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# Advances in E-learning-Experiences and Methodologies

## Knowledge Discovery from

Knowledge Discovery from E-Learning Activities M p( s) = ∏ p ( s ). A data vector x(t) = [x 1 (t)...x N (t)] T i= 1 i i is observed at each time point t, such that x(t) = As(t)where A is called mixture matrix and it is full rank N x M, (Hyvärinen, Karhunen, & Oja, 2001). There are several standard ICA algorithms as FastICA (Hyvärinen & Oja, 1998), Extended Infomax (Lee, Girolami, & Sejnowski, 1999), or TDSEP (Ziehe & Müller, 1998). Those algorithms rely on assumptions about the source signals, such that imply a given model for the source distributions or make assumptions that are only fitted to specific applications. We applied standard ICA algorithms and a new nonparametric ICA algorithm proposed in Annex 1. This latter algorithm yields the best results, because it was more adaptable to the data. It does not assume any restriction on the data, since the probability distributions are calculated directly from the training set through a nonparametric approach, and also focusing the independency between the source components directly from its definition based on the marginal distributions. ICA was applied on the UPA data in order to identify independent “sources” (independent event activity), that is, searching those event activity that can separate by an ICA algorithm as a source. Figure 4 shows the estimated activity for the 10 events (see Tables 2-11) on the UPA Web plus the average connection time and average grade for 1,072 students of graduate courses with grades. Note that data are displayed as signals (vectors of samples) for Figures 4 and 5. This latter show the sources estimated by an ICA algorithm, note that signal of event 8 (exercise practice) in Figure 4 is very similar (high correlated) to source 5 in Figure 5, it means that the activity corresponds to the workgroup document event could be recognized as an independent source for this subset of data. After analyzing the results from ICA applied to the different data subsets and considering additional information about the courses and students in the campus, we can infer the following conclusions: Figure 4. Data of the graduate courses with grades for the 10 events, and average grades and connection time: e1 (course access) … e10 (forum participation)

Knowledge Discovery from E-Learning Activities Figure 5. Sources calculated by an ICA algorithm for data of Figure 4 • E-mail exchange was independent in some cases. It could be due to weakness in teaching strategies for promoting the student interactivity. Then e-mail exchange is transformed in e-mail review done as a routine. • In courses with no grades, the workgroup document event was independent. The lack of evaluation and grades discourage the participation of students in collaborative tasks. • In some datasets the content consulting event was independent as reflect of a kind of distributed passive learning (DPL) nature of the Web platform. Thus content consulting becomes a routine consisting in download materials with no interactive learning process. • Exercise practice and course achievement also were found as independent events for some datasets. It could be due to the profile of some students that includes information and telecommunications background and knowledge about course contents. For those students participating in those event activities could be irrelevant. PrIncIPAL comPonent AnALysIs (PcA) And IcA PCA is a very well known technique that reduces the variable dimensionality in statistical multivariate analysis (Hardle & Simar, 2006). We applied PCA for grouping the events of the Web activity in learning dimensions taking into account the Felder’s framework (Felder & Silverman, 1988). PCA reduced 10 Web event activities to 5 components. To solve the problem of detecting learning styles in e-learning we assume that the underlying independent sources that generate the Web log data are dimensions of the learning styles of the students and we observe x linear combinations of those styles through the use of the facilities by the students at the virtual campus. Then, si ,( i = 1, ,5 learning style dimension) correspond to the “perception,” “input,” “organization,” “processing,” and “understanding” dimensions (see Table 1); and the mixture matrix A provides the relation between e-learning style dimensions and e-learning event activities, a ij , (i = 1,...,5 learning style dimension), (j = 1,...10 e-learning activity).

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Advances in E-Learning: Experiences

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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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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 Value and Student Experi

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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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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 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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