NEW RECOMMENDER SYSTEM USING NAIVE BAYES FOR E-LEARNING

Year 2016, Volume: 5 , 309 - 312, 01.09.2016

Mehmet Ozcan , Tansu Temel

Abstract

Coming
into prominence at the present time, e-learning is a great opportunity for
learners. It provides tremendous assets most valuable of which is distance free
learning. Besides, there is a great deal of e-learning resources on the web
that causes information overload. Accordingly, it turns into a requisite that
you ask for recommendation so as to find the resource you surely need. There
are readily available recommendation services arranged for that purpose. Such
systems have various rating systems; furthermore users tend to rate the
materials in different manners. Our goal with this paper is to generate
confidential referrals thanks to Naive Bayesian algorithm for e-learning
materials rated multifariously by learners. We also researched the effects of several
data preprocessing techniques on achieving this goal. 

Keywords

Naive bayes , data preprocessing , e-learning , recommendation systems

References

• Carmona, C., Castillo, G., & Millán, E. (2007). Discovering student preferences in e-learning. In Proceedings of the international workshop on applying data mining in e-learning (pp. 33-42). Chang, Y. C., Kao, W. Y., Chu, C. P., & Chiu, C. H. (2009). A learning style classification mechanism for e-learning. Computers & Education, 53(2), 273-285. Colace, F., & De Santo, M. (2010). Ontology for E-learning: A Bayesian approach. Education, IEEE Transactions on, 53(2), 223-233. García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles.Computers & Education, 49(3), 794-808. Melville, P., & Sindhwani, V. (2011). Recommender systems. In Encyclopedia of machine learning (pp. 829-838). Springer US. Özpolat, E., & Akar, G. B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53(2), 355-367. Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744-1754. Souali, K., El Afia, A., Faizi, R., & Chiheb, R. (2011, April). A new recommender system for e-learning environments. In Multimedia Computing and Systems (ICMCS), 2011 International Conference on (pp. 1-4). IEEE. Ueno, M., & Okamoto, T. (2007, July). Bayesian agent in e-learning. InAdvanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE International Conference on (pp. 282-284). IEEE.