Linked data for improving student experience in searching e-learning ...

2011

Linked data for improving student experience in searching e-learning resources Master Thesis Project

Author: Julieth Patricia Castelanos Ardila

Advisors: Annabella Loconsole Marie Gustafsson Friberger

Examiner: Paul Davidsson

03 February 2012

Linked data for improving student experience in searching e-learning resources 2011

Acronyms

BIBO

BIBliographic Ontology - provides main concepts and properties for describing citations and bibliographic references (i.e. quotes, books, articles, etc) on the Semantic Web. - http://bibliontology.com/specification

CC

Creative Commons - Nonprofit organization that develops, supports, and stewards legal and technical infrastructure that maximizes digital creativity, sharing, and innovation. - http://creativecommons.org/

DCMI

Dublin Core Metadata Initiative -

a non-profit organization engaged in the

development of interoperable metadata standards. - http://dublincore.org/ DOAP

Description Of A Project vocabulary - IT is a project to create an XML/RDF vocabulary to describe open source projects. - http://usefulinc.com/ns/doap

FOAF

The Friend Of A Friend (FOAF) vocabulary - project devoted to linking people and information using the Web. - http://xmlns.com/foaf/spec/

HTML:

HyperText Markup Language - the predominant markup language for web pages. -

HTTP:

HyperText

Transfer

Protocol

-

a

networking

protocol

for

distributed,

collaborative, hypermedia information systems. IEEE

The

Institute

of

Electrical

and

Electronics

Engineers

-

http://www.ieee.org/index.html LOD

Linking Open Data cloud diagram - Datasets that have been published in linked data format, by contributors to the Linking Open Data community project and other individuals and organisations. -

LOM

Learning Object Metadata - an IEEE standard for metadata descriptions of learning objects. -

MOAT

Meaning Of A Tag - It defines a lightweight ontology to represent how different meanings can be related to a tag. - http://moat-project.org/ontology

OAI

Open Archives Initiative. - a organization producing repository interoperability

2

Linked data for improving student experience in searching e-learning resources 2011 specifications. - http://www.openarchives.org/ OWL

Web Ontology Language – It is a modelling language for expressing formal semantics of RDF properties and classes. -

RDF:

Resource Description Framework - a W3C specification for metadata descriptions. - http://www.w3.org/RDF/

RDFS

The RDF Vocabulary Description Language, also known as RDF Schema

SIOC

Semantically-Interlinked Online Communities - provides the main concepts and properties required to describe information from online communities (e.g., message

boards,

wikis,

weblogs,

etc.)

on

the

Semantic

Web

-

http://www.w3.org/Submission/sioc-spec/ SKOS

Simple Knowledge Organization System - a W3C specification for represent knowledge

organization

systems

such

as

thesauri

or

taxonomies

using RDF. - http://www.w3.org/2004/02/skos/ SPARQL: SPARQL Protocol and RDF Query Language - a W3C query language for RDF. - http://www.w3.org/TR/rdf-sparql-query/ URI:

Universal Resource identifier - a globally unique identifier designed to be used on the WWW. -

URL

Uniform Resource Locator – It is the unique address for a file that is accessible on the internet. -

WGS84

The basic RDF vocabulary that provides the Semantic Web community with a namespace for representing lat(itude), long(itude) and other information about spatially-located things - http://www.w3.org/2003/01/geo/

3


Table of Contents 1.

2.

INTRODUCTION ............................................................................................................................. 10 1.1.

PROJECT IDEA ........................................................................................................................ 10

1.2.

AIMS AND OBJECTIVES.......................................................................................................... 11

1.2.1.

Main Goal ...................................................................................................................... 11

1.2.2.

Objectives...................................................................................................................... 11

1.3.

MOTIVATION ......................................................................................................................... 11

1.4.

RESEARCH QUESTIONS.......................................................................................................... 13

1.5.

EXPECTED OUTCOMES .......................................................................................................... 13

1.6.

OUTLINE ................................................................................................................................ 14

THE RESEARCH METHODOLOGY ................................................................................................... 16 2.1.

2.1.1.

Overview about the research methodologies .............................................................. 16

2.1.2.

Scientific Hypotheses .................................................................................................... 17

2.1.3.

Traditional Research in Computer Science ................................................................... 18

2.1.4.

Data Collection Method ................................................................................................ 18

2.2.

3.

METHODOLOGY OF THE THESIS PROJECT............................................................................. 16

CONSTRUCTING AND ADMINISTERING THE RESEARCH QUESTIONNAIRE ........................... 19

2.2.1.

Defining Questionnaire Objectives ............................................................................... 19

2.2.2.

Designing the questionnaire format ............................................................................. 20

2.2.3.

Pilot testing the questionnaire ..................................................................................... 23

2.2.4.

Target population and sample ...................................................................................... 23

2.2.5.

Precontacting the sample ............................................................................................. 24

2.2.6.

Writing a cover letter .................................................................................................... 24

2.2.7.

Following up with nonrespondents .............................................................................. 24

LITERATURE REVIEW ..................................................................................................................... 25 3.1.

LINKED DATA TECHNIQUES AND ITS USES IN E-LEARNING RESOURCES EXPLORATION....... 25

3.1.1.

Linked data overview .................................................................................................... 25

3.1.2.

Dereferencing URIs and Resource Description Framework (RDF) ................................ 26

3.1.3.

Taxonomies, vocabularies and ontologies to describe data ......................................... 27

3.1.4.

Learning Object Metadata (LOM) ................................................................................. 29

3.1.5.

Browsing the Web of Data .......................................................................................... 31

3.1.6.

e-Learning approaches for the linked data age ............................................................ 32

3.1.7.

Current state of LOD cloud diagram ............................................................................. 35

4

Linked data for improving student experience in searching e-learning resources 2011 3.1.8.

Use of vocabularies in the data sets in the LOD cloud diagram .................................. 36

3.1.9.

Data sources that provide origin metadata .................................................................. 38

3.1.10.

Data sources that provide licensing metadata ............................................................. 39

3.2.

THE INTERNET IN EDUCATION, AND SUITABLE SOURCES OF e-LEARNING RESOURCES. ..... 40

3.2.1.

Web 2.0 and collaborative e-learning ........................................................................... 40

3.2.2.

Learning resources and their potential ......................................................................... 41

3.2.3.

Educational issues in front of technological mediations .............................................. 42

3.3.

SURVEYS RELATED TO SEARCHING E-LEARNING RESOURCES BY E-LEARNERS. .................... 43

4. INVESTIGATION OF THE METHODS USED BY STUDENT FOR EXPLORING AND DISCOVERING eLEARNING RESOURCES.......................................................................................................................... 45 4.1.

5.

DATA ANALYSIS AND INTEPRETATION .................................................................................. 45

4.1.1.

Data collection .............................................................................................................. 45

4.1.2.

Data analysis ................................................................................................................. 45

4.1.3.

Answers to the key research questions of the questionnaire ...................................... 55

4.2.

INTERPRETATION OF THE RESULTS ....................................................................................... 57

4.3.

THREATS TO VALIDITY ........................................................................................................... 58

THE PROTOTYPE DESIGN............................................................................................................... 60 5.1.

FEATURES OF A e-LEARNING ENVIRONMENT PROTOTYPE................................................. 60

5.1.1.

Gaps found in the state-of-the art ................................................................................ 60

5.1.2.

A design proposal of an e-learning environment ......................................................... 62

5.1.3.

The role of the teacher in the e-learning collaborative environment .......................... 64

5.1.4.

Selection of the e-learning contents. ............................................................................ 66

5.1.5.

Comments and rankings ............................................................................................... 67

5.2.

REQUIREMENTS SPECIFICATION ........................................................................................... 68

5.2.1.

Functional requirements for the e-learning collaborative environment...................... 69

5.3.

QUALITY REQUIREMENTS FOR THE E-LEARNING COLLABORATIVE ENVIRONMENT ............ 70

5.4.

STAKEHOLDERS ..................................................................................................................... 70

5.5.

USE CASES DIAGRAMS .......................................................................................................... 71

5.5.1.

General use case for student Interactions .................................................................... 71

5.5.2.

Use case index............................................................................................................... 72

5.6. ARCHITECTURE OF THE e-LEARNING COLLABORATIVE ENVIRONMENTS WITH LINKED DATA TECHNIQUES ..................................................................................................................................... 73 5.6.1.

Transform mapping ....................................................................................................... 74

5.6.2.

Modules ........................................................................................................................ 76

5

Linked data for improving student experience in searching e-learning resources 2011 6.

REQUIREMENTS VALIDATION ....................................................................................................... 78 6.1.

REQUIREMENTS TRACEABILITY ............................................................................................. 79

6.1.1.

7.

Traceability matrix ........................................................................................................ 79

6.2.

REQUIREMENTS REVIEW CHECKLIST .................................................................................... 80

6.3.

LIST OF PROBLEMS AND LIST OF ACTIONS............................................................................ 81

CONCLUSIONS ............................................................................................................................... 82 7.1.

SUMMARY ............................................................................................................................. 82

7.2.

CONTRIBUTION ..................................................................................................................... 83

7.2.1.

Research justification .................................................................................................... 83

7.2.2.

Answering the research questions................................................................................ 84

7.3.

FUTURE WORK ...................................................................................................................... 85

8.

REFERENCES .................................................................................................................................. 87

9.

APPENDICES .................................................................................................................................. 93 9.1.

APPENDIX A: SURVEY ABOUT e-LEARNING RESOURCES ...................................................... 93

9.2. APPENDIX B: MAPPING BETWEEN THE AVAILABILITY OF DATA SOURCES IN THE LINKED DATA COMMUNITY AND THE PREFERENCES OF THE STUDENTS ..................................................... 99 9.2.1.

Contents selected by students and their availability in the LOD cloud diagram .......... 99

9.2.2.

According to Learning Object Metadata (LOM) criteria ............................................. 105

9.2.3.

Dataset selected and their vocabularies ..................................................................... 105

9.2.4. How comments, rankings and citations of the e-learning resources could be addressed with the Linked data approach ................................................................................................... 106 9.2.5. Reliability in Contributors of e-learning resources, and their availability en in the LOD cloud diagram ............................................................................................................................. 106 9.3.

APPENDIX C: USE CASES TEMPLATES ................................................................................. 107

9.3.1.

Provide Resources ....................................................................................................... 107

9.3.2.

Explore e-learning Resources ...................................................................................... 108

9.3.3.

Use Resources ............................................................................................................. 109

9.3.4.

View Revisions............................................................................................................. 110

9.3.5.

Manage Account ......................................................................................................... 111

9.3.6.

Create Account............................................................................................................ 113

9.3.7.

Delete Account............................................................................................................ 114

9.3.8.

Modify Account ........................................................................................................... 115

9.3.9.

Manage Personal resources information.................................................................... 116

9.3.10.

Save resources information ........................................................................................ 117

9.3.11.

Delete resources information ..................................................................................... 118

6

Linked data for improving student experience in searching e-learning resources 2011 9.3.12.

Modify resources information .................................................................................... 119

9.3.13.

Make revisions ............................................................................................................ 120

9.3.14.

Publish new Resources................................................................................................ 121

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List of Figures

Figure 1: An RDF Graph Describing Eric Miller. [12] ............................................................................ 27 Figure 2: A schematic representation of the hierarchy of elements in the LOM data model ............. 30 Figure 3: Linking Open Data cloud diagram, http://lod-cloud.net ...................................................... 34 Figure 4: The distribution of triples by domain. .................................................................................. 36 Figure 5: The distribution of links by domain. ...................................................................................... 36 Figure 6: The distribution of most widely used vocabularies .............................................................. 38 Figure 8: Discipline Area ....................................................................................................................... 46 Figure 7: Educational Level ................................................................................................................... 46 Figure 9: Sites/Sources of e-learning resources most visited ............................................................... 47 Figure 10: Use of social networks for learning ..................................................................................... 48 Figure 11: Initial approach in searching e-learning resources ............................................................. 48 Figure 12: Criteria in searching e-learning resources .......................................................................... 49 Figure 13: Resources recommended by teachers or other students................................................... 50 Figure 14: Choosing of resources with comments .............................................................................. 50 Figure 15: Rankings in e-learning resources ......................................................................................... 51 Figure 16: Citations on articles and books ........................................................................................... 51 Figure 17: Sense of reliability in recognized sources of learning resources ......................................... 52 Figure 18: Preferences in use of learning resources ............................................................................ 53 Figure 19: Ways for saving URL´s ......................................................................................................... 54 Figure 20: Possibility of making comments .......................................................................................... 54 Figure 21: Possibility of making rankings ............................................................................................. 55 Figure 22: Interest in belonging to a e-learning/s-science group ........................................................ 55 Figure 23: Contents in the e-learning environment prototype ............................................................ 63 Figure 24: Level decision in front of the selection of e-learning resources ......................................... 68 Figure 25: General use case for students interaction .......................................................................... 72 Figure 26: Data flow diagram for the environment ............................................................................. 75 Figure 27: General architecture or the e-learning collaborative environment .................................... 76 Figure 28: The process of requirements validation .............................................................................. 78

8


List of Tables

Table 1: Overview of the amount of triples as well as the amount of RDF links per domain in LOD cloud diagram, Bizer, C. & Jentzsch A. [29] ........................................................................................... 35 Table 2: Vocabularies widely used in LOD cloud diagram and links provided to the data sources...... 37 Table 3: Provide origin metadata by topical domain ........................................................................... 39 Table 4: Licensing metadata ................................................................................................................. 39 Table 5: Threat to Internal Validity ....................................................................................................... 58 Table 6: Threat to External Validity ...................................................................................................... 59 Table 7: Two Enactments of a Formative Assessment Cycle, Minstrell et al [46]. ............................... 64 Table 8: Use case index of the project ................................................................................................. 73 Table 9: Traceability matrix .................................................................................................................. 80 Table 10: Requirements review checklist ............................................................................................ 81 Table 11: Lectures in the LOD cloud diagram ..................................................................................... 100 Table 12: Online Research papers in the LOD cloud diagram ............................................................ 100 Table 13: e-Books in the LOD cloud diagram ...................................................................................... 101 Table 14: Tutorials/Manuals in the LOD cloud community ............................................................... 101 Table 15: Interactive resources in the LOD cloud diagram ................................................................. 101 Table 16: Statistics and government sites in the LOD cloud diagram................................................ 102 Table 17: Wikipedia and the LOD cloud diagram ............................................................................... 103 Table 18: Google scholar and the LOD cloud diagram ........................................................................ 103 Table 19: Google maps and the LOD cloud diagram ......................................................................... 103 Table 20: YouTube and the LOD cloud diagram................................................................................. 103 Table 21: Universities and the LOD cloud diagram ........................................................................... 104 Table 22: Data Sources selected and their vocabularies ................................................................... 105 Table 23: Comments in things in LOD cloud diagram ......................................................................... 106 Table 24: Use case for providing Resources ....................................................................................... 108 Table 25: Use case for exploring e-Learning Resources ..................................................................... 109 Table 26: Use case for Using e- resources .......................................................................................... 110 Table 27: Use cases for viewing revisions ......................................................................................... 111 Table 28: Use case for managing account .......................................................................................... 112 Table 29: Use case for managing account .......................................................................................... 113 Table 30: Use case for deleting account ............................................................................................. 114 Table 31: Use case for deleting account ............................................................................................. 115 Table 32: Use case for managing personal resources information ................................................... 116 Table 33: Use case for saving resources information ........................................................................ 117 Table 34: Use case for deleting resources information ..................................................................... 118 Table 35: Use case for modifying resources information .................................................................. 119 Table 36: Use case for making reviews ............................................................................................... 120 Table 37: Use cases for publishing new resources ............................................................................. 121

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1. INTRODUCTION

1.1. PROJECT IDEA The collection and the use of data on the internet with e-learning purposes are tasks made by many people every day, because of their role as teachers or students. The web provides several data sources with relevant information that could be used in educational environments, but the information is widely distributed, or poorly structured. Also, resources on the web are diverse, sometimes with high quality, but sometimes not. These situations involve a difficult search of e-learning resources,

and therefore a lot of time invested,

because the search process – typing, reasoning, selecting, using resources, bookmarking, and so forth -

is completely executed by humans, despite that some of them can be

executed by computers. Heath presents in [1], the linked data techniques as option to tackle the issues related to publishing and to exploring data on the internet, because these techniques are used for exposing, sharing and connecting the Web of Data, using Universal Resource Identifier (URIs) and Resource Description Framework (RDF) . Berners-Lee [2] expose the basis of linked data techniques and highlight the differences between the two modes of web information: the web of hypertext, and the web of data. Both are constructed with documents on the web, but the web of data describes information – it includes the connections - with RDF language, and the URI identifies objects or concepts – pages, people, resources, and so on - Instead, the web of hypertext uses relationships anchors in hypertext documents written in HTML (Hypertext Markup Language) and the URI concept is just for location. The features of the web of hypertext makes its information difficult to be crawled by machines.

linked data contributes with the growing of the Web of data by

applying four basic rules: ―Use URIs as names for things; Use HTTP (Hypertext Transfer Protocol) URIs so that people can look up those names; When someone looks up a URI, provide useful information, using the standards such SPARQL (Query Language for RDF); Include links to other URIs, so that they can discover more things”. Many efforts have been carried out in the last years using linked data techniques, and a great number of data sets are available for being used. Datasets available with dereferencable1 URIs are exposed in [3]. In summary, linked data provides designed practices for organizing, and for discovering information using the processing power of computers. At the same time, the community of

1

Dereferencing is the act of retrieving a representations of a resource identified by a URI. For more information, see section 2.1.2

10

Linked data for improving student experience in searching e-learning resources 2011 linked data provides data sets that are already connected, and this information could be consumed by people anytime as resources with e-learning purposes Additionally to practices of linked data, this master thesis will address the techniques used by students when searching e-learning resources, through a survey. The resources used by the students, as well as the sources preferred, will be compare with the current resources offered by the linked data community. Likewise, the strategies and techniques selected by the students will be taken into account, in order to establish the basic requirements of a elearning collaborative environment prototype

1.2. AIMS AND OBJECTIVES 1.2.1.

Main Goal

Improve the collaborative e-learning experience, through the design of an e-learning environment based on linked data techniques.

1.2.2.

Objectives



Identify linked data techniques as well as their uses in existing e-learning models.



Identify students preferences in regarding of searching e-learning resources.



Design the architecture of a prototype, based on the findings of the previous objective.

1.3. MOTIVATION Learning is an area where web-based technology has played an important role in recent years.

The web technology supports educational tasks, such as extracting/publishing

resources for teaching-learning purposes, real-time interaction among people involved in the educational process, and so forth. Since Web 2.0 appeared, students have found many ways of interaction with mates, and then, keeping in touch virtually, for example to share learning contents. Teachers as well as educational institutions like Universities, colleges, and so on, have found the web as the mean to interchange contents, points of view and many other activities with partners, scientists and their pupils. The web involves many resources for learning; not only web pages with information, but also knowledge communities, collaborative networks, and expert systems. Also, the idea of sharing one´s thoughts about things with others, - micro blogs, comments, rankings, photos, etc. -

11

Linked data for improving student experience in searching e-learning resources 2011 persuades more and more people every day, because of the easy spreading web-based systems. Consequently, the web provides not only several data sources with useful and relevant information with e-learning purposes, but

also information that is not easy to

retrieve, and therefore wasted data . Sometimes, the information is inappropriate, and they must be filtered, in order to be relevant.

