Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/12648


Title: Mining Learner Profile Based on Association Rule for Web-based Learning Diagnosis
Authors: Chen, Chih-Ming;Hsieh, Ying-Ling;Hsu, Shih-Hsun
陳志銘
Keywords: Web-based learning;Learning misconception diagnosis;Association rule mining;Learner profile
Date: 2007-02
Issue Date: 2008-12-05 12:01:51 (UTC+8)
Abstract: With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning fields. Most past researches for web-based learning focused on the issues of adaptive presentation, adaptive navigation support, curriculum sequencing, and intelligent analysis of student’s solutions. These systems commonly neglect to consider whether learner can understand the learning courseware and generate misconception or not. To neglect learner’s learning misconception will lead to obviously reducing learning performance, thus generating learning difficult. In order to discover common learning misconceptions of learners, this study employs the association rule to mine the learner profile for diagnosing learners’ common learning misconceptions during learning processes. In this paper, the association rules that occurring misconception A implies occurring misconception B can be discovered utilizing the proposed association rule learning diagnosis approach. Meanwhile, this study applies the discovered association rules of the common learning misconceptions to tune courseware structure through modifying the difficulty parameters of courseware in the courseware database so that learning pathway is appropriately tuned. Besides, this paper also presents a remedy learning approach based on the discovered common learning misconceptions to promote learning performance. Experiment results indicate that applying the proposed learning diagnosis approach can correctly discover learners’ common learning misconceptions according to learner profile and help learners to learn more effectively.
Relation: Expert Systems with Applications, 33(1), 6-22
Data Type: article
DOI 連結: http://dx.doi.org/10.1016/j.eswa.2006.04.025
Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

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