Abstract
Recently, recommendation systems have become an important tool to support and improve decision making for educational purposes. However, developing recommendation systems is far from trivial and there are specific issues associated with individual problems. Low-correlated input features is a problem that influences the overall accuracy of decision tree models. Weak relationship between input features can cause decision trees work inefficiently. This paper reports the use of features grouping method to improve the classification accuracy of decision trees. Such method groups related input features together based on their ontologies. The new inherited features are then used instead as new features to the decision trees. The proposed method was tested with five decision tree models. The dataset used in this study were collected from schools in Nakhonratchasima province, Thailand. The experimental results indicated that the proposed method can improve the classification accuracy of all decision tree models. Furthermore, such method can significantly decrease the computational time in the training period.
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References
Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education 56(3), 885–899 (2011)
Chen, W., Persen, R.: Recommending collaboratively generated knowledge. Computer Science and Information Systems 9(2), 871–892 (2012)
Vesin, B., Ivanovic, M., Klasnja-Milicevic, A., Budimac, Z.: Ontology-based architecture with recommendation strategy in java tutoring system. Computer Science and Information Systems 10(1), 237–261 (2013)
Özpolat, E., Akar, G.B.: Automatic detection of learning styles for an e-learning system. Computers & Education 53(2), 355–367 (2009)
Ocepek, U., Bosnić, Z., Nančovska Šerbec, I., Rugelj, J.: Exploring the relation between learning style models and preferred multimedia types. Computers & Education 69, 343–355 (2013)
Chen, C.-M.: Intelligent web-based learning system with personalized learning path guidance. Computers & Education 51(2), 787–814 (2008)
Lin, C.F., Yeh, Y.-C., Hung, Y.H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education 68, 199–210 (2013)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: C.45: Programs for Machine Learning (1993)
Grasha, A.F., Riechmann, S.W.: A rational to developing and assessing the construct validity of a student learning styles scale instrument. Journal of Psychology 87(2), 213–223 (1974)
Baykul, Y.A., Gürsel, M., Sulak, H., Ertekin, E., Yazıcı, E., Dülger, O., Aslan, Y., Büyükkarcı, K.A.: A Validity and Reliability Study of Grasha-Riechmann Student Learning Style Scale. International Journal of Human and Social Sciences 5(3), 177–184 (2010)
Maneenil, S., Srisa-ard, B., Chookhampaeng, C.: Causal Factors Influencing Critical Thinking of Students in Different Learning Styles. Journal of Education, Mahasarakham University 4(4), 88–95 (2010)
Thonthai, T.: Learning Styles of Graduate Diploma Students in Teaching Profession at Princess of Naradhiwas University. Princess of Naradhiwas University Journal 1(3), 145–157 (2009)
Dublin Core Metadata Element Set, Version 1.1, http://dublincore.org/documents/dces/
Final 1484.12.1 LOM Draft Standard Document, http://ltsc.ieee.org/wg12/20020612-Final-LOM-Draft.html
Knight, C., Gašević, D., Richards, G.: Ontologies to integrate learning design and learning content. Journal of Interactive Media in Education 7, 1–24 (2005)
Verbert, K., Jovanović, J., Duval, E., Gašević, D., Meire, M.: Ontology-based learning content repurposing: The ALOCoM framework. International Journal on E-learning 5(1), 67–74 (2006)
Verbert, K., Klerkx, J., Meire, M., Najjar, J., Duval, E.: Towards a Global Component Architecture for Learning Objects: An Ontology Based Approach. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 713–722. Springer, Heidelberg (2004)
Dunn, R., Dunn, K.: Teaching Secondary Students Through Their Individual Learning Styles Practical Approaches For Grades, pp. 7–12 (1993)
Wiley, D.A.: A Learning Object Design and Sequencing Theory. Brigham Young University, Provo (2000)
IMS learning resource meta-data information model, http://www.imsproject.org/
SCORM, 2nd ed., http://www.adlnet.org
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Wanapu, S., Fung, C.C., Kajornrit, J., Niwattanakula, S., Chamnongsria, N. (2014). Improving Performance of Decision Trees for Recommendation Systems by Features Grouping Method. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_22
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DOI: https://doi.org/10.1007/978-3-319-06538-0_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06537-3
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