Improving the way to exploit the web is an

important step in the development of e-learning technology. The web would be a useful mechanism in the learning process if we take advantage of the information which is placed there. These improvements are related to the distribution of tasks: computers can be faster than humans in searching information with organized data. For humans, the search process on the web becomes a difficult task, and also very tedious, as a result of the large amount of information disposable to be consulted.

For computers, large amounts of data is not a

problem. Computers can be very useful, if the data is reassigned in standard formats, in order to make connections between them.

Consequently, the computer can find the

information easily using adequate search engines, that crawl the Web of Data by following links between data sources and provide expressive query capabilities over aggregated data, similar to how a local database is queried today, as Bizer et al. [4] suggests. this condition, the current web is not ready for

According to

the new age of computers reasoning,

because data is published in several formats and the computers are unable to establish ways of connection without the human intervention. The linked data approach is a semantic web practice that allows structure on the web of data using RDF triples. RDF is a standard model for data interchange on the Web and it is presented as a language for representing information about resources in the World Wide Web [5]. Rdfization.

Data can become structured information with RDF.

This process is called

The structured information makes easier processing on computers, not only for

using the information, but also for creating better connections around the world wide web. eLearning resources could be reached, if the web of data enables the connections between several sources on the internet.

Universities, scientific sites as well as journals, have

published their own information for free, and these sources could be seriously considered when we search e-learning resources.

Metadata about the general resources provides

information to e-learners in order to know about the provenance of the data. Consequently, the e-learner obtains the freedom to select the most convenient according with their educational needs. Taking into account the amount of data located on the internet and the opportunity to make connections between information and sources that provide usable information for e-learning purposes, and also, the datasets currently connected in the linked data community, we could make the web a more interesting place, and also a relevant tool for e-learners, in order to improve their experience in searching e-learning resources.

12


1.4. RESEARCH QUESTIONS linked data is a technique that allows publishing and exploring data on the internet [1]. Searching information on the Web of Data is a process that is made by following links between data sources. This process provides expressive query capabilities over aggregated data, similar to how a local database is queried today [4]. This technique can be useful for crawling data on the internet for educational purposes, because learning is an area where web-based technology has played an important role in recent years, and lot of instructional information have been placed around the World Wide Web, using Learning Object Metadata (LOM).

LOM has established a standard (base schema) which defines a structure for

interoperable descriptions of learning objects [17].

In this context, our main research

question is: RQ1: How could linked data support the collection of information on the internet in order to enrich collaborative e-learning environments? The research must combine a depth literature review, in order to understand the current state of the art about the techniques around linked data issues and metadata in learning objects as well as the current preferences of the students in searching resources for self learning. The two following questions are placed for helping in answering these important aspects. RQ2: What are the remarkable features offered by existing learning environments based on linked data techniques? RQ3: To what extent are the students’ preferences in searching e-learning resources supported by the current search engines and/or learning environments?

1.5. EXPECTED OUTCOMES By developing this project, we are expecting to gain knowledge about searching e-learning resources, which use linked data. We are interested in establish connections between theoretical assumptions about linked data Techniques, and current practices developed by students when they search resources on the internet, in order to conduct future research around this topic. The outcomes of this project will support the answering of the research questions following the order of the thesis: RQ2 will be answered by performing a literature review on these aspects

13

Linked data for improving student experience in searching e-learning resources 2011 a. Linked data techniques and its uses in e-learning resources exploration b. The internet in education, and suitable sources of e-learning resources. c. Surveys related to searching e-learning resources by e-learners RQ3 will be answered with the results of a survey about the current techniques for searching e- learning resources on the internet, used by the students in Malmö University RQ1: will be answered by studying the following aspects: a. Mapping between the availability of datasets in the Linking Open Data Cloud diagram and the current preferences of the students: 

Data sets selected from the LOD (Linking Open Data) cloud diagram:

The

results of the survey will be contrasted with the information published using linked data techniques, in order to establish the availability of data sources for e-learning purposes. 

Identification of vocabularies used in the linked data community: Discovering and understanding the vocabularies used in the data sets selected for e-learning purposes

b. Design of an e-learning collaborative environment prototype, using the findings of the research 

Prototype overview: General goals of the prototype design.



Requirements specification: The basic requirements derived from the survey will be integrated in a model using UML use cases



General design of the e-learning collaborative environment:

which allows the

connection between students preferences found in the requirements elicitation, with the current data available on the internet, and the new ones published by the users.

1.6. OUTLINE Chapter 2 discusses the research methodology as well as the constructing and administering the research survey in which the current thesis based the requirements elicitation. Chapter 3 lays the groundwork for the rest of the thesis by presenting the principles and terminology of linked data, as well as related work about the internet in education, availability

14

Linked data for improving student experience in searching e-learning resources 2011 of e-learning resources, and surveys about the use of the internet in e-learning resources searching Chapter 4 presents the investigation of the methods used by student for exploring and discovering e-learning resources through the data analysis and interpretation of the survey. Chapter 5 Introduces to the prototype design.

It includes the prototype idea, the

requirements specification using the data analysis of the survey, and the architecture of the e-learning collaborative environment using the assumptions reached in the literature review and the dereferencable URIs found in the linked open data cloud diagram.

The design of

components in the environment will be addressed in terms of UML diagrams. Chapter 6 Validates the requirements of the prototype. Chapter 7 Tackles conclusions of the master thesis project in order to find incomes for further research in the area.

This chapter also shows the contributions e-learning world

evaluation is based on the benefits indentified by using this approach and gives indications of what future work can be done to improve the results.

15


2. THE RESEARCH METHODOLOGY In this section it is to describe the methodological research assumptions underpinning the present work.

It includes the general research strategy as well as the data collection

method. Furthermore, this section defines boundaries of the research design, and situates the research amongst existing research traditions in computer science.

2.1. METHODOLOGY OF THE THESIS PROJECT 2.1.1.

Overview about the research methodologies

The mixed-methods research is the methodological approach selected to address the present work. This approach was selected, taking into account the nature of the research in which take part computer science techniques and educational research. The assumptions of Gall [6] validate the selection of the method because he explains that this is a way of have broader understanding of the problem, making richer analysis:

―A review of quantitative

studies about a particular phenomenon combined with a review of qualitative studies about the same phenomenon can provide richer insights and raise more interesting questions for future research than is only one set of studies is considered‖. This kind of methodology includes an extensive data collection and analysis of both kind of data: numeric and text. The planning procedure in mixed methods is important and requires special attention. For this reason, it is useful to take into account the aspects that influence the designing of the method study as Creswell [7] explains ―Four important aspects are:

timing, weighting,

mixing and theorizing‖. In the case of the current research, these aspects will be addressed as follows: 

The timing of the qualitative and quantitative data collection is concurrently, that means: the information is gathered at the same time and the implementation is simultaneous.



The weighting means priority given to the approaches selected.

In this case, the

importance of the deductive approach allows to emphasize in the quantitative information to express, in better way, the hypothesis raised. 

The Mixing the data occurs in the three stages of the research: data collection, the data analysis and interpretation. The mixing consists of integrating the numbers with the text provided by questionnaire respondents.

16

Linked data for improving student experience in searching e-learning resources 2011 

The Theorizing is tackling the theoretical perspective established in the literature review and the understanding of the needs of the subjects in regards to search e-learning resources. It will be used an implicit theorization, that means theories are not discussed in front of the results of the survey because they will be already embedded in the creation of the survey and its subsequent analysis.

In essence, the strategy selected for addressing this research is a concurrent transformative strategy because as Creswell [7] explains "it is guided by the researcher´s use of a specific theoretical perspective as well as the concurrent collection of both quantitative and qualitative data". The concurrent transformative model is applied in data collected at the same time, prioritizing the quantitative one over the qualitative, because, qualitative data are used to explain some further issues of the questions in the survey in order to enrich the answers given by the respondents. This kind of approach is called Explanatory Sequential. The data collection method arises by this research is a survey.

Surveys are used in

research because allow to collect data about any topic, because surveys allow to collect data about Phenomena that are not directly observable: inner experience, opinions, values, interests and the likes, Gail [6]. The data collection tool supported by this research is a questionnaire managed by the respondent itself. This data collection method ensures reliability in the topic cited, because it allows respondents freedom for writing their own answers without the intervention of a interviewer, as well as anonymity. Despite the current research is based on mixed research approach, interviews are not conducted to gather any data. The questionnaire is designed to permit textual answers by the respondents after every close-ended question, in order to strengthen them with their particular comments or opinions.

2.1.2.

Scientific Hypotheses

If we develop a methodology

for searching e-learning resources based on linked data

approach, we develop e-learning environments more structured and more relevant, in order reduce the time for exploring, selecting, and use e-learning objects, as well as a framework for selecting reliable resources

17


2.1.3.

Traditional Research in Computer Science

The present project implies the design of a methodology for improving the students experience in searching e-learning resources in order to set the foundations for new developments in the field of e-learning

involving semantic web techniques.

The

questionnaire and its analysis will work as a elicitation of requirements in the normal process of software development. To compare the requirements elicited with the state-of-art are the second step in this research in order to configure a path for making a design that really contributes with the purpose of the project.

Then, the process will cover data design as

well as internal linkages proposal between data sources provided with the linked data community and new ones considered relevant in the present research in order to follow the practices involved in the linked data approach.

2.1.4.

Data Collection Method

The assumption underlying in the survey is that the students, in general, have adopted the information offered by the Web as a basic resource to consult their informational shortcomings, and used this data in their learning experience. The research will be used both descriptive and exploratory approach in order to understand the solutions of the research questions.

At the beginning, the descriptive research will be used to get data

about the current status of the phenomenon through a survey.

Then, the exploratory

research will be used to yield information discovered by other researchers.

This

assumptions allow the researcher familiarize herself with the concepts of the problem under study to facilitate development of insights. The present study is an exploratory attempt since it would try to gather information regarding the behaviour that

the students in Malmö

University show in front of exploration, selection and using of e-learning resources . The researcher makes use of existing literature in order to verify the information gathered with the surveys and come up with preliminary ideas regarding the research problem. The questionnaire will use close and open-ended questions as well as questions using Likert Scale answers.

The instrument is at the respondent hands over a paper-based

questionnaire. There are no participation of any

interviewer in the procedure of the

questionnaire answers, just at the beginning, in the introduction of the survey.

18


2.2. CONSTRUCTING AND ADMINISTERING THE RESEARCH QUESTIONNAIRE A relevant questionnaire must be structured in a precise way. In order to achieve relevant data, the procedure to follow will be obtained from some of the steps proposed by Neuman [8]: (1) defining survey objectives, (2) selecting a sample, (3) designing the questionnaire format, (4) pretesting the questionnaire, (5) precontacting the sample, (6) writing a cover letter and distributing the questionnaire, (7) following up with nonrespondents, and (8) analyzing questionnaire data. This approach is interesting and has a valuable framework for understand the whole process that must be performed in the current research.

2.2.1.

Defining Questionnaire Objectives

People involved in this data collection are students from Malmö University that are carrying out different careers, not only as undergraduate or postgraduate students, but also people who take single courses as well as college students. At the moment of this research, we are interested in making a broad descriptive study, neither to specify nor to compare different subgroups. The aspect of the topic to study is related to the methods that students use for exploring and discovering e-learning resources and also, the opinions they have in front of these resources, specially reliability, usability and usefulness. Finally, facts and attitudes gathered from the questionnaire will be used for interpret the information, related to preferences of students from Malmö University in front of uses of e-learning resources and develop a theory from the findings. 

Questionnaire Aim

Investigate the methods used by students for exploring and discovering e-learning resources as well as the opinion they have about these resources in terms of reliability, usability and usefulness. 

Questionnaire Objectives

The survey objectives are related to the research questions and the survey would seek answers to this question. The objectives are broadly spelt out as follows: a. To recognize the demographics of the population in terms of education level and discipline area studied in Malmö University.

19

Linked data for improving student experience in searching e-learning resources 2011 b. To investigate the types of exploration or search used by students in their search for elearning resources c. To find out if the students take into account other people´s suggestions when choosing of their e-learning resources d. To ascertain the reliability feeling in the sources of e-learning resources used by students e. To find out the preferences that students have in the use and manage or their e-learning resources f.

To find out how the student evaluates the e-learning resources supplied by the internet as well as the sharing intention with people that have similar interest

2.2.2.

Designing the questionnaire format

The questionnaire created are designed to request large amount of information around the exploration and the uses of e-learning resources, because the nature of the research was based on several LOM items (Learning Object Metadata).

However, keeping the

questionnaire as short as possible was a remarkable issue to solve. The first task consisted in organize the items, in order to confer logical sequence answers in the respondents, using key research questions.

Then, questions were

opened, in order to gain deeply

understanding on every main question. 

Key Research Questions of the Questionnaire

There are 6 questions closely linked with the objectives of the survey. The questions are as following: Question 1: What is the educational level and discipline that respondent is studying in Malmö University? Question 2: How do the students explore the internet for searching e- learning objects? Question 3: How important are suggestions of other people for the selection of e-learning resources? Question 4: What sources of e-learning resources shows more reliability in the student´s preference? Question 5: What kind of e-learning resources the students prefer and how they manage the source of that resource?

20

Linked data for improving student experience in searching e-learning resources 2011 Question 6:

How do the student would assess the usefulness of the online learning

resources as well as their intentions in sharing e-learning resources with mates? 

Splitting the main questions

This section was created to explain how the main questions were forked , in order to create a more accurate questionnaire. The main questions are enumerated and subquestions are presented below these numberings Question 1  Which educational level describes the respondents?  What discipline area the respondents are pursuing? Question 2  What kind of pages do the students usually visit for searching online learning objects?  Do the students use social networks to find e-learning resources?  What is the initial approach of the students when they search for e-learning resources?. How the students combine words to make a more precise search of their e-learning resources?  Are the students interested in search their learning resources using criteria like author´s name, field, topic, date, language, format, learning resource type, interactivity level or context? Question 3  Are the students interested in select online learning resources recommended for teachers or other students?  Are the students interested in select online learning resources commented by somebody else?  Are the students interested in the rankings people do about the usefulness elearning resources?  Are the people interested in the amount of citations that the articles have? Question 4  Do the students trust in sources of e-learning content like YouTube, Wikipedia, Google and so forth?

21

Linked data for improving student experience in searching e-learning resources 2011 Question 5  What kind of e-learning resources the students prefer to use?  How do the students remember the relevant web page URL´s? Question 6  Are the students willing to make comments about the quality of the learning object found?  Are the students interested in rank the usefulness of the e-learning resource they have used?  Are students interested in use social networking collaborative learning? 

Survey Instrument Completed

The survey instrument resulting in a questionnaire with 16 questions. It is able to tackle demographic information as well as the objectives given before. Finally, six sections are involved in the questionnaire.  The first section (questions 1 and 2) of the questionnaire gathered the respondents’ demographic characteristics such as their education level and discipline area of study.  The second section of the questionnaire (questions 3, 4, 5 y 6) collected the type of exploring respondents use in their e-learning resources search.  The Section 3 of the questionnaire (questions 7, 8, 9 and 10) highlighted the importance of suggestions of other people for the selection of e-learning resources.  The Section 4 of the questionnaire (question 11) was designed as a Likert scale to find out the reliability students have in some sources of e-learning resources .  The fifth section

of the questionnaire (questions 12 and 13) focused on

preferences of the students for use resources available and how they manage the Url´s of this resources.  The last section of the questionnaire (questions 14, 15 y 16) was specially included to find out the students orientation about assessing and sharing elearning resources.

22


2.2.3.

Pilot testing the questionnaire

The survey questionnaire was first piloted to a sample of 11 students of Malmö University on 23 February 2011, during a class in which the participants have different backgrounds, level of study as well as different

study areas.

The comments and the answers

from the

respondents were taken into consideration during the process of refining the questionnaire. The primary reason for this is to develop questions that are relevant and which could be understood easily by the respondents. The questions were further thoroughly checked for reliability and validity. The final product is a 4-pages questionnaire that was used for the survey. The questionnaire is shown in Appendix A.

2.2.4.

Target population and sample

The specific pool of population that the current research wants to study are the students of Malmö University which consists of 25 000 students enrolled in full- or part-time studies 20102. For tackling the current research, it was selected Nonprobability Sampling, which Neuman [8] explains as follow: "This means researchers rarely determine the sample size in advance and have limited knowledge about the larger group or population from which the sample is taken" But, according, also with Neuman [8], the specific type of Nonprobability sample selected was Quota Sampling, because the research designed is sure about the type of categories of students to investigate. These categories are related to the educational level that the students of Malmö University are pursuing: College, Undergraduate, Master, Doctoral and single courses. In order to improve the quota sampling, the individuals that answered the questionnaire were selected in several places of the university, such as Orkanen Library, Kranen Library, and rooms in which the teacher allowed the data collection in class.

Other questionnaires was

collected in Celcuisgården accommodation, which have many students who belong to Malmö University. designed.

This procedure ensures the correct representation of all categories

The final data collection was of 82 questionnaires that represents 0,328 percent

of the total population in these educational level categories in Malmö University

2

http://www.mah.se/english/About-Malmo-University/Facts-and-figures/

23


2.2.5.

Precontacting the sample

The rate of response is always a great problem to solve. In many cases, people are not interested in answering surveys, because they need the time for many other personal issues. Neuman [8] suggests that some kind of precontact, increases the rate of response. In the current survey the precontact was made in verbal way, opposite to the respondents and before give them the questionnaires, explaining the nature of the research as well as the importance of the reliable answers. Also, the teachers who allowed their classes to pick respondents up, offered time to think and answer the questions.

2.2.6.

Writing a cover letter

A cover letter is used to explain the purpose of the study concerned. It needs to be brief, but it must convey certain information and impression as Neuman [8] explains: "to persuade the respondents that the study is significant and their answers are important". For purposes of this study, a cover letter was not created, because the preliminary information was given to the respondents verbally. However, a short presentation was written at the beginning of the questionnaire.

2.2.7.

Following up with nonrespondents

As the surveys was doing in classrooms, libraries and in the accommodation corridor, the respondents were encouraged to filling the questionnaire at the same time it was given to them.

Nevertheless, some surveys were not returned in the same moment of the

application. These unanswered questionnaires were channelled in the next class, or in next meeting with the respondent, inquiring about them in a verbal way. 91, 11% of the surveys was collected.

24


3. LITERATURE REVIEW

To give an introduction to linked data technology, this chapter starts by explaining the basic elements of the linked data approach, as well as the uses of this technique in exploration/publication of e-learning resources in Section 2.1. Following, in Section 2.2. we explain the influence of the internet in education and the availability of the resources with elearning purposes. And finally, in Section 2.3 we describe the existing surveys related to searching e-learning resources by e-learners in different contexts.

