E-lEARNiNG, ExPERiENCES AND FUTURE

I

E-learning, experiences and future


Edited by

Safeeullah Soomro

In-Tech

intechweb.org

Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 In-teh www.intechweb.org Additional copies can be obtained from: [email protected] First published April 2010 Printed in India Technical Editor: Maja Jakobovic Cover designed by Dino Smrekar E-learning, experiences and future, Edited by Safeeullah Soomro p. cm. ISBN 978-953-307-092-6

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Preface Basically e-learning is new way of providing knowledge to peoples to interact with web based systems which is need of current world. E-learning based systems received tremendous popularity in the world since this decade. Currently most of the universities are using e-bases systems to provide interactive systems to students which can make communication fast to grow up the knowledge in every field of study. Many advantages comes to adopt e-learning systems like paper less environment, pay less to instructors, students can access systems from any part of world, advanced computer trainings provided at homes and access any material using web. In advanced countries e-learning systems play major role in an economy to produce productive output in the industries without having paid huge amount for personal staff that are locating physically and also provide big advantage to peoples of developed countries who can not attend physically courses neither afford experts in a professional fields. This is big achievement of e-learning bases systems to promote education online. This book is consisting of 24 chapters which are focusing on the basic and applied research regarding elearning systems. Authors made efforts to provide theoretical as well as practical approaches to solve open problems through their elite research work. This book increases knowledge in the following topics such as e-learning, e-Government, Data mining in e-learning based systems, LMS systems, security in elearning based systems, surveys regarding teachers to use e-learning systems, analysis of intelligent agents using e-learning, assessment methods for e-learning and barriers to use of effective e-learning systems in education. Basically this book is an open platform for creative discussion for future e-learning based systems which are essential to understand for the students, researchers, academic personals and industry related people to enhance their capabilities to capture new ideas and provides valuable solution to an international community. The editor and authors of this book hope that this book will provide valuable platform for the new researchers and students who are interested to carry research in the e-learning based systems. Finally we are thankful to I-Tech Education and publishing organization which provides the best platform to integrate researchers of whole world though this published book.

Dr. Safeeullah Soomro Yanbu University College, Kingdom of Saudi Arabia

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VII

Contents Preface 1. E-Learning Indicators: A Multidimensional Model For Planning Developing And Evaluating E-Learning Software Solutions

V 001

Bekim Fetaji and Majlinda Fetaji

2. Barriers to Effective use of Information Technology in Science Education at Yanbu Kingdom of Saudi Arabia

035

Abdulkarem Eid S. Al-Alwani and Safeeullah Soomro

3. The Use of Multi-Agents’ Systems in e-Learning Platforms

047

Tomasz Marcin Orzechowski

4. Quality Metrics of an Integrated E-Learning System – students’ perspective

071

Ksenija Klasnić, Jadranka Lasić-Lazić and Sanja Seljan

5. Intelligent Interaction Support for e-Learning

095

Takashi Yukawa and Yoshimi Fukumura

6. Supporting Technologies for Synchronous E-learning

111

Juan C. Granda, Christian Uría, Francisco J. Suárez and Daniel F. García

7. Bayesian Agent in e-Learning

129

Maomi Ueno

8. Data Mining for Instructional Design, Learning and Assessment

147

Lluís Vicent and Xavier Gumara

9. A learning style – driven architecture build on open source LMS’s infrastructure for creation of psycho-pedagogically – ‘savvy’ personalized learning paths

163

Kerkiri Al. Tania , Paleologou Angela-M., Konetas Dimitris and Chatzinikolaou Kostantinos

10. E-learning in information accessibility of disabled assistanttechnology Chunlian Li，Yusun

189

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11. Application of Data-Mining Technology on E-Learning Material Recommendation

213

Feng-Jung Liu and Bai-Jiun Shih

12. Loosely-Tied Distributed Architecture for Highly Scalable E-Learning System

229

Gierłowski and Nowicki

13. Evolution of Collaborative Learning Environments based on Desktop Computer to Mobile Computing: A Model-Based Approach

261

Ana I. Molina, William J. Giraldo, Francisco Jurado, Miguel A. Redondo and Manuel Ortega

14. From the discovery of students access patterns in e-learning including web 2.0 resources to the prediction and enhancement of students outcome

275

Raquel Hijón-Neira and Ángel Velázquez-Iturbide

15. Dependable e-learning systems

295

Ali Al-Dahoud, Marek Woda and Tomasz Walkowiak

16. Ontology-driven Annotation and Access of Educational Video Data in E-learning

305

Aijuan Dong, Honglin Li and Baoying Wang

17. When a robot turns into a totem: The RoboBeggar case

327

Gaetano La Russa, Erkki Sutinen and Johannes C. Cronje

18. Virtual patients as a practical realisation of the e-learning idea in medicine

345

Andrzej A. Kononowicz and Inga Hege

19. Data Warehouse Technology and Application in Data Centre Design for E-government

371

Xuanzi Hu

20. The Emergence of the Intelligent Government in the Second Society

385

Dennis de Kool and Johan van Wamelen

21. A Lightweight SOA-based Collaboration Framework for European Public Sector

397

Adomas Svirskas, Jelena Isačenkova and Refik Molva

22. Spatial Aided Decision-making System for E-Government

413

Liang WANG, Rong ZHAO, Bin LI, Jiping LIU and Qingpu ZHANG

23. Evaluating Local E-Government: A Comparative Study of Greek Prefecture Websites Prodromos Yannas and Georgios Lappas

441

E-Learning Indicators: A Multidimensional Model For Planning Developing And Evaluating E-Learning Software Solutions

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X1 E-Learning Indicators: A Multidimensional Model For Planning Developing And Evaluating E-Learning Software Solutions 1, 2 South

1Bekim

Fetaji and 2Majlinda Fetaji

East European University, (Computer Sciences Faculty) Macedonia

1. Introduction Many current e-learning initiatives follow the “one-size-fits-all” approach just offering some type of Learning Management System (LMS) to learners or Learning Content Management System (LCMS). Typically, this approach is related to lack of knowledge of the learner audience or factors influencing that audience and e-learning project overall and therefore fail to provide satisfactory support in the decision making process (Fetaji, 2007a). In order to address this issue, an approach dealing with e-learning indicators is proposed, assessed, measured and evaluated. The proposed E-learning Indicators Methodology enables successful planning, comparison and evaluation of different e-learning projects. It represents an empirical methodology that gives concrete results expressed through numbers that could be analysed and later used to compare and conclude its e-learning efficiency. With the application of this methodology in e-learning projects it is more likely to achieve better results and higher efficiency as well as higher Return on Investment ROI. The purpose of e-learning indicators was to raise the awareness of the factors influencing elearning project in order to identify the nature of obstacles being faced by e-learners. This research argues that if such obstacles could be recognized early in the process of planning and development of e-learning initiatives then the actions that remedy the obstacles can be taken on time. We believe that the absence of appropriate on-time actions is one of the main reasons for the current unsatisfactory results in many e-learning projects. The e-learning indicators approach is a multidimensional model used in planning, developing, evaluating, and improving an e-learning initiative. Thus, the model comprises e-learning projects as iterative development processes where at each iteration step appropriate actions to improve the initiative outcomes can be taken. The iteration steps of this development process include:  Planning phase with the initial measurement of e-learning indicators. The obtained results influence all the other phases.  Design phase where (group or so called “collective”) personalisation issues and pedagogical and instructional techniques and aspects are addressed.

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Implementation phase where a number of e-learning experiments are conducted based on the results from the previous phases.  Evaluation phase to obtain precise results of the initiative outcomes.  Analysis phase where guidelines and recommendations are written down. The proposed model defines 18 indicators that were practically applied in a number of case studies including their application with Angel LMS and a number of self developed and implemented e-learning interactive tools. E-learning indicators have been defined with help of different focus groups, realised literature review and a web based survey of academic staff and students in the framework of South East European University. In addition, the approach was revised closely with experts in the field during participation in several research projects (mentioned in acknowledgement). The experiences from these projects show that a more successful e-learning is not possible only if a generic approach or generic guidelines for the learners are applied. Rather, individual learning services are needed in supporting learners according to their personal preference profile. However, although not the focus of the research because of the interconnection with the above identified issues several projects and research initiatives that deal with personalization have been shortly reviewed. The reviewed projects are the OPen Adaptive Learning Environment (OPAL), (Dagger, et al 2002) and ADELE-Adaptive e-Learning with Eye Tracking (Mödritscher, et al 2006). The OPAL research shows personalization as difficult to achieve and “… are often expensive, both from a time and financial perspective, to develop and maintain.” (Dagger, et al 2002). Therefore, a conclusion is drown that learner personalisation should not bee addressed at to finely grained level. Typically, personalisation at that starting level is not practical based on the findings of OPAL project (Dagger, et al 2002) and since it has too include all of those learners preferences that change each time the learner uses the system clearly does not represent a constant factor that can be addressed (Fetaji, 2007g). Instead, a recommendation is to use the defined approach with elearning indicators as starting point when developing an e-learning initiative. Then after the measurements the learners are divided into groups so called ”collectives” (in Universities these are the departmental levels) were personalisation is offered to the specifics of the collectives majority primarily based on learning style categorization and type of learner they are (indicator 4, 4). We have adopted the Felder-Silverman model for learning style categorization (Felder, 1993). After that learner personalisation can be designed and offered tailored to each collective (Fetaji, 2007g). Furthermore, based on the measurements of these e-learning indicators a design of a sustainable e-learning initiative can be supported. Each elearning initiative is unique and involves specifics that can not be taken under consideration in the form of “one-size-fits-all” solution. However evaluating e-learning indicators in the planning phase is only the first step in more successful e-learning. E-learning indicators can be used in other phases as well in particular in evaluating different e-learning initiatives in conjunction with ELUAT methodology to assess e-learning effectiveness. Comparison of different projects can be realised comparing e-learning indicators measurements in conjunction with the evaluated e-learning effectiveness (how effective they have shown measured using the ELUAT methodology) (Fetaji, 2007g). 


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2. E-Learning Indicators Methodology E-learning indicators are defined as the important concepts and factors that are used to communicate information about the level of e-learning and used to make management decisions when planning an e-learning strategy for an institution or University according to the study of (Fetaji et al 2007a). The purpose was to raise the awareness of the factors and concepts influencing e-learning in order to enhance learning and identify the nature of obstacles being faced by e-learners and therefore proposed is a methodological approach in developing any e-learning initiative. Because there are too many factors, personalization and specifics related to each situation and circumstances it is considered that would be wrong offering one size solution for all. It is of great importance to have standardised guide of e-learning indicators accepted by scientific community to be able to compare and to evaluate the different initiatives regarding e-learning in a standardised manner. In order to define and assess the e-learning indicators the data have been gathered from interviews with e-learning specialists, 2 focus groups (one student and one instructors), web based survey of academic staff and students and literature review of similar previous research work found at (Bonk, 2004). The web based survey was realised through questionnaire that was developed in three cycles. In the first cycle the questions were developed based on the e-learning indicators. For most of the e-learning indicators there was just one question to cover it, while for some 2 (two) or more questions. At the beginning developed were more questions but after thorough consultations with survey experts shortened and come up with 23 questions. In the second cycle the developed survey questionnaire was tested on a 2 different focus groups. One group consisting of students and the other group from instructors. After analyses of the survey data they were presented to the focus groups and confronted to them how much do they agree and consider this results as realistic and accurate. The initial response was that although the survey captures in substantial level the real situation there were a lot of discussions especially on the student focus group regarding the appropriateness of the survey questions. In discussion with both of the focus groups most of the questions have changed according to the discussions and proposals of the group. In the third cycle both of the focus group were filled the new survey and after the survey data were given to them both of the focus groups agreed that it really gives an accurate clear picture of the participants. The survey was designed following the rule of thumb for all communications: Audience + Purpose = Design. This survey was divided into 18 (eighteen) sections to cover al the elearning indicators previously defined and had 23 (twenty three) questions in total. It was communicated to the participants and provided as link in the message board of the eservice system of the University. As e-learning indicators defined are: (1) learner education background; (2) computing skills level (3) type of learners they are, (4) their learning style and multiple intelligence, (5) obstacles they face in e-learning (e-learning barriers), (6) attention, (7) content (suitability, format preferences), (8) instructional design, (9) organizational specifics, (10) preferences of e-learning logistics; (11) preferences of e-learning design; (12) technical capabilities available to respondents; (13) collaboration; (14) accessibility available to respondents; (15) motivation, (16) attitudes and interest; and (17) performance-self-efficacy (the learner sense their effectiveness in e-learning environment); (18) learning outcomes. Recommendation is to use the defined e-learning indicators as starting point when developing e-learning

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initiative and based on the measurements of these e-learning indicators to tailor the specifics of e-learning. Each e-learning initiative should measure the provided indicators and based on them to design and build their e-learning sustainability.

3. Research Methodology The research methodology used was a combination of qualitative and quantitative research as well as comparative analyses of factors influencing e-learning. Background research consisted of an in depth literature review of e-learning. The background research consisted of analyses of e-learning trends, e-learning technologies and solutions, e-learning standards, learning theories, concepts and factors that influence e-learning. Then grounded theory research was realised through exploratory research to determine the best research design and then constructive research was undertaken to build the software solution followed by empirical research to describe accurately the interaction between the learners and the system being observed. The data for this research was gathered from research interviews with elearning specialists and participants, focus group and a web based survey as well as printed hard copy survey of academic staff and students. In order to develop a systematic methodology, either substantive or formal, about improving and enhancing e-learning by addressing the deficiencies from the findings and in this manner to contribute in enhancing e-learning effectiveness. In order to achieve this, the following research objectives have been tried to be addressed:  Review key authoritative literature on e-learning trends, e-learning standards, technologies and e-learning systems provided as e-learning solutions, and evaluation of e-learning effectiveness in order to provide a thorough understanding of e-learning in general and associated knowledge dissemination.  Discuss the advantages and disadvantages of different approaches to e-learning solutions.  Analyses of different e-learning environments and solutions  Asses, measure and evaluate concepts and factors influencing e-learning defined as e-learning indicators  Design, develop and conduct experiments in order to asses the best modelling approach to developing e-learning software solutions  Connect e-learning indicators with each e-learning software solution approach and learning theory and design  Analyse and discuss the data gathered from the experiments  Conclude and deliver recommendations for enhanced learning and future improvements. Key variables and themes that have been studied are: students needs analyses, usage environment feasibility analyses, e-learning indicators, e-content and learning processes issues, feasibility analyses of authoring issues, assessment of e-learning effectiveness, and discussion of the purpose and evaluation of results of the research and proposed recommendations for e-content and e-learning processes issues, applications specifics and requirements in correlation with the environment and situation of the Communication Sciences and Technologies Faculty at south East European University, accessibility and learning specifics based on learners needs, deployment, testing and evaluation of the solution.


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Interviewed and realised direct observation of students as program implementation case study for the three subjects: Advanced Elective course “Object Oriented Programming in Java” and the two core courses “Software Engineering” and “Algorithms and Data Structures”. There implemented the solutions proposed under the part of the research study on e-content issues and e-learning processes. Developed is a novel e-learning indicators-(ELI) model to be used for developing information retrieval courseware’s by concentrating on previously assessed e-learning indicators. Secondly, the research is conveying the need for close correlation of software development and e-learning pedagogy. Recommend that technology should adapt to theories of learning and e-learning indicators assessed earlier. This process modelling based on e-learning indicators should be used as guidelines in similar developments. A pilot study was conducted on e-learning interactive courseware applying network analyses method in order to find the critical activities and assess the risks. The main focus and aim of research was set on software development proposed and based upon the elearning indicators and the design of the courseware in compliance with theories of learning and didactical pedagogical approach. For the assessment of e-learning effectiveness proposed a methodology, called ELUAT (E-learning Usability Attributes Testing), for which developed an inspection technique the Predefined Evaluation Tasks (PET), which describe the activities to be performed during inspection in the form of a predefined tasks, measuring previously assessed usability attributes.

4. The Experiments In order to investigate the implementation strategy and try to address the above identified issues 7 (seven) experimental case studies were developed and evaluated. The experiments have been separated in 3 (three) groups based on their research nature and investigation focus. The first 2 (two) experiments concentrate on e-learning indicators and their usage in planning as well as evaluating e-learning projects. In the next 4 (four) experiments various e-learning software solutions as interactive tools are designed and developed in order to test several hypotheses as well as to investigate the new e-learning indicators methodology approach in developing e-learning software solutions and at the same time to investigate instructional strategies discussed and reviewed earlier. The final experiment is devised in order to investigate and analyse the e-content and attention correlation and conjunction in the e-learning process. Each case study experiment is tailored based on the information collected in the first step, evaluated e-learning indicators. The technological part of the research involved analyses of software engineering issues in designing e-learning environments. Proposed is ELI (E-Learning Indicators) model - as methodology for developing e-learning software solutions (Fetaji, 2007e). Further, the experiments also investigated applications of different instructional techniques and pedagogical learning models and how they are reflected in the software development process according to different devised scenarios in supporting instructional strategy. An analysis of Project, Problem, Inquiry-based and Task based learning instructional techniques and their appropriateness for different scenarios was realized. In the final step, each experiment and its underlying pedagogical model was once more evaluated using the evaluation methodology developed for this purpose. The developed methodology is called ELUAT (E-Learning Usability Attributes Testing) through the PET (Predefined Evaluation

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Tasks) inspection technique (Fetaji, 2007c). The developed 4 (four) e-learning software solutions as case study experiments were created under two research projects realised in a time framework of more than two years and later evaluated:  Intranet Gateway research project and  E-Learning Framework research project, The e-learning software solutions developed for the needs of the experiments are:  XHTML and XML e-learning Interactive tool,  E-learning interactive mathematical tool,  Information Retrieval Courseware system-Intranet Gateway.  Online Dictionary of Computer Science terms and nomenclatures. The results of this research show that e-learning indicators approach is of primary importance (Fetaji, 2007e). Having a standardised set of e-learning indicators accepted by scientific community enables comparison and evaluation of different e-learning initiatives and their e-learning projects in a systematic manner. Moreover this approach combined with experimental approach to e-learning brings new insights into the specifics of e-learning that might help in increasing the learning outcomes, especially knowledge transfer. Therefore, conclusion is that no new systems are needed but a series of experiments has to be conducted to see what does and does not work in a particular situation and to provide guidelines and recommendations for that situation. Furthermore, an investigation of issues in authoring e-learning content (e-content) was realised. The main purpose was to effectively identify the vehicles into increased knowledge dissemination and efficient knowledge transfer and thus improve the overall e-learning process. Preparing quality e-content delivered digitally is probably the major aspect for long term success of any e-learning endeavour. It is the content, however, that learners care for and judge how much they learn from it. Therefore we have identified and addressed most important authoring issues by analyzing different courses using an Learning Management System.

