Supporting openness of MOOCs contents through of an OER and OCW framework based on Linked Data technologies Nelson Piedra, Janneth Chicaiza, Jorge López
Edmundo Tovar Caro
Universidad Técnica particular de Loja, Ecuador Departamento de Ciencias de la Computación Loja, Ecuador
[email protected], {jachicaiza, jalopez2}@utpl.edu.ec
Universidad Politécnica de Madrid Dpto. Lenguajes y Sistemas Informáticos e Ingeniería Software
[email protected]
Abstract—The arrival of Massive Open Online Courses (MOOCs) and the growth of open and online education - Open Educational Resources (OER), OpenCourseWare (OCW)- is increasingly the focus to self-learners as the primary target group. The OER movement has tended to define “openness” in terms of access for use and reuse to educational materials, and to address the geographical and financial barriers, between students, teachers and self-learners with distinguished educational institutions. MOOC initiatives emphasize free access and interactive features rather than static content. The dominant message is of the quantity of access rather than the openness of educational resources for use, re-use, adaptation or repurpose. The purpose of this paper is to present the main aspects to considerer building a framework based on semantic web technologies to support the inclusion of open materials in massive online courses and significantly to improve discovery, accessibility, visibility, and to promote reuse of open educational content in massive courses. This framework will provide a set of services that allows the discovery and access of open educational resources that are extracted from open repositories distributed. Our principal OER providers are OCW institutions. In this context, we opted to apply the principles of Linked Data to integrate, interoperate and mashup data from distributed and heterogeneous repositories of open educational materials. Keywords—MOOC; openness; technologies; linked data
I.
reuse;
semantic
web
INTRODUCTION
Extending the paradigm of Open Source Software (OSS) and Open Access (OA) to the education domain, hundreds of institutions and enthusiasts have shared on the web thousands of free educational resources for teaching and learning. Open Educational Resources (OER), OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs), are part of this movement towards the aperture of knowledge. In 2001, the Massachusetts Institute of Technology (MIT) launched OpenCourseWare (OCW) initiative, one of the first ones institutionally backed. The following year, in a global forum of UNESCO the term Open Educational Resources was adopted and being officially defined it as “the open provision
of educational resources, enabled by information and communication technologies, for consultation, use and adaptation by a community of users for non-commercial purposes.” 1 This definition of OER has several connotations that go beyond having materials free of charge and licensed by means of open licenses. The recent emergence of Open Massive Open Online Courses (MOOCs) has helped to spread the Open concept but not to clarify the true meaning of Open. MOOCs are largescale online courses where thousands of participants may assist. For these courses there is an expert or group of experts which intent to create the large draw of the course, and facilitate a multi-week series of interactive lectures and discussion on critical issues from that field. Every participant is able to self-organize, to share and discuss the course material, in addition to this, every participant is able to create and publish new artifacts that represent their learning. Additionally, MOOC participation is recorded and published openly so that those who come upon it later may follow peripherally [1]. Although MOOCs are free as in gratis, they are not open in the sense of being reusable of openly accessible. Students are forced to sign up and get access to the course; so many researches highlight that it would be much more useful to have complete access at all times and reuse elements in other courses. In this paper, the authors focus on a type of Open concept as continuum of openness beyond a resource available at no cost. We make reference to the four separate aspects of reuse that creators wish to promote revising and remixing, the “Four R’s of Openness” [2,3]: adaptation and reuse in different contexts, open of contents as regards alteration i.e. freedom to reuse the material, combination with other materials, the possibility to adapt it and to share it further under an open license. 1 http://portal.unesco.org/ci/en/files/2492/10330567404OCW_forum_report_fin al_draft.doc/OCW_forum_report_final_draft.doc
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1112
The main motivation of this interpretation for this concept is to provide the means that people use the resources available and incorporate them into their teaching and learning practice. MOOCs OCW courses can provide the necessary support to students, teachers and self-learners around the world in their lifelong learning and encouraging to independent learning practices.
