Advances in E-learning-Experiences and Methodologies

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AI Techniques for Monitoring Student Learning Process Figure 5. A portion of a TANGOW log file . . .
AI Techniques for Monitoring Student Learning Process rithm (Bacstrom, 92) to eliminate the unnecessary causal links. Then, the solution generated by the de-order algorithm is given to the temporal and resource reasoners. During the search process, every time the planner chooses an operator, it consults the scheduler for the time and resource consistency. If the resource-time reasoner finds the plan inconsistent, then the planner backtracks. If not, the operator is applied, and the search process continues. To know more details about the algorithm, readers can refer to (R-Moreno, 2003; R-Moreno et al., 2006). Figure 7 shows the architecture of the system that results from the integration of the IPSS and the TANGOW systems. The monitored learning process can be described as follows: 1. The educators define: a. The teaching tasks and rules to build the adaptive course (by using the TANGOW tool). b. The educator assigns to each task both a priority and a time estimation associated to the task (a high priority means that the task is considered as essential in the learning process). c. The teacher selects the number and kind of dimensions that will be used to generate the metadata. In our example, the teacher has selected the knowledgestudent level (end user, application programmer, OS Designer). 2. The students interact with TANGOW. These interactions generate different logs that will be stored in the system. 3. By using the above-mentioned information (students logs, teaching tasks, rules, task priorities, time estimation) the metadata is generated based on both logs and educator estimations. 4. The metadata is appropriately mapped into an IPSS representation. This mapping process generates the domain and the initial problem that will be used by IPSS to solve the defined problem. 5. IPSS looks for solutions that solve possible problems existing in the initial learning design by taking into account the teaching Figure 7. IPSS integration for a specific TANGOW course TANGOW Student Data Course Contents Student Logs Students (End user, Appl. programmer, OS Designer ) Educators Teaching Tasks Rules Priority/Time module (teaching Tasks) Dimensions Log-based Metadata Priority-based Metadata Sol novice Sol normal P/S metadata-based mapping Sol advanced Solutions found (proposals) P/S Domain P/S Problem IPss Planner/Scheduler 0
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Advances in E-Learning: Experiences
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Table of Contents Preface .........
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Chapter XIV Open Source LMS Customi
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Chapter III Philosophical and Epist
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of constructive and cooperative met
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Chapter XIV Open Source LMS Customi
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contents, learning contexts, proces
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xv these organizations do not get a
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xvii QuALIty In e-LeArnIng Before t
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allow that the teachers in training
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xxi ISO. (1986). Quality-Vocabulary
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Chapter I RAPAD: A Reflective and P
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RAPAD in fields such as law, engine
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RAPAD mystery to the new student. B
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RAPAD example, whereas Laurillard h
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RAPAD Ontologically, systems philos
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RAPAD information related processes
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RAPAD methods and techniques accord
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RAPAD 2. An introduction to learnin
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RAPAD then asked to reflect on and
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RAPAD Figure 4. A rich picture to h
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RAPAD Again using techniques from t
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RAPAD university preparation course
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RAPAD The third interface is at the
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RAPAD Knight, P.T., & Trowler, P. (
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RAPAD AddItIonAL reAdIngs Goodyear,
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A Heideggerian View on E-Learning t
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A Heideggerian View on E-Learning (
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A Heideggerian View on E-Learning s
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A Heideggerian View on E-Learning r
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A Heideggerian View on E-Learning o
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A Heideggerian View on E-Learning n
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A Heideggerian View on E-Learning M
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A Heideggerian View on E-Learning W
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Philisophical and Epistemological B
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Chapter IV E-Mentoring: An Extended
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E-Mentoring However, what is unders
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E-Mentoring baugh, & Williams, 2004
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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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Training Teachers for E-Learning ht
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The Role of Institutional Factors i
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Page 132 and 133: The Role of Institutional Factors i
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Page 156 and 157: Integrating Technology and Research
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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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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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Evaluation and Effective Learning p
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Evaluation and Effective Learning r
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Evaluation and Effective Learning t
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Evaluation and Effective Learning p
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Evaluation and Effective Learning m
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Evaluation and Effective Learning c
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Evaluation and Effective Learning H
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Chapter XVI Formative Online Assess
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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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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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