5. Data Collection and Analysis Depending from the Software Lifecycle used for each e-learning software solutions developed in particular for the given experiment used is the ELUAT methodology and PET testing as described thoroughly at (Fetaji et al 2007a). Questionnaires, surveys, focus groups, usability testing and other software testing groups were used. Groups of students filled out different surveys discussing e-learning indicators, barriers to distance education and usability surveys of e-learning software solutions modelled and developed. The return rate for the surveys for each experiment was different and the highest was for distance education with 64.89 %, (The distance education program at the moment has 81undergraduate full time students, and 13 part time students, or in totals 94 students) while for the e-learning indicators the response rate was 9.7 % (There were in total 701 student surveys filled. The University at the moment of the research survey has 6.386 undergraduate and 188 postgraduate full time students, and 643 part time students, or in total 7217 students). The majority of the participants (63.8%) have used the e-learning software solutions discussed. Ten percent of the participants took fewer than all of the courses mentioned previously since Object Oriented Programming in Java was an elective subject. Large amount of data was collected and used from the literature reviews and inputs from other related projects.


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Several statistical procedures were conduct for data analysis. First, the zero-order correlations were computed among all variables. The aim of this operation is to have an initial test of whether there were relationships among the variables. The interaction of technology with teaching or social presence was considered if including those items would increase the power of the regression model substantially. The standard multiprogression procedures were conducted with course subjective satisfaction through the perceived learning outcome, learning engagement assessed through time to learn and time of performance as dependent variables. All assumptions of normality, usability, of residuals were checked in those regression analyses. In order to handle those data the triangulation technique from Dumas and Redish (1999) was used, were we look at all data at the same time to see how the different data supports each other.

6. E-Learning Indicators Specification and Analyses (1) Learner education background together with his cultural background is set as indicator since it is a direct factor that is associated and impacts e-learning. According to Gatling et al, (2005), students today come from a variety of cultural backgrounds and educational experiences outside of the traditional classroom. How do students construct meaning from prior knowledge and connect it with the new experiences? Based on this facts and interviews with e-learning specialist It was set it as important indicator. (2) Computing skills level of the learner is set as indicator since it directly influences the way elearning is conducted with the use of Information and communication technologies (ICT) and use of computers and the computing skills requirements are essential in learning. “As we move toward the 21st century, anyone who is not “computer literate” will find themselves at a disadvantage when competing in the job market.” (Johnson, Gatling, Hill, 1997). The indicator (3) type of learners they are depends primarily on the balance in the two dimensions of the Learning Style scale model formulated by Richard M. Felder and Linda K. Silverman of North Carolina State University according to Felder & Soloman (n.d) based on four dimensions (active/reflective, sensing/intuitive, visual/verbal, and sequential/global). According to Felder & Soloman (n.d) “students preferentially take in and process information in different ways: by seeing and hearing, reflecting and acting, reasoning logically and intuitively, analyzing and visualizing, steadily and in fits and starts. Teaching methods also vary. Some instructors lecture, others demonstrate or lead students to selfdiscovery; some focus on principles and others on applications; some emphasize memory and others understanding. Active learners tend to retain and understand information best by doing something active with it, discussing or applying it or explaining it to others. Reflective learners prefer to think about it quietly first. Sensing learners tend to like learning facts; intuitive learners often prefer discovering possibilities and relationships. Visual learners remember best what they see: pictures, diagrams, flow charts, time lines, films, and demonstrations. Verbal learners get more out of word, written and spoken explanations. Sequential learners tend to gain understanding in linear steps, with each step following logically from the previous one. Global learners tend to learn in large jumps, absorbing material almost randomly without seeing connections, and then suddenly getting it”. Therefore assessing and knowing the learning audience is crucial in order to know whom to support and there is an extensive need for this input data in order for the elearning initiative to be successful and effective. Then after the measurements the learners

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are divided into groups so called”collectives” were personalisation is offered to the specifics of the collective majority (in Universities these are the departmental levels) primarily based on learning style categorization and type of learner they are according Felder-Silverman model for learning style categorization (Felder, 1993). The importance of the type of learner and (4) their learning style and multiple intelligence is for the both sides: instructor and student. For instructors it is of importance since it reflects the preferences of Learning style in their teaching and delivery style to students. We advise to tend to use each learning style to teach also in a delivery type suited to other types of learners and truing to bring it closer and generalize to include all the types using visualization and verbal communications, as well as other communication tools. According to Tomas Armstrong (n.d.) Multiple Intelligences are eight different ways to demonstrate intellectual ability. 1) Linguistic intelligence ("word smart"), 2) Logical-mathematical intelligence ("number/reasoning smart"); 3) Spatial intelligence ("picture smart"); 4) BodilyKinesthetic intelligence ("body smart"); 5) Musical intelligence ("music smart"); 6) Interpersonal intelligence ("people smart"); 7) Intrapersonal intelligence ("self smart"); 8) Naturalist intelligence ("nature smart"). Again assessing the audience and having this input data is very important e-learning indicator in planning and developing e-learning initiative. The indicator (5) obstacles they face in e-learning (e-learning barriers) is set as important based on interviews and speaking with e-learning specialists. Each e-learning project has different barriers and they are specified as learner input and depend from a situation. Assessing what the learner audience faces as barrier is crucial in achieving effective elearning. Indicator (6) attention is set as very important. Attention cues when the learners begin to feel some mental workload, Ueno, M. (2004). (7) e-content (suitability, format preferences), e-learning content (e-content) considered as vehicle of the e-learning process and knowledge construction. The quality of the virtual learning environment is mainly depending on the quality of the presented e-learning content. Fetaji, B. (2006). Indicator (8) Instructional design has gained significant prominence in e-learning for a number of compelling reasons. One of them is the possibility for instructional design to systematically address the need for creating and evaluating students’ learning experience as well as learning outcome. The other is instructional design can help faculty to focus on using the appropriate format and tools for the appropriate learning objectives. Fetaji, B. (2006). Indicator (9) organizational specifics - every instituion has its specific business processes that influences and impacts e-learning, Galotta et. al. (2004) (10) preferences of e-learning logistics - targeted at learners of different experience levels and organizational background/hierarchy, based on the ELA model-the European Logistics Association (ELA), (Zsifkovits, 2003). The following 7 (seven) variables have been set as priority in determining viable learning environment and its e-learning logistics: 1) Interoperability; 2) Pricing; 3) Performance; 4) Content development; 5) Communication tools; 6) Student Involvement Tools; 7) Evolving technology. (11) indicator preferences of e-learning design; designing instruction that acknowledges that students differ in their learning preferences and abilities and that instruction needs to be flexible to address these differences, (Kumar 2006). The next indicators (12) technical capabilities available to respondents (13) collaboration; (14) accessibility available to respondents, ares defined as important indicators in discussions with e-learning specialist and experts. They represent the essential influencing


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factors on e-learning mentioned in different studies such as (Coleman, B., Neuhauser, J. & Fisher, M. 2004). (15) Motivation is essential to learning and performances, particularly in e-learning environments where learners must take an active role in their learning by being self directed (Lee, 2000). (16) Attitudes and interest. A review of studies on attitudes toward learning and using information technology in education has revealed that most studies have shown that students’ attitudes toward technology are critical, (Liu, et. al. 2004); (17) performance: self-efficacy (the learner sense their effectiveness in e-learning environment); Self-efficacy refers to people beliefs about their capabilities to perform a task successfully at designated levels, (Bandura, 1997). (18) According to Jenkins, A. and (Unwin, 1996) learning outcomes are defined as statements of what is expected that a student will be able to do as a result of a learning activity. Learning outcomes are usually expressed as knowledge transfer, skills, or attitudes (Unwin, 1996). Therefore, it is a very important indicator in planning, designing and evaluating e-learning.

7. E-Learning Indicators Assessment, Measurement and Evaluation 7.1 Definition E-learning indicators have been defined with help of different focus groups, realised literature review and a web based survey of academic staff and students in the framework of South East European University as well as revised closely with experts in the field during participation in several research projects. In order to investigate e-learning indicators in planning phase of e-learning projects a case study was initiated in order to asses, measure and evaluate e-learning indicators a web based survey has been used. The survey was designed following the rule of thumb for all communications: Audience + Purpose = Design. The survey was divided into 18 (eighteen) sections to cover al the e-learning indicators previously defined. It was communicated to the participants and provided as survey in Angel LMS. It was offered to two different department from two different Universities. One using angel LMs as e-learning platform and the other using Moodle as learning platform. There were in total 701 student surveys filled. The answer rate was 30.48%. There were 701 filled survey, and the total number of students in using Angel platform was 2300. The data was collected using Angel Learning Management System and further analyzed in Excel. The second e-learning project that is using Moodle as e-learning platform was focused on computer Science Faculty and in total 44 surveys were filled and the answer rate was 9.78%. 7.2 Analyses of indicator: Self efficacy in e-learning Please rate your self efficacy in e-learning. How effective and efficient you are? Bad Not so good OK Good Very good □1 □2 □3 □4 □5

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7.2.1 ANGEL LMS - Findings for indicator: Self efficacy in e-learning Most of the respondents, 43.7% have rated them self’s as good their efficacy in e-learning. While 24.1 % have rated them self’s as very good. On the other hand 1% of them were not satisfied with the e-learning environment and their efficacy and have rated them self’s as bad, 4.7 % not so good, and 26.5% rated them self’s as OK, meaning they are partially satisfied with the e-learning system and their effectiveness in it.

43,70% 26,50%

1,00% Bad

24,10%

4,70% Not so good

OK

Good

Very Good

Self Efficacy in e-learning

Fig. 1. ANGEL LMS - Findings for indicator 7.2.2 Moodle LMS- Findings for indicator: Self efficacy in e-learning Most of the respondents, 33.17%, have rated them self’s as good their efficacy in e-learning. While 26.54 % have rated them self’s as very good. On the other hand 1.12% of them were not satisfied with the e-learning environment and their efficacy and have rated them self’s as bad, 9.7 % not so good, and 29.47% rated them self’s as OK, meaning they are partially satisfied with the e-learning system and their effectiveness in it. 29,47%

33,17% 26,54%

9,70% 1,12% Bad

Not so good

OK

Good

Self Efficacy in e-learning

Fig. 2. Moodle LMS - Findings for indicator

Very Good


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7.2.3 Discussion of the Findings for Indicator: Self Efficacy in E-learning As Bandura (1997) defined it, self-efficacy refers to people beliefs about their capabilities whether or not they can perform successfully at designated levels using the e-learning environment. From the analyses of the findings it indicates that there is an increase in student’s achievement after their engagement in an e-learning environment. Overall 94.3% of the students in Angel and 89.18 % of students in MOODLE are satisfied with their selfefficacy and have shown progress moving in the new e-learning environment from the traditional classroom. However there are 5.7 % of the students (ANGEL) and 10.82 % (MOODLE) that are not satisfied with their achievement. The main reason among others for this result is identified in the usability issues of the two offered e-learning systems. Other reasons will be discussed in conclusions. However in general students rated their self efficacy as better in using ANGEL compared to MOODLE. 7.3 Analyses of Indicator: Type of Learner What type of learner you are? (Please Circle one option: a) or b) for each row) a) ACTIVE or b) REFLECTIVE Learner (Explanations: Active learners tend to retain and understand information best by doing something active with it--discussing or applying it or explaining it to others. Reflective learners prefer to think about it quietly first.) 7.3.1 ANGEL LMS - Findings for Indicator: Type of Learner Reflective; 29,24%

Type of Learner

Activ e Reflectiv e

Active; 72,61%

Fig. 3. ANGEL LMS - Findings for indicator On the whole, 72.61 % of respondents rated them self’s as Active learners while the others 29.24 % as Reflective learners. 7.3.2 MOODLE - Findings for indicator: Type of Learner Type of Learner Reflective; 45,72%

Active Reflective

Active; 54,28%


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On the whole, 54.28 % of respondents rated them self’s as Active learners while the others 45.72 % as Reflective learners. 7.3.3 Discussion of the findings for indicator: Type of Learner The indicator (3) type of learners they are depends primarily on the balance in the two dimensions of the Learning Style scale model formulated by Richard M. Felder and Linda K. Silverman according to Felder & Soloman (n.d). The findings indicate that students in using ANGEL are primarily of the Active type of learner 72.61% in comparison to 29.24% Reflective type of a learner. The students in using MOODLE are primarily of type reflective learners 54.28% in comparison to 45.72 %. These findings indicate that the structure and curriculum of the studies should change and embrace this type of learner more by preferring and choosing a hands on approach in comparison to the theoretical approach for the learners using ANGEL and the opposite for the learners using MOODLE were learners should be provided more reading materials and solved examples so they can reflect this and learn by doing this. 7.4.3 Analyses of indicator: Type of Learner a) SENSING or b) INTUITIVE Learner (Explanations: Sensing learners tend to like learning facts; intuitive learners often prefer discovering possibilities and relationships.) 7.4.3.1 ANGEL LMS - Findings for indicator: Type of Learner Type of Learner Sensing; 62,62% Intuitive; 37,37%

Sensing Intuitive

Fig. 5. ANGEL LMS - Findings for indicator On the whole, 62.62 % of respondents rated them self’s as Sensing learners while the others 37.37% as Intuitive learners. 7.4.3.2 MOODLE - Findings for indicator: Type of Learner Intuitive; 56,09%

Type of Learner

Sensing; 43,91%

Sensing Intuitive



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On the whole, 43.91 % of respondents rated them self’s as Sensing learners while the others 56.09% as Intuitive learners. 7.4.3.3 Discussion of the findings for indicator: Type of Learner The findings indicate that ANGEL LMS students are primarily of type sensing and they tend to learn by learning facts 62.62%. The minority group of the students are of type intuitive learners 37.37% and they prefer discovering possibilities and relationships for them self’s. These finding suggests that the content created and used in the e-learning environment should be concentrated around facts and detailed descriptions rather then on living this to students to discover for them self’s. MOODLE students are primarily of type Intuitive 56.09% compared to the sensing group with 56.09%. For the students of this type the recommendations are to provide more information and case studies for students in order to intuitively learn and find the answers. 7.4.4 Analyses of Indicator: Type of Learner a) VISUAL or b) VERBAL LEARNER (Explanations: Visual learners remember best what they see--pictures, diagrams, flow charts, time lines, films, and demonstrations. Verbal learners get more out of wordswritten and spoken explanations.) 7.4.4.1 ANGEL LMS - Findings for indicator: Type of Learner Type of Learner

Verbal, 40.66%

Visual Verbal Visual, 59.34%

Fig. 7. ANGEL LMS - Findings for indicator On the whole, 59.34 % of respondents rated them self’s as Visual learners while the others 40.66% as Verbal learners.

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7.4.4.2 MOODLE - Findings for indicator: Type of Learner Type of Learner Visual Verbal

Verbal, 49.58% Visual, 51.42

Fig. 8. Moodle LMS - Findings for indicator On the whole, 51.42 % of respondents rated them self’s as Visual learners while the others 49.58% as Verbal learners. 7.4.4.3 Discussion of the findings for indicator: Type of Learner The findings indicate that ANGEL students are 59.34% while MOODLE 51.42% primarily of type Visual learners and they tend to learn by pictures, diagrams, flow charts, time lines, films, and demonstrations. The other group of the students is of type verbal learners Angel 40.66% and MOODLE 49.58% and they prefer to learn out of words, written and spoken. This findings suggests that the e-content created and used in the e-learning environment should contain more multimedia elements like pictures, diagrams, flow charts and demonstrations rather then just text explanations. 7.4.5 Analyses of indicator: Type of Learner a) SEQUENTIAL or b) GLOBAL LEARNER (Explanations: Sequential learners tend to gain understanding in linear steps, with each step following logically from the previous one. Global learners tend to learn in large jumps, absorbing material almost randomly without seeing connections, and then suddenly "getting it.") 7.4.5.1 ANGEL LMS - Findings for indicator Type of Learner

Global, 38.37%

Sequential, 61.63%

Fig. 9. ANGEL LMS - Findings for indicator

Sequential Global


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On the whole, 61.63 % of respondents rated them self’s as Sequential learners while the others 38.37% as Global learners. 7.4.5.2 MOODLE - Findings for indicator Type of Learner

Global, 52.83% Sequential, 47.17%

Sequential Global

Fig. 10. Moodle LMS - Findings for indicator On the whole, 52.83 % of respondents rated them self’s as Sequential learners while the others 47.17% as Global learners. 7.4.5.3 Discussion of the findings The findings indicate that 61.63 % Angel students and 47.17% Moodle students are primarily of type Sequential learners and they tend to learn in linear steps, with each step following logically from the previous one. The other group of the students are of type Global learners 38.37% Angel students and 52.83% Moodle students and they prefer to learn in large jumps, absorbing material almost randomly without seeing connections, and then suddenly "getting it.". This findings suggests that the e-content created and used in the elearning environment should present the subject sequentially and then progressing step by step to the global and general issues for Angel environment students while for the Moodle environment students the content provided should contain information that provides global picture of the content. 7.4.6 Analyses of indicator: Learning Style and intelligence 1) Linguistic ("word smart", sensitivity and ability to spoken and written language): 2) Logical-mathematical ("number/reasoning smart", analyze problems logically, investigate issues scientifically) 3) Spatial ("picture smart", potential to recognize and use the patterns of wide space) 4) Bodily-Kinesthetic ("body smart", mental abilities to coordinate bodily movements) 5) Musical ("music smart", skill in the performance, composition, and appreciation of musical patterns) 6) Interpersonal ("people smart", capacity to understand the intentions, motivations and desires of other people) 7) Intrapersonal ("self smart", capacity to understand oneself, to appreciate one's feelings, fears and motivations) 8) Naturalist ("nature smart", recognize, categorize certain features of the environment)

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7.4.6.1 ANGEL LMS - Findings for indicator Learning Style Linguistic Naturalist; 14,78% Linguistic; 11,64% Intrapersonal ; Logical14,85% mathematical; 24,10% Interpersonal ; 15,75%

Logical-mathematical Spatial Bodily-Kinesthetic Musical Interpersonal

Spatial; 7,84%

Intrapersonal

Musical; 6,42% Bodily-Kinesthetic ; 4,63%

Naturalist

Fig. 11. ANGEL LMS - Findings for indicator 7.4.6.2 MOODLE - Findings for indicator Linguistic

Learning Style Intrapersonal ; 11,85% Linguistic; 5,16% Naturalist; 5,80% Interpersonal ; 15,75%

Logicalmathematical; 36,55%

Logicalmathematical Spatial BodilyKinesthetic Musical Interpersonal

Spatial; 17,84%

2,42% ily-Kinesthetic ; 4,63%

Intrapersonal Naturalist

Fig. 12. Moodle LMS - Findings for indicator 7.4.6.3 Discussion of the Findings The findings indicate that Angel and Moodle students are more or less with a balanced and similar learning style and intelligence were slightly prevails the Logical-mathematical, and linguistic style and intelligence preferences. 7.4.7 Analyses of indicator: Obstacles - Borders Please define the obstacles you face in e-learning?