Part of the fulfillment of the features mentioned in Figure 1, is the responsibility of the institutions that produce and promote OERs, to the extent that organizations observe openness and accessibility considerations during the design and release of open educational resources, greater the likelihood that these resources can be incorporated into educational practice, formal or informal.
According to facilitate successful experiences for these learning practices with semantic web technologies developed by the authors in the past, the purpose of this paper is to present the main aspects to consider building a framework based on Linked Data approach to support the inclusion of open materials in MOOCs. The authors focus on this type of openness with freedom to reuse the material, to combine it with other materials, to adapt it, and to share it further under an open license [4].
Efforts applied to the production and sharing of open educational resources have enabled people around the world can access thousands of educational resources in various media formats, levels of granularity, languages and topics. However, so far, sharing and reusing OERs means providing access across different repositories and platforms, to create legal support schemes for distributing resources, to promote some open educational practices, and mainly to use open content licenses such as Creative Commons or Public domain. In the field of open knowledge, use Open Licenses not enough; legally free / open does not mean it is easy to discover, use, reuse, adapt, remix and share.
This paper is organized in the following manner. Firstly, section II, issues of Openness is discussed deeper, as well as its implications. In section III the application of the principles Linked data is explained as the approach to integrate, interoperate and mashup data from distributed and heterogeneous repositories of open educational materials. Later, section IV presents the prototype of a service that allows you to discover and access open educational resources that are extracted from open repositories distributed. And finally the conclusions are discussed. II.
ISSUES OF OPENNESS
As mentioned, the Open Educational Resources (OERs) are part of a larger trend toward openness of higher education. Since starting OER movement, a growing number of academic institutions are joining and are striving to adopt and release their open academic contents and materials [1]. However, although educational content as OER / OCW / MOOCs offer the potential to expand access to knowledge to the world, there are some barriers to achieving this goal [6]. As "Open" educational resource are several the implications and characteristics which in itself should have the resource. The first level consequences of an OER directly based on the characteristics of a piece of open knowledge, are to provide people the freedom to use, reuse and redistribute. On the other hand, in a higher level, they contribute to address the geographical and financial barriers between students, teachers and self-learners with distinguished educational institutions. In [4] it is discussed the different dimensions of the term "Openness", as seen in Fig. 1, there are implications of type financial, social, technical and legal. Fig. 1. Degrees of Openness
Therefore, a challenge for the community is promoting OER initiatives with channels that facilitate the discovery, use and reuse for teachers, students and self-learners incorporate them in the educative practice. From a technical point of view, as shown in Fig 1, the openness of OERs covers issues such as interoperability and discovery. In previous work we have presented how these features can be enhanced by applying Semantic Web technologies and Linked Data [10]. Along the same line, the contribution of this paper is to bring OERs to users who need them, specifically, support the MOOCs openness by reusing OERs. III.
