Abstract
Various researches in E-learning mainly focused on improving learner achievements based on learner profile. Explosive growth of distance learning has caused difficulty of locating appropriate learning objects for learner in this environment, and it becomes relatively widespread learning method for learner. In this paper, an innovative learning approach is proposed by using recommender system to address this challenge. Based on this tool, a learning model is designed to achieve personalized learning experiences by selecting and sequencing the most appropriate learning objects. Moreover, some experiments were conducted to evaluate the performance of our approach. The result reveals suitability of using recommender system in order to support online learning activities to enhance learning.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Wiley, D.A.: Connecting learning objects to instructional design theory: a definition, a metaphor, and a taxonomy. In: Wiley, D.A. (ed.) The instructional use of learning objects, pp. 1–35. Agency for Instructional Technology (2002)
El Bachari, E.; Abelwahed, E.; El Adnani, M.: An adaptive teaching strategy model in e-learning using learners preference: learnFit framework. Int. J. Web Sci. 1(3), 257–274 (2012)
El Bachari, E.; Abelwahed, E.; El Adnani, M.: E-learning personalization based on dynamic learners’ preference. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 3(3) (2011). doi:10.5121/ijcsit.2011.3314
Gluga, R.; Kay, J.; Lever, T.: Modeling long term learning of generic skills. Intell. Tutor. Syst. 1, 85–94 (2010)
Khribi, M.K.; Jemni, M.; Nasraoui, O.: Automatic recommendation for e-learning personalization based on web usage mining techniques and information retrieval. Educ. Technol. Soc. 12(4), 30–42 (2009)
Mobasher, B.: Data mining for web personalization In: Brusilovsky, P., Kobsa A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Springer, Berlin, Heidelberg, pp. 90–135 (2007)
Salehi, M.: Application of implicit and explicit attribute based collaborative filtering and bide for learning resource recommendation. Data Knowl. Eng. 87, 130–145 (2013)
Belkin, N.; Croft, B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)
Lang, K.: Newsweeder: learning to filter news. In: Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, pp. 331–339 (1995)
Mooney, R.J.; Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, pp. 195–204. ACM Press, New York (2000)
Pazzani, M.; Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
Delgado, J.; Ishii, N.: Memory-based weighted majority prediction for recommender systems. In: 1999 SIGIR Workshop on Recommender Systems, pp. 1–5. University of California, Berkeley (1999)
Si, L.; Jin, R.: Flexible mixture model for collaborative filtering. In: Proceedings of the 20th International Conference on Machine Learning, Vol. 20, pp. 704–711. AAAI Press (2003)
Yu, K.; Schwaighofer, A.; Tresp, V.; Xu, X.; Kriegel, H.P.: Probabilistic memory-based collaborative filtering. IEEE Trans. Knowl. Data Eng. 16, 56–69 (2003)
Aggarwal, C.C.; Procopiuc, C.; Yu, P.S.: Finding localized associations in market basket data. IEEE Trans. Knowl. Data Eng. 14(1), 51–62 (2002)
Huang, C.L.; Huang, W.L.: Handing sequential pattern decay: developing a two-stage collaborative recommendation system. Electron. Commer. Res. Appl. 8(3), 117–129 (2009)
Wang, Y.; Dai, W.; Yuan, Y.: Website browsing aid: a navigation graph-based recommendation system. Decis. Support Syst. 45(3), 387–400 (2008)
Balabanovic, M.; Shoham, Y.: Fab: content-based collaborative filtering recommendation. Commun. ACM 40, 66–72 (1997)
Chih-Ping, W.; Chin-Sheng, Y.; Han-Wei, H.: A collaborative filtering based approach to personalized document clustering. Decis. Support Syst. 45(3), 413–428 (2008)
Choi, K.; Yoo, D.; Kim, G.; Suh, Y.: A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron. Res. Appl. 11(4), 309–317 (2012)
Liu, Z.; Qu, W.; Li, H.; Xie, C.: A hybrid collaborative filtering recommendation mechanism for P2P networks. Future Gener. Comput. Syst. 26(8), 1409–1417 (2010)
Salter, J.; Antonoupoulos, N.: CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intell. Syst. 21(1), 35–41 (2006)
Chen, M.S.; Han, J.; Yu, P.S.: Datamining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)
Anaya, A.R.; Luque, M.; Garcia-Saiz, T.: Recommender system in collaborative learning environment using an influence diagram. Expert Syst. Appl. 40, 7193–7202 (2013)
Manouselis, N.; Drachsler, H.; Verbert, K.; Santos, O.C.: Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), Procedia Computer Science, 1(2) (2010)
Vuorikari, R.; Manouselisn, N.; Duval, E.: Metadata for social recommendations: storing, sharing and reusing evaluations of learning resources. In: Goh, D., Foo, S. (eds.) Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively, pp. 87–107. Idea Group Inc., Calgary (2008)
Ochoa, X.: Modeling the macro-behavior of learning object repositories. Interdiscip. J. E-Learn. Learn. Objects 7, 25–35 (2011)