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7.4.7.1 ANGEL LMS - Findings for indicator Obstacles - borders Location based; 10,40% Organisational; 9,75%

Computer skills; 10,50%

Learning Style; 11,79%

Learning Style

content suitability; 9,65%

Personal; 12,97%

Computer skills content suitability Computer access Internet connection

instructional design; 12,86%

Internet connection; 13,29%

Computer access; 8,79%

instructional design Personal Organisational Location based

Fig. 13. ANGEL LMS - Findings for indicator 7.4.7.2 MOODLE - Findings for Indicator

Organisational; 14,98%

Location based; 9,04%

Learning Style; 14,49%

Computer skills; 1,00%

Obstacles - borders Computer skills Learning Style

content suitability; 24,85%

Personal; 22,92%

content suitability Computer access Internet connection

instructional design;Internet 3,60% Computer access; connection; 3,29% 6,83%

instructional design Personal Organisational Location based

Fig. 14. Moodle LMS - Findings for indicator 7.4.7.3 Discussion of the Findings The findings indicate that there are a lot of obstacles and barriers to e-learning and they are rated as follows in percentage: Angel: Based on these findings the internet connection and econtent not suited to learners learning style are rated as the biggest obstacles and barriers to enhanced learning. Moodle: Based on the findings content suitability, personal issues and learning style are rated as the biggest obstacles to enhanced learning.

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7.4.8 Analyses of indicator: Attention What captures best your attention in ANGEL that helps you learn best? 7.4.8.1 ANGEL LMS - Findings for indicator Other 9%

Attention Lessons

Em ail feature 15% Surveys 6%

Lessons 40%

Calendar Forum Chat

Forum 13%

Calendar 11%

Surveys Em ail feature

Chat 6%

Other

Fig. 15. ANGEL LMS - Findings for indicator The findings indicate that e-learning attention is based on different factors and they are rated as follows in percentage: 39.31% rated that their attention on Lessons; 11.40% rated that their attention on Calendar; 13.43% rated that their attention on Forum; 5.85% rated that their attention on Chat; 6.00% rated that their attention on Surveys; 14.70% rated that their attention on email feature; 9.30% rated that their attention on other factors. 7.4.8.2 MOODLE - Findings for Indicator Attention Lessons Calendar

LessonsEmail feature 90% 1%

Forum 7% Other Surveys Chat 1% 1% 0%

Forum Chat Calendar 0%

Surveys Email feature Other

Fig. 16. Moodle LMS - Findings for indicator The findings indicate that e-learning attention is based on different factors and they are rated as follows in percentage: 89.31% rated that their attention on Lessons; 0.14% rated that their attention on Calendar7.37% rated that their attention on Forum; 0.62% rated that their attention on Chat; 0.23% rated that their attention on Surveys; 1.03% rated that their attention on email feature; 1.30% rated that their attention on other factors.


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7.4.9 Analyses of indicator: Content format If you could choose different formats for the same content which one do you think is best to convey knowledge and to learn from? 7.4.9.1 ANGEL LMS - Findings for indicator Content format

Combination of all 59%

Text 15%

Animation 6% Graphics 9%

Text Animation Graphics

Video 11%

Video Combination of all

Fig. 17. ANGEL LMS - Findings for indicator 7.4.9.2 MOODLE - Findings for indicator Content format Text

Text 20% Combination of all 48% Video 10%

Animation 13% Graphics 9%

Animation Graphics Video Combination of all

Fig. 18. Moodle LMS - Findings for indicator 7.4.9.3 Discussion of the findings Most of the respondents, in both of the environments prefer mostly a combination of all media in representing the course e-content. Then the preferences are for Text as their representation of learning e-content, then respondents prefer Video as their e-content, Graphics and animation representation of their learning e-content. This data highlights the importance of the e-learning content and its format of representation which should be provided in different formats and most desirably as combination of all the media. The

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structure and interactivity should also be embedded in the content as well and provide clear summary and outcomes for the e-content. 7.4.10 Analyses of indicator: Optimal Course to Learn When is your optimal time to learn, what do you prefer? - a self-paced e-learning course completed independently - an e-learning course facilitated by an instructor who requires completed assignments and discussions with peers - a real-time e-learning course conducted online with a facilitator and participants in different locations 7.4.10.1 ANGEL LMS - Findings for indicator: Optimal Course to Learn Optimal time to learn a real-time elearning course conducted online w ith a facilitator 53%

a self-paced elearning course completed independently 35% an e-learning course facilitated by an instructor w ho requires 12%

a self-paced e-learning course completed independently an e-learning course facilitated by an instructor w ho requires a real-time e-learning course conducted online w ith a facilitator


Most of the respondents, 53% prefer a real-time (synchronous) class conducted by a facilitator and participants in different locations. 12%, prefer an asynchronous e-learning course facilitated by an instructor who requires completed work and participation in discussions. Only 35% prefer a self-paced course. This data highlights the importance of a facilitator who can structure interaction and provide assistance and accountability. 7.4.10.2 MOODLE - Findings for Indicator: Optimal Course to Learn

a real-time elearning course conducted online w ith a facilitator 34%

a self-paced elearning course completed an e-learning independently course facilitated 55% by an instructor w ho requires 11%


a self-paced Optimal e-learning time to learn course completed independently an e-learning course facilitated by an instructor w ho requires a real-time e-learning course conducted online w ith a facilitator


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Most of the respondents 55% prefer a self-paced course. Then, 34% prefer a real-time (synchronous) class conducted by a facilitator and participants in different locations. 11%, prefer an asynchronous e-learning course facilitated by an instructor who requires completed work and participation in discussions. This data highlights the importance of having a self paced course were the focus will be in the e-content since the content is the main vehicle into learning. 7.4.11 Analyses of indicator: Optimal time to learn When is the best time for you for a real-time online classes or online discussion with your instructor or colleague student? 7.4.11.1 ANGEL LMS - Findings for indicator Optimal time to learn Weekends (SatSun) morning 16% 13% Afternoon Weekdays (Mon22% Fri) Evenings/Nights 23% 26%

morning Afternoon Evenings/Nights Weekdays (Mon-Fri) Weekends (Sat-Sun)

Fig. 21. ANGEL LMS - Findings for indicator 7.4.11.2 MOODLE - Findings for indicator Weekends (SatSun) 0%

Weekdays (MonFri) 43%

Optimal time to learn morning

morning 17% Afternoon 30%

Evenings/Nights 10%

Afternoon Evenings/Nights Weekdays (Mon-Fri) Weekends (Sat-Sun)

Fig. 22. Moodle LMS - Findings for indicator In Angel: Most of the respondents, 26%, prefer Evenings/nights for online classes or online discussion. 23% prefer Weekdays Monday to Friday, 22% prefer afternoons, 16% prefer Weekends Saturday and Sunday, and 13% prefer morning for online classes and online

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discussions. This data suggests that e-learning most preferred efficient time is during evenings in the weekdays, second option is at least to be in the afternoon and very few learners desire to learn during weekdays. In Moodle: Most of the students 43% prefer weekdays as optimal time to learn. Then afternoon is the second choice with 30% and morning with 17% while evenings/nights with 10%. 7.4.12 Analyses of indicator: Online positives If you study at home or workplace, how much do you agree with the following statements? 7.4.12.1 ANGEL LMS - Findings for indicator E-Learning preferences working in groups 21% ngs explained in sequence 12%

have teacher to help 15%

learn at own pace 26% work at times repeat difficult bitssuited 7% 14%

learn at own pace work at times suited repeat difficult bits more time for reflection have teacher to help things explained in sequence working in groups

more time for reflection 5%

Slice 8

Fig. 23. ANGEL LMS - Findings for indicator 7.4.12.2 MOODLE - Findings for indicator help 9%

things explained in sequence working in groups 9% 4%

more time for reflection 25%

E-Learning preferences learn at own pace 19% work at times suited 11% repeat difficult bits 23%

learn at own pace work at times suited repeat difficult bits more time for reflection have teacher to help things explained in sequence working in groups Slice 8

Fig. 24. Moodle LMS - Findings for indicator 7.4.12.3 Discussion of the findings Angel: Most of the respondents, 26% prefer online learning because they can learn at their own peace. 21% prefer online working in groups, 15 % need teachers/instructors to help,


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14% prefer online because they can work at times suited to their schedule, 12% prefer things explained in sequence, 7% prefer online because they can repeat difficult bits, 5 % prefer online because they have more time for reflection. Moodle: Most of the respondents, 25% prefer online learning because they have more time for reflection. 23% because they can repeat difficult bias, 19 % prefer learning in their own pace, 11% prefer working at times suited to their schedule, 09% prefer things explained in sequence, 4% prefer working in groups. This data highlights the importance of the factors that drove the learners decision for choosing e-learning compared to traditional classroom. The most preferred positive option of e-learning for student learners are the facts that they can learn on their own peace, at times suited to their schedule, they can repeat difficult bias and they have more time for reflection. 7.4.13 Analyses of indicator: Learning preferences Do you prefer to study ALONE or as part of a TEAM? 7.4.13.1 ANGEL LMS - Findings for indicator: in team 49.08 %

alone 50.92 %

E-Learning preferences

alone

in team

Fig. 25. ANGEL LMS - Findings for indicator 7.4.13.2 MOODLE - Findings for indicator: in team 26.12 %

E-Learning preferences alone 74.88 %

alone

in team

Fig. 26. Moodle LMS - Findings for indicator 7.4.13.3 Discussion of the findings In Angel: Most of the respondents, 50.92 % prefer working alone and learn at their own peace. 49.08 % prefer team work. The preferences of the student learners are almost divided the same in favor of working alone or in team. In Moodle: Most of the respondents 74.92% prefer working alone, while 26.08% prefer working in team.

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Based on the findings we concluded that this is not such an issue for them and it is not influencing the learning process substantially. 7.4.14 Analyses of indicator: Communication preferences As Learner how do you usually work with fellow students on your course and share ideas with him/her? 1) Face to Face; 2) Telephone; 3) Email 4) chat room; 5) Moderated discussion forum 7.4.14.1 ANGEL LMS - Findings for indicator E-Learning preferences Face to Face 55% Discussion forum 9%

Email 13%

Face to Face Telephone 14%

Chat room 9%

Telephone Email Chat room Discussion forum

Fig. 27. ANGEL LMS - Findings for indicator 7.4.14.2 MOODLE - Findings for indicator E-Learning preferences Face to Face 53% Discussion forum 10% Chat room 2%

Face to Face Telephone

Email 13%

Telephone 22%

Email Chat room Discussion forum



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7.4.14.3 Discussion of the findings Most of the respondents, similarly in both cases angel and Moodle prefer Face to Face communication with their colleges. Then they prefer telephone communication to exchange ideas with their colleges, and then prefer email communication, afterwards prefer Discussion forum to communicate with their colleges, and at the end prefer chat rooms for communication. 7.4.15 Analyses of indicator: Technology usage extending learning To what extent have your skills and learning improved by your personal use of technology outside the University? 7.4.15.1 ANGEL LMS - Findings for indicator: Technology usage Good 42.98 % Very Good 25.61 %

OK 26.05 % Not so god 4.63 %

Not at all 0.723 %

Not at all

Not so god

OK

Good

Very Good

Fig. 29. ANGEL LMS - Findings for indicator 7.4.15.2 MOODLE - Findings for indicator: Technology usage

OK 33.45 %

Good 22.58 % Very Good 25.61 % Not so god Not at all 3.28 % 4.32 %


Not at all

Not so god

OK

Good

Very Good

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7.4.15.3 Discussion of the findings Most of the respondents, for both Angel and Moodle feel that they have improved their skills using technology and they have classified this as good. Most of the respondents classified their improvement as OK, then fewer respondents classified their improvement as Very Good, while on the other side although few there are some respondents that classified their improvement as Not so good, while fewer as Not at all. This data highlights the importance of technology usage in improving student learner’s skills and learning. The learning system usage influenced and improved student learning. 7.4.16 Analyses of indicator: Access to E-learning Material Describe your access to e-learning material? 7.4.16.1 ANGEL LMS - Findings for indicator: Access to e-learning material I do have home connection to internet but always use University facilities I have no home 10% connection to internet, always Use home use University connection 1/3rd facilities of time and 31% University 2/3rds of the time 8%

Access to learning materials I nearly always use my home connection I nearly always use my home connection 35% I use my home connection about 2/3rds of the time and University facility about 1/3rd of the time 16%


I use my home connection about 2/3rds of the time and University facility about 1/3rd of the time Use home connection 1/3rd of the time and University facility about 2/3rds of the time I have no home connection to internet, always use University facilities I do have home connection to internet but always use University facilities


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7.4.16.2 MOODLE - Findings for indicator: Access to E-learning Material Access to learning materials

I do have home onnection to internet but always use University facilities 8%

I nearly always use my home connection 35% I use my home connection about I have no home Use home connection 2/3rds of the time and connection to internet, 1/3rd of time and University facility about always use University University 2/3rds of the 1/3rd of the time facilities time 25% 19% 8%

I nearly always use my home connection

I use my home connection about 2/3rds of the time and University facility about 1/3rd of the time Use home connection 1/3rd of the time and University facility about 2/3rds of the time I have no home connection to internet, always use University facilities I do have home connection to internet but always use University facilities

Fig. 32. Moodle LMS - Findings for indicator 7.4.16.3 Discussion of the findings for indicator Most of the respondents, for both Angel and Moodle prefer using their own home connection to internet, then the largest group have no home connection and use the University facility for connecting online, then use their home connection around 2/3 of the time and 1/3 the University facilities to connect to internet, then few of the respondents use their home connection around 1/3 of the time and 2/3 of the time they use the University facility, and smallest group although do have home connection they always use the University facility to connect to internet. This data highlights the importance of the factors that drove the learner’s decision for choosing e-learning compared to traditional classroom. The most preferred positive option of e-learning for student learners are the facts that they can learn on their own peace, at times suited to their schedule, they can repeat difficult bias and they have more time for reflection. 7.4.17 Analyses of indicator: Online positives How often do you visit course contents on ANGEL??

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7.4.17.1 ANGEL LMS - Findings for indicator: Online Positives E-Learning preferences

Daily 2/3 day s

Daily 65% 2/ 3 days

Weekly

20.35 %

Rarely Never

Weekly

1.75 %

Rar ely

5.71%

Hardly Ev er

5.85 %

Har dly Ever

Nev er

1.31%

Fig. 33. ANGEL LMS - Findings for indicator 7.4.17.2 MOODLE - Findings for indicator: Online positives - Question 22: E-Learning preferences

2/3 days

Daily 71%

Weekly 2/3 days 18.23 %

Never 0% Hardly Ever 0.21 %

Rarely 5.85 %

Daily

Weekly 4.71 %

Rarely Hardly Ever Never

Fig. 34. Moodle LMS - Findings for indicator 7.4.17.3 Discussion of the findings Most of the respondents, in Angel (65 %) , Moodle (71.09%) access content inn LMS on Daily basis, Angel (20.35 %), Moodle (18.63%) of the respondents access the content each 2 or 3 days, Angel 5.71 % Moodle 4.71% of the respondents access the content on Weekly basis, while on the other hand Angel 5.85 %; Moodle 5.39% of the respondents access the content Rarely, Angel 1.31 % ; Moodle 0.21% access it hardly ever, and Angel 1.31 %; Moodle 0% never access content in LMS. 7.4.18 Analyses of indicator: Learning Outcomes What is the impact of this e-learning system regarding learning outcomes? 9) Knowledge transfer and understanding; 2) Intellectual (thinking) skills; 3) Practical skills; 4) Transferable skills


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7.4.18.1 ANGEL Findings for indicator: Learning Outcom es

Practical skills 24%

Transferable skills 11% intelectual (thinking) skills 31%

Know ledge Transfer

Knowledge Transfer 44%

intelectual (thinking) skills Practical skills Transferable skills

Fig. 35. ANGEL LMS - Findings for indicator 7.4.18.2 Moodle Findings for indicator: Learning Outcomes

Knowledge Transfer 39% Transferable skills 8%

intelectual (thinking) skills 38% Practical skills 25%

Knowledge Transfer intelectual (thinking) skills Practical skills Transferable skills

Fig. 36. Moodle LMS - Findings for indicator 7.4.18.3 Discussion of the findings Most of the respondents, Angel 44 % and Moodle 39 % declared that knowledge transfer was the most important outcome, 31 % in Angel and 38% in Moodle the respondents declared that intellectual thinking skills were the most important outcome, Angel 24 % and Moodle 25% of the respondents think that practical skills were the most important outcome, while only 11 % in Angel and 8% in Moodle the respondents declared most important the transferable skills.

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It is a conclusion that both e-learning projects using Angel and Moodle have been rated very similarly regarding the learning outcomes.