USE OF LINKED DATA ON OER DOMAIN
The framework we present in this paper aims to provide a service that allows you to discover and access open educational resources that are extracted from open repositories distributed. Our principal OER providers are OCW institutions. In this context, we opted to apply the principles of Linked Data to integrate, interoperate and mashup data from distributed and heterogeneous repositories of open educational materials. The purpose is to significantly improve discovery, accessibility, visibility, and to promote reuse of open educational content in massive course. The use of Linked Data has been the approach used by the authors for years to face to the previous challenge. Web of linked data constitutes an evolution of the current Web towards a global information space where browsing is driven by structured and linked data rather than by documents as is now the case. Unlike the current Web of linked documents, a Web of linked data is able to describe data models, concepts and properties that are then connected, consulted and mashupped on the Web, as if they were simply part of a global database. The goal of Linked Data is to enable human beings to easily share structured data via the Web just as they share documents now [7]. The philosophy of Linked Data is that the value and usefulness of data increase in proportion to their links with other data. On this ground, Linked Data uses the
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1113
Web to create different types of links among data from different sources. By publishing Linked Data we are sharing structured data that can be connected to other data by typed links using the global architecture of the World Wide Web [8]. Data links can be used to connect and browse heterogeneous and distributed repositories. The publication of Linked Data is governed by four principles introduced by Tim Berners-Lee [9]. These practices lead to the transition from a HTML-based Web to a Linked Data Web. The information published in the HTML-based Web targets the human user, whereas the Linked Data Web is expressed in Resource Description Framework (RDF), where software agents can automatically exploit (compile, add, interpret, publish, mix, etc.) data enhanced by vocabularies and ontologies [10]. The application of the Linked Data principles enables scaling to 5 progressive levels of the Open Data (Linked Open Data 5 Star). On site maintained by Michael Hausenblas 2, the costs and the benefits of ღ Web data are explained. At the highest level, key benefits for both consumers and providers of data on the Web are identified; mainly, the data is discoverable thus this can be found to be used for different purposes. The Table 1 shows the 5 star deployment scheme for Open Data suggested by Tim Berners-Lee TABLE I. ღ ღღ ღღღ
ღღღღ
ღღღღღ
With regard to the inclusion of OERs in MOOCs, two works may be mentioned: x
x
LINKED OPEN DATA 5 STAR
Educational data available on the web (whatever format) but with an open license, to be Open Data Educational data Available as machine-readable structured data (e.g. excel instead of image scan of a table) Educational data as (2) plus non-proprietary format (e.g. CSV instead of excel) All the above plus, Use open standards from W3C (RDF and SPARQL) to identify/describe things (e.g. educational resources), so that people can point at your stuff All the above, plus: Link data to other data sources to provide context
From the point of view of people: students, faculty and selflearners are interested in finding adequate resources to support their teaching process and learning but not the underlying technology. However, the benefits of Semantic Web technologies and Linked Data to the learning community will be evident; instead of having silos of undiscoverable OERs, users could navigate in a network of OERs, i. e. OERs link by predicates and related entities (e.g., subjects, authors, competency level, etc.). Specifically, the representation of areas or subjects and their relationships through semantic technologies, will help the discovery of such kind of resources for students and self-learners at worldwide. The use of Linked Data is widely documented, but in education is in particular remarkable. Several proposals and projects using Linked Data and principles within education have already been launched. Initiatives can be categorized into 2
two areas: (a) proposals contributing to the growth of the linked data cloud through academic information and educational resources; (b) efforts to generate Linked Data technologies and use the LOD cloud data for educational purposes. In [1] we present the Linked OpenCourseWare Data project (LOCWD), which published metadata of courses coming from different open educational datasets. The selected open educational content was converted to Linked Data using the LOCWD vocabulary. The resources described in Linked Data/RDF were stored in a RDF-Store. At this point, each resource was identified by a URI with a dereferencing option, and thus display the results retrieved as Linked Data.
Five Start Data http://5stardata.info/
OERu 3, “The OER university (OERu) aims to provide free learning to all students worldwide using courses based solely on OER” [11]. The process to choose the OERs was manual, based-on nominates and voting system. In [12], the author comments an initiative of the University of California, Irvine's, to publish OERs (in video format) to cover the entire undergraduate chemistry major. Besides the author comments “the next phase of the OER movement, where students are able to acquire open education by choosing a complete, coherent curriculum composed of many lectures and courses, providing a full mastery of a subject. Rather than trying to assemble a curriculum themselves from individual courses, scattered amongst different universities and online platforms”. IV.
DESIGN OF THE FRAMEWORK
In this section we will describe the purpose and goal of each module of a framework that help to build an OER recommender system based-on linked data to MOOC. A. Proposed Approach Our approach is based on identifying distinctive features with the help of MOOC preferences and resources needs data. As with all recommender systems, the main goal is to help users to find information or resources and match information that is important about needs with information that is important about resources. Accordingly, the process can be broken down to the following steps: 1.
Module 1 - data collect.
2.
Module 2 – OER Data Publication
3.
Module 3 – MOOC profiles provider.
4.
Module 4 - Seeker of resources.
5.
Module 5 - OER recommender.
