Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/137678


Title: 應用深度學習模型、時間序列分群方法和序列分析探討學生的學習表現
Applying deep learning models, time series clustering methods and sequential analysis to exploring students' learning performance
Authors: 林英儒
Lin, Ying-Ru
Contributors: 江玥慧
Chiang, Yueh-hui
林英儒
Lin, Ying-Ru
Keywords: 教育資料探勘
深度學習
長短期記憶模型
K-means
動態時間校正
序列分析
Education data mining
Deep learning
Long short-term memory
K-means
Dynamic time warping
Sequential analysis
Date: 2021
Issue Date: 2021-11-01 12:00:56 (UTC+8)
Abstract: 在面對面的實體教室中,教學現場的人員比較容易觀察學生於課堂中的學習狀況;當學生在學習過程中遇到問題時,也較能清楚地了解問題所在,幫助學生解決問題。不過在課堂以外的時間,教學人員不易得知學生的學習狀況與學習過程。因此,本研究希望透過學習管理系統收集學生在學習過程中的日誌資料(Logs),並使用深度學習模型、時間序列分群方法和序列分析探討學生於課程中的學習表現,最後將研究結果回饋給教學現場的人員,使老師和助教能夠幫助學習進度較緩慢、或是在學習過程中遇到問題的學生。
In a face-to-face classroom, it’s easier for teachers to observe students’ learning process. When students encounter problems in class, teachers can see the situations and help students solve the problems. However, it's not easy for teachers to know students’ learning conditions and learning process outside of class. Therefore, this study collected students’ learning log data from a learning management system, and used deep learning models, time series clustering methods, and sequential analysis to explore students’ learning performance in the courses. The results of this study can contribute to helping teachers identify low-performance students so as to provide necessary assistance.
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Description: 碩士
國立政治大學
資訊科學系
108753211
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108753211
Data Type: thesis
Appears in Collections:[資訊科學系] 學位論文

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