7. Conclusion The research study is following the e-learning trends needs and tries to address the issues and deficiencies from the findings realized in the secondary research. Most importantly the study recognises and tries to address the multidimensional aspects of e-learning. The research study results in several contributions. The main result of the realised research study was the development of the e-learning indicators methodology that could be used systematically in planning phase of e-learning initiatives and their corresponding e-learning project. Therefore, recommendations are to use the e-learning indicators methodology approach in developing any e-learning initiative and their corresponding e-learning project. Many current e-learning initiatives follow the “one-size-fits-all” approach just offering some type of LMS to learners. Typically, this approach is related to lack of knowledge of the learner audience or factors influencing that audience and e-learning project overall and therefore fail to provide satisfactory support in the decision making process. In order to address this issue, an approach dealing with e-learning indicators is proposed, assessed, measured and evaluated. The proposed E-learning Indicators Methodology enables successful planning, comparison and evaluation of different e-learning projects. Above is given comparative analyses of two different institutions using Angel and Moodle and focusing on comparison and evaluation of e-learning indicators of these two e-learning projects. E-learning indicators methodology represents an empirical methodology that gives concrete results expressed through numbers that could be analysed and later used to compare and conclude its e-learning efficiency. With the application of this methodology in e-learning projects it is more likely to achieve better results and higher efficiency as well as higher Return on Investment ROI. Recommend using the defined e-learning indicators as starting point when developing elearning initiative and based on the measurements of these e-learning indicators to tailor the specifics of e-learning. Each e-learning initiative is unique and involves specifics that can not be taken under consideration in general in the form of one solution suits all. On the contrary each e-learning initiative should measure the provided indicators and based on them to design and build their e-learning. From the perspective of all available evidence it points toward growing enrolments and provision albeit from a low starting point. The opinion is that the future quality development in e-learning has to be oriented at the learner’s needs and their specific situation that needs to be measured and evaluated using the e-learning indicators. Regarding the comparative analyses of two distinct e-learning projects: Angel and Moodle the fact is that after analyzing both of the systems, some main problems that these two systems contain, and some suggestions how these problems could be solved or recovered are given below: As it can be concluded from the data described above, Moodle really has a large number of options that it offers and when these tools come involved into the course they attract the student’s attention from his aim. This problem is not faced in ANGEL system, which has a cleaner interface with high usability. As a solution for such a problem, our recommendation


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is to simplify course pages in the Moodle system, and in this way make it more aesthetic, efficient and attractive. Of course, some necessary tools would have a proper place in a smaller and well readable format. Another problem of Moodle is that it has a difficult file management. The solution to this problem is allowing managing files and according to the latest news, the professional team of Moodle is currently working on this issue. ANGEL is not considered to have any problems with the templates and design, but it does not contain a glossary which the Moodle has, and it operates perfectly. I would necessarily put such an item in order to increase its functionality and effectiveness since Moodle is evidence how much it is useful for the learners. Another problem that ANGEL faces is that it does not target a UNIX based system. All of the above mentioned important issues and problems are the most important and essential ones that student, instructors and other roles mostly care about. That is why their improvements are important as much as their existence. All of the other tools such as surveys, quizzes, language supports and different options are very functional and efficient in both systems and these items are definitely the ones that I would not change in any of them. Although the e-learning indicators methodology has many positive aspects mentioned above, it also has several drawbacks. Some of the most important identified are:  Some e-learning projects are running for the entire University while some only for a Faculty or a department. Then the comparison of e-learning projects might not prove to provide accurate insights.  Comparison of e-learning projects with different types of collectives based on the learning type can not prove satisfactory results. The best results are achieved when comparing similar types of collectives. Although the methodology tries to capture the multidimensional nature of e-learning some of the indicators could be separated into several others in order to capture more precisely some multiple dimensions of e-learning. Based on the insights of the research study recommended and proposed is a strategy for implementing E-Learning at South East Europe. The developed strategy takes into account the Universities current mission in achieving a so-called borderless education within the regional Balkans context, but also in a wider European and global context. A number of issues related to such a specific context of the University, such as its multilingual and multicultural environment influenced the developed strategy and its implementation plan. The main principle of such a strategy is to support the university’s mission of borderless education by providing the widest possible access to national and regional excellence in learning and teaching by means of the current and novel technology. That technology includes but is not limited to the Web technologies such as Web-based media and multimedia technologies, broader Internet-based communication and collaboration technologies, as well as more general Knowledge Management technologies. Also, the strategy takes into account the traditional classroom education and classical methodologies and compares the options and possibilities to apply them in combination with the current technologies in a blended manner (Rosenberg, 2004). Following this main strategic principle a number of concrete goals have been defined. Through achievement of these measurable goals the SEE can move towards the fulfilment of its primary mission. Therefore we recommend strategic goals together with a detailed implementation plan for them.

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8. References Bandura, A. (1997), Self –efficacy: The exercise of control. New York. W.H. Freeman. Bonk, C. J. & Graham, C. R. (2004). Handbook of blended learning: Global Perspectives, local designs. San Francisco, CA: Pfeiffer Publishing. Coleman, B., Neuhauser, J. & Fisher, M. (2004). Accomplished Methods for Online Collaboration in Graduate Courses. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2004 (pp. 387-392). Chesapeake, VA: AACE. Dagger, D, and O’Connor, A,. and Lawless, S,. Walsh, E,. and Wade, V,. (2007) ServiceOriented E-Learning Platforms: From Monolithic Systems to Flexible Services, IEEE Educational Activities Department Piscataway, NJ, USA, May/June 2007 (Vol. 11, No. 3) pp. 28-35 Dumas, J. S., & Redish J. C. (1999) “A practical guide to Usability Testing” revised edition, Pearson Education Limited, pp.55-62 Eklund, J., Kay, M., & Lynch, H. (2003). E-learning: emerging issues and key trends. A discussion paper, Australian National Training Authority. Felder, R.M. (1993) "Reaching the Second Tier: Learning and Teaching Styles in College Science Education," J. Coll. Sci. Teaching, 23(5), 286--290 (1993). Fetaji, B. (2006). Issues and solutions in authoring e-learning content in South East European University. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2006 (pp. 254-259). Chesapeake, VA: AACE. Fetaji, B. (2007). Assessing, measuring and evaluating e-learning indicators. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007 (pp. 4640-4649). Chesapeake, VA: AACE. Fetaji, B., & Fetaji, M. (2007e). “E-learning indicators approach in developing e-learning software solutions” – Accepted and to be published in the proceedings of the IEEE EUROCON 2007 conference, Warsaw, Poland, USA, 09-12 September 2007. Fetaji, B., & Fetaji, M. (2007g). “E-learning indicators methodology approach in designing successful E-learning” –International E-Learning 2007 conference, Lisbon, Portugal, 06-08 July 2007. Fetaji, B., & Fetaji,. M. (2007). Assessing, measuring and evaluating e-learning indicators. In P. Kommers & G. Richards (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007. Chesapeake, VA: AACE. Galotta, C., Zanetti, D., Krejci, D., Oliveira, K. & Rocha, A. (2004). Organizational Learning Based on a Customizable Environment for Knowledge Management Using Intranet. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2004 (pp. 2626-2633). Chesapeake, VA: AACE. Harrison, L. & Smith, R. (2003). All I Really Need to Know About E-Content I Learned In Kindergarten: Share and Share Alike. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2003 (pp. 2004-2006). Chesapeake, VA: AACE. Helic, D., Hrastnik, J. & Maurer, H. (2005). An Analysis of Application of Business Process Management Technology in E-Learning Systems. In G. Richards (Ed.), Proceedings


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of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2937-2942). Chesapeake, VA: AACE. Kumar, P. (2006). Using Universal Design Principles for e-learning. In T. Reeves & S. Yamashita (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2006 (pp. 1274-1277). Chesapeake, VA: AACE. Liu, L., Maddux, C. & Johnson, L. (2004). Computer Attitude and Achievement: Is Time an Intermediate Variable?. Journal of Technology and Teacher Education. 12 (4), pp. 593-607. Norfolk, VA: AACE. Mödritscher, F., Gütl, C., Garcia Barrios, V. & Maurer, H. (2004). Why do Standards in the Field of E-Learning not fully support learner-centred Aspects of Adaptivity?. In L. Cantoni & C. McLoughlin (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2004 (pp. 2034-2039). Chesapeake, VA: AACE. Moore, M. G. (1989). Three modes of interaction. A presentation of the NUCEA forum: Issues in instructional interactivity. Presented at the annual meeting of National University Continuing Education Association, Salt Lake City, UT. Ueno, M. (2004). Animated agent to maintain learner’s attention in e-learning. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2004 (pp. 194-201). Chesapeake, VA: AACE. Whitehouse, C. (2003). Examining usability issues of e-learning. In P. Kommers & G. Richards (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2003 (pp. 2748-2755). Chesapeake, VA: AACE. Zsifkovits, H. (2003). E-Learning for E-Logistics: Planning and Implementing A Modular Training Program. In G. Richards (Ed.), Proceedings of World Conference on ELearning in Corporate, Government, Healthcare, and Higher Education 2003 (pp. 845-849). Chesapeake, VA: AACE.

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Barriers to Effective use of Information Technology in Science Education at Yanbu Kingdom of Saudi Arabia

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X2 Barriers to Effective use of Information Technology in Science Education at Yanbu Kingdom of Saudi Arabia Abdulkareem Eid S. Alwani and Safeeullah Soomro Emails: [email protected], [email protected] Yanbu University College Yanbu-Al Sinaiyah, Yanbu Kingdom of Saudi Arabia

Abstract This study examined the barriers to use of information technology (IT) in science education at the Yanbu school district in Saudi Arabia. Sub-domains investigated included: infrastructure and resources, policy and support, science teachers’ personal beliefs, and staff development. This chapter is using a survey which shows common barriers encountered by the science teachers were identified. Demographic data enabled description of science teachers based on similarities and differences of gender, location of the school, training, years of teaching experience, and age. Science teachers rated the barriers limiting their use of technology in teaching on a scale ranging from 0 (does not limit) to 3 (greatly limits). We present results which showed that all four domains were highly significant barriers: infrastructure and resources (M = 2.06, p 0.33 0.1 …. UserRole, Pro-Intermediate 0.55 Interactivity,high 0.55 0.5 … …

By having so far explained the entire integrative platform described above as aimed at resembling a stage of action rather, than merely an assortment pallet, the prospects of its structuring may now be more clearly understood: Along with its modules, this platform

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carries a number of notions encapsulated in each step of its processes that are here believed to ensure both its expansiveness and its compliance with cognitive constructivist principles. The third component of the proposed architecture is presented below. III. The bridging algorithm refers to a method that would be robust enough to unambiguously rule, stem and sort information, material, and sequences while being generated in a creative psycho-pedagogic spirit by respect to cognitive constructivist tenets, that would in turn allow the attempted personalization be implemented in the best interest of all e-users involved in the educative e-interactivity. It is stipulated that this algorithm is not to serve as merely a static formula for connecting different entities of the proffered platform or for linking its contents with existing structures of the Semantic Web; it rather is designed to function as a circuit amid them, thus ensuring interoperability, hence efficacy. It markedly uses the enhanced super-structure offered here, and is shaped in a mature psycho-pedagogic milieu inscribing eclectic methods of the platform that emerged, in a conscious orientation to wisely use the vast pool of the learning objects so as to choose which of the latter shall offer the e-user the optimal learning path. The personalization algorithm of the presently proffered framework bares different content for the individual learner, so it highly depends on the existence of different LRs for the same educational topic. On this account, in each node of the hierarchy several educational objects are attached and can be used alternatively to teach the same didactic subject to learners of different profiles. The algorithm that selects the most suitable LRs for a dedicated learner follows: Step 1: For a specific learner, his/her properties are compared to the properties of all the learning resources of each node through the alignment-structure, Step 2: For each LR of the specific node the weights of all matching properties are multiplied, Step 3: For each LR a total weight is calculated (formula 1) by summarizing all the intermediate results,

weight li

 

t k 1

( wlr , i * wLR , k * wL , k )



t k 1

( wLR , k * wL , k )

(1)

where wLR and wL are the weightLR, and weightL, as defined in the alignment structure, the weight wlr,i is the total weight of the instances of the LR, and k is the number of the records of the alignment-structure for which the properties of the LR li are matched to this learner. As seen, the weight wl,m, ,i.e., the weight of the learner, does not participate in this formula. Step 4: From each node the LR having the greater weight is selected, Step 5: The process is repeated for the LRs of the ontology. This method selects the best ranked LR from each node according to the selection-criteria; consequently, the whole learning path turns out to be the most suitable one. The concluding choice to be made in this module is a custom-made learning path based on characteristics of the specific learner. The use of weights is brainwave approach as the fluctuation of their values is very easy. In this way examination of the influence of these parameters can be done, and the promotion of the desired parameters is feasible. For example, if it were supposed that one has to examine whether or not the property “semanticDensity” influences the “Performance” of a specific type of learner, e.g. the one who has “MSc”, then, the same course can be delivered

A learning style – driven architecture build on open source LMS’s infrastructure for creation of psycho-pedagogically – ‘savvy’ personalized learning paths

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twice: firstly using a high weight to this parameter, and later a lower one. For the specific course, inspection of the outcomes of his/her “Performance” as run in parallel results in that the influence of the characteristic “semanticDensity” is checked.

5. Implementation To attest these proposals a web-based application was implemented and used in parallel with the most popular open source LMS, the Moodle. The software was used to implement the personalization algorithm and Moodle was used to deliver the ensuing course. This application implements the proposed super-structures that store the properties of the LRs and handles these metadata. The maintenance of the LRs properties is seen In Figure 5.

Fig. 5. The LRs Repository and the treatment of their properties As seen, the properties of the LRs are described using the LOM educational standard. The available resources of the repository used here are shown on the left side and the number of the available properties of a single LR of the repository is shown on the right. To create the e-course hierarchy, the web was exhaustively searched to discover suitable LRs that met the experiment specifications for alternative learning resources varying in formats, interactivity degree, semantic density degree, context type, etc. While planning the courses, a great effort was put to include a variety of LRs categorized into 20 scrutinized educational activities, intended to cover the overall experimental prerequisites. The ensuing material was thoroughly inspected, evaluated and accordingly attached to the ontology. The adopted personalization method was based on the Felder and Silverman (1998) learning style model. The factors under examination in the pertinent experiment were the effect of the “UserRole” to “Difficulty”, “InteractivityLevel”, “SemantciDensity” and the effect of “LearningStyle” to “InteractivityLevel”, “Format”, “LearningResourceType" and “SemanticDensity”. The weights that were used to connect these factors are shown in Table 3.

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E-learning, experiences and future Combination of properties of LRs – Learners based Learner property

LR property hasDifficulty

hasUserRole

hasLearningStyle

0.55

0.45

0.33

hasInteractivityLevel

0.33

hasSemanticDensity

0.33

hasInteractivityLevel

0.25

hasFormat

0.25

hasLearningResourceType

0.25

hasSemanticDensity

0.25

Table 3. Combination of properties of Learners and LRs From a specific node of the hierarchy this personalization bridging algorithm ‘chose’ one specific LR which is picked as the optimal object for each learner. The final learning path was provided to each individual. After the course completion, two decisive dimensions were examined: i) the satisfaction of the learners from their participation in the experiment and ii) the difference of their performance in this course lesson as compared to the performance of other students, who had not participated in the experiment. Albeit these indicators could hastily be viewed as insufficient for a denotative feedback, however, due to the novel and purely tentative character of the experiment per se, they were reckoned as reasonably fitting the aims of the whole effort. This was thought because the current effort in its entirety, was, as initially indicated, primarily centered in the theoretical and mathematical verification of merging two important axes: on the one hand, a) the potential refinement of the e-system supporting educational actions and activities by pondering into its technical methodologies and attempting mathematical manipulations that would perhaps endow further sophistication to its treating e-material, and, on the other hand b) the potential expatiation of even the very theoretical framework of cognitive constructivism along with the prospects of its applications into its largely unfamiliar e-environment, via precisely an algorithmic expression. This vision would have been adequately proven even if a single case-study was involved in the experimentation process, of a pair of a single e-tutor interacting with a single e-learner together dealing with the existing system’s capacity and the currently proposed intervention’s ability to: store information, retrieve material and process e-education in a smooth and functional manner. Again, in this light, the success indicators selected for the present purposes could prove sufficient, provided that the relevant measurements would be markedly high in the gauge. Indeed, the related hypotheses were confirmed, with outcomes of these experiments being considered as very promising: i) the percentage of the participants who reported great satisfaction with taking part in the project reached a quite impressive 78%, and ii) their mean performance in the course increased against the mean performance of the control group at a considerable 18%. Further Improvement Tactics: Consecutive steps for improving results in both i) quantitative and ii) qualitative terms are readily conceivable: i)For example, more indicators on outcomes would include general course performance ratings, intermediary grades, marks on midway assignments, participants’ assessments of partial provisions, time management pointers, and gauges on speed of retrieval of information. It was also deemed that in order to arrive at even more reliable results, the experiment would have to be continued and


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enriched towards qualitative improvements: ii) For example, variables which should also be included at such a future phase include: variety of several courses assessed in parallel, larger and varied (e.g., in demographics) samples of e-learners, a greater quantity of educational material qualitatively assessed as for, e.g., the conditions of delivery of its components, the appropriateness of the format of its components as ascribed to different users, the degree of users’ satisfaction in the cooperation with specific e-tutors, different weights in the properties that are examined, etc. More specifically, given that the proffered architecture has satisfactorily inter alia shown its expansiveness due to its integrative platform of operations as conjunct with its bridging algorithm; and given that it does provide the capability to easily: i) change values of the existing parameters included in the system and ii) add more in order to cover a wider spectrum of characteristics to each tally, improvement tactics as the aforementioned should and could (Kerkiri et al, -in press) be promptly followed.

