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Advances in E-learning-Experiences and Methodologies

Knowledge Discovery from

Knowledge Discovery from E-Learning Activities Cabena, P., Hadjnian, P., Stadler, R., Verhees, J., & Zanasi, A. (1997). Discovering data mining: From concept to implementation (IBM Books). Pearson Education. Caragea, D., Pathak, J., & Honavar, V. (2004). Learning classifiers from semantically heterogeneous data. Lecture Notes in Computer Science, 3291, 963-980. Cichocki, A., & Amari, S. (2001). Adaptive blind signal and image processing: Learning algorithms and applications. New York: John Wiley & Sons. Duda, R., Hart, P.E., & Stork, D.G. (2000). Pattern classification (2 nd ed.). Wiley-Interscience. Elton, L.R.B., & Laurillard, D.M. (1979). Trends in research on student learning. Studies in Higher Education, 4(1), 87-102. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. New York: The MIT Press. Felder, R., & Silverman, L. (1988). Learning and teaching styles. Journal of Engineering Education, 78(7), 674-681. Fridman, N., & McGuinness, D. (2001). Ontology development: A guide to creating your first ontology (Rep. No. KSL-01-05, SMI-2001). Garcia, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluting Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. Gasar, S., Bohanec, M., & Rajkovic, V. (2002). Combined data mining and decision support approach to the prediction of academic achievement. In Workshop on Integrating Aspects of Data Mining (pp. 41-52). Gomez-Perez, A., & Manzano-Macho, D. (2004). An overview of methods and tools for ontology learning from texts. Knowledge Engineering Review, 19(3), 187-212. Gruber, T.R. (1995). Towards principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 43, 907-928. Haldiki, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2- 3), 107-145. Hardle, W., & Simar, L. (2006). Applied multivariante statical analysis. New York: Springer. Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. New York: John Wiley & Sons. Hyvärinen, A., & Oja, E. (1998). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 1483-1492. Kimber, K., Pillay, H., & Richards, C. (2007). Technoliteracy and learning: An analysis of the quality of knowledge in electronic representations of understanding. Computers & Education, 48(1), 59-79. Kotsiantis, S.B., Pierrakeas, C.J., & Pintelas, P.E. (2003). Preventing student dropout in distance learning using machine learning techniques. In Proceedings of 7th International Conference on Knowledge-Base Intelligent Information an Engineering Systems. Kristofic, A., & Bielikova, M. (2005). Improving adaptation in Web-based educational hypermedia by means of knowledge discovery. In ACM Conference on Hypertext and Hypermedia (pp. 184-192). Larsen, J., Hansen, L.K., Szymkowiak, A., Christiansen, T., & Kolenda, T. (2002). Web mining: Learning from the world wide Web (Special Issue of Computational Statistics and Data Analysis). Computational Statistics and Data Analysis, 38, 517-532.

Knowledge Discovery from E-Learning Activities Lee, T., Girolami, M., & Sejnowski, T. (1999). Independent component analysis using an extend infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11, 417-441. Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185-204. Liaw, S., Chen, G., & Huang, H. (in press). Users’ attitudes toward Web-based collaborative learning systems for knowledge management. Computers & Education. Liu, H., & Yang, M. (2005). QoL guaranteed adaptation and personalization in e-learning systems. IEEE Transactions on Education, 48(4), 676-687. Ma, Y., Liu, B., Wong, C.K., Yu, P.S., & Lee, S.M. (2000). Targeting the right students using data mining. In KDD’00: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 457-464). Maimon, O., & Rokach, L. (2005). Data mining and knowledge discovery handbook (1 st ed.). Springer. Merceron, A., & Yacef, K. (2003). A Web-based tutoring tool with mining facilities to improve learning and teaching. In 11th International Conference on Artificial Intelligence in Education (pp. 41-52). Michalsky, R.S., & Stepp, R.E. (1983). Automated construction of classifications: Conceptual clustering versus numerical taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(4), 396-410. Minaei, B., Kashy, D.A., Kortemeyer, G., & Punch, W. (2003). Predicting student performance: An application of data mining methods with an educational Web-based system. In Proceedings of 33rd Frontiers in Education Conference. Mor, E., & Minguillón, J. (2004). E-learning personalization based on itineraries and long-term navigational behavior. In Thirteenth World Web Conference (pp. 264-265). Pils, C., Roussaki, L., & Strimpakou, M. (2006). Location-based context retrieval and filtering. Lecture Notes in Computer Science, 3987, 256- 273. Piramuthu, S. (2005). Knowledge-based Web-enabled agents and intelligent tutoring systems. IEEE Transactions on Education, 48(4), 750-756. Pituchs, K.A., & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244. Puntambekar, S. (2006). Analyzing collaborative interactions: Divergence, shared understanding and construction of knowledge. Computers & Education, 47(3), 332-351. Quinlan, R.J (1992). C4.5: Programs form machine learning. San Mateo, CA: Morgan Kaufmann. Reilly, R. (2005). Guest editorial Web-based instruction: Doing things better and doing better things. IEEE Transactions on Education, 48(4), 565-566. Romero, C., Ventura, S., De Bra, P., & Castro, C. (2003). Discovering prediction rules in AHA! courses. In 9th International Conference on User Modeling (pp. 25-34). Salazar, A., Gosalbez, J., Bosch, I., Miralles, R., & Vergara, L. (2004). A case study of knowledge discovery on academic achievement, student desertion and student retention. In IEEE 2th International Conference on Information Technology: Research and Education (pp. 150-154). Schellens, T., & Valcke, M. (2006). Fostering knowledge construction in university students through asynchronous discussion groups. Computers & Education, 46(4), 349-370.

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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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    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 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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  • Page 172 and 173: Chapter IX AI Techniques for Monito
  • Page 174 and 175: AI Techniques for Monitoring Studen
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  • Page 196 and 197: Chapter X Knowledge Discovery from
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  • Page 222 and 223: Chapter XI Swarm-Based Techniques i
  • Page 224 and 225: Swarm-Based Techniques in E-Learnin
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  • Page 236 and 237: Chapter XII E-Learning 2.0: The Lea
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  • Page 248 and 249: E-Learning 2.0 Finally, it is impor
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  • Page 252 and 253: E-Learning 2.0 McPherson, K. (2006)
  • Page 254 and 255: E-Learning 2.0 Rosen, A. (2006). Te
  • Page 256 and 257: 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 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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