3.1. LINKED DATA TECHNIQUES AND ITS USES IN E-LEARNING RESOURCES EXPLORATION

3.1.1.

Linked data overview

Computers today can show information for almost any source, but they have difficulties to crawl most of the information published, because the conventional data format i.e. HTML, is not sufficiently expressive to enable individual entities described in a particular document to be connected by typed links to related entities, Bizer et al. [4].

Computers have been

considered as passive counters of information, because sometimes they just show the information that the human find, but their potential on searching data on the internet is wasted. Semantic Web could make the computers a more active tools in order to help us in tasks like searching information, because semantic web refers to possibility of allowing the formal communication of a higher level of language (this is called metalanguage) between computers.

Linked data is a Web semantic technique and refers to a set of best practices

for publishing and interlinking structured data on the Web, Heath et al. [6].

Linked data

conducts the possibility to configure a new kind of web called Web of Data.

Heath et al.

[9] as well as Bizer et al. [4], the principles of linked data: 1. URI references are not just Web documents and digital content, but also real world objects and abstract concepts; 2. The HTTP protocol is seen as a well-understood retrieval mechanism;

25

Linked data for improving student experience in searching e-learning resources 2011 3. The Resource Description Framework (RDF) as Klyne et al. says [10] ―is a model that has been designed for use in the context of the Web‖. RDF is used for publishing structured data on the internet and tools was created to crawl this data in a proper manner. 4. Finally, linked data context have types which describe the relationship between the things. This feature allows the discovery of more things.

3.1.2.

Dereferencing URIs and Resource Description Framework (RDF)

Intelligent agents (IAs) “are software programs intended to perform tasks more efficiently and with less human intervention” Kravari et al [11].

It implies that this kind of complex

software shall operate in the world wide web, performing a variety of tasks from caching and routing to searching, categorizing, and filtering.

They may retrieve representations of

resources by dereferencing URIs. Dereferencing is a useful concept in the context of semantic web, because, is described as the act of retrieving a representation of a resource identified by a URI, Lewis [12] Heath et al. [9], also implies ―Any HTTP URI should be dereferenceable, meaning that HTTP clients can look up the URI using the HTTP protocol and retrieve a description of the resource that is identified by the URI” RDF allows URLs (Uniform Resource Locator) dereferencing because the data format could be determined by content negotiation inside the graph created.

RDF was created for

representing metadata about Web resources as well as for representing information about things that can be identified on the Web, even when they cannot be directly retrieved on the Web, Manola et al. [13].

For this reason, RDF is useful when information needs to be

processed by applications, rather than HTML, which is oriented to display the information to people. RDF is based on the idea of identifying things using Web identifiers or URIs, and describing resources in terms of simple properties and property values.

For this reason,

RDF serves as mechanism for describing things. RDF statement is composed by three basic parts: subject, predicate and object; Groups of statements are represented by corresponding groups of nodes and arcs. In Figure 1 there are a group of statements that refers to Eric Miller. This representation can be explained in words: "there is a Person identified by http://www.w3.org/People/EM/contact#me, whose name is Eric Miller, whose email address is [email protected], and whose title is Dr."

26


Figure 1: An RDF Graph Describing Eric Miller. [12]

3.1.3.

Taxonomies, vocabularies and ontologies to describe data

RDF is an abstract data model for describing resources. RDF does not have any domain terms for describing classes, things, and relationships. For this reason, taxonomies, vocabularies and ontologies are designed and expressed in SKOS (Simple Knowledge Organization System), RDFS (the RDF Vocabulary Description Language, also known as RDF Schema) and OWL (the Web Ontology Language) SKOS is described as a common data model for sharing and linking knowledge organization systems via the Web, Miles et al. [14]. The idea consists in sharing similar structure, and similar applications. SKOS is used to represent thesauri, taxonomies, subject heading systems, and topical hierarchies. OWL is designed for being used by applications that need to process the content of information instead of just presenting information to humans, McGuinness et al. [15]. This Ontology allows that machines can read and interpret the web content created with XML,

27

Linked data for improving student experience in searching e-learning resources 2011 RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. RDFS and OWL are used in cases where subsumption relationships between terms, as well as inheritance, should be represented. There are vocabularies created with different purposes.

The recommendation is to reuse

them instead of creating a different one, in order make the data being consumables by applications that may be tuned to well-known vocabularies. The following list, developed by Heath et al, [9], describes some vocabularies, used in common applications: 

The Dublin Core Metadata Initiative (DCMI) Metadata Terms vocabulary defines general metadata attributes such as title, creator, date and subject.



The Friend-of-a-Friend (FOAF) vocabulary defines terms for describing persons, their activities and their relations to other people and objects.



The Semantically-Interlinked Online Communities (SIOC) vocabulary (pronounced "shock") is designed for describing aspects of online community sites, such as users, posts and forums.



The Description of a Project (DOAP) vocabulary (pronounced "dope") defines terms for describing software projects, particularly those that are Open Source.



The Music Ontology defines terms for describing various aspects related to music, such as artists, albums, tracks, performances and arrangements.



The Programmes Ontology defines terms for describing programmes such as TV and radio broadcasts.



The Good Relations Ontology defines terms for describing products, services and other aspects relevant to e-commerce applications.



The Creative Commons (CC) schema defines terms for describing copyright licenses in RDF.



The Bibliographic Ontology (BIBO)provides concepts and properties for describing citations and bibliographic references (i.e., quotes, books, articles, etc.).



The OAI Object Reuse and Exchange vocabulary is used by various library and publication data sources to represent resource aggregations such as different editions of a document or its internal structure.



The Review Vocabulary provides a vocabulary for representing reviews and ratings, as are often applied to products and services.



The Basic Geo (WGS84) vocabulary defines terms such as lat and long for describing geographically-located things.

28

Linked data for improving student experience in searching e-learning resources 2011 These vocabularies are designed to classify things - concepts, people, animals, etc - that can be used in a determined applications, create relationships, and define constraints on using those terms. In practice, vocabularies are to help data integration, and for this reason, they must be work together. This integration could be very complex to understand if they have several terms. Fortunately, there are metadata harmonization initiatives that provide frameworks to set up the vocabularies coordination in order to raising the expectations for metadata interoperability, Nilsson [16]. The relevance of this harmonization is defined in a successful exploration of the web. The metadata standards discussed in this thesis have all been chosen based on some kind of relevance for the field of e-learning and learning objects. The next section will be oriented completely to LOM (learning Object Metadata) because LOM is regarded as the dominant standard in this field.

3.1.4.

Learning Object Metadata (LOM)

IEEE (The Institute of Electrical and Electronics Engineers) has established a standard (base schema) which defines a structure for interoperable descriptions of learning objects. It was called LOM (Learning Object Metadata). Learning Object in terms of the IEEE document is: "any entity -digital or non-digital- that may be used for learning, education or training‖ [17]. LOM metadata describes relevant characteristics of the learning object, and this is appropriate because the standard is suitable for facilitating search, evaluation, acquisition, and use of learning objects. LOMv1.0 base schema is composed by metadata in terms of a hierarchy of 76 elements classified into nine categories, specifying vocabularies, and allowing syntaxes for the value of each element. LOM metadata could be used to know not only metadata about for resources, but also information such as aspects of the lifecycle of a learning object, its pedagogical features, technical aspects and so on. Figure 2 shows a schematic representation of the hierarchy of elements in the LOM data model. Dublin Core Metadata Initiative has developed data elements related to the current LOM version. There are several works that talk about LOM standards, its usefulness and its features developed in learning environments. For instance, Svensson et al. [18] says that the IEEELOM1 metadata specification is a popular standard for enriching learning content with metadata to promote their reusability, discoverability and interoperability. This is a good starting point in terms of creation of vocabularies or reuse of existing vocabularies.

29

2011

Figure 2: A schematic representation of the hierarchy of elements in the LOM data model

3

http://upload.wikimedia.org/wikipedia/commons/1/14/LOM_base_schema.png

3

2011 Another citation of LOM are found in Santally et al. “The aim of those entities (Learning Objects) is to provide a tremendous set of learning knowledge that once developed can be exchanged among organisations, and be used to build individual lessons and courses”, [19]. LOM provides not only flexibility, but also a standardised description about the kind of objects used in education. Recently, Mogharreban et al. [20] realize that the conventional LOM lacks of control in their structure. Some of the problems are derived in impediment to reusability. They explain that Learning objects have been metaphorically described as pieces of something, like chunks, nugget s, LEGO™ blocks, Lincoln Logs™, atoms, molecular compounds, and crystals, that could be difficult to sort. For this reason, their proposal are related to a new vision of LOM, that encloses the use of a learning pod that "compass the continuum from discrete digital components to full-fledged aggregate coursework". With this approach, they have adopted a strictly granular approach to retain the highest level of reuse. The use of LO (Learning Object) in the current research work is relevant.

The general

concept has been developed by many authors in order to lay out the principles of its foundation. Polsani [21], walked towards a clear concept of the reusable learning object. He says that any digital object can acquire the status of a learning object if it is wrapped in a learning intention, which has two aspects: form and relation. Form means the setting, context and environment for viewing the learning object, while relation refers interface that establishes a connection between the user and the bits of information stored in the computer memory banks. In the other hand, reusability suggests that the learning object is predisposed for reuse by multiple developers in various instructional contexts. We are interested in understand the nine categories described in [17] and showed in Figure 2, and how they are applicable in the context of searching e-learning resources by students in Malmö University,

selecting relevant features of each category and using them in the

gathering of data from students. The complete standard is large and must be read in [17]

3.1.5.

Browsing the Web of Data

The web of data has the ability to discover new things through the tracking of data. There are linked data browsers that allow users to navigate between data sources by following RDF links. Some of these browsers are the following:


Disco hyperdata browser. Bizer et al, explains that it is a simple browser for navigating the Semantic Web as an unbound set of data sources, [29] .



The Tabulator browser. It is an user-friendly Semantic Web browser, Berners Lee et al, [23], that becomes a generic browser for linked data on the web. Heath et al, [9] also explains: ―the results of the query form a table that can then be analyzed with various conventional data presentation methods, such as faceted browsing, maps, timelines, and so on”

Any browser selected works with SPARQL. This is the query language used for querying RDF data. Its name is a recursive acronym that stands for SPARQL Protocol and RDF Query Language. The language works with a similar structure of SLQ language. SPARQL contains capabilities for querying required and optional graph patterns along with their conjunctions and disjunctions, Prud'hommeaux et al, [24].

3.1.6.

e-Learning approaches for the linked data age

The web of linked data is a repository based on semantic technologies. Several researchers have been oriented to this kind of interoperable e-Learning repositories and establish that the Linked Data approach has the potential to fulfil the e-Learning vision of Web-scale interoperability of eLearning resources as well as highly personalised and adaptive eLearning applications.

Dbpedia (http://dbpedia.org/) is the core of this approach and

presents a first view related to data connection, using linked data techniques. Dbpedia as a community effort to extract structured information from Wikipedia, Auer et al. [25].

This

project has mechanisms in which new authors are allowed to contribute in order to facilitate contents. Dbpedia dataset is accessible using three access mechanisms: linked data, the SPARQL protocol, and downloadable RDF. Also, It has been used in several e-learning projects, because their linked open datasets are suitable to be selected using the linked data methodology to almost any topic. Dbpedia project is still growing and it has open data A work based on weaving social e-learning platforms is the one described by Selver et al. [26]. They highlights the relationships among several vocabularies, such as FOAF (Friend of a friend), SIOC(Semantically-Interlinked Online Communities), and MOAT(Meaning of a tag) for interlinking data. These ideas present how is possible to use semantic web in order to enrich contents, using social networks and dbpedia. It explains an interlinking model for finding information, based on semantic enhancement of user contributed content in social elearning platforms, using information about users, tags, and related resources. It includes the explanation of weaving process using the vocabularies: MOAT describes the meaning of

32

Linked data for improving student experience in searching e-learning resources 2011 tags, FOAF links to user profile and SIOC shows the representation of a user’s blog post to the FOAF profile. The ELGG community (http://community.elgg.org/), which is an open source social networking engine, provides the social e-learning platform as a start point for search the information related. The idea proposed by [26] is very useful, because the authors show the possibility to establish dialogue between several ontologies, thereby setting interoperability and enhance the navigability inside the ELGG community. Ways for combining RDF vocabularies is also tackled by Aleman et al. [27]. Contents source are subordinated to the production inside the community and dbpedia. Video search with educational content was tackled by Waitelonis et al. [28]. This work establishes "the use Linked Open Data to complement already existing information about entities within the scope of the domain of our academic video search engine". This works shows how Yovisto's (http://www.yovisto.com) database provides recordings of speakers about scientific subjects. Yovisto, also has information about the source of this recordings such as name of the lecturer, country, city, type (university or other), and website. Dbpedia provides information about the university that is the source of the recording . The dbpedia dataset is integrate automatically inside Yovisto. Also, dbpedia

is used for setting

information about the speaker in the lecture recorded. The confidence in data is subject to DBPEDIA dataset only and it represents one of the major gaps of this work, because the reliability must be searched in more reliable sources, such as the universities that provides the recording. However, the approach achieved with this work is a fundamental contribution to the web of linked data. In order to recognize the advances inside the linked data Community, and their possible uses in e-learning collaborative networking, we considered relevant to explain, briefly, the topology of the Web of data. Data sets are classified into the following topical domains: Media, Geographic, Publications, user-generated content, Government, cross-domain and Life sciences. Figure 3 shows the Linking Open Data cloud diagram, organized by topical domain, which are represented in different colors. The organization of these domains must be understand for establishing a complete awareness of the possibilities that the Web of data has today, and how we can contribute for the creation of new data sets, that will implies the use of LOM. Our interest will focus in the publications and cross-domain, because these are suitable to LOM standard.

33

2011

Figure 3: Linking Open Data cloud diagram, http://lod-cloud.net

2011

3.1.7.

Current state of LOD cloud diagram

LOD cloud diagram provided by the Comprehensive Knowledge Archive Network (CKAN)4, is a registry of open knowledge packages and projects.

It provides statistics and relevant

information about the current state of the data stored in the LOD cloud diagram, and also, allows to understand the current number of connections between domains and the size of the linked data community. The state of these statistics enable the creation of new projects around domains in order to enlarge the community, taking into account the size of triples and also the size of connections between different kind of data sources. The concept triples5 is used in semantic web as the following: A concept with three components: 

the subject, which is an RDF URI reference or a blank node



the predicate, which is an RDF URI reference



the object, which is an RDF URI reference, a literal or a blank node

The datasets, shared by the linked data community, cover different topical domains. The Table 1, gives an overview of the amount of triples, as well as the amount of RDF links per domain. The number of RDF links refers to out-going links that are set from data sources within a domain to other data sources. This statistics change with the time, because many projects are developing every day. Table 1: Overview of the amount of triples as well as the amount of RDF links per domain in LOD cloud diagram, Bizer, C. & Jentzsch A. [29] Domain

Number of

Triples

%

(Out-) Links

%

datasets Media

27

1,855,413,060

6.01 %

50,491,015

10.72%

Geographic

22

6,096,504,422

19.75 %

35,747,820

7.59%

Government

39

13,229,470,882

42.85 %

19,261,998

4.09%

Publications

82

2,868,088,257

9.29 %

135,336,031

28.72%

Cross-domain

32

3,708,240,740

12.01 %

32,254,790

6.85%

Life sciences

41

3,001,943,206

9.72 %

194,672,433

41.32%

User-generated

13

114,442,475

0.37 %

3,423,613

0.73%

256

30,874,103,042

100.00%

471,187,700

content TOTAL

4 5

http://ckan.net/ http://www.w3.org/TR/rdf-concepts/#section-triples

100.00%

Linked data for improving student experience in searching e-learning resources 2011 The percentages of the distributions of triples by domain and distribution of links by domain are shown in the figures 4 and 5 respectively.

10%

0%

Media

6%

Geographic

12%

20%

9%

Government

Media

1% 11%

7% 4%

41%

Publications

43%

Geographic Government Publications

29%

Cross-domain

Cross-domain

7% Life sciences

Figure 4: The distribution of triples by domain.

Life sciences

Figure 5: The distribution of links by domain.

Government sites, and also geographic sites have the high percentage of information located in triples. It means that these sites have a good connection inside them, and the information related is easiest to find using RDF crawlers. This two kind of sites allow the discovery of more and more information related to a single item, following the information located in the triples. Connections between different kind of data sources are high in the domains related to Life sciences and publications. The crawling of information using this kind of links allow not only the discovery of new information, but also the comparison of different kinds of data sources related to the same topic. It could be offer to the user, different perspectives of the same theme, in order to provide some kind of truthfulness to the information found in the web.

3.1.8.

Use of vocabularies in the data sets in the LOD cloud diagram

Standards would make it easier to write an application to mesh distributed databases together, so that a computer could use the data sources together to help an end-user make better decisions. Another important thing is that vocabularies must be able to be mixed together. Several vocabularies are used in the data sets provided by LOD cloud diagram. Table 2, shows the most important vocabularies used in the current stare of the linked data community.

36

Linked data for improving student experience in searching e-learning resources 2011 Table 2: Vocabularies widely used in LOD cloud diagram and links provided to the data sources

Vocabulary prefix

Vocabulary link

Number of usages in data sets 105

Data sets that use the vocabulary

dc

http://purl.org/dc/elements/1.1/

Data sets that use dc

foaf

http://xmlns.com/foaf/0.1/

72

Data sets that use foaf

skos

http://www.w3.org/2004/02/skos/core#

46

Data sets that use skos

geo

http://www.w3.org/2003/01/geo/wgs84_pos#

22

Data sets that use geo

akt

http://www.aktors.org/ontology/portal#

17

Data sets that use akt

xhtml

http://www.w3.org/1999/xhtml/vocab#

16

Data sets that use xhtml

mo

http://purl.org/ontology/mo/

13

Data sets that use mo

bibo

http://purl.org/ontology/bibo/

12

Data sets that use bibo

sioc

http://rdfs.org/sioc/ns#

8

Data sets that use sioc

vcard

http://www.w3.org/2006/vcard/ns#

7

Data sets that use vcard

cc

http://creativecommons.org/ns#

7

Data sets that use cc

geonames

http://www.geonames.org/ontology#

6

Data sets that use geonames

frbr

http://purl.org/vocab/frbr/core#

6

Data sets that use frbr

event

http://purl.org/NET/c4dm/event.owl#

5

Data sets that use event

dbpedia

http://dbpedia.org/resource/

5

Data sets that use dbpedia

time

http://www.w3.org/2006/time#

5

Data sets that use time

xsd

http://www.w3.org/2001/XMLSchema#

5

Data sets that use xsd

ore

http://www.openarchives.org/ore/terms/

4

Data sets that use ore

dbo

http://dbpedia.org/ontology/

4

Data sets that use dbo

bio

http://purl.org/vocab/bio/0.1/

4

Data sets that use bio

uniprot

http://purl.uniprot.org/core/

3

Data sets that use uniprot

void

http://rdfs.org/ns/void#

3

Data sets that use void

dbp

http://dbpedia.org/property/

3

Data sets that use dbp

http

http://www.w3.org/2006/http#

3

Data sets that use http

scovo

http://purl.org/NET/scovo#

3

Data sets that use scovo

rev

http://purl.org/stuff/rev#

3

Data sets that use rev

umbel

http://umbel.org/umbel#

3

Data sets that use umbel

metalex

2

Data sets that use metalex

vu-wordnet

2

Data sets that use vu-wordnet

tl

http://purl.org/NET/c4dm/timeline.owl#

2

Data sets that use tl

swrc

http://swrc.ontoware.org/ontology#

2

Data sets that use swrc

gr

http://purl.org/goodrelations/v1#

2

Data sets that use gr

2

Data sets that use api

api sawsdl

http://www.w3.org/ns/sawsdl#

2

Data sets that use sawsdl

wdrs

http://www.w3.org/2007/05/powder-s#

2

Data sets that use wdrs

tag

http://www.holygoat.co.uk/owl/redwood/0.1/tags/

2

Data sets that use tag

txn

http://lod.taxonconcept.org/ontology/txn.owl#

2

Data sets that use txn

doap

http://usefulinc.com/ns/doap#

2

Data sets that use doap

geospecies

http://rdf.geospecies.org/ont/geospecies#

2

Data sets that use geospecies

37

Linked data for improving student experience in searching e-learning resources 2011 Figure 6, shows the distribution of most widely used vocabularies. This figure shows that the most used vocabularies are Dublín Core (dc), Friend Of A Friend (foaf), Simple Knowledge Organization System (skos), Geospatial metadata (geo) and Advanced Knowledge Technologies (akt).