6. Conclusions The present work attempted to abridge tentative, yet influential findings and concepts of two distinctly disparate doctrines traditionally ascribed to ICTs and psycho-pedagogy respectively. By focusing on relevant illustrative evidence it aspired to primarily provide some indication as to the feasibility of linking these two seemingly unrelated fields of study in a way that would benefit the end-users of this alliance. The open source LMSs as a fertile environment on the one hand and the cognitive constructivist approach on the other were purposefully selected as it was deemed that almost by definition they could be exploited in synergy with respect to the contemporary needs of savvy e-users. Although certain exemplary theoretical studies have been asserting the legitimacy of such alliance in the past (e.g., Dalgarno 2002), however, no one known to the current one has proceeded beyond theoretical to empirical experimentation, which was the general objective of the present effort. On a consecutive layer, this work chose to deal with the popular, yet delicate issue of contemporary e-educational methods addressed to undeniably savvy and demanding elearners, whose special, idiographic characteristics craving for e-material personalization did not appear to be practically taken into account by most of the thoroughly investigated existing e-systems that support electronically conducted teaching processes. In this way, it not only hoped to trail this shortcoming, but also to offer a viable solution for it. The focal point of interest in this study being indeed the issue of personalization, it was accordingly led to advance beyond examining the capacity, yet inertia of the most popular open source LMSs to cover individual e-learners’ needs for effective knowledge accessibility, and thus to present a coherent architecture of novel factually interoperable components that were ad hoc attested to efficiently serve such personalization purposes by applications compatible to the existing e-LMSs. These components were I. the creation of a superstructure that would secure fundamental functions of the LMSs environment on which the proffered architecture would unfold, II. the design of an integrative platform that would stage methods, tools and features derived from both the theoretical and empirical realms in question, and finally III. a bridging algorithm, that would function as a circuit amongst these features in order to uphold, not only interoperability, but also expansiveness of the spectrum of tools and services provided –again, not only to the e-learners themselves, but also to their e-tutors. In the research involved in this work an architectural structure and a personalization algorithm for e-Learning systems were presented focusing on a gradually apparent need of

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e-users for what was here depicted as a double concern: namely, the personalization of the content that the e-learner seeks for, and, likewise, the personification of the quest of the very same e-learner. The underlying concept dictates that once the characteristics of a learner are fed into a psycho-pedagogically founded and indeed cognitive constructivism-inspired esystem, it is not only the system itself that would take care of the user by supplying her/him with information suitable to his/her type by use of the proposed architecture; it would automatically empower the user to choose and change the type of the e-educational material that s/he seeks for, by simply altering/modifying/enriching the previously stored information about him/her. A significant finding in this research aspect of the study is that the proposed architecture also appeared to promise cost-effectiveness, inasmuch as it was shown that it can be also applied even in already developed LMSs. The design of this system is coordinated through the utilization of learning style outcomes (Kritikou et al., 2008), educational standards (cf LOM, SCORM, PAPI, LIP), Semantic Web technologies (Berners-Lee et al., 2001; Kerkiri et al., 2009). The capabilities of this design rely on the creation of self-defined LRs. This leads to i) appreciating and exploiting their educational properties at a glance and ensuring their suitability for a specific learner by abiding to a multidimensional matrix of parameters, ii) promoting its characteristics so that they can be discovered by proper software (e.g. s/w agents), through an aptly computerized process, and iii) steering their automate classification in the right position of a learning path. The personalization algorithm of the proposed system is based on divergent properties of equally divergent weights for the matching. This offers the maximum flexibility to a researcher to produce a dedicated course for the individual, as s/he can easily change these parameters depending on the outcomes s/he wants to examine. In this work’s future plans the layered development of a number of functionalities are scheduled. Accordingly, next steps to be taken aim primarily to the creation of a repository of alternative LRs for a number of courses. A collation of courses shall follow which are to be created from the personalization algorithm with, and without evaluations. To this end, the relevant evaluation strategy shall be more refined and based on evaluating both educational approaches, observing students that follow the learning-styles approach courses and those that do not. With all the outcomes of the first experiment having being very promising, an even deeper examination is foreseeable, so that in the long term using different LRs, and a number of learner profiles of a wide variety of learning styles may well be available to extensively serve a multitude of users in progressively more meaningful ways.

7. Future Orientations In an effort to respond effectively to e-learners’ increasing demands and at the same time follow the Dalsgaard’s three-fold proposition by also upholding Hannafin and colleagues’ suggestions while continuously verifying the capacity of this architecture for meaningful expansiveness, it is further postulated that the hardiness model could be employed in the future, as the next step for an even more inclusive, unifying framework, that combines these qualities by actually coordinating distinct principles into an applicable instrumentation. Hardiness theory holds that three distinguishable, yet interoperable and, ideally well balanced, personality components are interwoven in the cognitive schemata construction modes of each individual in the form of a highly idiographic/personalized frame of (subjective) reference. The latter may be reconstructed, inasmuch flexibility is encouraged or


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not by use of, both, inner and outer, inherent and interpersonal/communicable resources. These components are defined as [self-]Control, Commitment –Responsibility and Challenge– Enthousiasm, repsectively (Maddi, 1980). Found in both, balance and, fairly, but not excessively, high levels in the individual profile, these “3C’s” ensure i) good general, both mental and physical health, ii) fulfilling goal-setting, iii) adept goal-pursuing, iv) effectual stress-management, v) emphatic interpersonal/communicational skills and vi) general successfulness in an overall vii) productive, creative and pleasing quality of life. In contrast, when detected at both, imbalance and low levels in the individual profile, their fusion entails Alienation, i.e., aloofness and widespread hazy dissatisfaction with the self, others and any future prospects (Paleologou, 1996). Regardless of the degree of sturdiness indicated and irrespectively of the breadth of balance between these ‘3C’s’, the hardy-vsalienated profile is portrayed as the compass of a person’s navigation in life. Because repeated scrutinization of its components and consequent adaptations to the concurrent measurements of individual and group profiles have been optimized through three decades of study and applications, this theory’s revisions and enrichments have led to detailed systemization of i) principles and ii) practical steps aiming to reconstruct a) dysfunctional schemas underlying the attitudes of alienated profiles and b) functional, yet potentially upgradeable, hardy profiles towards bettering their already satisfying lifestyle (Paleologou, 2001). The constellation of the emerging cognitive re-scripting approaches is now at a stage that may be addressing and treating information and interpretation processing that secures body, mental, social and spiritual harmoniousness of a person’s inter-, and intra- cohesion in competency (e.g., idem, 2009). This structural system appears to be appealing to serve as a common denominator for an effective stabilization of both theoretical psycho-pedagogical plus learning principles and e-tools as well as functions of LMSs in favour of e-users. Hence, almost by definition, the hardiness components seem appropriate for a concrete future inclusion in the currently proposed architecture at a level beyond mere profiling of the e-learners’ individual persona, but by proceeding to aid them also in corrective actions towards precisely improving their learning style, capacities and future projections

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Ismail, J. (2002). The design of an e-learning system, beyond the hype. Internet and Higher Education, Vol. 4, pp. 329 – 336 Jonassen, D.H. & Grabowski, B.L. (2000). Handbook of Individual Difference, Learning, and Instruction. Hillsdale, NJ: Lawrence Erlbaum Associates Jonassen, D.H. (1999). Constructing learning environments on the web: Engaging students in meaningful learning. EdTech 99: Educational Technology Conference and Exhibition 1999: Thinking Schools, Learning Nation Kerkiri T. & Paleologou A.–M. (2009). Do open source LMSs support personalization? A comparative evaluation. Proceedings of the 2nd WSKS 2009, LNCS/CCIS Springer Verlag Kerkiri, T., Konetas D., Paleologou, A.-M. & Mavridis, I. (in press), Semantic Web Technologies Anchored in Learning Styles as Catalysts towards Personalizing the Learning Process, International Journal of Learning and Intellectual Capital, ISSN (Online) Kerkiri, T., Mavridis, I. & Manitsaris, A. (2009). How e-Learning systems may benefit from ontologies and recommendation methods to efficiently personalize knowledge, International Journal of Knowledge and Learning ISSN (Online): 1741-1017 - ISSN (Print): 1741-1009 Kritikou, Y., Demestichas, P., Adamopoulou, E., Demestichas, K., Theologou, M. & Paradia M. (2007), User Profile Modeling in the context of web-based learning management systems. Science Direct, Journal of Network and Computer Applications, Vol. 12, No. 6, pp. 603-627 Larochelle, M. & Bednarz, N. (1998). Constructivism and Education: beyond epistemological correctness. In: Constructivism and Education, Larochelle, M. Bednarz, N. and Garrison, J. (Ed.), ISBN 0521621356, 9780521621359, Cambridge, Cambridge University Press Lazarus, R.S. (1999). Stress and emotion: A new synthesis. NY: Springer Publishing Co, Inc. Lee, S. (2008), The Gates Are Shut: Technical and Cultural Barriers to Open Education, In: Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge, Iiyoshi, T., Kumar, V., John Seely Brown (Ed.), Publisher: The MIT Press, ISBN-10: 0262033712, London, England Maddi, S.R. (1980). Myth and Personality. The Journal of Mind and Behavior, Vol. 1, No. 2, 145154 Mitchell, D.P. (1994). Learning style: critical analysis of concept and its assessment. In: Design for learning: educational technology aspects, R. Hoey (Ed.), UK: Kogan Page Moodle, http://www.moodle.org, [Last Visit 01/04/2009] Muntean, C., H. & Muntean, G., M. (2008). End-User Quality of Experience-Aware Personalized E-Learning, In: Architecture Solutions for E-Learning Systems, S. Clarke (Ed.), pp. 281-301, Dublin, Ireland Nardi, B.A. (1996). Context and Consciousness: Activity Theory and Human-computer Interaction. MIT Press, Cambridge, MA Nash, S., S. (2005). Learning objects, learning object repositories, and learning theory: Preliminary best practices for online courses. Interdisciplinary Journal of Knowledge and Learning Objects, Vol. 1, pp. 217-228, Available at http://ijklo.org/Volume1/v1p217-228Nash.pdf Ounnas, A., Liccardi, I., Davis, H. C., Millard, D. E. & White, S. A. (2006). Towards a Semantic Modeling of Learners for Social Networks, Proceedings International

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Workshop on Applications of Semantic Web Technologies for E-Learning (SW-EL), (AH’06), Dublin, Ireland Paleologou, A. – M. & Dellaporta, A. (2009). Hardiness vs Alienation Personality Construct Essentially Explains Burnout Proclivity and Erroneous Computer Entry Problems, World Academy of Science Engineering Technology Proceedings, Vol. 40, pp. 1-17. Paleologou, A. – M. (1996). Hardiness Manual on Stress Management. R. Athens: ELKEPA Paleologou, A. – M. (2001). Orientations in Clinical Psychology, Athens: Ellinika Grammata Rogers, A. (1999), Teaching Adults, Open University Press, ISBN 960-375-015-8 Roy, M. & Chi, M. (2003). Gender Differences in Patterns of Searching the Web. Journal of Educational Computing Research, Vol. 29, No. 3, pp. 335-348 Schunk, D. H. (2004). Learning Theories: An Educational Perspective, ISBN:0-13-038496-8, Upper Saddle River, New Jersey Simpson, T. L. (2002). Dare I oppose constructivist theory?. The Educational Forum, Vol. 66, No. 4, pp. 347- 354, ISSN-0013-1725 Tuparov, G. & Dureva, D. (2008). Learning Styles Testing in Moodle. Procedures of CompSysTech '08, Bulgaria Von-Glasersfeld, E. (1984). An introduction to radical constructivism. In: The Invented Reality, P. Watzlawick (Ed.), pp. 17-40, New York: W.W. Norton & Company Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Process, M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Ed.), Cambridge, Harvard University Press. W3C Recommendation Web Ontology Language -OWL. (2004). Retrieved online 14/09/2008 at: http://www.w3.org/TR/owl-features/ Wallace, B., Ross, A., Davies, J.B. & Anderson, T. (2007). The Mind, the Body and the World: Psychology after Cognitivism. Imprint London: Imprint Academic

E-learning in information accessibility of disabled assistanttechnology

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10 X

E-learning in information accessibility of disabled assistanttechnology Chunlian Li and Yu Sun

Changchun University China

1. E-learning in information accessibility of disabled assistant technology 1.1 The disabled higher education and network teaching 1.1.1 Development of higher education for the disabled Around the 18th century, the European started to take cognizance of disability and disabled people scientifically. In 1864, American congress passed a resolution on merging the Washington schools for the blind and for the deaf into Gallaudet University, and it became the first university in the world for the disabled. The famous IDEA (the Individuals with Disabilities Education Act) is mainly about the stage of compulsory education for the disabled students. But the protected by the Act also includes the people who accepted higher education. The Act strengthened the disabilities’ awareness on right to protect the personal needs and use the facilities in higher education, enhanced the people’s awareness of the possibility and legitimacy on the disabled sharing the higher education. Data shows that the U.S. has more than 1500 universities that accept disabled students, almost one the thirds of the American university. In 1995, almost all the disabled people at the proper age enrolled into the universities or colleges. And there were 42.8 million disabled students in American colleges in 1999. For example, in the famous University of Michigan, there are 200 disabled students among each 3500 ones.The colleges provide all kinds of services for disabled students to adapt their special needs of material conditions, such as the use of braille books, reading software, etc. Each school has its accessible environment for the various needs of the special students. In Japan, the higher education for disabilities follows the principles of democratic education with neither special preferential policies nor the restrictions. While in previous Soviet Union, it runs in two ways, study together with the common students or in the special education institute. After the collapse of the Soviet Union, Russia retained partial special schools. In the United Kingdom and some other countries, almost all the people with disabilities study in ordinary colleges or universities in ordinary class. Most of the people with disabilities of Yugoslavia have obtained higher education levels, many of them became masters or doctors. Any chance is equal to people with disabilities in the aspect of education. In China, the higher education for people with disabilities can be traced back to the early 20th century. The government approved the establishment of the formal department for the blind and dumb schools, and the excellent graduates will be sent to the special educational college of

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National Central University without examination. After new China was founded, Communist Party of China and the government attached great importance to the cause of the handicapped and the higher education for the disabled. Higher education for the disabled planed by government began after the mid-1980s in China. Since the recent 20 years, there has more than 30000 disabled students studying in Chinese universities and the Changchun University keeps the Chinese level on special higher education. 1.1.2 Network resources of higher education for disabled In most countries, people with disabilities shared equally college education with common students. So the services provided by library must meet the different needs arisen due to all kinds of physiological defects. The first digital library for the blind of Paraguay is created in 2007 offering free service for the blind in Spanish. At the web site, a reading-software called JAWS is available to help the blinds “view” pages or “read” electronic books. Another digital library for the blind where the visually impaired can listen to electronic books, music or on-line lectures for free has been opened in the National Library of China. About 200 electronic books, 500 audio programs and 500 video programs are available in the library, covering ancient Chinese culture, medicine, modern literature and daily life. Some of the materials were provided according to the requests from the blind. The library plans to add at least 200 electronic books, 30 lecture videos and 500 audio programs every year. Australia, Canada, Croatia, Denmark, Japan, Korea, Netherlands, South Africa, Sweden, UK and the USA have joined to issue a report on the governance and funding of library and information services for visually impaired people. The report is aimed to support a long-term dialogue among stakeholders about how to meet opportunities and challenges for widening access to reading materials for visually impaired people best. The professional membership organization AHEAD (Association on Higher Education And Disability, http://www.ahead.org) is organized for individuals involved in the development of policy and in the provision of quality services to meet the needs of people with disabilities involved in all areas of higher education. Nowadays, it has more than 2,500 members throughout the United States, Canada, England, Australia, Ireland, Northern Ireland, New Zealand, South Africa, Sweden, Japan and Greece. In addition to the international membership, AHEAD has formal partnerships with 30 regional affiliates and numerous other professional organizations working to advance equity in higher education for people with disabilities. Since 1977 AHEAD has delivered quality training to higher education personnel through conferences, workshops, publications and consultation. AHEAD members represent a diverse network for professionals who actively address disability issues on their campuses and in the field of higher education. There are many websites of "Educational Resources", "Job Training/Placement", "University Resources" for people with disabilities in higher education. Such as "College Funding for Students with Disabilities" at http://www.washington.edu/doit, "Financial Aid for Students with Disabilities" at http://www.finaid.org/otheraid/disabled.phtml, "Scholarships Available for College Students with Disabilities" at http://www.spinalcord.org/ , "On-line Disability Information System (ODIS)" at http://dll.umaine.edu/cd/html/online.html, etc. The National Science Foundation, the State of Washington, and the U.S. Department of education have provided the primary funding for DO-IT (Disabilities, Opportunities, Internetworking, and Technology) to serve to increase the successful participation for people with disabilities in challenging academic


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programs and careers. ODIS is created by the center for Community Inclusion and Disability Studies of the University of Maine to offer courses in many disciplines throughout the year. Courses are delivered through various technologies and formats including interactive television, compressed video, internet, intranet, video streaming technologies and combinations of these systems. Depending on the technology utilized, students participate in the class "live" or asynchronously through materials archived online. Besides, some universies have open courses that are offered by Tufts Open Course, UMass Boston Open Course, MIT Open Course, Utah State University Open course Yale Open Course, Notre Dame Open Course, UcIrvine, Johns Hopkins Open Course, etc. Also, some other university resources are available at George Washington University , Iowa State University, Johns Hopkins University, Northwestern University, Oklahoma State University, Ohio State University, Oklahoma State University, Tarleton State University, University of Delaware, University Of Georgia, University of Illinois at Urbana-Champaign, University of Iowa Division of Developmental Disabilities, University of Kansas Medical Center, University of Minnesota, University of New Hampshire, University of Virgina, University of Washington, Victorian University, Wright State University. 1.2 The e-learning system 1.2.1 The ideas and goals of e-learning E-learning is a new education concept which has been applied in various fields beyond formal education, such as adult education, corporate or enterprise training, business training institutions. The original interpretation of e-learning refers to the study action assisted by electronic means and that the learning process is usually created by interaction with digitally delivered content, network-based services. These lessons can be made more interesting using multimedia i.e. combination of text, graphics, sound and animation. Lessons can be delivered to the learner via various means e.g. CD-ROM, video conference, Internet. Nowadays, the much talked about e-learning is referred to its narrow sense that the learning by Internet. The main goals of e-learning are to make learning more convenient, more effective, to reduce more educational cost, and what’s more to do great benefit to the resource sharing throughout the world. It deliveries the digital content, provides a learner-oriented environment for the providers and learners. It promotes the construction of life-long learning opinions and learning society. To both the provider and the learner, web-based learning options offer terrific flexibility and cost savings. The learning process can be carried at anytime, anywhere by anyone via any media and anyway. Benefit from the powerful combination of audio, animation and software simulations that produce highly engaging multimedia training. Courses today use realistic simulations, hands-on exercises, and role-playing scenarios to help learners "learn while doing". As a Internet-based learning, e-learning has the following three characteristics: i.

Network: Knowledge can be updated in time, and meanwhile ensuring knowledge consistency.

ii.

Personalization: The terminals can be computers or mobile telephones. Learners can arrange personal progress according to his (her) time, can decide the appropriate

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content he (her) need. iii. Trackability: All learning activities can be recorded as the basis for the assessment of the learning result and the analysis of training needs. 1.2.2 Some typical e-learning systems The original concept of e-learning is presented about 30 years ago, and the narrow concept is appeared about over 10 years ago, but its real use began only few years ago. In the most recent period, especially since 1998, network-based "e-Learning" concept began frequently in the mass media as well as in the industry, and gradually become a "popular language". The application spreas rapidly around the world. Learning management systems (LMSs) and course management systems (CMLs) are the two similar modes of e-learning. 1.2.2.1 LMSs LMSs refer to software that primarily acts as an electronic registrar by electronically performing various enrollment and related tasks. LMSs were originally designed for workplace learning environments, and specifically perform some or all of the following tasks: � Registration � Track participation (classroom attendance, sign-ons and sign-offs of online courses) � Track of completions (including final scores or grades) � Testing � Follow-up discussions with participants � Aggregated reports, such as the number of people registered for particular courses � Transfer of information to other systems, such as human resource information systems � Process charges for courses, such as tuition payments and transfer payments among departments � Course catalog � Skills management. From a participant perspective, an LMS provides a central point from which learners access activities. It provides a list of courses available, and lets learners enroll in courses. If learners must complete prerequisite courses, the system can check that. After learners enroll in a course, the system can automatically generate an enrollment confirmation and, later, a reminder about the class. After class, the system can be used to test knowledge, record a course completion, and send the information to the learner’s permanent employment record, as well as send follow-up correspondence to the learner. For an e-learning course, the system can launch the course, track student progress, record completions, and send information to learner’s permanent employment record For administrators, LMSs can be used to manage both classroom courses and e-learning. For e-learning, the system starts the course after the learner registers in the course. For classroom courses, the LMS can provide a variety of resources, including schedules for individual classrooms (that is, facilities tracking capbilities) and class lists for instructors. LMSs can also be used to record and assess training satisfaction (Level 1). In addition, LMSs can generate a number of reports, from the number of students enrolling in particular courses to aggregated records of student performance in particular courses.


