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Advances in E-learning-Experiences and Methodologies

RAPAD then asked to

RAPAD then asked to reflect on and write about their thoughts on the accuracy and relevance of the measures. Later in the unit, each student had to develop a Web-based personalized e-learning environment (PELE) to a series of e-learning related information resources. This required the application of elements of the cognitive profile to the design and development process. In addition, the students were asked to document the reasons for their design. A range of qualitative and quantitative measures was collected. Student reflections on and responses to the process were considered via the use of a questionnaire, reflective journal and interviews. The comments on the form and content of the Web sites created contained in the documentation were also analysed. Two related metaphors were used to help the students to conceptualise the design of the PELE. The first was that of the Learning Resource Centre (LRC) which is basically a modern university library integrating digital information management and learning support services. One definition used was: The Learning Resource Centre (LRC) is a meeting place for all those who wish to learn. It is the electronic hub of the university and our surrounding communities, linking us to the wider global community. It harnesses new technologies effectively to make learning more adaptable and flexible and more widely available. The LRC is at the centre of the university’s concept of a new learning environment. This environment focuses all our available resources into a teaching and learning strategy based on our understanding of the changing trends in the learning community. The second metaphor was that of the PELE conceptualised as a small personal house which the student could enter and find the personalized learning resources in a set of rooms design to support each specific learning activity. This is a similar, but more personal and individual use of the “house” metaphor to that used in the “Bookhouse” (Pejtersen, 1989). emergent Issues The initial period of analysis involved using the quantitative data to provide a broad overview of the profiles, responses, and attitudes of the respondents. This was done using the data from each of the cognitive profile measures plus the quantitative data from the survey. However, as would be expected and as suggested by Summerville (1999), the qualitative data provided much greater insights into the individual aspects of e-learning. The student comments and associated qualitative data indicated that engaging in the process of reflecting on the characteristics of one’s own individual cognitive profile did have an effect on the design, development, and content of the individual e-learning environment. Several students queried their prior lack of knowledge of this type of information and commented that they would have preferred to have access to this type of metacognitive information in their high school (or even their university) careers. The participants often had a vague awareness and sketchy understanding of their preferences for information handling, but this remained in an unstructured and unfocused form. The information from their cognitive profile gave them an opportunity to look at this scenario and their preferences in a much more informed and structured manner. This then helped inform the PELE design, from the perspective of an impact on both the structure and form of the environment. Feedback and comments indicated that the CSA and its dimensions provided the most useful data and criteria in terms of developing the “look and feel” of the PELE. The MBTI and ASSIST measures also provided personal learning and information processing preference details and these, while having less impact on the design and construction of the PELE, proved useful with specific reference to the learning process. This then impacted on the PELE in terms of materials accessed to support e-learning preferences.

RAPAD More important, however, was the manner in which several students commented on broader aspects of their learning experiences and approaches to learning and sometimes identified key incidents which affected their learning development. Others commented on the difficulties they had in adjusting to the different demands of studying at university. They also pointed out that the way they studied in the later parts of their time at university was very different from that adopted in the earlier stages. The manner of this transition appeared to be a random one, often enabled by personal recognition of the problem and self-help or the requested intervention of a lecturer, tutor, or counsellor. Consideration of these and other examples from the difference types of data sources, especially the reflective journals, process documentation, survey comments, and interviews indicated several emergent issues. The first issue to emerge was that the real impact of the cognitive profile measures was in enabling students to reflect on their e-learning habits and processes in a structured manner. The actual scores were less important than providing each student with a set of relevant learner categories and characteristics—whether imager or analytic, extraversion or intuition, “interest in ideas” or “fear of failure”—which could be used to think about their own e-learning experiences. The measures and activities provided a framework and a structured set of processes with which the participants could engage reflectively with important features of the own learning. By critically assessing their own learning needs and applying their assumptions and conclusions to an iterative design process aimed at supporting their personal learning requirements, the students could effectively engage with understanding how they learn at an individual level. This leads to a much needed “conceptual shift” in students understanding of individual (and thus collaborative) learning, the need for which was suggested by Vermetten et al. (2002). To improve the quality of student learning, instructional measures should address the conceptual domain of learning conceptions and beliefs, of which students have to become aware, and which they have to develop, for example by means of critical reflection. (Vermetten et al., 2002, p. 263) In addition, the responses suggested that both the range of issues students considered as affecting their learning and the manner in which these issues interacted was very wide yet produced an individual mix for each student. This outcome appeared to support the comments of Summerville (1999) and Pillay (1998) on the need for a more process based approach comprising the collection of qualitative data. In addition, social issues such as the intervention of others or the need to make sense of a process which students felt they should understand (how to study effectively at university) yet clearly didn’t, indicated a need for a revision and extension of the methodology and e-learning system. Phase three: the Introduction of ssm techniques The third phase saw the development and reformulation of ideas from the first two phases with post-graduate conversion students taking several iterations of an information systems development course. A major outcome of this phase was the introduction of specific techniques from Checkland’s Soft Systems Methodology (SSM) (Checkland, 1981, 2000), especially Rich Pictures, in the process and research. The use of Rich Pictures at the student modelling phase was introduced after the initial research and Human Computer Interaction unit iteration. The purpose of its introduction was to see if it could be used to draw out issues relating to the social and interactive elements of learning. It then provided the basis for the “organisational interface” by allowing the student to place him or herself at the centre of the university as organisation in a pictorial format. An example Rich Picture is shown in Figure 4.