Number of usages in data sets dc foaf 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 1% 1% 1% 1% 1% 25% 1% 1% 1% 1% 1% 1% 1% 2% 2% 2% 3% 3% 17% 4% 4% 5% 11%

skos geo akt xhtml mo bibo sioc

Figure 6: The distribution of most widely used vocabularies

3.1.9.

Data sources that provide origin metadata

Our project is oriented not only for the data itself but also for the provenance of data in order to be sure about the quality of them and their usability in e-learning. The data sets in LOD cloud diagram offers the following information about provenance metadata: Currently: 

76 (33.63 %) out of the 256 data sources provide provenance information.



150 (66.37 %) out of the 256 data sources do not provide provenance information.

Many data sources provide useful information about the source of the data.

Table 3

provides information about the provenance metadata by domain:

38

Linked data for improving student experience in searching e-learning resources 2011 Table 3: Provide origin metadata by topical domain

Domain Media Geographic Government Publications Cross-domain Life sciences User-generated content

Provenance information 5/27 (18.52 %) 10/22 (45.45 %) 7/39 (17.95 %) 41/82 (50.00 %) 7/32 (21.88 %) 1/41 (2.44 %) 5/13 (38.46 %)

3.1.10. Data sources that provide licensing metadata Metadata provides a way to protect copyright of digital resources by embedding ownership and contact information directly into files. Currently the LOD cloud diagram can provide: 

34 (14.98 %) out of the 256 data sources provide licensing information.



193 (85.02 %) out of the 256 data sources do not provide licensing information.

Table 4 shows information about the licensing metadata by domain:

Table 4: Licensing metadata

Domain Media Geographic Government Publications Cross-domain Life sciences User-generated content

Licensing information 4/27 (14.81 %) 7/22 (31.82 %) 5/39 (12.82 %) 8/82 (9.76 %) 6/32 (18.75 %) 1/41 (2.44 %) 3/13 (23.08 %)

When users know the restrictions about the data, they can take better decisions about their use. There is a 34% of the information shared with linked data technology that allows these data license in order to be sure about legal basis.

39

2011

3.2. THE INTERNET IN EDUCATION, AND SUITABLE SOURCES OF e-LEARNING RESOURCES.

3.2.1.

Web 2.0 and collaborative e-learning

The start point of the present review was taken from Crook et al. [30] and Tiropanis et al. [31]. These works talk about web 2.0 and its impact not only in the social life of people but also in their new learning approach and suggest that Web 2.0 is an opportunity to be independent in their study and research, because e-learners can find on the internet interesting communication ways, unlimited data sources, free data storage, and so on. The conclusions in Gillet et al. [32]

involve also, the uses of PLE´s (Personal Learning

Environments) as an approach to conducting learning using the advantages of the information available on the internet not only as a collection of artifacts but also as a collective intelligence with proper participatory approach. The features of the web 2.0, in addition, encourage learning in collaboration with any people and a certain level of trust in their knowledge. The assumptions of Kukulska [33] implies a new concept of ―Learning cultures‖ referring to the new ways of acquiring knowledge in a pervasive way. In [30], [31], [32] y [33], is established ―the learner-centred education‖ as the pinpoint of the new learning era.

In fact, the internet,

has impacted the learning approaches in

individuals, because the internet promote new ways of inquiry, literacy, collaboration, and publication. More people are interested in showing their work to others in order to receive some kind of feedback, and more people are interested in commenting and ranking the work of others.

Remarkable characteristics of the previously cited works are the collection

of online communities for building a knowledge sharing through different kind of web 2.0 activities as well as web 3.0, or semantic web; such as media sharing, web mashups, conversational arenas, and so forth. Taking into account the previous assumptions, we are interested in establishing a path to follow in our investigation: the need to consult students´ opinions and convey their own thoughts about their relationships with Web 2.0 in building their own knowledge.


3.2.2.

Learning resources and their potential

By digging on the internet applications with learning purposes, it is feasible to find several valuable contents for e-learning published in online learning environments such as LMS (Learning Management System), and VLE (Virtual Learning Environment) which exploits advantages of web as interaction media. Examples of those environments are: Blackboard (http://www.blackboard.com/),

Moodle

(http://moodle.org/),

Its

learning

(http://www.itslearning.co.uk/), and so forth. These environments offer to students, different kind of contents with high level of reliability, because teachers, who work in these platforms, create contents to use in their own e-learning classes and their contents would be considered of high quality. The quality in the e-learning resources offered by educational institutions is considered high because the material is derived from mainstream teaching activities that are already subject to quality assurance process, Lane [34]. There are many other e-learning environments disseminated on the internet. These web environments have been filled with courses previously created for being used in a normal classroom and these courses are available free on the internet. An interesting example of the e-learning environments mentioned is OOPS (Opensource Opencourseware Prototype System - http://oops.editme.com/). OOPS is a community project that hosts several videos waiting to be transcribed in order to be used as a written resource. The contributions are posted on the web site as well as the videos and recordings. The dataset used for showing the information is a good example of monitoring the work around the recordings. Other valuable examples of this kind of resources are Open Courseware Consortium (http://www.ocwconsortium.org/) and MIT Open Courseware (http://ocw.mit.edu/index.htm). These e-courses provide resources that could be identified as a good alternative of sharing reliable knowledge, because they corresponds to open courseware initiatives under way at many colleges and universities, Katz [35]. Moreover, there are other interesting sources of knowledge presented in a more informal way than previous and they are available on the web for free, for example blogs, journals, libraries, and so on. Respect of them, Lane [34] also notes: ―Open content is largely digital stuff (music, images, works, animations) created by somebody who has attached an open license to it‖. But, of course, data collection on the internet with learning purposes must have a process carefully designed, because some information are irrelevant. Around this issue, Katz [35] comments: "Today´s superabundant but decontextualized, filtered, mashed up, photoshoped, crowd-source and opaque information environment also contains the potential for leaving people in the dark".

41

Linked data for improving student experience in searching e-learning resources 2011 The conclusion of these assumptions is that several e-learning contents are available on, but these are hard to be found and need to be evaluated.

Furthermore, these e-learning

contents need to be linked in order to be found easily to improve the experience of elearners.

It is also significant to understand, how the student assumes their process from

the motivation point of view. The work of Keller et al. [36] proposed a model called ARCS (Attention, Relevance, Confidence, Satisfaction). This model could determine the availability of published contents, enabling, for example, ranking or qualification of them. From here, it is feasible to arise an interesting question: what would happen if the e-learners are enabled to judge the contents and make rankings with them?

Are the e-learners

interested to make rankings and qualifications?

3.2.3.

Educational issues in front of technological mediations

To make available technological resources for e-learners, is a task that demanding not only the advantages of the semantic web but also pedagogical features, like monitoring of the learning, relevance of the contents, and so forth. Maintaining pedagogical strategies is, for sure, the most difficult task in these kind of e-learning environments, because the teacher is not available all the time in the web. Therefore, the contents selected must be as good as possible. A way to realize this kind of contents is keep the connectivity among several educational institutions as well as other kind of formal data sources like newspapers, virtual libraries, social networks oriented to education, scientific authors and so on. Also, distance learning and its contents have growing acceptance because they are taken seriously as is done with learning face to face, Geith [37]. The access to data collections, the creation of personal knowledge, as well as the contributions by the scientists, could be enabled in many sources on the web. But in this context, both the teacher and the learner, must be active part of the process of information verification, being carefully attentive about the learning design. Learners need to think about or be helped to understanding their own learning process as well as collaborate or cooperate in that learning design, Lane [34].

Alexander

[38] also says: ―Another way forward is to learn more about those who have already leaped into the web to teach, and to follow the emerging learning 2.0‖. This establishes the need to create an awareness of learning on minds of those who have access to resources: a really important task, that is part of any educational environment placed on the web.

42


3.3. SURVEYS

RELATED

TO

SEARCHING

E-LEARNING

RESOURCES BY E-LEARNERS.

The way in which learners use the information technology in their learning process has been addressed by several studies. Adaptation profiling systems have been used to understand the self-learning experience.

Tzouveli et al. [39]

describe an integrated e-learning

application, able to continuously adapt its services to its learners’ preferences, usage history, and particular needs as they evolve during its usage. In this application, the researchers use questionnaires to identify the needs and how the learners improve their learning process.

They are interested in knowing the professional background of the participants,

the uses of the internet with learning purposes, availability of the internet connection in the learner´s home and in the work environment, the use of additional sources of educational material and so on.

The usage of learners’ responses to the e-questionnaires leads

additionally to statistics analysis, and so,

reassign learners to different learner profiles,

according to [39]. Two issues have been studied by Kirkwood [40]: Why and how independent learners people who use Web-based resources, exploring not only the academic context of the courses studied, but also any relevant personal, domestic and employment-related circumstances - tackle the exploration in the world wide web. The study shows that the participants are familiar with using search engines like Google or Yahoo. Also participants have connected the internet use with the learning of hobby activities. The study has also found factors that were related to the educational context of studying with the university or with a particular course module. Other aspects like the technical ones (password-protected facilities), pedagogic design of courses and courses assessments were taken into account. The conclusion was that the students in this study are not averse to using the Web to find information that they feel will be of benefit to them, in the context of their own learning experience. Hargitti et al., have evaluated the process of information searching,

highlighting reliable

features of web sites, and how these sites receive credibility by users, or perceptions of credibility when evaluating information online, [41]. This work shows evidence of users’ trust in the information gathered by search engines. The gap in this study is that students often did not investigate the results of the search engines with regard to who authored the information. Also, Web sites from educational organizations and government entities were often trusted more than the average commercial site. The use of the web to contact people

43

Linked data for improving student experience in searching e-learning resources 2011 are important for the participants in this survey research. The questions of the study were made from a young population (first-year students at an urban public research university), but the results can be compared with those which could be taken from more experienced students (in master courses or doctoral ones). Students´ opinions about social networking used with literacy purposes was tackled by Connell [42]. Most students indicated that they accept to be contacted by library through Facebook or MySpace, but a sizable minority reacted negatively to the concept. The important thing regards to this survey is the acceptance of the social networks to get information not only for social aspects but also for educational goals. Moreover, 58 % of the participants respond that they would proactively add librarians as friend. The results allows to think to the possibility of using social network sites for educational outreach. Surveys are useful tool to address people opinions. The results of [39], [40], [41], and [42], are close to the intention of our research, because most of the results could be compared with similar ones taken from students selected in different levels, in Malmö University, in order to understand the behaviour of them when searching e-learning resources and how they address reliability in the sources. The survey described in this thesis in chapter 3, section 3.2., is shown in Appendix A. This survey outlines a longer aspects about the behaviour of the students in searching resources, when they use the web to learn. These behaviours are related to the use of specific sources, as well as social networks, to get learning information. Also, the survey addresses the students´ opinions about the way of exploring, the criteria for searching, the uses of other people´s opinions in front of e-learning resources and how they are interested in contribute with their own opinions.

44


4. INVESTIGATION OF THE METHODS USED BY STUDENT FOR EXPLORING AND DISCOVERING eLEARNING RESOURCES

4.1. DATA ANALYSIS AND INTEPRETATION 4.1.1.

Data collection

The total number of subjects in the sample was 90 students from all the study levels in Malmö University. At the end of the data collection, the total amount of questionnaires collected was 82 that corresponds to a 91,11% of the sample. rejected, because the data collected were incomplete.

8 questionnaires were

The data collection made in

classroom, with the supervision of a teacher, was more effective and did not require as much time as the collection made in open places like the library or student accommodation. Also, people must be motivated about the importance of the research and how it could affect in a positive way their life as a students.

We are sure that the questionnaires collected

have reliable and relevant information to address the current study, because respondents were observed by the researcher, while they answered the questionnaires. The time in responses was the estimated one (the estimated time was among 15 and 20 minutes).

4.1.2.

Data analysis

Subjects, who answered the questionnaire, are students of Malmö University. In total, 82. They belong to different careers and different levels of education. Of these 33% are undergraduate students, 32% are master students, 24%

are college students, 6% are

students of single courses and 5% are doctoral students. In terms of discipline area, 27% of the entire sample are studying computer science; 21% social science; 17% other areas like cultural studies, humanities, sign language, economics, languages and culture, global political science, translation and interpreting, physical education and sport science, fine art, performing arts design, scene production, construction and creative writing; 11% architecture/design; 10% medical/oral science; 8% business and 6% pedagogy. The majority of the groups are

undergraduate students, and the area of computer science.

Nevertheless, the difference with the second group is not significant because the

45

Linked data for improving student experience in searching e-learning resources 2011 percentages are similar. The second largest group are students belong to master courses and study area in social science. Figures 7 and 8 show the total amount of students in the different levels as well as discipline areas.

Level

Number

Undergraduate Student

27

Master Student

26

College Student

20

Student of single course

5

Doctoral Student

4

Total

5%

6%

33% 24%

32%

82

Undergraduate Student Master Student College Student Student of single course Doctoral Student

Figure 7: Educational Level

Area

Number

Computer Science

22

Social Science

17

Other

14

Architecture/Design

9

Medical/Oral Science

8

Business

7

Pedagogy

5

Total

Computer Science

Social Science

Other

Architecture/Design

Medical/Oral Science

Business

Pedagogy 10% 11%

8%

6% 27% 17%

21%

82

Other: Cultural studies, humanities, Sign language, Economics, Languages and culture, Global political science, Translation and interpreting, Physical education and sport science, Fine Art, Performing arts design, scene production, construction, creative writing. Figure 8: Discipline Area

46

Linked data for improving student experience in searching e-learning resources 2011 Students have different ways to search e-learning resources. The questionnaire invited students to prioritize their searches.

66% of the respondents make their searches using

Google in first place and Google Scholar in the second place with 11%. 28% selected Wikipedia in first place, 22% Google Scholar again and in third place also Google with 12%. 18% chose Wikipedia in first place, 14% selected Google scholar in the second place and with 12% each one

were selected two sources;

University/teachers web pages and

YouTube. 16% selected YouTube in first place Wikipedia as well as free e-books, were selected by 14%of subjects.

Figures 9 presents the most important sites and sources of

resources of e-learning resources used by students. 100% of the students have selected resources in their first option. 90.24% of the respondents also have second option. 79.27% of the respondents have third option and the 52.44% have fourth option. The results show that the most used sites for searching e-learning resources are: Google, Google scholar, Wikipedia, University/teachers web pages, YouTube, scientific journals, and free e-books

60 50 40 30 Priority 1

20

Priority 2 Priority 3

10

Priority 4 0

Figure 9: Sites/Sources of e-learning resources most visited

47

Linked data for improving student experience in searching e-learning resources 2011 In order to emphasize the use of social networks in the personal e-learning experience, was necessary to examine how the students are behaving in front of social networking. The results shows that social networks are not used by the majority students, 57%, but the social collaborative networking learning is chosen by many students 43% as figure 10 shows. The most social network used in learning is Facebook as well as Flicker. Students also mention LinkedIn, Google Docs and Twitter. They said in their comments that these social networks are used to set academic meetings/events, or to share documents.

Do you use any kind of social networking collaborative learning to search elearning resources ?

43% No

57%

Yes

Figure 10: Use of social networks for learning

The most common initial approach, when students search e-learning resources, is to write a descriptive phrase of the topic searched 71%. In second place 15% of the subjects try with one keyword. It means that, frequently, students have a clear idea about the topic they are looking for. Figure 11 explains the percentage reached in this particular question.

When you search for e-learning resources, which of the following statements best describes your initial approach? 4%

I try with a descriptive phrase such as "Teaching Pets skills".

2%

8%

I try with a keyword for example "pets"

15%

I try with advanced search 71% I don't always have a clear idea how to describe what I'm Other

Figure 11: Initial approach in searching e-learning resources

48

Linked data for improving student experience in searching e-learning resources 2011 The question related to the criteria in searching e-learning resources, evaluated by the respondents. It was used a 5-point Likert-scale regarding the following nine LOM (learning object metadata) features: availability, source, date of creation, type, author, format, rights, target age and interactivity level.

The Likert-scale was coded in the following way:

extremely important, somewhat important, neutral, somewhat unimportant and extremely unimportant. Most participants classified their responses in the scale as Somewhat Important, while other important rate of responses was Neutral, and Extremely important. It could be interpreted, in general, that the students are interested in the classification criteria proposed. These criteria were taken from the Learning Object Metadata Standard. In terms of searching criteria, 31% of the selections of the respondents considered extremely important that the e-learning resources have information about the availability, it means if the resources are free or payable;

the sources of the resource, such as universities, training centers, with 21% are

also extremely important. 16% of the selections of the respondents are interested in the date of creation of the resource. Author is also considered as the somewhat important with 14%. Respondents are neutral with information about the interactivity level 20% and the format as well the type with 15%. As the Unimportant selection are not largely than 15%, these answers were not taken into account. The result in general shows the following order of importance in Information of learning resources: Availability, Source, Date of creation and Author. Format and Type are features that are considered neutral by the respondents. Figure 12, shows the choices of the students.

How important could be the following criteria to your search for e-learning resources? 60 50 40 30 20 10 0

Extremely important

Somewhat important

Somewhat unimportant

Extremely unimportant

Neutral

Figure 12: Criteria in searching e-learning resources

49

Linked data for improving student experience in searching e-learning resources 2011 Recommendations placed in front of e-learning resources are part of this study. 71% of the respondents show tendency in paying attention to the recommendations about e-learning resources made by other people, especially if they are teachers or students. Of course, it depends of the confidence in the author of this recommendation as well as the subject selected. Figure 13 shows this responses.. Numb er 58 13 11 82

Option Yes, All time In some occasions Never Total

Comments  Of course, If teacher give an idea or example I will look it up  It depends on what kinds of resources as some elearning resources would be useful for searching particular resources.  When I don´t know anything about the subject  For my bachelor thesis, or other kind of bigger work/research  Depends of my confidence in the person recommending it

Are you interested in using elearning resources recommended by somebody else (specially teachers or other students)? Yes, All time 13% 16% 71%

In some ocassions Never; I prefer to make my own choices

Figure 13: Resources recommended by teachers or other students

To a lesser extent, 46% of the respondents would select e-learning resources with comments.