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Many LMSs are available on the market. Here, we will take the SkillSoft PLC, Saba, and SumTotal Systems as examples. Case 1: SkillSoft PLC[http://www.skillsoft.com] SkillSoft was incorporated in Ireland on Aug. 8, 1989. After completing a merger with SmartForce in 2002 became known as SkillSoft PLC. It is a leading provider of e-learning and performance support solutions for global enterprises, government, education and businesses with small to medium-sized. SkillSoft enables business organizations to maximize business performance through a combination of comprehensive e-learning content, online information resources, flexible learning technologies and support services. Their products offer the most comprehensive content alongside the most experienced and qualified consulting team in the e-learning industry. Content offerings include business, IT, desktop, compliance and consumer/SMB courseware collections, as well as complementary content assets such as Leadership Development Channel video products, KnowledgeCenter™ portals, virtual instructor-led training services and online mentoring services. The main models include the following. Digitized business books and reports, learning management system and virtual classroom Online business and IT training SkillPort: learning management system Books 24x7: online business and technical books resource SkillSoft Dialogue: web collaboration tool Case 2: Saba[http://www.saba.com] Founded in 1997, Saba is the premier global provider of strategic human capital management (HCM) software and services. Saba ’s people management solutions are used by more than 1,300 organizations and over 17 million end users worldwide. Saba ’s solutions increase organizational performance by aligning workforce goals with organizational strategy; developing, managing and rewarding their people; and improving collaboration. Here, we introduce Saba learning suite. Saba learning suite is the most comprehensive solution available for addressing the strategic goals of learning organizations. It delivers effective formal and informal learning that is tied to clear business outcomes and strategic human capital management initiatives. Combined with Saba Centra, Saba Learning Suite is the first product from a single company that offers the full continuum of learning methods – formal, informal, self-paced, live, and a blend to provide seamless and truly effective learning that builds organizational competencies. The suite gives learners the ability to target competencies and capabilities, measure improvements, and assess the impact of those improvements on performance, revenues, and cost savings. The Saba Learning Suite includes capabilities for: enterprise learning management, Certification management, For-profit customer education, Learning content management systems, Content authoring tool, Saba Centra: Virtual classrooms, Informal learning and collaboration, Career and competency management. Case 3: SumTotal Systems[http://www.sumtotalsystems.com] SumTotal Systems, Inc. is a global No.1 provider of learning, performance, and talent management solutions[http://www.hiray.net/No.1-a.htm], its partners spread over Asia Pacific, Europe, Middle East, Africa, Latin America, North America. Their Learning Management System (TotalLMS) is an industry’s leading and the backbone of SumTatal’s learning management offerings. TotalLMS combines best-of-breed functionality with simple

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configuration to help companies easily administer a variety of learning activities for both internal and external audiences. By providing the latest information, training and certifications, companies can help their customers to use their products more effectively, become productive more quickly and decrease their need for support. SumTotal's customer education solutions provide global, scalable capabilities to maximize the effectiveness, coverage and consistency of training to a company's customers. The recently released ResultsOnDemand Learning provides a best-of-breed learning management platform in an easy-to-use, rapidly deployed solution. This Software-as-a-Service (SaaS) solution provides organizations a low risk opportunity to leverage the robust functionality in ResultsOnDemand Learning. More than 1,500 organizations, large and small, depend on SumTotal learning solutions to improve learning management efficacy, meet training compliance requirements, and report on training’s impact. 1.2.2.2 CMSs CMSs are online systems that were originally designed to support classroom learning in academic settings, such as universities and high schools. CMSs provide instructors with the ability to perform the following tasks:  Place course materials online: Most CMSs provide pre-programmed buttons for the course syllabus, course schedule, and course materials linked to specific lessons, such as copies of readings and PowerPoint slides from lectures.  Track student progress through assessment features, which enable instructors to give quizzes and tests online, and an online grade book, where instructors can post student grades  Discussion board, where instructors and students can discuss readings and continue class discussions between formal class sessions  Other communication tools, which let instructors send announcements to classes and communicate individually with students  Lock box for students, where students can store class materials in a safe place—either a presentation to give later in class or backing up class assignments in a safe place  Course statistics, which provide information on the use of the course site, including who and when used the course site Because CMSs enable instructors to easily create a course website by following a template and uploading existing documents in PowerPoint, Word, Excel, Acrobat and other popular formats without converting them to a web format (like HTML), they require few specialized skills. As a result, CMSs are easy to learn and were quickly adopted by instructors, even those who might claim to be luddites. Indeed, some universities report that well over 70 percent of their instructors have created course websites using CMSs. CMSs also have been proven popular in managing asynchronous academic distance courses, too, because of their ability to manage discussions. In addition, given that CMSs were already installed and in wide use only adds to their popularity. When using a CMS to manage a distance course, instructors post a core lesson—a master script, of sorts, that guides students through readings, discussions, and learning activities—instead of merely posting readings and PowerPoint slides for each lesson,. Instructors then use the discussion board to manage the course discussions, which are usually more extensive than those used in classroom courses.


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Examples of CMSs include the commercial products Blackboard and WebCT (emerged with Blackboard), and the open source system, Moodle, but not limited to these. The following are the simply introduce to them. Case 4: Blackboard Learning System [http://www.blackboard.com] The Blackboard Learning System of Blackboard Inc. is an industry–leading software application used to power virtual learning environments, supplement classroom education and as a platform for distance learning programs. Featuring a robust core set of capabilities that enable instructors to efficiently manage courses, author content, create assignments, and foster collaboration, among other key functions, the Blackboard Learning System help institutions accomplish mission-critical objectives related to instruction, communication and assessment. These key capabilities include:  Course Management  Content Authoring  Adaptive Release  Syllabus Builder  Learning Units  Online Textbook Content (Course Cartridges)  Teaching and Learning Tools  Personal Information Management  Discussion Board  Virtual Classroom / Collaboration Tool  Group Projects  Assessments and Surveys  Assignments  Gradebook  Reporting and Performance Dashboard  Enterprise Scalability  Multi-Language Support  Standards  Blackboard Building Blocks (Open APIs)  System Integration Case 5: Moodle [http://moodle.org] Moodle is an Open Source Course Management System (CMS), also known as a Learning Management System (LMS) or a Virtual Learning Environment (VLE). It has become very popular among educators around the world as a tool for creating online dynamic web sites for their students. To work, it needs to be installed on a web server somewhere, either on one of your own computers or one at a web hosting company. The current version is 1.9, which was released in March of 2008. It has been translated into 61 different languages. Major improvements in accessibility and display flexibility were developed in 1.5. As of March 2008, the Moodle user community with over 400,000 registered users on their site alone. As there are no license fees or limits to growth, an institution can add as many Moodle servers as needed. The largest single site has reported over 19,000 courses and over 41,000 students, and the Open University of the UK is building a Moodle installation for their 200,000 users. Its capabilities will be divided into two aspects, i.e. the system

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management functions and the network teaching functions. Ⅰ The system management functions Site integration The easy-to-use design Dynamic modular design The role management User Registration Management Styles in a variety of themes Multi-language supported Well opened Perfect management tools Other new technical features Ⅱ Network teaching functions Multiple types of courses supported Flexible curriculum management Tracking analysis of study record Classing and grouping functions Courses resource management Two-way evaluation Test & item bank Multi-styles online teaching module Industry-standard supporting Other new features 1.2.3 Discussion on the application of e-learning in the HEFD As we covered at the beginning of section 1.2.1, the much talked about e-learning is referred to the learning by Internet, and which has contributed a lot to the higher education. In this part we will review the application among universities around the world. 1.2.3.1 E-learning in higher education Since the middle of the 1990s, the number of colleges and universities which provide courses and degree programs via e-learning has been growing dramatically. Internet-based e-learning has become one of the most concerned paths for people to acquire their expected knowledge. More and more universities have been invested a huge amount of resources to implement their Internet-based e-learning platform or environment. Many universities established its e-learning system based on the commercial platforms. Following are the details. Cornell University offers over 2,000 SkillSoft courses in information technology and business-related topics such as administrative support, communication, customer service, human resources, leadership, management, personal development and team building. The University of Houston became SkillSoft's first enterprise-wide education partner in the United States in 2004, offering the SkillChoice Complete business skills, IT and desktop solution to the entire campus community. University of North Carolina Wilmington had used SkillSoft IT courseware since 1998. In 2005, the university became enterprise-wide users and began offering SkillSoft's SkillChoice Complete solution of business skills, IT and


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desktop courseware, etc. to all faculty, staff and students. Besides, The Western Governors University and Minnesota School of Business are all the costumers of SkillSosft. What’s more, SkillSoft has extended its service to Chinese market. East Carolina University puts a new face on academic outreach with Saba Centra Live, Hosei University Builds a New Education Model With Saba Centra Live, University of Tennessee Nearing a Decade of Distance Education Using Saba Centra Live. The University of Manchester, a Blackboard client since 2003 recently made a strategic decision to adopt the Blackboard Managed HostingSM solution for its Blackboard Learn - Vista™ delivery. The University of Manchester aims to be one of the top 25 universities in the world and is committed to achieving this by 2015. Moodle is an open source system and its known sites have reached to 55,499 till January in 2009. Besides the commercial platform-based ones, there are also various kinds of e-learning systems around the world. The first pioneer intelligent and adaptive Web-based educational systems were developed in University of Pittsburgh 1995-1996 by Brusilovsky[1] named Adaptive and Intelligent Web-based Educational Systems (AIWBES). Since then many interesting systems have been developed and reported. In 2004, Brusilovsky presented a distributed architecture for adaptive e-learning: Knowledge Tree[2]. Subsequently, He worked out the ontology-based framework for user model interoperability in distributed learning environments in 2005 [3]. In 2001, the Department of Education, Training and Youth Affairs (DETYA) commissioned one of the largest educational research projects ever undertaken in Australia, to focus on innovation in schools [4]. The Innovation and Best Practice Project (IBPP) consisted of research into 107 schools throughout Australia in relation to teaching and learning toward a ‘knowledge society’. Through the innovation of e-learning environments, teachers found they were more able to cater to the differences of different students than previously attempted in the conventional classroom. Jianming Yong [5] proposed a new method to design an efficient Internet-based e-learning platform by combining an Internet-based e-learning environment with the Internet-based workflow mechanism in University of Southern Queensland, Australia. The whole system can be divided into four parts, Teaching Workflow System (T), Learning Workflow System (L), Admin Workflow System (A), Infrastructure Workflow System (I). Mirko Cesarini [6] has described a platform designed on the power of process management abstractions provided by WFMS. When teachers set a course, they can benefit of the flexibility, expressiveness and control capabilities. At the same time a student, having its learning process driven by a workflow designed by a teacher, earns the possibility to customize her/his learning path while maintaining it within the bounds of practicability and usefulness. Teachers' effort in creating courses for the new platform is eased by some visual tools and by improving reusability. Every created course can be a topic of another course. A new visual designed course is translated into workflow code through intermediate representation levels. These levels allow a teacher to customize the course for different situations by explicitly cutting some branches in the resulting learning paths. The use of an orchestration engine allows enrich a course not only with static learning material but also with the services provided by third programs or institutions. Fox an Trinidad (2006)[7] developed a structural framework to assist educationalists implementing e-learning or as they refer to “technology-enhanced learning”. This framework is based on the work of Herrington (2001) [8] and describes three components of

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importance when developing new resources and providing professional development for staff. These components are resources, pedagogies and delivery strategies. The resources are the learning content and forms of information that are provided to the learner. These resources are best provided as a variety of forms, which need not be all online. Pedagogies are the activities used to engage the learner. This underpins the manner in which the environment is structured and should include opportunities for people with disabilities learner to construct their own meaning through collaboration and a learner-centred environment. The delivery strategies element refers to the reliability and accessibility to the e-learning environment. The ability to design inclusively for learners who may be geographically located from the teacher is critical to this component (Oliver & Herrington, 2001) [9]. Since 1999, the Chinese Ministry of Education has authorized 67 ordinary universities and the central committee Radio and Television University does the modern distance learning pilot work, allows them to develop the certificate and non-certificate education. Related resources are available at the site http://www.ceta.edu.cn/Plone/resource. 1.2.3.2 E-learning in the HEFD The advantages of the online learning have been widely described in the literature. However, the prospect promised with the digital era has not become the reality for most people with disabilities, especially in educational context. Some studies of the statistical accesses of e-learning web pages of the major organizations have revealed a high percentage of inaccessible pages. A large number of organizations have realized the importance of the services for people with disabilities. A survey on e-learning for disabled people is been make by Electronic Training Village (ETV) (http://www.trainingvillage.gr). In the survey over 320 respondents across Europe gave their views about accessibility to e-learning for people with disabilities. Over one fifth of disabled e-learners require voice or speech recognition software to communicate with computers. Half the respondents thought e-learning would offer users with disabilities more opportunities to learn. 54.8% expected it to open up new and innovative opportunities. From the survey it appears that most people still believe modern e-learning involves merely reading off a screen. In fact e-learning technology has now evolved into a set of interactive multimedia applications. The Web Accessibility Initiative of W3C has presented the Web Content Accessibility Guidelines and its current version is 2.0 (WCAG2.0). WCAG 2.0 covers a wide range of recommendations for making Web content more accessible. Publicly Available Specification (PAS) 78 has been developed by the Disability Rights Commission (DRC) in collaboration with BSI. This PAS outlines good practice in commissioning websites that are accessible to and usable by disabled people. Besides, various actions have been taken to help disabled persons attain the e-learning education they deserve. In European Union, the "European Network for Visually Impaired Training, Education and Research" has worked a lot including the completed and actual projects, the following are the detailed. �MEBUS/Let's Vitalize (1998-2000) Developing a training course for IT trainer to educate ViP. �MARQUISA/UMBRELLA (1998-2000)


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Developing a training course long term unemployed ViP. �EVASA (2000-2002) Developing a training course long term unemployed ViP with low qualification �PROBAT I-III (2000-2003) Developing a helpdesk software for Microsoft Office optimized for blind computer-user. �PROBIQ (2000-2003) Developing a BORLAND 5.0 C++training course for ViP �SOBES (2003-2005) Developing interactive & case-sensitive diagnosis software to analyze the optimal computer setup for computer users with visual handicaps �E-Learn-ViP (2005-2006) Evaluation of available E-Learning products for ViP. �PROVIPTRAIN (2004-2006) Developing a modular training course for trainer in institutes for ViP. �PROZESS (2006-2007) Self diagnosis software for eye test considering computer setup and driving licence �PRIAMOS (2006-2007) Pilot project for empowering people with low vision to achieve a driving licence by using Bioptics and technical driving assistance systems �SEE-ViP (2005-2007) An E-Learning project aimed to develop an accessible LMS and CBT. Creating blended learning courses for the subjects English and Information Management. �ValorViP (2007.1-2007.2) Evaluation of EC funded project for the target group of visually impaired people. �DICOMP-S.NET (2007.1-2008.6) Developing a free screenreader. �Discovering Hands (R) (2006.8-2008.7) Education of blind women as doctors assistant in palpation diagnostics. �AHVIIT-ACCESS (2006.10 – 2008.9) Developing innovative audio-tactile learning materials �eYES (2006-2008) Creation of an e-learning interface for the blind and the visually impaired �KnowProViP(2007.10-2009.9) Developing a Knowledge Management and training system for trainers who educate: - visually impaired - and elderly people - immigrants �E-COVIP (2006.10-2008.9) Developing a network and certified blended learning course for E-Learning Coaches for visually impaired people Developing a network and certified blended learning course for E-Learning Coaches for visually impaired people Apart from the above, Margaret E.S [10] describes a case study undertaken by the University of Dundee at its library in 2006-2007 in which an e-learning module was developed to support library staff training in disability awareness. Jane Seale published a book named "E-learning and Disability in Higher Education" [11]. Examining the social, educational and

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political background behind making e-learning accessible in higher and further education, this book considers the role of, and provides advice for, the key stake-holders involved in e-learning provision: lecturers, learning technologists, student support services, staff developers and senior managers. M.Arrigo[12] introduces an online learning environment designed for blind students based on the client-server model. F. Colace, et. al.[13] presented a standardized design approach to e-learning contents for people with disabilities based on the use of accessibility and usability guidelines. In America, 54 million Americans with disabilities use information technology (IT) at colleges and universities, which accounts for 20 percent of the population. So there starts a movement on many college campuses to become more proactive in using e-Learning as a strategic tool for students with disabilities. 1.2.4 References [1] Peter Brusilovsky , Christoph Peylo. Adaptive and Intelligent Web-based Educational Systems, International Journal of Artificial Intelligence in Education 13 (2003) 156–169 [2] Brusilovsky, P. (2004) KnowledgeTree: A distributed architecture for adaptive e-learning. In: Proceedings of The Thirteenth International World Wide Web Conference, WWW 2004 (Alternate track papers and posters), New York, NY, 17-22 May, 2004, ACM Press, pp. 104-113 [3] Brusilovsky, P., Sosnovsky, S. & Yudelson, M. (2005). Ontology-based Framework for User Model Interoperability in Distributed Learning Environments. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2851-2855) [4] Department of Education, Training and Youth Affairs. (DETYA) (2001). School innovation: Pathway to the knowledge society. Canberra: Commonwealth of Australia. [5] Jianming Yong, Internet-based E-learning work flow process, 9th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2005) Computer Supported Cooperative Work in Design II, 0302-9743, 2006: p.516-524 [6] Mirko Cesarini; Mattia Monga; Roberto Tedesco, Carrying on the e-Learning process with a Workflow Management Engine, Association for Computing Machinery(ACM) Annual Symposium on Applied Computing(SAC 2004) vol.2; 20040314-17; Nicosia(CY) Applied Computing 2004 vol.2, 2004 [7] Fox, R., & Trinidad, S. (2006). Technology's role in leveraging curriculum reform. Paper presented at the Conference on ICT in Teaching and Learning, ICT2006, July 7-10, Hong Kong. [8] Herrington, A., Herrington, J., Oliver, R., Stoney, S. & Willis, J. (2001). Quality guidelines for online courses: The development of an instrument to audit online units. In G. Kennedy, M. Keppell, C. McNaught & T. Petrovic (Eds.), Meeting at the crossroads: Proceedings of ASCILITE 2001 (pp.263-270). Melbourne: The University of Melbourne. [9] Oliver, R. & Herrington, J. (2001). Online learning design for dummies: professional development strategies for beginning online designers. In P. Barker & S. Rebelsky (Eds.), Proceedings of EDMEDIA 2002, World Conference on Educational