Manouselis, N.; Drachsler, H.; Vuorikari, R.; Hummel, H.; Koper, R.: Recommender systems in technology enhanced learning. In: Kantor, P.B., Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook. Springer, US, pp. 387–415 (2011)
Recker, M.M.; Walker, A.: Supporting ’word-of-mouth’ social networks via collaborative information filtering. J. Interact. Learn. Res. 14(1), 79–98 (2003)
Recker, M.M.; Walker, A.; Lawless, K.: What do you recommend? implementation and analyses of collaborative filtering of Web resources for education. Instr. Sci. 31(4/5), 229–316 (2003)
Walker, A.; Recker, M.M.; Lawless, K.; Wiley, D.: Collaborative information filtering: a review and an educational application. Int. J. Artif. Intell. Educ. 14, 3–28 (2004)
Anderson, M.; Ball, M.; Boley, H.; Greene, S.; Howse, N.; Lemire, D.; McGrath, S.: RACOFI: a rule-applying collaborative filtering system. In: Paper Presented at the Conference IEEE/WIC COLA, Halifax (2003)
Dorca, F.A.; Araujo, R.D.; Carvalho, V.C.; Resende, D.T.; Cattelan, R.G.: An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: an experimental analysis. Inf. Educ. 15(1), 45–62 (2016)
Zaiane, O.R.: Building a recommender agent for e-learning systems. In: The International Conference on Computers in Education, ICCE’02. IEEE Computer Society, Auckland, New Zealand, pp. 55–59 (2002)
Imran, H.; Belghis-Zadeh, M.; Chang, T.; Graf, K.S.: PLORS: a personalized learning object recommender system. Vietnam J. Comput. Sci. 3(1), 3–13 (2016)
Avancini, H.; Straccia, U.: User recommendation for collaborative and personalised digital archives. Int. J. Web Based Commun. 1(2), 163–175 (2005)
Bobadilla, J.; Serradilla, F.J.; Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl. Based Syst. 22, 261–265 (2009)
Aher, S.B.; Lobo, L.M.R.J.: Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowl.-Based Syst. 51, 1–14 (2013)
Cechinel, C.; Sicilia, M.A.; Sanchez-Alonso, S.: Evaluating collaborative ltering recommendations inside large learning object repositories. Inf. Process. Manage. 49, 34–50 (2013)
Tang, T.; McCalla, G.: Smart recommendation for an evolving e-learning system: architecture and experiment. Int. J. E-Learn. 4(1), 105–129 (2005)
Garcia, E.; Romero, C.; Ventura, S.; Castro, C.: A collaborative educational association rule mining tool. Internet High. Educ. 14(2), 77–88 (2011)
Klasnja-Milicevic, A.; Vesin, B.; Ivanovic, M.; Budimac, Z.: E-Learning personalization based on hybrid recommendation strategy and learning style identication. Comput. Educ. 56, 885–899 (2011)
Dwivedi, P.; Bharadwaj, K.: Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Educ. Technol. Soc. 16(4), 201–216 (2013)
Verbert, K.; Drachsler, H.; Manouselis, N.; Wolpers, M.; Vuorikari, R.; Duval, E.: Dataset-driven research for improving recommender systems for learning. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 44–53 (2011)
Kris, J.; James, H.; Dan, H.; Jason, H.; Jan, R.; Paul, F.; Victor, H.: Mendeley’s reply to the DataTEL challenge. In: 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSys-TEL 2010) (2010). http://www.teleurope.eu/pg/pages/view/50630/. Accessed 13 June (2015)
Martin, W.; Katja, N.: dataTEL challenge: CAM for MACE. In: 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010) (2010). http://www.teleurope.eu/pg/pages/view/50630/. Accessed 13 June 2015
Verbert, K.; Manouselis, N.; Drachsler, H.; Duval, E.: Dataset-driven research to support learning and knowledge analytics. Educ. Technol. Soc. 15(3), 133–148 (2012)
Koedinger, K.R.; Baker, RSJd; Cunningham, K.; Skogsholm, A.; Leber, B.; Stamper, J.: A data repository for the EDM community: the PSLC DataShop. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D. (eds.) Handbook of Educational Data Mining. CRC Press, Boca Raton (2010)
Chonghuan, X.: Personal recommendation using a novel collaborative filtering algorithm in customer relationship management. Discrete Dyn. Nat. Soc. 2013 (2013). doi:10.1155/2013/739460
Mabroukeh, R.; Ezeife, C.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1), 3–41 (2010)
Chan, P.: A non-invasive learning approach to building web user profiles. In: 5th International Conference on Knowledge Discovery and Data Minining Workshop on Web Usage Analysis and User Proling, San Diego (1999)
Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)
Feng, X.; Zhen, C.; Jiaxing, S.; Geoffrey, F.C.: Grey forecast model for accurate recommendation in presence of data sparsity and correlation. Knowl. Based Syst. 69, 179–190 (2014)
Andreas, M.; Thomas, R.: An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. J. Retail. Consum. Ser. 10(3), 123–133 (2003)
Acknowledgments
We used the “Geometry 2006–2007” data set accessed via DataShop (www.pslcdatashop.org). We used the “Algebra I 2005–2006 (3 schools)” data set accessed via DataShop
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bourkoukou, O., El Bachari, E. & El Adnani, M. A Recommender Model in E-learning Environment. Arab J Sci Eng 42, 607–617 (2017). https://doi.org/10.1007/s13369-016-2292-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-016-2292-2