21% of the surveyed student recognize that sometimes they take into account

the comments of other people but if they can assess the relevance thereof. Figure 14 shows the frequencies of this behaviour Option Yes, all time No Sometimes Total

Number 38 27 17 82

Comments  Only when I have time  If I am suspicious about the content  Depending on in which it is and where it´s published  I try assess the quality of the information and cross-reference it with other sources  If the quality is poorly, but the subject matches my approach/study. I´ll still check it out or/and use it

Some e-learning resources have comments about their quality. Do you pay attention to these comments when you select the resource? 46%

21%

Yes, all time 33%

No Sometimes

Figure 14: Choosing of resources with comments

50

Linked data for improving student experience in searching e-learning resources 2011 In terms of rankings of e-learning resources, 41% of the respondents prefer not use this kind of information when they are selecting the resource. However, 38% of the respondents are sure to use it, while 21% use it sometimes as Figure 15 shows. Option No Yes, all time Sometimes

Number

Some e-learning resources have been ranked by people, according to their usefulness. Do you use these rankings for support your selection?

34 31 17 82

Comments  Specially if I am to quote some material  I´ll take a look the rankings but I´ll decided the quality by myself  If I think the opinions given actually make sense. Often I find opinions without substance  Depending on the forum, it I have confidence in rest of the forums people

No 21%

41% Yes, all time

38%

Sometimes

Figure 15: Rankings in e-learning resources

Citations on articles and books are not extremely important for students when they exploring and selecting e-learning resources.

57% of the respondents prefer not to use citations,

while 29% used it all the time. Figure 16 shows this aspect Option No Yes, All time Sometimes

Number

When you select articles on internet, do you check the amount of citations that have in other articles or books?

43 22 11 76

Comments  It depends of the purpose of the article. If a need as primary source or just to review a topic  Depends on it I´m going to use it myself as a important source  If I will use the information for my own work  If the articles doesn´t feel reliable  Depends on what I´m looking for

14%

No Yes, All time

29%

Sometime s 57%

Figure 16: Citations on articles and books

51

Linked data for improving student experience in searching e-learning resources 2011 In terms of the sense of reliability in sources of e-learning resources,

most of participants

have decided that the sources indicated are Somewhat Trustworthy, while other important amount of subjects have decided that the same sources are Extremely Important as well as that the resources presented in the list, are not applicable in their cases selections of Somewhat Untrustworthy, Extremely Untrustworthy and

Because of

Depends of the

specific author or source, are below of 10% , this selections will be not take into account for this analysis. In this vein, students present somewhat trustworthy in resources taken from Google maps 12%, Wikipedia 10%, Scientific Online Magazines 10%, Digital Libraries almost 10%, Google scholar almost 10%, and Government Sites 9%.

Students show

extremely trustworthy in Academic Learning Resources 19%, Scientific web pages 18%, Scientific Online Magazines 11%, as well as Google Scholar with the same percentage, and Government Sites with almost 11%.

In the other hand, respondents shows neutrality

with Yahoo Answers 13%, Thematic Blogs and Thematic Forums with 12%, and Free eBooks with 10%. Resources that are not applicable in their personal selections are Slide Share with 20%, Thematic Mailing List 19%, and Thematic Forums with 12%. Figure 17 shows the results and make a comparison between them. In general, respondents shows some kind of reliability in the following resources: Google maps, Wikipedia, Scientific Online Magazines, Digital Libraries, Google scholar, Government Sites, Academic Learning Resources and Scientific web pages.

60 50

Extremely trustworthy

40 Somewhat trustworthy

30 20

Neutral

10 Somewhat untrustworthy Yahoo answers

Thematic mailing list

Thematic forums

Thematic blogs

Wikipedia

Youtube videos

Slideshare

Free e-books

Digital Libraries

Google maps

Google scholar

Goverment sites

Scientific online magazines

Scientific Web pages

Academic Learning Resources

0 Extremely untrustworthy NA Depends of the specific author or source

Figure 17: Sense of reliability in recognized sources of learning resources

52

Linked data for improving student experience in searching e-learning resources 2011 The questionnaire asks to the respondents about the selection and frequent use of elearning resources. 100% of the respondents selected, in their first option:

lectures 29%, Online research

papers 17%, Videos 15%, e-Books 12%, and Tutorials or manuals 11%. 95% of the respondents have a second option and the results are: Online newspapers information 19%, Online research papers 18%, Videos as well as Interactive resources (questionnaires, exercise, simulations…) with 15%. 85% of the respondents have a third option. The results of this third option is: Statistics 16% as well as Interactive resources (questionnaires, exercise, simulations…), Thematic blogs and e-Book with 14%. Only 54% of the respondents have a fourth option and this selection agree with resources selected before. Figure 18 shows the statistics in this question.

25 Lectures

Online research papers

20

Videos 15 E-books

10

Tutorials or manuals

Online newspapers information 5 Oficial websites information (government sites) 0 Option 1

Option 2

Option 3

Option 4

Figure 18: Preferences in use of learning resources

53

Linked data for improving student experience in searching e-learning resources 2011 Participants surveyed prefer to use automatic procedures for keeping the URL of the elearning resources found on the internet. In first place, students prefer to bookmark the URL. In the second place, students prefer to copy de URL in a word processor. Figure 19 shows the frequencies of the procedures. I bookmark the page in my web browser

45 40 35 30 25 20 15 10 5 0

I copy the URL in a word document I check the history in my web browser

Option 1

Option 2

I write by hand the name of the web page in my notebook

Option 3

Figure 19: Ways for saving URL´s

There are three relevant aspects that were taken into account for evaluating the usefulness of the online learning resources:

consider the possibility to make comments about the

quality of the e-learning resource, the possibility to make

rankings in

the e-learning

resources according to their usefulness and the intention in belonging to an e-learning/escience group. In general, respondents are not interested in make comments, or make rankings, or belong to any social networking collaborative learning. However, the 48% of respondents are interested in have some kind of connections using

any e-learning/e-

science group. Figures 20, 21 and 22 show the percentages reached in these questions. Criteria Never Always Sometimes total

Number

Do you consider the possibility to make comments about the quality of the e-learning resource you have used in order to publish your own opinion about the resource and facilitate the learning experience of other people?

56 15 11 82

Comments  If it´s unreliable I would want to tell others so.  Depends on how important my comment is- based on my research. But not comment just to comment  When there is clear and easy opportunity to do so  Sometimes I answer queries  Sometimes share my own opinion, but not often  Google scholar or serious e-learning resources

14% 18%

Never 68%

Always Sometimes

Figure 20: Possibility of making comments

54

Linked data for improving student experience in searching e-learning resources 2011 Criteria Never Always Sometimes Total

Number

Are you interested in ranking the elearning resources according to their usefulness?

42 21 19 82

Comments  Never: What is useful for me is not necessarily useful to another  Sure, It would be a good idea  If it seems to be useful for other users  When there is clear and easy opportunity to do so  Like now  If I feel that I am contributing . In that case it´s to improve quality  For help other people  Not always

23% Never 51%

Always Sometimes

26%

Figure 21: Possibility of making rankings

Criteria

Number

Yes

39

No I already belong to a elearning/e-science group

42

Total

82

Comments

Are you interested in belong to an elearning/e-science group ?

No 1%

1 48%

Yes 51%

 Internet. I follow several sites

I already belong to a elearning/escience group

Figure 22: Interest in belonging to a e-learning/s-science group

4.1.3.

Answers to the key research questions of the questionnaire

Question 1: What is the educational level and discipline that respondent is studying in Malmö University? Students belong to different levels and different careers.

Most of them are from

undergraduate and from master levels. The discipline areas of the students are, in their

55

Linked data for improving student experience in searching e-learning resources 2011 majority, students of computer science and social science. However, a sample of many other disciplines was selected.

Question 2:

How do the students explore the internet for searching e- learning

objects? The most used sites for searching e-learning resources are:

Google,

Google scholar,

Wikipedia, University/teachers web pages, Youtube and free e-books. Social networks are not used by the majority students, but the social collaborative networking learning would be a phenomena considered by many students. The initial approach, when students search elearning resources, is to write a descriptive phrase of the topic searched and the most important information of e-learning resources required by the students are:

Availability,

Source, Date of creation and Author.

Question 3: How important are suggestions of other people for the selection of elearning resources? Recommendations of teachers and other students are very important as well as citations of the books and articles in other resources. Comments and rankings made by other people are used by the students for selecting their own learning resources but not to a large extent

Question 4:

What sources of

e-learning resources shows more reliability in the

student´s preference? Students shows some kind of reliability in the following resources: Google maps, Wikipedia, Scientific Online Magazines, Digital Libraries, Google scholar, Government Sites, Academic Learning Resources and Scientific web pages

Question 5: What kind of e-learning resources the students prefer and how they manage the source of that resource? Students surveyed prefer to use automatic procedures for keeping the URL of the e-learning resources found on the internet like bookmarking. For learning themselves, students prefer

56

Linked data for improving student experience in searching e-learning resources 2011 to use Lectures, Online research papers, Videos , e-Books , Tutorials or manuals, Online newspapers information, Interactive resources (questionnaires, exercise, simulations…), Statistics and thematic blogs.

Question 6: How do the student would assess the usefulness of the online learning resources as well as their intentions in sharing e-learning resources with mates? In general, student are not interesting in making comments or in ranking the resources. However, many of them are interested in belonging to any kind of social networking collaborative e-learning.

4.2.

INTERPRETATION OF THE RESULTS

The survey shows interesting aspects. a. There are many resources on the internet that have largely acceptance by the students.

People prefer those that are free on the internet like the resources

canalized by Wikipedia, Google Scholar, Google maps, free e-Books and YouTube. Many other resources are also largely accepted. These resources was selected by students because the sources come

from universities,

or the resources were

created by teachers.

b. Some social networks are used, for funnelling relevant information about academic activities like meetings or important dates. c. Students have clear ideas on how to search their e-learning

resources on the

internet and the majority of them use descriptive phrases of the topic.

d. The resources selected by students are better if they have information about Availability (free or payable), Source, Date of Creation and Author.

e. An interesting issue is related with the evaluation of the resources. The majority of the students are interesting in finding comments, rankings and citations about the elearning resources but the majority of them, also, are not interested in commenting,

57

Linked data for improving student experience in searching e-learning resources 2011 ranking or citating. People who thinks about making comments sometimes, argues about the significance of the resource in their own work could not be significant for others. f.

Expertise and education level of people, who belong to academic and research organizations, are widely accepted by the students because of their reliability. Their comments, recommendations and rankings have been took into account greatly. Academic resources are also accepted. Examples of these resources are Lectures, Online research papers, e-books, tutorials or manuals, and Interactive resources (questionnaires, exercise, simulations…). Statistics and government sites are good sources of information used in academic stuffs.

4.3. THREATS TO VALIDITY Creswell [7] explains the two types of threats of validity, ―that will raise questions about an experimenter´s ability to conclude that the intervention affects an outcome,”: Internal and external.

This specific research presents the following kinds of threats to internal validity:

Selection, because the participants with certain characteristics may predispose the responses; Diffusion, because the responses of one participant could be supported by the responses of other participant; and some kind of demoralization, because the questionnaire has many questions and it takes time to be answered. In response of these threats of validity, we applied the actions presented in Table 5: Table 5: Threat to Internal Validity

Type of Threat to

Actions

Internal Validity Selection



Selections of participants was in a Nonprobability sample specifically Quota Sampling, because the research designed is sure about the type of categories of students to investigate. These categories are related to the educational level that the students of Malmö University are pursuing.

Diffusion



Respondents were encouraged to answer without any kind of support

Demoralization



The researcher provides information about the importance of the study

58

Linked data for improving student experience in searching e-learning resources 2011 External validity refers to the extent to which the results of a research study are able to be generalized confidently to a group larger than the group that participated in the study. As Creswell explain ―External validity threats arise when experimenters draw incorrect inferences from the sample data to other persons, other settings, and past and future sitiations‖ There are two major threats to external validity tackled in this study: people and places. 

For people, the results of the study are due to the type of people who were in the study.



For place, the relevance of the specific place that determines the sample.

We can determine the importance of the study and the cover it has, explaining these two kind of treats of validity.

In response of these threats of validity, we applied the actions

presented in Table 6: Table 6: Threat to External Validity

Type of Threat to

Actions

External Validity People



People selected are part of the Malmö University. They must belong to any program the university offers for students. People involved in the university with just other kind of linkage, such as teachers or administrative, were not selected.



The current study restricts claims about the results just to the case of Malmö University, but the results can generalized in universities with the similar characteristics.

Places



In order to gather people involved in the University programs, the selection of participants take place in the university places such as the

library,

the classrooms and students corridors

(accommodation).

Before conduct the survey, the surveyor

made sure about the belonging of the participant to the university.

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5. THE PROTOTYPE DESIGN

5.1.

FEATURES OF A e-LEARNING ENVIRONMENT PROTOTYPE

The previous chapter mentions the methods used by students for exploring and discovering e-learning resources. A review of the LOD cloud diagram, shown in Appendix B,

reveals

that there are several internet information sources with deferencable URI´s and metadata, that follow the rules of linked data techniques.

These findings allow to create groups of

information sources, according to the students preferences and so, to make available the relevant information with better search results. This chapter is written to show how the gaps found in the literature review could be adapted to the new findings..

5.1.1.

Gaps found in the state-of-the art

There are many e-learning environments in the market with relevant features that offer solutions to the educational world.

The e-learning scenarios called LMS (Learning

Management systems) and VLE (Virtual Learning environments) such as Moodle, Blackboard and its Learning, (see section 2.2.2), require intervention of administrators in order to maintain the pertinence of the contents, and to control the participants contributions. The teachers’ activities are established as the main role, because they are the developers of all the relevant resources for the interaction in the environment. In addition, participation of the teachers are associated to the evaluation of the students, and also the reviewing of the contents that students are adding during the learning process. Also, teacher´s actions are oriented to solve the pedagogical issues that educational institutions have designed. Those who are called students are authorized to add contributions related with their personal work with the purpose of receive teachers assessment, or feedback.

Participants of the e-

learning environment must be registered in order to have access, explore and use the contents, and contribute. The behaviour of these sites is understandable because they belong to academic/scientific institutions that need to have control of the use of their technological facilities for avoiding security issues.

However, the contents placed in this

kind of environment are currently considered valuable because they are created by teachers

60

Linked data for improving student experience in searching e-learning resources 2011 to be used in classes both on-campus or at a distance. The gaps found in the literature review about this kind of learning environments are: 

Gap 1: The information placed in these environments are used for specific purposes like courseware, for a target audience and it has not free access for everybody



Gap 2: Typically these environments work isolated. There are no evidence of how these learning environments contribute with the web of data, using semantic web techniques, with the purpose of discovering new knowledge or making connections around the web of data



Gap 3: In most of the cases, learners are not authorized to make contributions in order to swell the contents information, although sometimes students can participate in forums or e-mailing list.



Gap 4: Usually, learners are not allowed to make comments or rankings in order to assess the contents.

On the other hand, the world wide web have sites for getting free information with e-learning purposes, allowing self-training. The information placed there, sometimes have significant features for being used as e-learning resource, but sometimes, the information is irrelevant. For this reason, many initiatives have been designed with the purpose of provide alternative ways to get information and use data for being independent in the learning activities. Some of these web solutions have used the principles of linked data techniques, but they do not offer to the learners the possibility to add more contents, or evaluate the resources, see section 2.1.6. The gaps found in this kind of initiatives are: 

Gap 5: These sites offer information about e-learning contents but do not provide a list of domains by subjects to follow the information



Gap 6: Mostly of these sites offer information of e-learning resources related to one kind of them, for example, sites for videos, sites for lectures, sites for articles, and so on, but there is no a site that includes information of several resources, in order to offer to the e-learners more than one chance to learn.



Gap 7: These sites lack of networking features to activate collaboration between the participants.



Gap 8:

There are no options for qualifying the resources either as comments or

rankings

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5.1.2.

A design proposal of an e-learning environment

We are interested in design an environment that shall remedy the gaps described above. The proposed collaborative e-learning environment will be structured to provide a frame of agreed rules and directives for exploring and publishing e-learning resources, as well as resources assessments in the forms of comments and rankings, with the purpose of ensuring trust of the information for other users, according with the subject topics selected. The rules and directives correspond with the principles of the linked data techniques mentioned in the section 2.1.1., 2.1.2.

as well as dereferencing URIs addressed in the section

By linking resources, the e-learning environment shall take into account the LOD

cloud diagram offered by the linked data community and the LOM metadata, explained in the section 2.1.4.

The data sets provided by this diagram allow the discovering of more

knowledge in the web of data, see LOD cloud diagram in the page 30. The participants in this e-learning environment will have two connotations: those who want to explore and use the e-learning resources and those who, in addition, wants to make contributions, in order to enlarge the community of e-learning.

These latter participants will

be called from now on e-learners. Taking into account the interpretations of the survey results, see section 4.2, and each e-learner shall have the possibility to read and annotate information about e-learning resources provided by the e-learning environment as well as to link information about new resources. Also, e-learners could structure information about resources according to their preferences. The knowledge-building is also available in social way, according to the given conditions for assessing resources, that means:

every member of the e-learning environment could

comment and rank the e-learning resources linked to the environment. These contributions will stored using linked data techniques in order to maintain the connectivity with the resources, and to enlarge the linked data community facilities. Figure 23 shows the solutions for managing the learning resources in the environment.

62


Figure 23: Contents in the e-learning environment prototype

Inside the e-learning collaborative environment, the resources shall be organize in the seven groups, described in the figure 23. The information about the resources would be provided by the linked data community and the e-learner himself, because he can publish new ones, and also, qualify the resources that are already linked in the environment. The LOD cloud diagram can enrich itself with the contributions of the e-learner (new published resources, and comments and rankings). Summarizing, the e-learning collaborative environment will be a web semantic application, elearner-centred, for both exploring and publishing knowledge, working together as a unified resource. Members and non-members of the environment would explore the information of the knowledge previously linked. Just members, or e-learners, could link the information of new resources from distributed and heterogeneous source, which belong to the principal categories selected by the students in the survey, that means lectures, online research papers, e-books, tutorials, interactive resources, statistics and government sites. Videos will be also an available category in the e-learning environment. environment

This characteristic of the

enables to discover relationships and resources that was not formerly

connected, using semantic web techniques (linked data principles). Also, members can make their own contributions in order to qualify the resource and organize their data following their own preferences.

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5.1.3.

The role of the teacher in the e-learning collaborative environment

The teacher’s role is very important in every learning process, and their job in e-learning is valuable and relevant.

