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# Advances in E-learning-Experiences and Methodologies

## Knowledge Discovery from

Knowledge Discovery from E-Learning Activities Table 6. E-mail exchange description Type E-mail exchange The global mean and limits of the event activity were calculated using the following equations: (2) 1 Sporadically exchange it 2 Usually exchange it 3 Copiously exchange it mean event total instance number = student number event Table 7. Chat description (3) Type Chats limit event = mean event ± (maximum - minimum) event 0.3 1 Not very active 2 Fairly active 3 Very active Table 8. Workgroup documents Type Workgroup documents 1 Low collaboration 2 Average collaboration 3 High collaboration Table 9. Exercise practice description Type Exercise practice 1 Few exercising Equation 3 was applied after checking the normality of the event instance distributions. The superior limit (suplim) for event instance was calculated using plus in equation 3 and inferior limit (inflim) was calculated using minus in equation 3. Thus the 60% of the probability density distribution of the event instance was contained between the superior and inferior limits. Given the event total instance numbers (event activity) for a student, the corresponding description values were calculated using the description tables and the event limits. To allocate a value of the description table from an event activity value for a student, the following algorithm for 4-entries description tables was used: 2 Enough exercising 3 Much exercising Table 10. Course achievement description Type Course achievement 1 Sporadically 2 Usually 3 Very frequently Table 11. Forum participation description Type Forum participation 1 Little 2 Average 3 High If event_activity < inflim event allocate to Type 1 If event_activity >= inflim event & < mean event allocate to Type 2 If event_activity >= mean event & < suplim event allocate to Type 3 If event_activity >= suplim event allocate to Type 4 In the case of 2-entries tables, Type 1 was allocated when event_activity < mean event and Type 2 was allocated when event_activity >= mean event . For tables with 3 entries, Type 1 was allocated when event_activity < inflim event , Type 2 was allocated when event_activity >= inflim event & < suplim event , and Type 3 was allocated when event_activity >= suplim event .

Knowledge Discovery from E-Learning Activities Besides of assigning description values to event activity values, the instance time of the events was discretized using the following hour intervals: [0-6) =dawn, [6-12) =morning, [12-18) =evening, and [18-24) =night. The qualitative value for average grade was calculated using the following discretization intervals: [0-5) =unsatisfactory, [5-7] =fair, [7-9) =good and [9-10] =excellent. At the end of the data preprocessing stage, the data warehouse obtained consisted of a 8,909 (student records) x 27 (variables) as follows: 3 identification record variables (group, course, student code), 4 variables for quantitative and qualitative values of average grade and average time, and 20 variables for quantitative and qualitative values of the activity for the different kind of e-learning events. dAtA mInIng scheme Figure 3 shows a general schema of the relationship between the data mining techniques applied on the data warehouse. Quantitative clustering was made by applying the fuzzy c-means algorithm (Bezdek & Pal, 1992) and qualitative clustering using the conjunctive conceptual algorithm (Michalsky & Stepp, 1983). Total population of the data warehouse was divided accordingly to the course types in: graduate (informal courses), doctorate, and regular academic career courses. In addition, each of those population divisions was divided in two subsets: cases with grades and cases with nogrades. Therefore six disjunctive data subsets to analyze were generated. IndePendent comPonent AnALysIs (IcA) ICA is a powerful statistical technique that has had a successful application in different areas of signal processing (Cichocki & Amari, 2001; Hyvärinen, Karhunen, & Oja, 2001). ICA assumes that there is a M-dimensional zero-mean vector s(t) = [s 1 (t),...,s M (t)] T , such that the components s i (t) are mutually independent. The vector s(t) corresponds to M independent scalar-valued source signals s i (t). The multivariate probability density function (p.d.f.) of the vector can be rewritten as the product of marginal inde pendent distributions Figure 3. Interconnection of the applied data mining techniques 0

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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 2. An introduction to learnin

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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 The third interface is at the

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RAPAD Knight, P.T., & Trowler, P. (

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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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Philisophical and Epistemological B

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Philisophical and Epistemological B

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Philisophical and Epistemological B

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Philisophical and Epistemological B

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Philisophical and Epistemological B

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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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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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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 Figur

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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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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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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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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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Designing an Online Assessment in E

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Designing an Online Assessment in E

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Designing an Online Assessment in E

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Designing an Online Assessment in E

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Designing an Online Assessment in E

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Designing an Online Assessment in E

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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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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 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 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 María D. R-

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Index e-learning tools, automated p

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Socrates 55 Sophists 55 student-foc

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