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Multimedia, Hypermedia and Telecommunications. Norfolk, VA: AACE, (pp 1500-1505). [10] Margaret E.S. Forrest, E-learning to support the development of disability awareness skills: a case study, WORLD LIBRARY AND INFORMATION CONGRESS: 74TH IFLA GENERAL CONFERENCE AND COUNCIL 10-14 August 2008, Québec, Canada [11] E-learning and Disability in Higher Education (Paperback), by Jane Seale (Author) ， UK, Routledge Tavlor & Francis Group, 2006, USA & Canada, New York, NY [12] M.Arrigo. E-Learning Accessibility for blind students. Recent Research Developments in Learning Technologies, 2005, 1-5 [13] F. Colace, M. De Santo, P. Rocca Comite Mascambruno, E-Learning Contents for People with Disabilities: a Standardized Design Approach, ICTA’07, April 12-14, Hammamet, Tunisia,109-114 1.3 Technology of information accessibility 1.3.1 Solutions to information accessibility Accessibility is initially used in the environment construction referred to that the surrounding environment is accessible to all kinds of people with disabilities to live, study and work. Then it is introduced into the field of information science and technology which generates the concept of information accessibility. Information accessibility refers to that any one (whether disabled or not, whether young or not) at any circumstance can equally, conveniently, accessibly access and make use of information. Information accessibility including two main areas, one is the accessible design of the hardware and software related to the electronic and information technology and its assisted products and techniques, and the other is the network accessibility which included the accessibility of web site content, web application, etc.. In this chapter we will pay much attention to the accessibility with the help of computer technology especially the network technology. Most people today can hardly imagin life without the internet. It provides access to news, email, shopping, and entertainment, at any hour of the day or night. But these are all based on the facts that you can use a mouse and see the screen and hear the audio—in other words, if you don't have a disability of any kind. So the solution to information accessibility is to do everything to make all people with various kinds of disabilities can easily access the web. And it can be concluded into the category of web accessibility. Web accessibility [1] refers to the practice of making websites usable by people of all abilities and disabilities. When sites are correctly designed, developed and edited, all users can have equal access to information and functionality. For example, when a site is coded with semantically meaningful HTML, with textual equivalents provided for images and with links named meaningfully, this helps blind users using text-to-speech software and/or text-to-Braille hardware. When text and images are large and/or enlargeable, it is easier for users with poor sight to read and understand the content. When links are underlined (or otherwise differentiated) as well as colored, this ensures that color blind users will be able to notice them. When clickable links and areas are large, this helps users who cannot control a mouse with precision. When pages are coded so that users can navigate by means of the keyboard alone, or a single switch access device alone, this helps users who cannot use a mouse or even a standard keyboard. When videos are closed captioned or a sign language

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version is available, deaf and hearing impaired users can understand the video. When flashing effect is avoided or made optional, users who prone to seizure what has been caused by these effects are not put at risk. And when content is written in plain language and illustrated with instructional diagrams and animations, users with dyslexia and learning difficulties are better able to understand the content. When sites are correctly built and maintained, all of these users can be accommodated while not impacting on the usability of the site for non-disabled users. The needs that Web accessibility aims to address include: i. Visually impaired: Visual impairments including blindness, various common types of low vision and poor eyesight, various types of color blindness; ii. Motor/Mobility: e.g. difficulty or inability to use the hands, including tremors, muscle slowness, loss of fine muscle control, etc., due to conditions such as Parkinson's Disease, muscular dystrophy, cerebral palsy, stroke; iii. Hearing impaired: Deafness or hearing impairments, including individuals who are have difficulties in hearing; iv. Seizures: Photo epileptic seizures caused by visual strobe or flashing effects. v. Cognitive/Intellectual impaired: Developmental disabilities, learning disabilities (dyslexia, dyscalculia, etc.), and cognitive disabilities of various origins, affecting memory, attention, developmental "maturity," problem-solving and logic skills, etc. Individuals living with a disability use assistive technologies such as the following to enable and assist web browsing: i. Screen reader software, which can read out, using synthesized speech, either selected elements of what is being displayed on the monitor (helpful for users with reading or learning difficulties), or which can read out everything that appears on the computer (used by blind and vision impaired users). ii. Braille terminals, consisting of a Refreshable Braille display which renders text as Braille characters (usually by means of raising pegs through holes in a flat surface) and either a QWERTY or Braille keyboard. iii. Screen magnification software which enlarges what is displayed on the computer monitor, making it easier to read for vision impaired users. iv. Speech recognition software that can accept spoken commands to the computer, or turn dictation into grammatically correct text - useful for those who have difficulty using a mouse or a keyboard. v. Keyboard overlays, which can make typing easier and more accurate for those who have motor control difficulties. 1.3.2 International developing process of information accessibility Every region in the world has experienced over 100 percent growth in Internet usage in the last five years according to the table of World Internet Usage and Population Statistics provided by Internetworldstats.com. With the Internet becoming a more important part of society throughout the world, many countries are recognizing and acting upon the need to ensure access to the web for people with disabilities. It seems as if each nation is taking a slightly different approach to the problem of Internet access for people with disabilities. Some have established laws that provide human or civil rights. Others, like the United States, have addressed the access issue through the technology procurement process. One fairly common approach throughout the world is for


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nations to support and adopt the Web accessibility guidelines (WCAG 2.0) created by the Web Accessibility Initiative (WAI). In 1998 the US Congress amended the Rehabilitation Act to require Federal agencies to make their electronic and information technology accessible to people with disabilities. Section 508[2] was enacted to eliminate barriers in information technology, to make available new opportunities for people with disabilities, and to encourage development of technologies that will help achieve these goals. The law is applied to all Federal agencies when they develop, procure, maintain, or use electronic and information technology. Under Section 508, agencies must give disabled employees and members of the public access to information that is comparable to the access available to others. Summary of Section 508 technical standards： i. Software Applications and Operating Systems: include usability for people that are visually impaired, such as alternative keyboard navigation. ii. Web-based Intranet and Internet Information and Applications: assure accessibility to web page graphics by the visually impaired using assistive technology such as screen readers and refreshable Braille displays. This is accomplished by using text labels and descriptors for graphics. iii. Telecommunications Products: address accessibility for people who are deaf or hard of hearing. This includes technology compatibility with hearing aids, assistive listening devices, and TTYs. iv. Videos or Multimedia Products: include requirements for captioning of multimedia products such as training or informational multimedia productions. Captioning or video descriptors must be able to be turned on or off. v. Self Contained, Closed Products: product with embedded software, such as information kiosks, copiers, and fax machines, cannot be used with assistive technology. This standard requires that access features be built into these systems. vi. Desktop and Portable Computers: discuss accessibility related to mechanically operated controls such as keyboards and touch screens. The Disability Discrimination Act 1995 (c.13) (DDA 1995) [3] is an Act of the Parliament of the United Kingdom which makes it unlawful to discriminate against people in respect of their disabilities in relation to employment, the provision of goods and services, education and transport. It is a civil rights law. Other countries use constitutional, social rights or criminal law to make similar provisions. The Equality and Human Rights Commission provides support for people with disabilities Act. Equivalent legislation exists in Northern Ireland, which is enforced by the Northern Ireland Equality Commission. In 2001, the United Kingdom passed the special education regulations of disabilities, requiring all educational institutions and education-related services necessary to anticipate on people with disabilities in the educational services required. For example, while organizations do not have the visually impaired access to services, but they have required the agency to provide services immediately when necessary. Some internal companies involved into the area of information accessibility at a very long time ago. For example, IBM began hiring the employees with disabilities in 1914. IBM has eight research centers all over the world, the IBM global accessibility center "IBM Ability and Accessibility Center" is linked under the rank of IBM's research centers. The essential mission is to collect the best and the most advanced technology from the 8 centers to serve the R&D of accessibility. Up to date, IBM has established information accessibility

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centers in America, Japan, German, France and Australia. Microsoft introduced two ways to support the technology of information accessibility. One is to adding accessibility performance to the popular products. Such as pre-setting some APIs to extend the products' existing practicabilities, some accessible products are developed for the people with disabilities. In addition, Microsoft has associated with more than 100 companies for the accessibility on the products R&D and manufacturing, so that its windows and other products can provide better services for all people. In Chinese, Microsoft has been a partner of China Braille Press on promoting application of information technology for the blind. On Microsoft's website, there are accessibility information technologies pages providing mass free resources for the developers. 1.3.3 Design criterion for network recourses accessibility The World Wide Web Consortium (W3C) [4] is an international consortium where member organizations, a full-time staff, and the public work together to develop Web standards and guidelines designed to ensure long-term growth for the Web. Over 400 organizations are members of the consortium. W3C is jointly run by the MIT Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) in the USA, the European Research Consortium for Informatics and Mathematics (ERCIM) headquartered in France and Keio University in Japan, and has seventeen outreach offices worldwide. W3C's Web Accessibility Initiative (WAI) [5] works with organizations around world to make the web more accessible for people with disabilities and older users. WAI pursues web accessibility by ensuring that web technologies support accessibility; developing guidelines for Web content, browsers and media players, and authoring tools; developing resources to support improved evaluation tools; developing resources for education outreach; and coordinating with research and development efforts that can affect future accessibility of the Web. WAI is supported in part by the U.S. Department of Education's National Institute on Disability and Rehabilitation Research (NIDRR), European Commission's Information Society Technologies Programmed, HP, IBM, Microsoft Corporation, SAP, and Wells Fargo. On 11th December 2008 W3C announces a new standard that will help Web designers and developers to create sites that better meet the needs of users with disabilities and older users. Drawing on extensive experience and community feedback, the Web Content Accessibility Guidelines (WCAG) 2.0[6] improve upon W3C's groundbreaking initial standard for accessible web content. This new standard from the WAI will advance accessibility across the full range of Web content (such as text, images, audio, and video) and Web applications. WCAG 2.0 can be more precisely tested, yet it allows Web developers more flexibility and potential for innovation. Together with supporting technical and educational materials, WCAG 2.0 is easier to understand and use. Web Content Accessibility Guidelines (WCAG) 2.0 defines how to make Web content more accessible to people with disabilities. Accessibility involves a wide range of disabilities, including visual, auditory, physical, speech, cognitive, language, learning, and neurological disabilities. Although these guidelines cover a wide range of issues, they are not able to address the needs of people with all types, degrees, and combinations of disability. These guidelines also make Web content more usable by older individuals with changing abilities due to aging and often improve usability for users in general.


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WCAG 2.0 is developed through the W3C process in cooperation with individuals and organizations around the world, with a goal of providing a shared standard for Web content accessibility that meets the needs of individuals, organizations, and governments internationally. WCAG 2.0 builds on WCAG 1.0 [WCAG10] and is designed to apply broadly to different Web technologies now and in the future, and to be testable with a combination of automated testing and human evaluation. The individuals and organizations that use WCAG vary widely and include Web designers and developers, policy makers, purchasing agents, teachers, and students. In order to meet the varying needs of this audience, several layers of guidance are provided including overall principles, general guidelines, testable success criteria and a rich collection of sufficient techniques, advisory techniques, and documented common failures with examples, resource links and code. The WCAG is of the most effect all over the world. And many other standards made by many countries have made references to it. The Disabilities' Right Committee and the British Institute of Standard have set down a criterion about the web accessibility and the government has introduced it into the technology standard for governmental stock. In Jun 2004, Japan Industry Standard Association released JISX 8341. In its third section, accessible demands on web service are regulated according to the WCAG. 1.3.4 Some cases of information accessibility  Kyoto City Web The challenge: Leverage the Internet to increase tourism in Kyoto City, a large, culture-rich metropolis in Japan. Help make sure that the Kyoto City Web site is more accessible to all people regardless of ability or disability. Include both foreign language support and text-to-speech functionality for Japanese, English, Korean and Chinese. The solution: IBM Easy Web Browsing technology is being offered, at no charge to end users, on the Kyoto City Web site, to allow users who have low vision to enlarge text or read text aloud. The benefit: follows the direction and "Grand Vision" of Kyoto for the 21st Century. Helped establish an accessible Web site that is attracting the global community. Increased traffic on the Web site. 1）Kyoto City government uses Easy Web Browsing as part of its 'Grand Vision' for 21st Century： Seeking to create a direction for civic life and city development, the Kyoto City government devised a "Grand Vision" for the 21st Century. In 1978, the city made a declaration to the world that Kyoto would be "a city open to the free exchange of world cultures." As part of the plan to achieve this ideal, Kyoto formulated "The Master Concept of Kyoto City" in 1983 with the aim of meeting many of its goals by the year 2000. This Master Concept asserts that the city will keep developing, but at the same time preserve its tradition. From Kyoto City Web site: (see Figure 1)

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Fig. 1. Kyoto City Web site home "To attract the world's cultures, Kyoto City created a Web site that allowed all people, regardless of their abilities or native language, to be able to access information. The Web site is available in four languages, and uses IBM Easy Web Browsing technology to enlarge text and read it aloud. The technology is very easy to use and is offered free to the user. Senior citizens, who often have vision difficulties such as low vision or cataracts, can now access all the information on the Kyoto City Web site through enlarged text or as a "screen reader." The screen size can be customized from 50% to 600%. It also enables the user to change the color of the Web site background to help those with color blindness and other color-related vision impairments. For children and non-Kyoto natives, the IBM Easy Web Browsing "reading aloud" function helps increase comprehension. Offering this enhanced communications functionality as part of its Web site increases the user's comfort level with technology — even for novice users." 2）Vision for the future of Kyoto City In the coming years, government agencies, local municipalities and private companies will plan how to prepare their Web content in various languages — such as English, Hangul and Chinese — in order to enhance global information transmission. They can use the Kyoto City Web site as the model. By adopting the multilingual text-to-speech technology from IBM, Easy Web Browsing can be used to help those agencies address the needs of their constituents.


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1.3.5 References： 1、http://en.wikipedia.org/wiki 2、http://usability.gov/web_508/tut-b.html 3、http://www.york.ac.uk/admin/hr/resources/policy/disability_dda_1995.htm 4、http://www.w3.org/ 5、http://www.webaim.org/ 6、http://www.w3.org/TR/WCAG20/ 7、http://www-03.ibm.com/able/index.html 1.4 Higher education grid for disabled 1.4.1 Grid and education Grid is the forefront technology in the field of IT and Internet. It is considered the most significant breakthrough at computer architecture, operating system, user interface since 1995 to 2010. It integrates the whole Internet into one huge super computer and will keep it to share the resources of compute, storage, data, information, knowledge, expert, etc. There is no fixed definition for grid but the following features:  Distribution and Resource Sharing 

High degree of abstraction



Self-similarity



Dynamic nature and diversity

 Autonomy and the multiple of management The classified method of grid can be various. According to the type of managed resources, grid can be categorized into compute grid, data grid, information grid, service grid, knowledge grid, semantic grid, etc. Applying the grid to education to provide all kinds of educational resource becomes the education grid. Here, the educational resources include the course ware and very type of media materiel as well as the computing ability, storage space, databases, sensors and demote devices. China Education and Research Grid (ChinaGrid[1]) is an important project of the fifteenth national “211 project” and is responsible by Chinese Ministry of Education. Up to now[2], it has integrated a large number of grid resources of 20 famous universities from 13 provinces and cities, the computing power of polymerization is more than 16 trillion times per second, the storage capacity is more than 170 TB, it has become one of the world’s largest super grid. Five application grid have been developed successfully on the bioinformatics, image processing, computational fluid dynamics, mass information processing and online university courses. There are more than 120 types of bioinformatics software tools, 30 kinds of services related to database, about 100,000 medical images, 10,000 pieces of diagnostic data. Chinagrid supports the typical grid application such as the optimized design of digitized virtual man and aircraft, integrates digital museum resources of 18 universities, provides more than 300 college courses and approximately 3,500 hours course videos. It can provide many grid services such as the video-on-demand education system through the 22 servers distributed in 17 cities.