Figure 2 summarizes the architecture in a general model of recommendation of OER.
3
The OER University, web site: http://oeruniversitas.org
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1114
Fig. 2. Architecture for Seeker of OER from LOCWD Triplestore .
B. Module 1, Data Collect Goal: Identify and select data sources, and Description: Authors selected and extracted information from 80 heterogeneous OCW repositories from OCWC and OCW-Universia members, sifting through a total of 7,239 OCW courses and 90.000 OERs approx. Scope: 15 associate consortia, as well as 212 higher education institutions and 57 organizational members compose OCWC; all courses are available for adoption and adaptation by faculty and students around the world. There is a large amount of unstructured data of an OCW resource available on the Web, but only in a human-readable representation (HTML). Most OCW web sites do not have APIs for data consumption. So, the only other alternative for automatically reconstitute the underlying data from an OCW web site is to use web-scraping techniques. Data scraping were used to extracts data from OCW platforms that was later structured and stored in a database. Scraping eliminated the need for having to do the retrieval manually.
C. Module 2, OER – Data Publication In (Piedra et al., 2013) authors described LOCWD RDFS vocabulary using W3C's RDF technology, for open educational resources with the aim of describing the specific types and classes of resources in OCW domain. This vocabulary was called Linked OpenCourseWare Data (LOCWD). A machinefriendly version is also available in http://purl.org/locwd/schema on RDF/XML format. LOCWD is a RDF(S) vocabulary devoted to linking OERs, open licenses, OCW repositories, and other academic information using the Web. Different kinds of applications can use or ignore different parts of LOCWD. With LOCWD, the OER/OCW initiatives can retain some control over their information of materials and courses in a non-proprietary format. LOCWD reuses a set of RDF(S) vocabularies. Each vocabulary includes a set of terms and classes that are common to a particular knowledge domain. The aim of these vocabularies is to connect the described OCW domain with Datasets in the LOD cloud. D. Module 3, MOOC profiles provider Goal: Serves as a view of filter onto the whole universe.
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1115
Description: Users don't know precisely what they can find on OCW site, or what to search for. Self-learners are trying to discover relationships or trends between MOOC profile and LOCWD data. In the context of this paper, authors opted to apply the design issues of Linked Data to integrate, interoperate and mash up data from MOOC Designer. The idea is evolving into a more interoperable and integrated system to sharing, connecting and discovering data and metadata of MOOC profiles. E. Module 4, Seeker of resources The architecture will use recommendation seekers based on SPARQL to express preferences and resources needs by rating OERs. The goal the module 5 will be merge the functionalities of recommendation seeker and preferences and resources need provider. The system will focuse on SPARQL query-based algorithms for matching OER based on MOOC preferences and weighting the interest of MOOC designer with similar taste to produce a recommendation for the resources seeker. The module will be designed for accessing two data sources: x The first one reline on LOCWD data, which provides RDF data extracted from OCW and OER websites. x The second one use the LOD Project, particularly from DBPEDIA, which provides RDF data extracted from the infoboxes of Wikipedia pages in a structured way. Linked Data technologies can also help to integrate the work of disperse institutions producing diverse linked data. Linked Open Data (LOD 4) is well known for providing a extensive amount of detailed and structured information. The architecture not only uses LOCWD dataset, but also uses information from Linked Open Data project. This allows exploiting the LOD community benefits. F. Module 5, OER Recommender Goal: For our purpose, a preference is an individual mental state concerning a subset of items from the universe of alternatives. Users can use the architecture because a single taxonomic order or a single folksonomy is not suitable or sufficient for explorer OER resources. Description: The architecture proposed attempt to recommend OERs that are similar to educational resources planned by the MOOC Designer and others records of social activity, such as OCW Syllabus and system usage history. Conclusions. In the architecture, the recommendation seeker (Module 4) Using Sparql, is possible filter OER using multiple category or taxonomy terms at the same time, and combine text searches, category term filtering, and other search criteria. Then, may ask for an OER recommendation based on MOOC data profile (Module 3). CONCLUSIONS The use of linked data approach on OER repositories provides the framework for their evolution into a more interoperable and integrated system to sharing, connecting and 4
