Abstract
In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the relationship between student characteristics and learning performance, DM to generate classification rules for learning outcomes which are further used to generate cases for the case base and CBR for reasoning. This adaptive system helps in facilitating the course content of different difficulty level to individuals according to their features. The result shows the above method provides the learning material to student as per their need and helps them to enhance their learning.
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Carchiolo, V., Longheu, A., & Malgeri, M. (2002). Adaptive formative paths in a web based learning environment. Educational Technology and Society, 5, 64–75.
Chang, T. Y., & Chen, Y. T. (2009). Cooperative learning in e-learning: a peer assessment of student-centered using consistent fuzzy preference. Expert Systems with Applications, 36(4), 8342–9.
Chen, T. S., & Hsu, S. C. (2007). Mining frequent tree like patterns in large datasets. Data & Knowledge Engineering, 62, 65–83.
Chen, G. D., Liu, C. C., Ou, K. L., & Liu, B. J. (2000). Discovering decision knowledge from web log portfolio for managing web based classroom teacher. Journal of Educational Computing Research, 19(3), 307–328.
Chen, C. M., Liu, C. Y., & Chang, M. H. (2006). Personalized curriculum sequencing utilizing modified item response theory for web based instruction. Expert System with Applications, 20, 378–396.
Chookaew, S., Panjaburee, P., Wanichsan, D., & Laosinchai, P. (2012). A personalized e-learning environment to promote students conceptual learning on basic computer programming. Procedia – Social and Behavioral Sciences, 116, 815–819.
Dayhoff, J. E. (1990). Neural network architecture. New York: Van Reinhold.
Despotovic-Zrakic, M., Markovic, A., Bogdanovic, Z., Barac, D., & Krco, S. (2010). Providing adaptivity in moodle LMS courses. Educational Technology and Society, 15(1), 326–338.
Eyesenck, H.J., & Eyesenck, S.B.G. (1964). Manual for the personality inventory.
Ganschow, L., & Sparks, R. (1996). Anxiety about foreign language learning among high school women. Modern Language Journal, 80, 199–212.
Gilbert, J. E., & Han, C. Y. (1999). Adapting instruction in search of “a significant difference”. Journal of Network and Computer Application, 22, 149–160.
Horwitz, E. K., Horwitz, M., & Cope, J. A. (1986). Foreign language classroom anxiety. Modern Language Journal, 70, 125–132.
Hsia, T. C., Shie, A. J., & Chen, L. C. (2006). Course planning of extension education to meet market demand by using data mining technique. Expert system with Applications. doi:10.1016/j.eswa.2006.09.025.
Hsu, M. H. (2008). Proposing an ESL recommender teaching and learning system. Expert System with Applications, 34, 2102–2110.
Huang, M. J., Huang, H. S., & Chen, M. Y. (2007). Constructing a personalized e-learning system based on genetic algorithm and case based reasoning approach. Expert System with Applications, 33, 551–564.
Khamparia, A., & Pandey, B. (2015a). Performance analysis of SPARQL and DL-QUERY on electromyography ontology. Indian Journal of Science and Technology, 8(17), 2015.
Khamparia, A., & Pandey, B. (2015b). Knowledge and intelligent computing methods in e-learning. International Journal of Technology Enhanced Learning, 7, 3.
Lee, C. (2005). Diagnostic, predictive and compositional modeling with data mining in integrated learning environment. Computers and Education. doi:10.1016/j.compedu.2005.10.010.
Lin, C. F., Yeh, Y., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: an application of decision trees. Computers & Education, 68, 199–210.
Liu, H. I., & Yang, M. N. (2005). QoL guaranteed adaptation and personalization in e-learning systems. IEEE Transactions on Education, 48(4), 676–87.
Pandey, B., Mishra, R.B., Khamparia, A (2014). CBR based approach for adaptive learning in e-learning system. In proceedings of IEEE Asia pacific World Congress on Computer Science 1–6 Fiji.
Patil and Sherekar. (2013). Performance analysis of Naïve Bayes and J48 classification algorithm for data classification. International Journal of Computer Science and Applications, 6, 20–29.
Rodrigues, L., Antunes, B., Gomes, P., Santos, A., Barbeira, J., Carvalho, R. (2007). Using textual CBR for e-learning content categorization and retrieval. Knowledge Engineering Review.
Romero, C., Ventura, S., Delgado, S., & Bra, P.D. (2007). Personalized link recommendation based on data mining in adaptive educational hypermedia systems. Expert System with Applications, 2465–2479.
Salem, M. A. B. (2005). Case based intelligent e-learning systems, WSEAS Transactions on Computer.
Sarasubm, K. (1998). Learning style perspectives: Impact in classroom. Kanas City: Atwood publishing.
Seters, J. R., Ossevoort, M. A., Tramper, J., & Goedhart, M. J. (2012). The influence of student characteristics on the use of adaptive e-learning material. Computers and Education, 58, 942–952.
Trantafillou, E., Poportsis, A., & Demetriadis, S. (2003). The design and the formative evaluation of an adaptive educational system based on cognitive. Computers and Education, 41, 87–103.
Tseng, S. S., Su, J. M., Hwang, G. J., & Tsai, C. J. (2008). An object oriented course framework for developing adaptive learning systems. Educational Technology & Society, 11(2), 171–191.
Wang, T. H. (2011). Developing web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-learning environment. Computers & Education, 57(2), 1801–12.
Wang, Y., Tsen, M., & Liao, H. (2009). Data mining for adaptive learning sequences in English language instruction. Expert System with Applications, 36, 7681–7686.
Wang, Y., Tseng, M. H., & Lia, H. C. (2011). Data mining for adaptive learning in a TESL based e-learning system. Expert System with Applications, 38, 6480–6485.
Witkin, H. A., Moore, C. A., Goodenough, D. E., & Cox, P. W. (1977). Field dependent and field independent cognitive styles and their educational implications. Review of Educational Research, 47(1), 1–64.
Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2008). Development of an adaptive learning system with multiple perspectives based on students learning styles and cognitive styles. Educational Technology and Society, 16(4), 185–200.
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Khamparia, A., Pandey, B. A novel method of case representation and retrieval in CBR for e-learning. Educ Inf Technol 22, 337–354 (2017). https://doi.org/10.1007/s10639-015-9447-8
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DOI: https://doi.org/10.1007/s10639-015-9447-8