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The Kerala Education Grid Project is a path-breaking initiative of the Kerala government to provide quality education to all the students pursuing higher studies in the state. The project vision is to provide quality education to all, independent of geography. The project aims to facilitate multiple independent course-specific collaborative information and knowledge networks across distributed servers over the Internet as well as affordable and pedagogically effective online assisting learning environment for the colleges in any subject. American Education Grid Portal[3] is provided as a platform for the educational community to support generation and sharing of education resources across the different institutions, universities and colleges of higher education. The portal is also intended to facilitate collaboration, sharing of knowledge, best practices and co-operation to improve the quality of education offered in the colleges and in the open learning mode. In many ways, Education Grid is complementary to the National Program on Technology Enhanced Learning that is being executed by the IITs and IISc to develop content in large number of courses for engineering colleges. It provides development and deployment of systems and processes that use NPTEL and other open content for conducting quality education in the colleges. 1.4.2 Grid resources for special education 1.4.2.1 The status of higher special education The higher special education in China began in the medium 80s in 20th century. From 1985, limb-impaired students have been recruited by Shandong Binzhou medicine college. In 1987, the special education school of Changchun University was established and became the first higher special education academy in China. Till now, there are more than 10 high schools recruits the various disabilities. There are four manners exist present, such as establishing special education academy or setting the corresponding department and major in common colleges, let the people with disabilities study together with the healthy students, setting up some junior colleges to recruit people with disabilities and is managed by the cooperation of independent vocational school and adult education college, and all other regular and irregular network education forms [4]. The prevalent computer & network technology has penetrated into the high special education colleges to assist the teaching and researching and many relevant works have been reported. In the teaching application of network technology in higher special educational colleges, study on how to exert the function of multimedia and how to make full use of the advantages that network resources made to teaching is worth to investigate. Studies show that information technologies have laid out obvious advantages at the compensating the dumps for their physiology limitation and increasing the interests in study. British Staffordshire University began the development of virtual learning system COSE in 1996 and now the third version is come into birth. In 2001, an e-learning junior college was held in Shenzhen Radio & Television University that is serviced to the people with disabilities and is aroused strong reverberation. It shows that modern distance education model is the efficient method to make the disabilities get the equality education and obtain employment. Subsequently the education academy for disabled people of central Radio &


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TV University is established. But the e-learning study on the high special education in high school is of no reports. According to the result of the second countrywide disabilities sampling investigation, the total number of disabled people in China is 8296 and it takes the portion of 6.34% to the national total. Throughout the country there are only more than 10 colleges that recruit disabled students. Under the whole background of constructing socialism harmonious society, it is of most important to resolve the equality education for the disabled. Engaging in the study of special education e-learning system in common colleges can benefit to the development of our national special education and also can make more people with disabilities obtain higher education. 1.4.2.2 Implementation of e-learning system for higher special education based on grid Distance education is one of the most important aspects of informationization education. It can enable the special needs group breakthrough the limitation of traditional teaching mode and obtain high-quality education. It’s well known that the distance education system is of a distributed circumstance in which the men and men, men and computer can share the information independent of the time and space limits. As the third generation network technology, grid can provide better platform for resources sharing. Grid is a new technology based on the internet and mean while integrates the whole internet into a vast super computer to realize the comprehensive sharing of computer resources, data resources, information resources, knowledge resources and expert resources. Resource sharing is the most importance feature of grid and it can efficiently manage and schedule the processor time, memory, network and storage. International grid technology develops quickly and the distance education research reports based on grid are familiar, such as the Birmingham Grid for Learning, the education grid of Washington University, etc. Although they are not specialized for the special education, they provide some convenience offers to the people with disabilities and all kinds of users to meet their needs in it. By contraries, the research on high education service based on grid in Mandarin China is appeared lately. But several institutes and colleges have taken their work into different degree. The work of Beijing Jiaotong University, Zhejiang University of technology, Guangzhou University, etc. are reported. China-grid plan also includes the relevant content about distance education. By reading through all the available literature, a conclusion can be drawn that there is no instance of study on e-learning system for higher special education based on grid in colleges. 1.4.3 A case study on special higher education grid In this section we will introduce the layout of e-learning teaching resources for higher special education worked by our group. It expands the application fields of grid and meanwhile wishes that the more convenience network study circumstances is offered to more and more people with disabilities. 1.4.3.1 Grid structure The main purpose of the proposed grid is to establish a distributed and massive education resource library, offer a set of resource sharing and storage management mechanisms by which users can conveniently and quickly access network information anytime and

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anywhere. Because of that the service objects is the special needs group, a special dealing is necessary to the preparation of educational resources and the offered services also has its special feature. The structure of the proposed system is shown in fig. 2. Education resource servers distributed in different regions will continue to input the corresponding information into the e-learning grid included network courseware, video on demand (VOD), assistant study information and assistant supports. User can capture all kinds of information service by distance logging in and upload or storage some resources to share with other users. The physical position and topological structure of the whole network are transparent to all users. So users do not need to know that where the coursewares were stored. The relevant work is done by grid middleware. In this system, high reliable service can be obtained as far as there is one grid node in work. The assist support module at the server end will offer the client downloading and video format transferring function. All the resources provided by the grid will suite to all persons including the visual impairments. So the downloaded client will offer text-to-speech function. Network courseware and VOD files can be played online or in local machine. The function of video format transferring is equipped in any grid node. Storing format Information service

Network courseware Video on demand Assist studies

E-learning grid for higher special education

Assist Supports

Information service Information service Information service

Register management

Fig. 2. Structure of e-learning grid for higher special education transferring makes that the downloaded video can be played in computer, mp3 and even portable cellular phone. Register management is accomplished by main grid node and it includes the log in for node user and member user. Two kinds of users can share the grid service. Besides, the node user can storage its private resources or offer some service and the member users can give its own resource storage in the grid and share it with all the other users. 1.4.3.2 Grid components and its access model Three components exist in the grid, they are grid client, grid manager and grid node. There is an integrative web server at grid client but it can only send the requirement to the grid manager. In the grid node, Java technology is adopted to deal with the requirements came from client or grid manager. Grid manager is responsible to the whole grid management. It not only deals with the web requirement but also the grid database. Grid client can directly access the grid node to obtain various services. This scheme is useful to keep the load balance. See fig. 3 for the access model.


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Resource search Grid manager

Grid node

Search results

Grid client

Grid node

Grid node

Fig. 3. Access model of e-learning grid for higher special education From the above fig. we can see that the first role of the grid is resource storage. Both the grid node and client can submit its own teaching resource to the grid and the grid manager decides which node will be used. During the operation, the requirement is first transferred from grid node or client to grid manager, then the manager searches the management database to confirm which node can offer the service according to the requirement and then sends the result (including the node ID) to the required node or client. When the required end got the responds information it can directly get connection with the grid node signed by node ID. The second role of this grid is to offer information service. When the service requirement is sent to the grid manager from client, the grid manager also searches the inner information to find the grid nodes which can meet the needs and then send all the available nodes’ IDs to client. The client can select one node, but the default setting is to connect with the first node of the nodes list. Grid manager in this access model is the kernel of the whole grid system. So we take a special server as the grid manager and its service management function is baked up in the main node. The grid manager responds to the client’s requirements regularly and once the exception occurred, the main grid node takes the place of grid manager immediately. As for the important or the unique resources of the grid, it always is stored a copy on server or other nodes. The grid system can be regularly run as long as one node is in work. The data storages at the grid manager include: (1)Node user information table: userID, userName, password, realName, address, nodeParameter. (2)Member user information table: userID, user-Name, password, realName, address, memberLevel. (3)Resource information table: ResourceID, cap-tion, metadata, nodeIDs. Among the resource information table, “Resource-ID” is used to sign every teaching resource. It is confirmed when the resource is registered or uploaded. “caption” signs the resource name and “metadata” refers to the feature parameters of the resource such as the size, storage format, knowledge-point, content introduction, etc. “nodeIDs” shows the nodes’ IDs that storage the resource. 1.4.3.3 Design of grid resource library The particularity of the service object determines the particularity of resource library in the proposed e-learning grid. Every teaching resource in the grid can be lies in both the video and audio media. Such as the network courseware is played as video by default and its service objects are deaf-and-dumb students. Before playing these kinds of resources user can

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make selection whether needs the speech support. On the contrary, video on demand resources will be played by default for visual impairments. So before playing user can make selection that whether needs the text-caption support. The resource storage names is the integrated identifier which is composed of resource ID, course identifier, resource type, knowledge point. Each part is separated by “-“. Resource ID is the unique and uniform code of all resources and is consists of year (4bits), month (2bits), day (2bits) and an order number (3bit). Course identifier implies the course name and it is expressed by the first spelling letter of Chinese word. Resource type refers to the network courseware or VOD media, etc. In this system is coded by two bits number such as 01. Knowledge point is also expressed by the first spelling letter of Chinese content. For example, if the resource code is 20060122001-jsjwhjc-01-gaishu,it means that the resource is a network courseware for computer basic knowledge (in Chinese is : ji suan ji wen hua ji chu), the knowledge point is summarize (in Chinese is: gai shu) and it is the first resource submitted on January the 22nd in 2006. The system resource list will be shown to the user when the client access grid server. The total meaning of the resource code can benefit to both the user and the management people. 1.4.4 References： [1] http://www.chinagrid.edu.cn [2] http://news.xinhuanet.com/newmedia/2006-07/25/content_4875147.htm [3] http://www.edugrid.ac.in [4] YU Huiyun, WEI Xiaoman. A Review of Researches on Higher Special Education in China. Chinese journal of special education. 2006, 4:66-70(in Chinese)

Application of Data-Mining Technology on E-Learning Material Recommendation

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Application of Data-Mining Technology on E-Learning Material Recommendation Feng-Jung Liu and Bai-Jiun Shih Tajen University Ping Tung, Taiwan

1. Introduction “Information overload” problem is even more emphasized with the growing amount of text data in electronic form and the availability of the information on the constantly growing World Wide Web (Mladenic et. al., 2003). When a user enters a keyword, such as “pencil” into a search engine, the result returned is often a long list of web pages, many of which are irrelevant, moved, or abandoned (Smith et. al., 2003). It is virtually impossible for any single user to filter out quality information under such overloading situation (Shih et. al., 2007). Designing appropriate tools for teaching and learning is a feasible approach to reduce the barriers teachers might encounter when adopting technology in their teaching (Marx et. al., 1998; Putnam & Borko, 2000). With potentially hundreds of attributes to review for a course, it is hard for the instructor to have a comprehensive view of the information embedded in the transcript (Dringus & Ellis, 2005). Computer-based systems have great potential for delivering learning material (Masiello, Ramberg, & Lonka, 2005), which frees teachers from handling mechanical matters so they can practice far more humanized pedagogical thinking. However, information comes from different sources embedded with diverse formats in the form of metadata, making it troublesome for the computerized programming to create professional materials. (Shih et. al., 2007).The major problems are: (1)Difficulty of learning resource sharing. Even if all E-earning systems follow the common standard, users still have to visit individual platforms to gain appropriate course materials contents. It is comparatively inconvenient. (2) High redundancy of learning material. Due to difficulty of resource-sharing, it is hard for teachers to figure out the redundancy of course materials and therefore results in the waste of resources, physically and virtually. Even worse, the consistency of course content is endangered which might eventually slow down the innovation momentum of course materials. (3) Deficiency of the course brief. It is hard to abstract course summary or brief automatically in efficient way. So, most courseware systems only list the course names or the unit titles. Information is insufficient for learners to judge quality of course content before they enroll certain courses.

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To solve the problems mentioned above, we propose an automatic inquiring system for learning materials which utilize the data-sharing and fast searching properties of LDAP. Our system not only emphasizes friendly search interfaces, but also excavates the association rule from log data of learning activities. Meanwhile, collaborative filtration is employed to improve the reliability of the searching results. However, the result lacked meaningful associations or visual accessibility in current approach. We then intend to employ some AI techniques to improve the results. A topic map seems to be a good solution. Fisher, Wandersee and Moody (2000) said that a topic map presents a group of assets in a network structure; the knowledge network is completed by interlinked conceptual nodes, and spread out in a framework. It not only involves sets of concepts but also the organization of concepts in terms of their inter-relationships. In this Chapter, we will give a brief review of what has been done on addressing the information overload problem on e-Learning, and explain how related techniques were applied. After then, a detailed description of the system implementation will be provided, followed by pedagogical application suggestions to possible alternatives for the integration of the technology into the learning field. Both the technical and educational evaluations of the system were discussed. At last, our conclusion brings some ideas and suggestions to the alternative possible integration of technology into learning in all fields.

2. Literature Review A key factor of hypermedia-based learning is customizable cognitive style as it suffices users’ information processing habits, representing individual user’s typical modes of perceiving, thinking, remembering and problem solving. Cognitive psychologists recognize that knowledge has a basic structure which presents the inter-relationships between concepts. Anderson (1980) distinguished knowledge into declarative and procedural types to identify their characteristics as abstract or practical functions (Anderson, 1980). In understanding knowledge, Ausubel (1968) provided two strategies including progressive differentiation and integrative reconciliation for making meaningful learning focusing on the systematic methods to learning (Ausubel, 1968). The implication of these theories is twofold; one, knowledge has an internal structure to sustain itself; two, systematic retrieval and understanding of knowledge is an effective method for learning (Shih et. al., 2007). In order to effectively use technology to assist the education process, helping learners to collect, process, digest, and analyze the information, we introduce data mining, association and collaborative filtering technologies, describing how the technology facilitates the processing of data, and how the dynamic map can achieve the pedagogical goal. Generally speaking, there are two approaches to organize and present knowledge: topdown and bottom-up. Top-down approaches consist of supervised-learning. They require human-experts to define knowledge ontology (Noy & McGuinness, 2001) and taxonomy (Bruno & Richmond, 2003). Accordingly, the machine classifies the knowledge by those predefined rules, based on co-occurrence and ANN (Artificial Neural Network). Inversely, bottom-up approaches are fully-automatic and represent unsupervised-learning, including SOM (Kohonen, 2001) and knowledge maps. However, most applications took hybrid approaches, that is, a human expert (SME: subject material expert) to predefine ontology and taxonomy and then clustering and organizing the knowledge by using ANN or SOM. This is sometimes called reinforcement learning. (Bruno & Richmond, 2003)


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2.1 Data Searching, Storage and Retrieval The Lightweight Directory Access Protocol, LDAP, is an application protocol for querying and modifying directory services running over TCP/IP. LDAP was originally intended to be a "lightweight" alternative protocol for accessing X.500 directory services through the simpler (and now widespread) TCP/IP protocol stack. The advantages are: (1) fast searching: LDAP utilizes the properties of data hierarchy, caching technology and the innovative index technology to offer fast inquiry service; (2) Extendable Data schemas: The data schema mainly describes and defines the attribute of entries in the directory tree. LDAP allows users to define data schemas by themselves and let schema specification more flexible; (3) Multiple access permissions and data encoding: Except that be able to establish the access permissions according to users’ specifications individually, LDAP also supports some security mechanisms, such as SSL (Secure Socket Layers), TLS (Transport Layer Security) and SASL (Simple Authentication & Security Layer); (4) Suitable for the inquiry of a large amount of data: The directory database is designed under the assumption that the frequency of reading is greater than frequency of writing. It can improve the usability and dependability of data by duplicating data extensively. XML and Java technology are recognized as ideal building blocks for developing Web services and applications that access Web services. JAXB (Java Architecture for XML Binding) is an XML binding model that defines the way of automatic mapping XML documents into objects in a programming language. Two major processes, marshalling and unmarshalling, take care of the mapping between Java objects and XML documents, which makes JAXB surpass traditional SAX and DOM approaches. Its advantages are: (1)Simplicity: It is Java procedure too to derive the classification (Schema-Derived Classes & Interfaces) through the outline that Binding Compiler compiles out, so does not need to deal with XML file by oneself, and does not deposit and withdraw the content tree without according to the order; (2)Extensibility: The programmer can revise the schemas and derive the classification independently, and let the procedure accord with systematic requirements even more. Additionally, when XML Schema is changed to some extents, it just needs to recompile Schema, and increase some more procedures newly, instead of needing to revise the original procedures, (3)Efficiency: Because all of data of the content tree are produced of JAXB according with the definition of XML Schema, there not exist any invalid methods or objects. Even it could use the Unmarshaller class to verify whether XML file is effective. 2.2 Data Association The Apriori algorithm(Agrawal, 1993) is one of the most representative algorithms of association rule. Its key steps are: (1) produce the candidate set from the database, and then find out the largest item-set according with the minimum support degree; (2) find out the items from the large item-set derived in previous step, in accord with the minimum confidence degree. However this approach is time-consuming because the database are usually scanned too many times. Therefore, many algorithms, like the DIC algorithm (Brin et. al., 1997), DHP

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algorithm(Park. et. al., 1995) , are proposed successively to improve the performance. We suggest using association rule mining techniques to build an e-learning material recommendation system that intelligently recommend on-line learning activities or shortcuts in the course web site to learners based on the actions of previous learners to improve course content. 2.3 Collaborative Filtering Collaborative filtering is one of recommendation mechanism for personage, also called “people to people correlation”. Currently, it is applied to all kinds of e-commerce extensively to infer user's interest in other products or the service through the analysis of user's materials or the behavior. Currently, the filtration technologies are classified into the following three methods: (1)Rule-based filtration: By the questionnaire or other ways to obtain the data, such as user's preference, or interest, etc. which will be made use of the reference as recommending. (2)Content-based filtration: The system recommends other relevant contents according to the user's selected content. And, the recommended content mostly were used and hived off or consulted by other user's experience before. (3)Activity-based filtration: By collecting user's activity information, infer the relation of some contents. Such mechanism is usually applied on these websites without member system.Prior works in collaborative filtering have dealt with improving the accuracy of the predictions. In the proposed system, we adopted the collaborative filtering instead of the complicated content processing, and provided recommendation on possible keywords of contents with the accuracy.

3. Implementation We utilized the techniques of the LDAP directory server and JAXB to reduce the load of development of search engine and the complexity of parsing the contents (Li et al., 2005). The data-mining techniques will be applied to support the e-learning materials recommendation. Relevant techniques including LDAP, JAXB, association rule and collaborative filtering are illustrated below.


Fig. 1. System architecture.

Fig. 2. The data flow of the recommendation system.

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In the recommendation system, it consists of three participants: learning management system, web spider and recommendation server as shown in Figure. 1. Learning management system, LMS, not only manages and provides the content services, but also registers the content URL address to web spider for collecting content index data. Through the URL address of content, the web spider will visit and parse all of the content recursively and store resulting contents into LDAP server. This scheme is applicable for all of e-learning content accessible by HTTP protocol. At last, a web-based querying system is presented to the users for friendly learning. As shown in the Figure. 1, while teachers or content providers uploaded their learning materials to the LMS systems, the background process stored in the LMS will pass the pathname or URL address of the uploaded materials to the web spider-like process for collecting index data. The web spider parses and traces all linked documents of the course contents. As an example of course content made by StreamAuthor package (CyberLink. http://www.cyberlink.com/multi/products/main_7_ENU.html), the content mostly consisted of a lot of HTML document translated from MS Powerpoint formatted files. In implementation, we defined and realized the PptHtmlParser class derived from HtmlParser interface to parse the materials created by the StreamAuthor package. The web spider will recursively visit these links embedded in the parsed documents. In Figure. 2, it describes the data flow in the recommendation system including three data sources. In implementation, we developed the backend program for collecting user behavior-related data in the JAVA programming language. In addition to the sake of the properties of crossplatform and easy usages, JAVA can directly utilize the abundant packages with powerful functions, such as HttpClient in Apache Jakarta project (http://jakarta.apache.org/commons/httpclient/), HtmlParser (http://htmlparser.sourceforge.net/) created by Derrick Oswald, etc.., is good for reducing the cost of system implementation. Additionally, we adopted the JAXB mechanism to transform the result, generated by HtmlParser process, to the XML documents, fit to the predefined schema as shown in Figure. 3. Because of the unmarshalling mechanism, it is easy to access data elements in XML documents with the minimum burden of programming.