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    E-Mentoring Table 2. Contact. Diffe

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    E-Mentoring Table 10. Ethical impli

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    E-Mentoring Table 15. Technology st

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    E-Mentoring Table 21. Coaching. Bes

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    E-Mentoring Table 27. Moment. Best

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    E-Mentoring Moreover, existing rese

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    E-Mentoring Kasprisin, C. A., Singl

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    E-Mentoring Ensher, E. A., Heun, C.

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    Chapter V Training Teachers for E-L

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    Training Teachers for E-Learning FL

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    Training Teachers for E-Learning ne

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    Training Teachers for E-Learning A

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    Training Teachers for E-Learning yo

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    Training Teachers for E-Learning Di

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Chapter IX AI Techniques for Monito

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    Chapter X Knowledge Discovery from

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Knowledge Discovery from E-Learning

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    Chapter XI Swarm-Based Techniques i

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Chapter XII E-Learning 2.0: The Lea

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    E-Learning 2.0 Table 1. Different s

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    E-Learning 2.0 Figure 1. Difference

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    E-Learning 2.0 where the blog is al

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    E-Learning 2.0 process. Along this

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    E-Learning 2.0 forth, and, of cours

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    E-Learning 2.0 Finally, it is impor

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    E-Learning 2.0 never be a hotchpotc

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    E-Learning 2.0 McPherson, K. (2006)

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    E-Learning 2.0 Rosen, A. (2006). Te

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Open Source LMS Customization Intro

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    Open Source LMS Customization or ev

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    Open Source LMS Customization compa

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    Open Source LMS Customization Figur

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    Open Source LMS Customization Haina

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    Chapter XVI Formative Online Assess

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    0 Chapter XVII Designing an Online

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    Designing an Online Assessment in E

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Chapter XIX E-QUAL: A Proposal to M

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    E-QUAL is proposed to evaluate the

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    E-QUAL provide competent, service-o

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    E-QUAL 2004; Scalan, 2003) and qual

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    E-QUAL benchmarks address technolog

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    E-QUAL E-learning added two differe

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    E-QUAL Table 6. Application of the

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    E-QUAL Future trends The future of

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    E-QUAL (EQO) co-located to the 4 th

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    E-QUAL SMEs: An analysis of e-learn

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    E-QUAL Meyer, K. A. (2002). Quality

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    Compilation of References Argyris,

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    Compilation of References Biggs, J.

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    Compilation of References Cabero, J

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    Compilation of References Comezaña

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    Compilation of References Downes, S

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    Compilation of References Fandos, M

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    Compilation of References national

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    Compilation of References Hudson, B

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    Compilation of References Harbour.

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    Compilation of References Little, J

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    Compilation of References Metros, S

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    Compilation of References ONeill, K

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    Compilation of References Preece, J

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    Compilation of References Sadler, D

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    Compilation of References Shin, N.,

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    Compilation of References tional Co

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    Compilation of References Vermetten

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    Compilation of References Yu, F. Y.

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    About the Contributors Juan Pablo d

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    About the Contributors part: “An

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    About the Contributors María D. R-

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    About the Contributors Applications

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    Index e-learning tools, automated p

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    Socrates 55 Sophists 55 student-foc

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