However, web-based collaborative environments ―are a special

category of e-learning tools that support a group of learners in achieving a common learning goal‖, Bonk, CJ & Wisher [43]. Also, theories about social constructivism emphasize the human dialogue, interaction, negotiation, and collaboration as special points to follow in new ways to learn. Harasim et al. [44] and Warschauer [45] argue that online classrooms hold great potential for collaborative educational approaches because they feature many-to-many communication, place and time independence, and computer-mediated. Such environments are betting on collaborative e-learning interaction in which the role of the teacher shall be minimized. Although the importance of the teacher supervision, there are several instructional changes that take place in collaborative e-learning environments, because of the learner-centered movement. This approach includes all actions taken not only by the teacher but also by the student for the purpose of gathering information about the own learning process. Table 7 shows the questions that are appropriate, according to Minstrell et al [46], when learning is in progress: Table 7: Two Enactments of a Formative Assessment Cycle, Minstrell et al [46].

Teacher and teaching focus (less

Learning and learner focus (more

effective and most typical)

effective)

Gather data - How much have my students Collect data intentionally - What and how learned of what I have taught?

are students learning in relation to the learning goal?

Evaluate - How many ―got it‖? Did enough of Interpret - What are the strengths and them get it so I can move on or do I need to problematic aspects of the students thinking? slow down?

What experience or particular cognition do they need next to deepen their learning?

64

Linked data for improving student experience in searching e-learning resources 2011 React - Do I re-teach to the entire class or Act intentionally - What specific learning assign a review to a few? How can I teach experience or feedback will address more effectively next time?

Learning and learner focus allow students to be aware about their own learning and how they can manage their own needs related to e-learning resources, and also the importance to assess the resources, according to their usefulness in particular circumstances. The proposed e-learning collaborative environment shall facilitate contents sharing by the elearners gathered from trustworthy sources, as well as generation of new information.

The

confidence in the e-learning contents is the clue of the environment proposal, because this feature enables to the students a good selection of useful resources. The direct contribution of teachers have to be linked to the contents generated in virtual environments previously created by themselves, in their teaching activities. The teachers contributions are valuable in the e-learning process but we need to take into account that in online learning also are important the autonomy and self determination of the e-learner. We have studied in section 2.2 how, nowadays, the internet supports education. Web 2.0 provides to e-learners channels for be independent in their study or research. Also, there are communities of learning guided by experienced people in some specific knowledge areas, who are willing to provide good learning resources.

The students have

to be the builders of their own knowledge not only by using sources of information with the good quality offered by the e-learning environment, but also by judging them with contributions in rankings and comments about the usability of the resources and how they support their own e-learning experience. Tutors, teachers and teaching assistants are in charge of the contents placed in e-learning environments, as we mention in section 2.2.2.

These kind of professionals are also in

charge about evaluation of contents placed by the students, like homeworks or assignments. Students receive feedback from their teachers every now and then. The comments in the feedback, could be also valuable for other students. As it is working in many e-learning environments, the comments about the students performance are not available in a public way. The student´s performance just engages the student who is involved.

However,

some feedback could be interesting for everyone, and the student could mark it as public is he decided he want to shared. This information and many other activities used in e-learning sites by every teacher, tutor or teaching assistant could be shared and considered with high

65

Linked data for improving student experience in searching e-learning resources 2011 quality.

They are able to create resources with good quality for being used with e-learning

purposes and this is the kind of resources we are interested in linked in our environment. The origin of the data must ensure that e-learning resources have been created, revised or commented by people attached to an educational or scientific environment. This is the clue of the design proposed, in order to offer to the e-learners, effective data when they are interested in learning by themselves. In conclusion, the e-learning environment proposed is oriented to the learning mediated through the semantic search and the criteria of the e-learner, as well as the collaboration offered by other e-learners in the forms of comments and rankings of the resources, instead of the guidance given by

an specific teacher attached to an any kind of educational

institution For Future work, we are interested in design specifications that tackle a different kind of teachers participation in the environment. A teachers area will be designed in order to allow an special kind of profiling that would eliminate the mandatory link to educational institutions for getting trustworthy to the data. Also, in a public way, people could comment about the confidence they have in the people involved in the environment, as a teacher, if they decided to do.

5.1.4.

Selection of the e-learning contents.

The e-learning collaborative environment proposed in this thesis, is focused in the e-learner centered approach. Appendix B shows the mapping between the availability of data in the LOD cloud diagram, and the preferences of the students. Information placed in Appendix B allow to understand the current state of art about data that use linked data techniques. The availability and existence of these data are considered pertinent to start new developments in educational areas, because they derive from educational and scientific institutions like universities and research environments. The list of pages was organized according the students preferences found for the survey.

It was placed in the following

order: a. Students resources 

Lectures



Online research papers



e-books



Interactive resources

66


Stadistics and goverments sites

b. Academic sources 

Wikipedia



Google maps



Youtube



Universities/teacher web pages

The main criterion for selecting contents is the availability of them using Learning Object metadata LOM, see section 2.1.4.

Neven, F & Duval [47], made a survey the field of

learning object repositories and demonstrated with this study that this field is growing. Other work is found Sicilia et al [48]. This paper presents the completeness of learning object metadata of samples obtained from the MERLOT and CAREO repositories analysed, using the IEEE LOM standard as a reference framework.

5.1.5.

Comments and rankings

The success of the internet sites depends on the number and activity levels of their user members. The same thing happens with the educational environments. Users of the elearning collaborative environment have an important role in the selection of the e-learning resources that cover their educational needs, and also in the building of their own e-learning space in which they are going to interact with the resources. However, the e-learning collaborative environment proposed shall suggest to the e-learners the contents more visited by other e-learners, and also the highest rankings. The average numerical star rating assigned to a e-learning resource will be show in the corresponding information place. Also, the e-learner has to read the actual reviews to examine which of the positive and which of the negative aspect of the resource are of interest. For this particular project, the best effort for ranking reviews for e-learners shall come in the form of peer reviewing in the review space for each e-learning resource, where e-learners could give helpful votes to other reviews. We are interesting in the textual content of each comment, because they play an important role in the influence of the e-learner decisions, and thereby, the change of the approach used by the e-learners in front of selection of the contents.

67


Comments

Comments ranking

Star ranking

Figure 24: Level decision in front of the selection of e-learning resources

The corresponding prototype will cover the first stage of level decision, shown in the figure 24.

It means that the environment shall be enabled for calculate the average of the

numerical rankings provided by the users of the environment.

5.2. REQUIREMENTS SPECIFICATION In this section, we will transform the data collected into requirements specification, for the common software process. Requirements specification is the process of writing down the user and systems requirements in a requirements document, Sommerville [49].

Also,

Robertson says [50]: "Requirements are not an extra burden, but something that will enhance your analytical life". Requirements are important because allow to know about the functions or qualities of any product.

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5.2.1.

Functional requirements for the e-learning collaborative environment

Functional requirements are statements about the services that the system must provide. In this chapter we will describe the functional requirements of an e-learning collaborative environment that allow not only the exploration of e-learning resources but also the publishing of new ones. The following list corresponds to the requirements gathered from the interpretations of the results of the survey analysis in section 4.2.

F1: The e-learning environment shall have e-learning resources that allow the discovery of new resources or information related to new resources. F2:

The e-learning environment shall have social networking features for allowing

communication among the participants like postings of meetings, or publishing important dates F3: The resources available in the e-learning environment shall have information about their availability (free or payable), source, date of creation and author. F4: The e-learning environment shall provide mechanisms for evaluation of the resources and the author of this evaluation, in the form of comments or rankings. F5: The e-learning environment shall provide profiling management in which the e-learners can create, delete and modify their accounts F6: The e-learning environment shall provide configuration management for allowing the elearner links their contents as well as to provide structure according to their preferences. F7: The e-learning environment shall have forms for insert the information of new resources by e-learners. F8: The e-learning environment shall have the resources organized by topics in order to provide a help in terms of searching to the user. F9:

The e-learning resources information shall be categorized in the following order:

lectures, online research papers, e-books, interactive resources, statistics and government sites, videos, and tutorials, according with the preferences selected by the students.

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5.3. QUALITY

REQUIREMENTS

FOR

THE

E-LEARNING

COLLABORATIVE ENVIRONMENT 

Performance: The e-learning environment shall complete the retrievals and display the requested information, within one minute of the user entering the query.



Exception handling: The e-learning environment shall generate error messages when a query fails to run to completion within the allotted time.



Availability:

The e-learning environment shall operate through a commercially

available browser such as the Internet Explorer or Netscape. 

Liability of the contributors: a member of the environment.

People, who want to make contributions, must become Everybody can access the data and follow the

connections, but in order to make contributions, or organize personal information inside the environment, is necessary to have an user and password. 

Connectivity: The resources selected shall use semantic web techniques in order to facilitate the discovery of new resources.



Accessibility: Anyone could explore and use the information placed in the e-learning environment. Generic users of the environment are not obliged to be a member with user and password. The process of exploring information in the environment has no required membership.



Reliability: the e-learning environment shall be failure-free in 90% of the cases

5.4. STAKEHOLDERS Participants of the e-learning collaborative environment shall be people interested in gaining knowledge through the e-learning contents or contribute with the e-learning environment, publishing new contents. The types of participants shall be categorized in two:

Generic

user and e-learner Generic users shall be able to explore the resources information, and to use it. e-Learner shall be able to do the same actions than generic, and also make contributions inside the system.

The e-learner actions shall be recorded in the system, in order to

maintain a history of them. Their contributions to the systems shall be save using semantic web techniques.

70

Linked data for improving student experience in searching e-learning resources 2011 LOD cloud diagram shall provide the information previously linked by other projects that used the linked data technology. This community could provide many information and the discovery of new information, and the new resources published by the learners in the elearning collaborative environment are enabled to provide new links to the community.

5.5. USE CASES DIAGRAMS Use cases are used for understanding the process better. Sommerville [49] explains that ―a use case identifies the actors involved in an interaction and names the type of interaction.‖ Also Larman, [51] explains: ―use cases are requirements; primarily they are functional requirements that indicate what the system will do‖.

After applying the survey, many

conclusions were achieved and now they allow the description of the requirements for current project.

5.5.1.

General use case for student Interactions

The design of collaborative e-learning environment must allow students to develop some activities in a free way (without registration) and other activities in a conditional way (with registration). Figure 25 shows the general use case for students interactions

71


Figure 25: General use case for students interaction

5.5.2.

Use case index

The following table (table 8) corresponds to the use case index.

Every use case will have

various attributes relating both to the use case itself and to the project. At the project level, these attributes include primary actor, complexity, and priority.

72

Linked data for improving student experience in searching e-learning resources 2011 Table 8: Use case index of the project

Use case

Use case name

Primary Actor

Complexity

Priority

1

Provide e-learning resources

LOD cloud diagram

High

1

2

Explore e-learning resources

Generic user

High

1

3

Use e-learning resources

Generic user

Medium

1

4

View revisions of e-learning

Generic user

Low

2

ID

resources 5

Manage account

e-learner

High

1

5.1



Create account

e-learner

High

1

5.2



Delete account

e-learner

High

1

5.3



Modify account

e-learner

High

1

6

Manage personal e-learning

e-learner

Medium

2

e-learner

Medium

2

e-learner

Medium

2

e-learner

Medium

2

resources information 6.1



Save e-learning resources information

6.2



Delete e-learning resources information

6.3



Modify e-learning resources information

7

Post ads

e-learner

Medium

2

8

Make revisions of e-learning

e-learner

Medium

3

e-Learner

Medium

3

resources 9

Publish new e-learning resources

Use case templates are available in Appendix B

5.6. ARCHITECTURE OF THE e-LEARNING COLLABORATIVE ENVIRONMENTS WITH LINKED DATA TECHNIQUES

The software architecture implies the structure of the system, including the components and their relationships.

The architecture enables a software engineer to "(1) analyze the

73

Linked data for improving student experience in searching e-learning resources 2011 effectiveness of the design in meeting its stated requirements, (2) consider architectural alternatives at a stage when making design changes is still relatively easy, and (3) reducing the risks associated with the construction of the software",

Pressman [52].

The

architecture of the environment proposed shall enable the location of information about elearning resources, information about e-learner, e-learner´s preferences, and collaboration between e-learners.

This collaboration is important because the e-learners will provide

useful information to people around the environment through comments about their own experience with the resource. As we know, the personal experience of the people is always subjective, and for this reason, the way in which the resources will be commented is special, because the e-learner not only will write the comment itself, but also student needs to add some context to the annotations. Rankings on the e-learning resources information do not need additional context.

The resources published by e-learners must be contained all the

information required by the environment in order to provide enough data

in a way of

resources verification by other e-learners. E-Learners are also invited to evaluate the data in the environment in order to enabled the high quality of them. Collaborative e-learning is a process that helps e-learner become members of the knowledge and create the feeling of belonging. For this reason, this is the clue issue we need to reach in the architecture proposed.

5.6.1.

Transform mapping

There are many architectural styles. Transform mapping allows a data flow diagram (DFD) for describing steps in the design, Pressman [52].

The following diagram, figure 26

summarize the design idea into a DFD figure, showing the more relevant actions in the environment and the data flow inside them.

74

2011

Figure 26: Data flow diagram for the environment

2011

5.6.2.

Modules

Modularity allows software to be split into components with separate names. Also, the functionality is conducted individually, Pressman [52].

The e-learning collaborative

environment will be based in the following modules:

Figure 27: General architecture or the e-learning collaborative environment



Account module: It has the information for creating new accounts. In this module the e-learner could manage its own account: create, delete and modify the account. Use cases associated with this module are:



o

5: Manage account

o

5.1: Create account

o

5.2: Delete account

o

5.3: Modify account

Access module: It has the information related to the users previously created and their preferences. Use cases associated with this module are: o

6: Manage personal e-learning resources information




o

6.1: Save e-learning resources information

o

6.2: Delete e-learning resources information

o

6.3: Modify e-learning resources information

o

7: Post ads

o

8: Make revisions of e-learning resources

o

9: Publish new e-learning resources

Exploring module: It has the information about the resources linked from the LOD cloud diagram or new ones, created by the e-learner.

In this module, the e-

learner/generic user could explore and use the resources, using a SPARQL endpoint. This module are in charge of providing the resources to the e-learning collaborative environment. Use cases associated with this module are: o

1: Provide e-learning resources

o

2: Explore e-learning resources

o

3: Use e-learning resources

o

4: View revisions of e-learning resources

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6. REQUIREMENTS VALIDATION Validation and verification is the process of checking whether the requirements, as identified, do not contradict the expectations about the system of various stakeholders, and do not contradict each other. This process is also, the step in which the software analyst detects inconsistencies, omissions and errors, Pressman [52]. The main mechanism for validating the requirements is the formal technical reviews.

This method involving a structured

encounter in which a group of technical personnel analyses an artefact according to a welldefined process.

The process has to be clear for the stakeholders. Requirements analyst

must define some inputs for this process and also, he needs to define some outputs to continue the analysis.

Figure 28: The process of requirements validation

There are several validation methods. The three most common are:



Requirements traceability: Consist in create a traceability matrix for checking relationships that exist within and across software requirements, design, and implementation



Requirements reviews checklist:

Consist in meetings in which an analysts team

trying to locate errors in the specification document.



Prototyping: This method involves the creation of a model about the future software system from the requirements that have been picked up in the specification. This model will be evaluated by the customer and users to verify their correctness and completeness.

78


Generating test cases (requirements test):

This method shall check the

requirements testability. It consists in create test cases definition that allow to verify compliance with functional requirements. In the case of this thesis we are going to select two methods for validate requirements: Requirements traceability and requirements review checklist.

6.1. REQUIREMENTS TRACEABILITY Requirements traceability, as part of the software engineering process, is an important activity within requirements management.

According to Gotel & Finkelstein [53]

requirements traceability is ―the ability to describe and follow the life of a requirement in both forwards and backwards direction‖. The definition of requirement traceability by Gotel & Finkelstein [53] is considered as comprehensive definition that’s why researcher like Gorschek [54] Jane Cleland [55] Aurum and Wohlin [56] refer the same definition. SWEBOK [57] defines requirements tracing as, ―Requirements tracing is concerned with recovering the source of requirements and predicting the effects of requirement‖. Another definition of requirement traceability is given by Palmer [59] as ‖traceability gives essential assistance in understanding the relationships that exist within and across software requirements, design, and implementation‖.

All of these definitions ensure the importance

of doing a good traceability through the requirements, in order to reach a good definition of the software planed.

6.1.1.

Traceability matrix

A traceability matrix is a document, usually in the form of a table, that correlates any two baselined documents that require a many to many relationship to determine the completeness of the relationship. It is often used with high-level requirements (these often consist of marketing requirements) and detailed requirements of the software product to the matching parts of high-level design, detailed design, test plan, and test cases.6

In table 6

we can see the traceability matrix for this project, taken into account the requirements in section 5.2.1, the uses cases defined in section 5.5.2., and the modules presented in section 5.6.2.

6

Taken from http://en.wikipedia.org/wiki/Traceability_matrix

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Linked data for improving student experience in searching e-learning resources 2011 Table 9: Traceability matrix

Req. ID

Use

Use case name

Module associated

case ID F1

1

Provide e-learning resources

Exploring module

F1

2

Explore e-learning resources

Exploring module

F1

3

Use e-learning resources

Exploring module

F3

4

View revisions of e-learning

Exploring module

resources F5

5

Manage account

Account Module

F5

5.1



Create account

Account Module

F5

5.2



Delete account

Account Module

F5

5.3



Modify account

Account Module

F8, F9

6

Manage personal e-learning

Access Module

resources information F8, F9

6.1



Save e-learning resources

Access Module

information F8, F9

6.2



Delete e-learning resources

Access Module

information F8, F9

6.3



Modify e-learning resources

Access Module

information F2

7

Post ads

Access Module

F4

8

Make revisions of e-learning

Access Module

resources F6, F7

9

Publish new e-learning resources

Access Module

6.2. REQUIREMENTS REVIEW CHECKLIST For the purposes of this project we will follow a list of requirements in order to assess the feasibility of the design created before. This checklist was verified with a group of computer system engineers, who checked the documents provided by this master thesis project. These engineers are involved in the world of e-learning, and they are interested in new possibilities to get data from the World Wide Web. The documents reviewed where: 

Data analysis and interpretation of the survey results (chapter 4)



Requirements specification (Section 5.2)



Use case templates (Appendix C)

80


Standards in education (LOM, learning Object metadata, IEEE [13]) Table 10: Requirements review checklist

ID

State

1

Complete

Items to examine Does the software design have a succinct name, and a described purpose?

2

Complete

Are the characteristics of users and of typical usage mentioned?

3

Complete

Are all external interfaces of the software explicitly mentioned? (No interfaces missing.)

4

Complete

Does each specific requirement have a unique identifier?

5

Complete

Is each requirement atomic and simply formulated?

6

Complete

Are requirements organized into coherent groups?

7

Complete

Is each requirement prioritized?

