Abstract
Many recent studies have proposed several e-learning personalization systems that could be used in the learning field. In particular, the divergence of the personalization parameters used in the literature makes the selection of the appropriate personalization strategy to apply for a given course complicated. Therefore, this paper presents a web-based system which aims to help teachers select the appropriate combination of personalization parameters for a personalization strategy for a given course. The selection process made by the system is based on Dynamic Programming. Thirty one student-teachers participated in evaluating the system using Technology Acceptance Model (TAM) questionnaire. The obtained results were very promising where the student-teachers revealed a high perceived ease of use and usefulness toward the system. Besides, they reported that they are willing to use the system in the future to select the appropriate personalization strategy of a given course.
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References
National Academy of engineering, Accessed in 20 December 2015 from: http://www.engineeringchallenges.org/File.aspx?id=11574&v=ba24e2ed (2014)
West, D. M.: Using technology to personalize learning and assess students in real-time. Washington, DC: Brookings Institution. (2011)
Essalmi, F., Ayed, L. J., Jemni, M., Graf, S.: A fully personalization strategy of E-learn- ing scenarios. Computers in Human Behavior. 26, 581-591 (2010)
Essalmi, F., Ayed, L. J. B., Jemni, M., Graf, S.: Generalized metrics for the analysis of e-learning personalization strategies. Computers in Human Behavior. 48, 310-322 (2015)
John, O. P., Srivastava, S.: The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research. 2, 102-138 (1999)
Silver, M.: Trends in school reform. New horizons for learning. 5,2005 (2004)
Smith, A., Blandford, A.: Mltutor: An application of machine learning algorithms for an adaptive web-based information system. International Journal of Articial Intelligence in Education. 13, 235-261(2003)
Brusilovsky, P., Schwarz, E., Weber, G.: A tool for developing adaptive electronic textbooks. on www. ERIC. (1996)
Kelly, D., Tangney, B.: Incorporating learning characteristics into an intelligent tutor. In: Intelligent Tutoring Systems. Springer, pp. 729-738 (2002)
Gardner, H.: Frames of mind: The theory of multiple intelligences. (1984)
Vassileva, J., Deters, R.: Dynamic courseware generation on the www. British Journal of Educational Technology. 29, 5-14 (1998)
Specht, M., Weber, G., Heitmeyer, S., Schöch, V.: Ast: adaptive www courseware for statistics. In: Proceedings of Workshop” Adaptive Systems and User Modeling on the World Wide Web” at 6th International Conference on User Modeling, UM97, pp. 91-95. Chia Laguna, Sardinia, Italy (1997)
Latham, A., Crockett, K., McLean, D., Edmonds, B.: A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education. 59, 95-109 (2012)
Essalmi, F., Jemni Ben Ayed, L., Jemni, M., Kinshuk, Graf, S.: Selection of appropriate e-learning personalization strategies from ontological perspectives*. Interaction Design and Architecture(s) Journal. 65-84 (2010)
Bellman, r.: The theory of Dynamic Programming. California, Santa Monica: Rand Corporation (1954)
Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Transac- tions on graphics (TOG). 26, 10 (2007)
Nakajima, N., Tamura, T., Yamanishi, Y., Horimoto, K., Akutsu, T.: Network completion using dynamic programming and least-squares fitting. The Scientific World Journal. 2012 (2012)
Tsai, R. T., Lai, P. T.: Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles. BMC bioinformatics. 12, 60 (2011)
Soloman, B. A., Felder, R. M. : Index of learning styles questionnaire. NC State Univer- sity. Available online at: http://www.engr.ncsu.edu/learningstyles/ilsweb.Html. (2005)
Honey, P., Mumford, A.: Learning styles questionnaire. Organization Design and Development, Incorporated. (1989)
Chorfi, H., Jemni, M.: Perso: A system to customize e-training. In: 5th International Conference on New Educational Environments, pp. 26-28 (2003)
Kleinberg, J., Tardos, É. : Algorithm design. Pearson Education India (2006)
King, W.R., He, J.: A meta-analysis of the technology acceptance model. Information & Management. 43, 740-755 (2006).
Park, N., Lee, K. M., Cheong, P. H.: University instructors acceptance of electronic courseware: An application of the technology acceptance model. Journal of Computer Mediated Communication. 13, 163-186 (2007).
Saadé, R.., Nebebe, F., Tan, W.: Viability of the technology acceptance model in multimedia learning environments: a comparative study. Interdisciplinary Journal of E-Learning and Learning Objects. 3, 175-184 (2007).
Davis, F. D., Bagozzi, R. P., Warshaw, P. R.: User acceptance of computer technology: a comparison of two theoretical models. Management science. 35, 982-1003 (1989)
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Haddaji, R., Essalmi, F., Hamzaoui, S., Tlili, A. (2017). Toward the selection of the appropriate e-learning personalization strategy. In: Popescu, E., et al. Innovations in Smart Learning. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-2419-1_10
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DOI: https://doi.org/10.1007/978-981-10-2419-1_10
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