Abstract

Adaptation and individualization of learning is a major challenge when using flipped class as a teaching method. In this paper, we propose a recommendation system for flipped classroom to individualize learning in the classroom based on Data Mining algorithms. This system allows the teacher to predict a classification of learners before administering the tasks to be accomplished and the adapted teaching resources, using attributes related to the activity logs on the e-learning platform, to the online evaluations (Quiz) and to demographic data. The results show that the use of this model as a learning strategy optimizes the time of learning and improves the learner’s performance.

References

• Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems.
• Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
• Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
• Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
• Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
• Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
• Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
• Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
• Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender.
• El Hajji, M., et al., New Blended Learning Strategy Based on Flipped-Learning for Vocational Work-Linked Training. Journal of Education and Practice, 2016. 7(36): p. 126-130.
• Plasencia, A. and N. Navas, MOOCs, the flipped classroom, and Khan Academy practices: The implications of augmented learning, in Innovation and Teaching Technologies. 2014, Springer. p. 1-10.
• Fine, J. and C. Vermandele, La statistique dans le secondaire et retour sur les MOOCs. Statistique et Enseignement, 2014. 5(1): p. 1-3.
• Alvarez, B., Flipping the classroom: Homework in class, lessons at home. The Education Digest, 2012. 77(8): p. 18.
• Fulton, K., Upside down and inside out: Flip your classroom to improve student learning. Learning & Leading with Technology, 2012. 39(8): p. 12-17.
• Baker, R., Data mining for education. International encyclopedia of education, 2010. 7(3): p. 112-118.
• Peraya, D., F. Lombard, and M. Bétrancourt, De la culture du paradoxe à la cohérence pédagogique. Bilan de 10 années de formation à l’intégration des TICE pour les futurs enseignants du primaire à Genève. Formation et pratiques d'enseignement en question, 2008(7) : p. 11-28.
• Laveault, D. La régulation des apprentissages et la motivation scolaire. in Document présenté au Ministère de l’Éducation, du Loisir et du Sport.[En ligne] www. mels. gouv. qc. ca/REFORME/conf/conflaveault. pdf (Consulté le 26 janvier 2009). 2000.
• Quinlan, J.R., C4. 5: programs for machine learning. 2014: Elsevier.
• Frank, E., et al., Naive Bayes for regression. Machine Learning, 2000. 41(1): p. 5-25.
• Vandamme, J.P., N. Meskens, and J.F. Superby, Predicting academic performance by data mining methods. Education Economics, 2007. 15(4): p. 405-419.
• Baker, R. S. and K. Yacef (2009). "The state of educational data mining in 2009: A review and future visions." JEDM-Journal of Educational Data Mining 1(1): 3-17.
• Breiman, L., J. Friedman, C. J. Stone and R. A. Olshen (1984). Classification and regression trees, CRC press.
• Breuel, T. and F. Shafait (2010). AutoMLP: Simple, effective, fully automated learning rate and size adjustment. The Learning Workshop, Utah.
• Brijain, M., R. Patel, M. Kaushik and K. Rana (2014). "A survey on decision tree algorithm for classification."
• Castro, F., A. Vellido, À. Nebot and F. Mugica (2007). "Applying data mining techniques to e-learning problems." Evolution of teaching and learning paradigms in intelligent environment: 183-221.
• Etchells, T. A., À. Nebot, A. Vellido, P. J. Lisboa and F. Mugica (2006). Learning what is important: feature selection and rule extraction in a virtual course. ESANN.
• Frank, E., L. Trigg, G. Holmes and I. H. Witten (2000). "Naive Bayes for regression." Machine Learning 41(1): 5-25.
• Quinlan, J. R. (1986). "Induction of decision trees." Machine learning 1(1): 81-106.
• Quinlan, J. R. (2014). C4. 5: programs for machine learning, Elsevier.
• Romero, C. and S. Ventura (2013). "Data mining in education." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3(1): 12-27.
• Witten, I. H., E. Frank, M. A. Hall and C. J. Pal (2000). Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann.
• Y. Es-Saady, A. Rachidi, M. El Yassa, D. Mammass, Amazigh Handwritten Character Recognition based on Horizontal and Vertical Centerline of Character, International Journal of Advanced Science and Technology, vol. 33, pp. 33-50, August, 2011.