8

Incomplete Are all unstable requirements marked as such? (TBC=`To Be Confirmed', TBD=`To Be Defined')

9

Incomplete Is each requirement verifiable (in a provisional acceptance test)?

10

Complete

Are the requirements consistent? (Non-conflicting.)

11

Complete

Are the requirements sufficiently precise and unambiguous?

12

Incomplete Are the requirements complete? Can everything not explicitly constrained indeed be viewed as developer freedom? Is a product that satisfies every requirement indeed acceptable? (No requirements missing.)

13

Complete

Are the requirements understandable to those who will need to work with them later?

14 15

Incomplete Are the requirements realizable within budget? Complete

Do the requirements express actual customer needs (in the language of the problem domain), rather than solutions (in developer jargon)?

6.3. LIST OF PROBLEMS AND LIST OF ACTIONS The current design corresponds to the first prototype of an e-learning collaborative environment, based on the student’s preferences.

There are four important problems

detected in the requirements validation. The solutions of this problems will be worked in next versions of the prototype, because they corresponds to the building of more specific models, like class diagrams and activity diagrams, not covered by the purpose of this project.

The future work, provided by the section 7.3 will explain better how the next

prototype could be solve the problems found.

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7. CONCLUSIONS This chapter concludes this thesis by summarizing the results of the research.

Then we

show the contributions of our research and finally we give directions for future research.

7.1. SUMMARY In education, collaboration has long been considered an effective approach for generate knowledge. Around the world wide web, there are many contributions in terms of e-learning resources.

These resources can solve the information needs of the people and can

enhance the e-learning world. However, there are big challenges related to the resources information. One of them is the correct appropriation of these information, and other is the appropriate storage and use of these resources. The topic of this thesis is recognizing semantic web ideas from a e-learning perspective. Much work has been done on developing architectures, and web applications have been created, as an infrastructure for enhance better interaction between e-learning resources and e-learners.

The literature review has allowed us to know about the techniques in

semantic web, specially linked data, and how this approach has been used in e-learning data collection. Now, we are aware about the LOM metadata and their structure, as well as the students preferences when they are interested in searching information by their own, on the internet. Also, we made a review of the LOD cloud diagram, provided by the contributors to the Linking Open Data community project and other individuals and organisations. This project is big and it is showing that the semantic web is growing, and is spreading the data around the world, offering clever connections, easy to follow by machines and easy to access by human beings. Furthermore, new doors for exploration of diverse information shall be open with this kind of technology, allow people the access not only the information but also to built the knowledge around the world. In this thesis, we make use of the criteria selected by the students for creating an approach related to a collaborative e-learning environment, in which information provided by the LOD cloud diagram could be explored and use by different kind of e-learners, and also, we open the possibility to extend the linking open data community, giving the e-learners a place in which they can contribute, not only with new resources but also with qualifications of the information previously linked. This information would be used by other people in order to enlarge the collaboration regarding the knowledge around the world wide web.

82


7.2. CONTRIBUTION

7.2.1.

Research justification

In this thesis, we have designed an architecture that allows exploring, selecting, using, publishing and evaluating

several e-learning resources based on the semantic web

techniques. The resources could be from different

types. The resources alternatives

(lectures, online research papers, e-books, videos, tutorials, interactive resources, and sadistic and government sites) offer options to the students according to their personal preferences.

Also, the design allows the collaboration between people, giving them the

opportunity to evaluate resources and make personal appreciations around these e-learning objects. Moreover, social networking is allowed, in order to establish connections between people with relevant information located in ads.

All new information generated by the

environment shall be stored using the four main rules designed to follow the linked data principles. Gaps found in the state-of-the-art and listed in section 6.1.1. have been tackled in the following way: 

Gap 1: The e-learning collaborative environment prototype is open. Everybody can access the environment, explore it and use the resources. The information that shall be placed inside it, would be selected from the linked data community. This community information is derived from several topical domains, making the platform an enriched source or e-learning resources.



Gap 2: The e-learning collaborative environment is designed for working in connection with the linked data community, in order to establish more and more connections around the web of data, and also with the purpose of enriching this community.



Gap 3: e-Learners can contribute with publishing new e-learning resources information.



Gap 4 and Gap 8:

The e-learning collaborative environment allow the participation of

the e-learners with evaluation of the resources such as comments and rankings, in order to generate usability tips for other participants in the environment. 

Gap 5:

The e-learning collaborative environment is designed for offering a list of

domains to the e-learners in order to establish a criteria for searching e-learning resources. 

Gap 6: The e-learning collaborative environment is designed shall be structured in a way taht allow the students to find the subjects easily.

83


Gap 7: The e-learning collaborative environment shall be structured in a way that allow the students find several types of information related to one specific topic in the forms of videos, lectures, articles, and so on, according with the students preferences found in the survey.

7.2.2.

Answering the research questions

This section is intended for explaining the connection between the conclusions and the research questions.

RQ2: What are the remarkable features offered by existing learning environments based on linked data techniques? Linked data is a semantic web technique that allows the organization of the Information, using metadata.

There are several projects around this technique that connect the e-

learning world with the technology.

The section

2.1 explain the uses of linked data

techniques in e-learning resources exploration. The remarkable features that we found in this literature review are the following: 

Section 2.1.1. shows that the principles allow the tracking of information via data networks.

LOD cloud diagram are a huge source of information

that could be

validated in order to find reliability in the authors, or in the contents. 

Section 2.1.3. shows that there are several taxonomies that contribute with the construction of new kind of knowledge in the web of data. These vocabularies are completed for supporting many of the developments in metadata, and they are useful in the achievement the new projects around the e-learning environments, because they have inputs related to educational issues.



Section 2.1.4. is related to Learning Object Metadata. LOM is the base schema for reaching the organization of e-learning object.

It provides several characteristics to

support the e-learning environments and it is the standard followed by a number of projects 

Section 2.1.6. Are focused in the e-learning approaches for the linked data age. Interesting projects have been made in order to exploit the linked data techniques. The efforts around this issue show that linked data techniques are useful in the achievement of data related to specific types.

LOD cloud diagram shows new

paradigms in the good practices, using the classification of topics in domains related.

84

Linked data for improving student experience in searching e-learning resources 2011 RQ3: To what extent are the students preferences in searching e-learning resources supported by the current search engines and/or learning environments? The students preferences are covered by many kind of e-learning environments, and also, by semantic search engines.

However, an integrated environment, that cover all the

students interest, was not found in the state-of-the-art. The current project pretends to show how the advances of semantic web techniques can support the students needs in order to offer them tools for being more productive when they try to learn by themselves. RQ1: How could linked data support the collection of information on the internet in order to enrich collaborative e-learning environments? 

Results from the survey reveal preferences of the students when finding resources for their own learning.

The e-learning approaches can be enhanced with the use of

Semantic Web techniques, allowing describing the participants preferences. 

Linked Data techniques can be used to model e-learning collaborative environments with strong sources of data from the LOD cloud diagram.

An optimal solution can be

obtained from this approach, using the datasets from many sources, and giving more data to the cloud with new ones. 

There are several semantic web ontologies as knowledge representation mechanism we be built up on existing ontologies, which encourages the re-use of data.



e-Learning resources can be evaluated is different ways depending on the usefulness they give in the collaborative environment. e-Learners’ satisfaction would be useful for other participants of the environment.



Semantic techniques in the building of approaches for education can be helpful to both autonomous learning and cooperative or collaborative learning, finding new links between the participants, as well as e-learning objects.

7.3. FUTURE WORK There are several issues that were not covered by this master thesis project. The project was focusing in the preferences of the students and how the semantic web techniques can cover all these needs using semantic web approaches like linked data. The following items are the most important issues that could be covered by future work:

85


One of the most important thing to make in the future is the analysis the data and search patterns related to students preferences and the connection to the educational level, because our current approach is focusing in a general behaviour of the students.



Obtaining a more complete and refined ontology, used to to get all relevant information that was basic in this project. There are many ontologies, but the reused of some of them can be useful in the development of new projects.



The construction of more detailed models for design, like class diagrams and activity diagrams, that shall express better the requirements for

communicating

between

software architects and developers. 

Requirements

engineering

frameworks

focusing

in

e-learning

environments

development could be studied, in order to generate better requirements reviews. 

Frameworks for educational environments must be selected, studied, and compared with the prototype designed in this master thesis project in order to generate an integrated model based in the linked data techniques combined with the methodological and pedagogical solutions already found in the educational world supported by computers tools.

86


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Linked data for improving student experience in searching e-learning resources 2011 [50] Robertson, S & Robertson, J 1999, Mastering the Requirements Process First., Addison-Wesley. [51] Larman, C 2002, Applying UML and Patterns. An Introduction to Object-Oriented Analysis and Design and the Unified Process. Second. P Guerrieri (ed), prentice Hall PTR. [52] Pressman, RS 2005, Software engineering: A practitioner´s approach 6th ed., Mc Graw Hill. [53] O. Gotel, A. Finkelstein, ―Extended Requirements Traceability: Results of an Industrial Case Study‖, Proceedings of the Third IEEE International Symposium on Requirements Engineering, IEEE, 1997, pp.169-178. [54] T. Gorschek, Requirements Engineering Supporting Technical Product Management, PhD Thesis no. 2006:01, ISBN 91-7295-081-1, Blekinge Institute of Technology, Ronneby, Sweden. [55] J. Cleland-Huang, ―Just Enough Requirement Traceability‖, Proceedings of the 30th Annual International Computer Software and Applications Conference (COMPSAC'06), IEEE, vol. 1, 2006, pp. 41-42. [56] A. Aurum, C. Wohlin, Engineering and Managing Software Requirements, Springer, Berlin Heidelberg, 2005 [57] C.Wholin, P. Runeson, M. Host, M.C. Ohlsson, B. Regnell and A. Wesslen, Experimentation in Software Engineering An Introduction, Kluwer Academic Publishers, Dordrecht, the Netherlands, 2000. [58] B. Ramesh, M. Jarke, ―Towards Reference Models for Requirements Traceability‖, IEEE Transactions on Software Engineering, IEEE, vol. 27, no.1, 2001, pp. 58-93.

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9. APPENDICES 9.1. APPENDIX A: SURVEY ABOUT e-LEARNING RESOURCES This survey is being conducted by Julieth Patricia Castellanos Ardila ([email protected]) and it is targeted at students of Malmö University who undertake studies in different programs. It has been created to investigate the opinion of the students about the exploration, discovery, usability and usefulness of e-learning resources. The results of this survey will be part of the master thesis project in computer science called: "Linked data for improving student experience in searching e-learning resources". Any information you provide will be confidential and anonymous. There are 16 questions that will probably take around 10-15 minutes to complete. Your contribution is much appreciated and we would like to thank you in advance for taking the time to fill in this survey. 1. Which education level describes you best? College student Undergraduate student Master student Doctoral student Student of single courses Other (please specify):

2. What discipline area do you study? If you work in more than one area, please indicate the area in which you spend most time. Social sciences Medical/Oral Health science Computer science Physical science Mathematics Architecture /Design Pedagogy Business Visual communication/Mass media Other (please specify):

3. When searching for e-learning resources (defined as any entity that maybe used for learning) what kind of sites or sources you use most often? (if you select more than one, please rank your selection writing numbers 1, 2, 3,... in the order of your highest preference, where 1 is the highest priority)

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Google Google scholar Yahoo Bing Ask.com Wikipedia Amazon JorumOpen Intute Scientific journals Google maps Jstor iTunesU Flickr Youtube Scrib Universities/Teachers web pages Theme blogs Digital libraries Free e-books OpenCourseWares Virtual classrooms (elluminate, WebCT...) Virtual Learning Environments(it´s learning. Moodle, Blackboard...) Learning mailing lists Thematic forums Social Networks (Flickr, twitter, facebook,...) Other (please specify):

4. Do you use any kind of social networking collaborative learning to search e-learning resources (Examples of social network are Facebook, Flickr, Twitter , ResearchGate, and so on) Yes No If your answer is yes, write the name of the social network you are using for e-learning purposes.

5. When you search for e-learning resources, which of the following statements best describes your initial approach? I try with a keyword for example "pets" I try with a descriptive phrase such as "Teaching Pets skills".

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I try with advanced search that let me specify other search features such as language, file type ... I don't always have a clear idea how to describe what I'm looking for Other (please specify):

6. How important could resources? CRITERIA

be the following criteria to your search for e-learning

Extremely Somewhat important important

Neutral

Somewhat Extremely unimportant unimportant

Name of the resource´s author Source (If the source is recognized by its continuous experience in the creation of learning resources such as universities)

Date of creation (How new or old is the learning resource found)

Format

(pdf,

word,

mpg,...)

Type

(simulation, questionnaire, diagram, graph, index, slide, table, narrative text, exam, experiment, self assessment, lecture) Interactivity level: (It refers to the degree to which the learner can interact with the resource) Target age (for example, a resource created for being used with children between 610 years old.) Rights (It describes the intellectual property rights and conditions of use for this learning

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Linked data for improving student experience in searching e-learning resources 2011 object.)

Availability (free or payable)

7. Are you interested in using e-learning resources recommended by somebody else (specially teachers or other students)? Yes, all the time Never, I prefer to make my own searches In some occasions. (please specify):

8. Some e-learning resources have comments about their quality. Do you pay attention to these comments when you select the resource? Yes, all time No. Sometimes. (please specify):

9. Some e-learning resources have been ranked by people, according to their usefulness. Do you use these rankings for support your selection? Yes, all time No. Sometimes. (please specify):

10. When you select articles on the internet, do you check the amount of citations that have in other articles or books? Yes, all time No. Sometimes. (please specify):

11. What is the level of trust you have for the e-learning contents of the following: (if you do not know, please, select NA = Not Applicable)

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2011

e-Learning Resource

Extreme ly trustwor thy

Somewhat trustwort hy

Neutral

Somewhat untrustwort hy

Extremely untrustwort hy

NA

Depends of the specific author or source

Youtube videos Wikipeda contents Slideshare contents Google scholar articles Google maps Thematic blogs Academic e-learning resources (created by universities, teachers and researchers) Scientific web pages (national geographic, discovery, Nasa...) Scientific online magazines Yahoo answers Thematic mailing lists Thematic forums Free e-books Digital libraries Statistics from government sites

If you consider another source of reliable information online with learning purposes, please specify

12. What kind of e-learning resources do you prefer to use in your own experience? (Please rank your selection, writing numbers 1, 2, 3,... in the order of your highest preference, where 1 is the highest priority) Videos Tutorials or manuals


Lectures Online newspapers information Thematic blogs Online research papers Interactive resources (questionnaires, exercise, simulations…) E-books Statistics Oficial websites information (government sites) Pictures from Google images Other (please specify):

13. How do you remember the URL (the address of a web page on the world wide web) of the e-learning resources when they are relevant to you? (Please rank your selection, writing numbers 1, 2, 3,... in the order of your highest preference, where 1 is the highest priority) I write by hand the name of the web page in my notebook (or any other paper-based resource) I copy the URL in a word document (or any other word processor) I bookmark the page in my web browser I check the history in my web browser If I need the same resource again I start a new search. Other (please specify):

14. Do you consider the possibility to make comments about the quality of the elearning resource you have used in order to publish your own opinion about the resource and facilitate the learning experience of other people? Never Always Sometimes, please, specify

15. Are you interested in ranking the e-learning resources according to their usefulness? Never

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Always Sometimes, please, specify

16. Are you interested in belong to an e-learning/e-science group (groups interested in create connections with people around the world that have similar interest and doing learning/science activities collaboratively, using the internet for sharing resources) Yes No I already belong to a e-learning/e-science group, which is called:

9.2. APPENDIX B:

MAPPING BETWEEN THE AVAILABILITY OF

DATA SOURCES IN THE LINKED DATA COMMUNITY AND THE PREFERENCES OF THE STUDENTS This appendix was created

to show the availability of data sources in the LOD cloud

diagram that could be linked in the platform, following the preferences of the students found in the survey

9.2.1.

Contents selected by students and their availability in the LOD cloud

diagram The following data sets references have been taken from -The Comprehensive Knowledge Archive Network (CKAN)7- a registry of open knowledge 'packages', including plenty of open data. a.

According to academic resources

All of the datasets in the LOM cloud diagram could be used in the e-learning collaborative environment designed, because of the knowledge they are spreading.

These are

interesting and relevant in the case of general purpose e-learning environment. However, 7

http://ckan.net/

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Linked data for improving student experience in searching e-learning resources 2011 the survey results encourage the use specific data sources. Also, we are interested in linked data sets in English.

According with the data analysis made in the previous chapter,

students are interested in academic resources such as lectures, online research papers, ebooks, tutorials or manuals, Interactive resources (questionnaires, exercise, simulations…), statistics and government sites. Linked data community is able to provide some examples of references to resources with interesting data sources like the information show in the tables 4 to 13 Table 11: Lectures in the LOD cloud diagram

Lectures Some universities publish their lectures in an open way. Examples of these universities are: 

data.open.ac.uk, Linked data from the Open University: The result of extracting and interlinking data from various institutional repositories of the Open University and making it available for reuse.



linkeduniversities.org/video Dataset: It aims to link videos of lectures from different universities. For this purpose, information of video lectures published by different universities and institutions has been extracted and structured under a set of well-known vocabularies. In addition, to interlink this information, the extracted videos have been categorized under the same taxonomy, the one defined by the Open Directory project.

Table 12: Online Research papers in the LOD cloud diagram

Online research papers 

BibBase: BibBase.org facilitates the dissemination of scientific publications over the internet.



Association for Computing Machinery: Linked data version of publications of the Association for Computing Machinery (ACM), along with details of their authors.



RKBExplorer: RKB Explorer is a Semantic Web application that is able to present unified views of a significant number of heterogeneous data sources regarding a given domain.



ECS Southampton EPrints: This is live data produced by the EPrints server since its upgrade to EPrints v3.2.1 and is distinct from the service provided by RKB Explorer.



DBLP Bibliography Database in RDF (FU Berlin): This is an RDF conversion of DBLP. The DBLP database provides bibliographic information on major computer science journals and conference proceedings including the WWW2006. The database

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Linked data for improving student experience in searching e-learning resources 2011 contains more than 800.000 articles and 400.000 authors.

Table 13: e-Books in the LOD cloud diagram

e-Books There are many information in the LOD cloud diagram about books. Collection of e-books are the following: 

eBooks@Adelaide - Free Web Books: The University of Adelaide Library’s collection of classic works of Literature, Philosophy, Science, and History.



Project Gutenberg: It is the first and largest single collection of free electronic books, or eBooks.



Collaborative publishing house: A place where you could publish your work, request a free book, or be assigned to a project, where a distribution of valuable information could be found. Help us develop the project

In second term, we can find examples of libraries: 

English Language Books listed in Printed Book Auction Catalogues from 17th Century Holland: The books are those listed in the English language section of Dutch printed book auction catalogues of collections of scholars and religious ministers.



The Open Library:

One web page for every book ever published. It's a lofty, but

achievable, goal. 

LinkedLCCN: A wrapper for the Library of Congress' LCCN Permalink service which models the MARCXML output as Linked data in RDF.