LOD cloud – http://lod-cloud.net
discovering data and metadata of OCW initiatives. Based on the perspective of Linked Open Data, free open OER data also fosters interoperability and creates a basis on which the use, reuse, remix, and adaptation of open educational tools or commercial applications can be built more easily. Linked Data vision enables a new generation of open educational resources that can be semantically described and connected with other data and discoverable sources. The framework provides an approach that allows you to discover and access open educational resources that are extracted from open repositories distributed. Our principal OER providers are OCW institutions. In this context, we opted to apply the principles of Linked Data to integrate, interoperate and mashup data from distributed and heterogeneous repositories of open educational materials. The purpose is to significantly improve discovery, accessibility, visibility, and to promote reuse of open educational content in massive course. The implementation of this work is in progress. We are currently developing services for modules 4 and 5. When we completed the construction of the proposal will be made available to different types of users interested in creating personalized learning experiences, reusing OERs available. ACKNOWLEDGMENT The eMadrid Excellence Network is being funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. S2009/TIC165. REFERENCES [1]
N. Piedra, E. Tovar, A. Dimovska, and J. Chicaiza, “OCW-S: enablers for building sustainable Open Education, “ In Proceeding of IEEE Global Engineering Education Conference (EDUCON), Berlin, March 2013. [2] L. Petrides, L. Nguyen, C. Jimes, and A. Karaglani, "Open educational resources: inquiring into author use and reuse," International Journal of Technology Enhanced Learning, 2008, pp. 98 - 117, Vol. 1 [3] J. Hilton III, D. Wiley, J. Stein, A. Johnson, The Four R’s of Openness and ALMS Analysis: Frameworks for Open Educational Resources, http://contentdm.lib.byu.edu/cdm/ref/collection/IR/id/774 [4] C. Hodgkinson-Williams, and E. Gray, “Degrees of Openness: The emergence of Open Educational Resources at the University of Cape Town,” International Journal of Education and Development using Information and Communication Technology (IJEDICT), 2009, Vol. 5, Issue 5, pp.101-116 [5] ESVI-AL. “E1.1.4 Informe de estado del arte de Recursos Educativos Abiertos que puedan apoyar la formación superior virtual de personas con discapacidad,” Delivery of ESVI-AL project, 2012 [6] J. Glennie, K. Harley, N. Butcher, and T. van Wyk, “Open Eduactional Resources and Change in Higher Education: Reflections from Practice”, UNESCO-Commonwealth of Learning, Vancouver, 2012 [7] C. Bizer, R. Cyganiak, and T. Heath, “How to Publish Linked Data on the Web,” 2007 [8] T. Heath, and C. Bizer, “Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology,” 1:1, pp. 1-136, Morgan & Claypool, 2011 [9] T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web,” Scientific American, 284:5, pp. 34- 42, 2001 [10] N. Piedra, E. Tovar, R. Colomo-Palacios, J. López, and J. Chicaiza, “Consuming and producing linked open data: the case of Opencourseware,” Emerald EarlyCite, (PROG-Jul-2012-0045.R2), in press, 2013. [11] Mackintosh, W. “Opening Education in New Zealand: A Snapshot of a Rapidly Evolving OER Ecosystem”. In J. Glennie, K. Harley, N.
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1116
Butcher, T. van Wyk (Eds.), Open Educational Resources and Change in Higher Education: Reflections from Practice, 263-279, 2012 [12] Matkin, G.W. (2013). “Open Educational Resources in the Post MOOC Era”. eLearn Magazine. ACM. Volumen, Issue 4, 2013
978-1-4799-3190-3/14/$31.00 ©2014 IEEE 3-5 April 2014, Military Museum and Cultural Center, Harbiye, Istanbul, Turkey 2014 IEEE Global Engineering Education Conference (EDUCON) Page 1117
