2024-03-29T16:34:35Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001414252022-10-21T05:24:51Z00581:07706:07709
A Predictive Model to Evaluate Student PerformanceA Predictive Model to Evaluate Student Performanceeng[特集:学生・若手研究者論文] comments data mining, latent semantic analysis (LSA), similarity measuring method, overlap methodhttp://id.nii.ac.jp/1001/00123017/Journal Articlehttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=141425&item_no=1&attribute_id=1&file_no=1Copyright (c) 2015 by the Information Processing Society of JapanFaculty of Specific Education, Kafr Elsheik University/Graduate School of Information Science and Electrical EngineeringFaculty of Information Science and Electrical Engineering, Kyushu UniversityKyushu Institute of Information ScienceResearch Institute for Information Technology, Kyushu UniversityShaymaaE.SorourTsunenori, MineKazumasa, GodaSachio, HirokawaIn this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.23(2015) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.23.192------------------------------In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.23(2015) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.23.192------------------------------AN00116647情報処理学会論文誌5632015-03-151882-77642015-03-11