Table 14: Tutorials/Manuals in the LOD cloud community

Tutorial/Manuals With the specific tag of tutorial or manual, it was not found any reference.

Table 15: Interactive resources in the LOD cloud diagram

Interactive resources 

DBTune.org Magnatune RDF server: It is an independent music label, allowing people to buy records for as much as they want. This package contains the Magnatune catalog in RDF format.

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DBTune.org Musicbrainz D2R Server: MusicBrainz is an open music encyclopedia that collects, and makes available to the public, music metadata.



Linked Movie DataBase: The project aims at publishing the first open semantic web database for movies, including a large number of interlinks to several datasets on the open data cloud and references to related webpages.



European Nature Information System: Welcome to EUNIS biodiversity database - find species, habitats and sites across Europe

 Freebase: It is an open database of the world’s information. It is built by the community and for the community—free for anyone to query, contribute to, built applications on top of, or integrate into their websites."

Table 16: Statistics and government sites in the LOD cloud diagram

Statistics and government sites Government is a topical domain in the LOD cloud diagram. This domain has 26 available datasets, but we are interested in the following: 

2000 U.S. Census in RDF (rdfabout.com): Population statistics at various geographic levels, from the U.S. as a whole, down through states, counties, sub-counties (roughly, cities and incorporated towns)



Eurostat RDF datasets: Eurostat country codes; (2008);



Eurostat NUTS statistical regions

Database of Eurostat-related legal acts

RDFizing and Interlinking the EuroStat Data Set Effort:

The statistical data

published on riese was originally published by Eurostat. 

patents.data.gov.uk: Namespace for patent applications from data.gov.uk. This data comes from the BIS dataset.



research.data.gov.uk: Reference data for linked UK government data: Departments;



People;

Namespace for various time intervals

statistics.data.gov.uk:

Linked data about administrative areas used within UK

government official statistics.

b. According to academic sources Students trust in the information provided by the following sources:

Wikipedia, Google

Scholar, Google maps, free e-Books, YouTube, and universities or teachers web pages.

102

Linked data for improving student experience in searching e-learning resources 2011 The tables 14 to 18, tackle the current state of these sources in terms of their generation of references using RDF vocabularies. Table 17: Wikipedia and the LOD cloud diagram

Wikipedia  DBpedia:

DBpedia.org is a community effort to extract structured information from

Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia and to link other datasets on the Web to Wikipedia data. 

dbpedia lite: It takes some of the structured data in Wikipedia and presents it as Linked data. It contains a small subset of the data that dbpedia contains; it does not attempt to extract data from the Wikipedia infoboxes. Data is fetched live from the Wikipedia API.

Table 18: Google scholar and the LOD cloud diagram

Google scholar Data, directly linked from Google scholar, are not available in the LOM cloud diagram

Table 19: Google maps and the LOD cloud diagram

Google maps Google maps are not available in the LOM cloud diagram. However, LOM cloud diagram has other sources of geographical information that could be used in our project.

The

following are examples of these datasets: 

LinkedGeoData: It uses the information collected by the OpenStreetMap project and makes it available as an RDF knowledge base according to the Linked data principles



Ordnance Survey Linked data: Scotland;

Geographical data about England, Wales, and

Provides identifiers for counties, cities, wards, census areas;

identifiers for postcodes;

Provides

Relationships between geographical areas (containment,

borders)

Table 20: YouTube and the LOD cloud diagram

You tube YouTube are involved directly in the LOD cloud diagram in EventMedia. However, other

103

Linked data for improving student experience in searching e-learning resources 2011 data sets with videos are taking into account in this investigation. 

EventMedia: This dataset is composed of events and media descriptions associated with these events. It is obtained from three large public event directories (last.fm, eventful and upcoming) represented with the LODE ontology and from large media directories (flickr, youtube) represented with the W3C Media Ontology.



EdShare Video (University of Southampton):

Dataset from University of

Southampton Open Data Service. Currently these videos are stored on a separate server from the rest of EdShare, due to resource constrains, and in due course this will be merged into EdShare. Only categories where all or most videos are available to the public have been included in the export to the open data server. 

DBTropes: DBTropes.org is a Linked data wrapper for the TVTropes.org community wiki. It contains descriptions of numerous movies, books, and other items, and associates these with tropes (writing devices and conventions).

Table 21: Universities and the LOD cloud diagram

Universities/teachers web pages  education.data.gov.uk: Linked data about schools, nurseries and universities within the UK 

lobid. Index of libraries and related organisations: lobid-organisations is a service of lobid.org. It is an international directory of libraries and similary organisations



data.dcs: Describes people, research groups and publications within the Department of Computer Science based at the University of Sheffield.



Scholarometer: It is a social tool for citation analysis, which provides a service to scholars by computing citation-based impact measures. Scholarometer data provides information about authors and disciplines based on citation analysis.



Nottingham Trent University Resource Lists



University of Plymouth Reading Lists: Search for lists, modules & courses



University of Sussex Reading Lists: Search for lists, modules & courses



VIVO: It has been funded by NIH to create a semantic Facebook for scientist. It utilizes Semantic Web technologies to model scientists and provides federated search to enhance the discovery of researchers and collaborators across the country.

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9.2.2.

According to Learning Object Metadata (LOM) criteria

LOM provides important criteria for classifying e-learning resources. Students prefer to use the following criteria:

Availability (free or payable), Source, Date of Creation and Author.

LOM metadata was developed using Dublin Core and for this reason, this vocabulary must be included in the research. The selection of data sources according to the sources, and the resources themselves, provided by the previous section, will provide the vocabularies needed.

9.2.3.

Dataset selected and their vocabularies

The prototype will show the possibility to built an e-learning environment using the LOD cloud diagram presented in the Linked data community. The criteria chosen by the students will be the starting point of this design.

The datasets were selected because of their

availability for the design (Open data and Open Content), and the possibility to find information about the date of creation and author. The reliability in the sources was tackled, using datasets from universities or government. However, the link to Dbpedia would be maintained in order to gather general information of this relevant source of information Table 22: Data Sources selected and their vocabularies

Source/Resource Lectures

DataSet Data.open.ac.uk

Online Research papers e-books

BibBase Open Library

Vocabulares  Bibliographic Ontology bibo8  WC3 Media Ontology9  SKOS10  CourseWare Ontology11  Creative Commons Rights Expression vocabulary 12  Nice Tag Ontology13  SIOC Ontology14  FOAF ontology15  BibTex Ontology16  Bibliographic Ontology bibo

8

http://bibliontology.com/specification http://www.w3.org/TR/mediaont-10/ 10 http://www.w3.org/2004/02/skos/ 11 http://courseware.rkbexplorer.com/ontologies/courseware 12 http://creativecommons.org/ns 13 http://ns.inria.fr/nicetag/2010/09/09/voc.html 14 http://sioc-project.org/ontology 15 http://xmlns.com/foaf/spec/ 16 http://data.bibbase.org/ontology/ 9

105

Linked data for improving student experience in searching e-learning resources 2011 Tutorial/Manuals Stadistics/Goverment sites Interactive Resources Wikipedia

9.2.4.

NO SELECTED NO SELECTED Freebase Dbpedia



Dublin Core

 

tsv: tab separate values DbPedia ontology

How comments, rankings and citations of the e-learning resources

could be addressed with the Linked data approach There are linked data sets that are collected from microblog posts like twitter: An approach in the linked data community is Twarql.

It

enables annotations and management of

streaming tweets in order to alleviate information overload, using linked data principles. However, this approach are not linked with any dataset selected before. Additionally, The elearning resources selected do not have comments. interesting approach called Revyu.com.

The LOD cloud diagram has an

It is an universal review site that

provide

comments about many resources. Table 20 shows the information about REVYU. Table 23: Comments in things in LOD cloud diagram

Comments in things Revyu.com - Review Anything: It is a web site where you can

Vocabularies

review and rate things. Unlike many other reviewing sites on the 

FOAF

web, Revyu.com lets you review and rate absolutely anything you 

Tag Ontology17

can name.

Furthermore, the new approach will tackle this shortcoming, making possible the contributions by the user. User contributions will be in the forms of comments and rankings.

9.2.5.

Reliability in Contributors of e-learning resources, and their availability

en in the LOD cloud diagram Reliability will address using Data Set derived from acknowledge organizations like universities. Wikipedia is also a pertinent source because students trust in this information. In most of the cases Wikipedia in the form o DbPedia, would be an starting point in the 17

http://www.holygoat.co.uk/projects/tags/

106

Linked data for improving student experience in searching e-learning resources 2011 discovery of new knowledge. It is important to show to the user the origin of all information in order to left the decision to he/she

9.3. APPENDIX C: USE CASES TEMPLATES 9.3.1.

Provide Resources

Use Case ID: Use Case

1 Provide Resources

Name: Created By: Date Created:

Julieth Castellanos 21/04/2011

Actors: Description:

Last Updated By: Date Last Updated:


Contributor In this use case, e-Learning environment would be filled with the information selected form LOD cloud diagram.

Preconditions: Postconditions:

e-Learning Source must be available for being linked e-learning resources connected

and ready to use using linked data

Techniques Normal Course:

1.0. The resources information must be selected from a the sources selected in section 5.2.5. and 5.2.6. Then, the resources will be linked using linked data techniques

Alternative Courses: 1.1. Resources could be selected from other available sources in the LOD cloud diagram. The resource must be open content and open data Exceptions:

None

Includes:

None

Priority:

High

Frequency of Use:

High

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Linked data for improving student experience in searching e-learning resources 2011 Business Rules: Special Requirements:

None Data Sets must be selected from the LOD cloud diagram. Reliability is provided by the profile of the institutions or people directly associated with educational environments like universities, scientific groups or scientific magazines/journals.

Assumptions:

None

Notes and Issues:

None Table 24: Use case for providing Resources

9.3.2.

Explore e-learning Resources


2 Explore e-Learning Resources



Actors: Description: Preconditions:



Generic user In this use case, students can explore e-learning resources None

Postconditions:

Resources selected have to be in the forms of links.

Normal Course:

2.0. User explore the e-learning resources provided by the system. User can select resources by the topic. Then, options related to the topic will show in the forms of

Lectures, Online Research Papers,

e-books and interactive resources. The final resource selected will have references to general information from dbpedia and reviews form Revyu, if this information exists. Alternative Courses: 2.1. The same than before, but the student can´t save the findings because he/she are not registered

108

Linked data for improving student experience in searching e-learning resources 2011 Exceptions:

2.0.E.1. Students do not find any resource. The system must show the message: ―The resources, with the criteria specified, is not found‖ 2.0.E.2. Students can´t save their findings. The system must show the reason with the message: ―The student must be registered‖

Includes: 

View revisions

Priority:

High

Frequency of Use:

High

Business Rules:

None

Special

None

Requirements: Assumptions:   Notes and Issues:

e-Learning resources must be linked before e-learning resources have revisions

None Table 25: Use case for exploring e-Learning Resources

9.3.3.

Use Resources


3 Use Resources



Actors: Description: Preconditions: Postconditions:



Generic user In this use case, students can use e-learning resources Users must explore the e-learning resource before. The user have access to the resource. This resource is a web page

109

Linked data for improving student experience in searching e-learning resources 2011 with the resource selected. Normal Course:

3.0. Students visit the e-learning resources selected in the exploration. Students can use the resources. After the resource is used, the students could make recommendations in the forms of comment or rankings but only if they are registered.

Alternative Courses: 3.1. The

same

than

before,

but

the

student

can´t

save

the

recommendations because he/she are not registered Exceptions:

3.0.E.1. Students can´t use the resource explored. Student can inform this problem in the option ―Resource could be broken‖. The system must put this characteristic to the information of the resource in order to warning new user about this anomaly. 3.0.E.2. Students can´t save their recommendations. The system must show the reason with the message: ―The student must be registered‖

Includes: Priority: Frequency of Use:

None Medium High

Business Rules:

None

Special

None

Requirements: Assumptions:   Notes and Issues:

Students search the e-learning resources before e-learning resources are disposable to be commented or ranked

None Table 26: Use case for Using e- resources

9.3.4.

View Revisions


4 View Revisions

Name:

110

Linked data for improving student experience in searching e-learning resources 2011 Created By: Date Created:

Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


Generic user In this use case, users can see the revisions of the resources Users must explore the e-learning resource before.

Postconditions:

Reviews about resources searched.

Normal Course:

4.0. Users click on the resource selected and then, they can see the

revisions Alternative Courses: Exceptions:

None 4.0.E.1. Users can not find the revision of the specific resource. The system must show to the user the following message ―No reviews yet” This message is provided by revyu.com

Includes:

None

Priority:

Low

Frequency of Use:

Medium

Business Rules:

None

Special

None

Requirements: Assumptions:  Notes and Issues:

Information of the contributors must exist in the system

None Table 27: Use cases for viewing revisions

9.3.5.

Manage Account

Use Case ID:

5

111

Linked data for improving student experience in searching e-learning resources 2011 Use Case

Manage Account


Julieth Castellanos

Date Last Updated:

21/04/2011


Last Updated By:


e-Learner In this use case, e-learners can manage their own account None

Postconditions:

e-learners will be entitled to manage their accounts

Normal Course:

5.0. Students can select the options for managing their accounts

Alternative Courses: Exceptions:

None 5.0.E.1. Students can´t do anything with the information of their profile specific. The system must inform about the problem.

Includes: 

Create account



Delete account



Modify account

Priority: Frequency of Use:

Hight Medium

Business Rules:

None

Special

None

Requirements: Assumptions:

None

Notes and Issues:

None Table 28: Use case for managing account

112


9.3.6.

Create Account


6 Create Account


Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


e-Learner In this use case, e-learners can create their own account None

Postconditions:

The account created

Normal Course:

6.0. E-Learner fill the form with information such us name, surname, email, username, password and picture.


None 6.0.E.1. Students can´t create the information of their profile. The system must inform about the problem.

Includes:

None

Priority:

High

Frequency of Use:

High

Business Rules:

None

Special

None


None

Notes and Issues:

None Table 29: Use case for managing account

113


9.3.7.

Delete Account


7 Delete Account


Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


e-Learner In this use case, e-learners can delete their own account None

Postconditions:

The account deleted

Normal Course:

7.0. E-learners logged in the site, select the option unsubscribe. The

system must ask about the decision. If the e-learner is sure about this, the account will be closed. Alternative Courses:

None

Exceptions:

None

Includes:

None

Priority:

High

Frequency of Use:

Low

Business Rules:

None

Special

None


None

Notes and Issues:

None Table 30: Use case for deleting account

114


9.3.8.

Modify Account


8 Modify Account


Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


e-Learner In this use case, e-learners can modify their own account None

Postconditions:

The account modified

Normal Course:

8.0. E-learners logged in the site, select the option modify account.

Then, e-learners are able to modify the information about their profile. System must ask about the decision. If the e-learner is sure about this, the account will be modified. Alternative Courses:

None

Exceptions:

None

Includes:

None

Priority:

High

Frequency of Use:

Medium

Business Rules:

None

Special

None


None

Notes and Issues:

None Table 31: Use case for deleting account

115


9.3.9.

Manage Personal resources information


9 Manage Personal Resources information






e-Learner In this use case, students can manage their findings The resources information must exist in the environment

Postconditions:

The information of the e-learning resources will be managed

Normal Course:

9.0. Students select the e-learning resources and manage. .


None 9.0.E.1. Students can´t manage the resource selected. Student must be informed by the system with the message ―Resource can´t be manage, try again‖.

Includes: 

Priority: Frequency of Use:

Save resources information



Delete resources information



Modify resources information

Medium Low

Business Rules:

None

Special

None


E-learners must be registered

None Table 32: Use case for managing personal resources information

116


9.3.10. Save resources information Use Case ID: Use Case

10 Save Resources information


Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


e-Learner In this use case, students can save their findings The resources information must be searched before

Postconditions:

The information of the e-learning resources will be saved

Normal Course:

10.0.

Students select the e-learning resources and save. Students

can rename their own findings. . Alternative Courses: Exceptions:

None 10.0.E.1. Students can´t save the resource selected. Student must be informed by the system with the message ―Resource can´t be saved, try again‖.


None Medium Low

Business Rules:

None

Special

None


E-Learners must be registered

None Table 33: Use case for saving resources information

117


9.3.11. Delete resources information Use Case ID: Use Case

11 Delete Resources information


Last Updated By:

Julieth Castellanos

Date Last Updated:

18/06/2011



e-Learner In this use case, students can delete their saved findings The resources information must be saved before

Postconditions:

The information of the e-learning resources will be deleted

Normal Course:

11.0.

E-learners select the e-learning resources and delete. System

must ask about the decision. If the e-learner is sure about this, the resource will be deleted. Alternative Courses: Exceptions:

None 11.0.E.1. Students can´t delete the resource selected. Student must be informed by the system with the message ―Resource can´t be deleted, try again‖.


None Medium Low

Business Rules:

None

Special

None


E-learners must be registered

None Table 34: Use case for deleting resources information

118


9.3.12. Modify resources information Use Case ID: Use Case

12 Modify Resources information


Last Updated By:

Julieth Castellanos

Date Last Updated:

18/06/2011



e-Learner In this use case, students can modify their saved findings The resources information must be saved before

Postconditions:

The information of the e-learning resources will be modified

Normal Course:

12.0.

E-learners select the e-learning resources and modify. System

must ask about the decision. If the e-learner is sure about this, the resource will be modify. Alternative Courses: Exceptions:

None 12.0.E.0. e-learner can´t delete the resource selected. Student must be informed by the system with the message ―Resource can´t be modify, try again‖.


None Medium Low

Business Rules:

None

Special

None


E- learner must be registered

None Table 35: Use case for modifying resources information

119


9.3.13. Make revisions Use Case ID: Use Case

13 Make revisions


Julieth Castellanos

Date Last Updated:

18/06/2011


Last Updated By:


e-learner In this use case, revisions of the resources will be made by e-leaners. None

Postconditions:

e-learners revisions are connected and ready to use by other users

Normal Course:

13.0.

The e-learners select the resource. Then, He/she selected the

option ―Make review‖.

E-Learner can comment or ranking the

resource Alternative Courses: Exceptions:

None 13.0.E.0. e-learners can´t add any comment or ranking for the resource selected. Student must be informed by the system with the message ―Resource can´t be modify, try again‖.

Includes:

None

Priority:

Low

Frequency of Use:

Low

Business Rules:

None

Special

None


None

Notes and Issues:

None Table 36: Use case for making reviews

120


9.3.14. Publish new Resources Use Case ID: Use Case

14 Publish Resources


Julieth Castellanos

Date Last Updated:

18/06/2011

Actors: Description:

Last Updated By:


e-learner In this use case, new publications of resources will link directly in the environment

Preconditions: Postconditions:

e-learners contributions must be available for being linked e-learners contributions are connected and ready to use using linked data Techniques

Normal Course:

14.0.

The e-learners contributions must be search from the resources

involved. Then, the resources will be linked using linked data techniques Alternative Courses:

None

Exceptions:

None

Includes:

None

Priority:

Low

Frequency of Use:

Low

Business Rules:

None

Special

None


None

Notes and Issues:

None Table 37: Use cases for publishing new resources

121

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