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研究生:許甄珉
研究生(外文):Hsu, Chen-Min
論文名稱:以相空間分割與階層序列合成預測學生表現
論文名稱(外文):Predicting Student Performance with Phase Space Partition and Hierarchical Sequence Synthesis
指導教授:郁方郁方引用關係
指導教授(外文):Yu, Fang
口試委員:蔡瑞煌陳鎮洲林翠芳陳婉萍
口試委員(外文):Tsaih, Rua-HuanChen, JennjouLin, Tsui-FangChen, Wan-Ping
口試日期:2020-07-08
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:40
中文關鍵詞:相空間預測階層序列合成
外文關鍵詞:GHSOMPhase Space PartitionHierarchical Sequence
相關次數:
  • 被引用被引用:1
  • 點閱點閱:161
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
由於科技發展迅速,許多學校與教師皆使用e-learning平台作為教學輔助的工具,因此平台記錄了學生大量的學習行為,而如何充分利用這些數據來提高課程的有效性,是大多數學者關注的議題。我們希望利用平台的大量紀錄資料,透過機器學習方法來預測學生的學習行為,滿足不同學生的個別需求,也可對具有風險的學生採取補救的措施,讓e-learning平台從原本只提供數據的被動角色轉換為具有評估學習狀態能力的主動角色,從學生學習的進程預測未來的學習狀態與表現,進而提供教師額外的資訊,協助改善學生學習狀態。
Due to the rapid development of science and technology, many schools and teachers use the e-learning platform as a teaching aid tool. Therefore, the platform records a large number of students' learning behaviors. How to make full use of these data to improve the effectiveness of the curriculum is what most scholars are concerned about. We hope to use a large amount of recorded data on the platform to predict students' learning behaviors through machine learning methods to meet the individual needs of different students. We can also take remedial measures for students at risk, with the ability to assess the learning status, predicting the future learning status and performance from the student's learning process, and then providing teachers with additional information to help improve the student's learning status. So we can turn the e-learning platform from playing a passive role on data-access-record to an active one with evaluation-caution-transformation. There are three major challenges in predicting student performance. The first is that student records are often high-dimensional data, which makes the prediction effect poor. The second problem is that the data is time-series data, so the order of the data must be considered. In order, the third is that in most cases, we only have partial information, so it is challenging to use partial data to make accurate predictions. In this paper, we will use phase space partition to split high-dimensional data, and use the symbolic label to represent partitions. These symbolic label sequences can be regarded as discrete sequences. Finally, HiSeqGAN, the neural network of sequence synthesis is used to generate a large amount of data, and use Label Error to calculate the symbol label distance between the generated data and the real data to predict the future performance of students. Then use the methods mentioned above to solve the three major challenges.
1 Introduction . . . . . . . . . . 1
2 Related Work . . . . . . . . . . 4
2.1 Intelligent e-learning systems. . . . . . . . . . 4
2.2 Phase Space Partition . . . . . . . . . . 5
2.3 Discrete Sequence Synthesis . . . . . . . . . . 6
3 Methodology 8
3.1 Overview . . . . . . . . . . 8
3.2 Phase Space Partition . . . . . . . . . . 8
3.2.1 Growing Hierarchical Self-Organizing Map (GHSOM) . . . . . . . . . . 9
3.2.2 Symbolic Labels with Hierarchy . . . . . . . . . . 10
3.2.3 Label Error with Symbolic Labels. . . . . . . . . . 11
3.2.4 Label Sequence Error. . . . . . . . . . 12
3.3 Discrete sequence synthesis: HiSeqGAN . . . . . . . . . . 13
3.3.1 HiSeqGAN Overview . . . . . . . . . . 13
3.3.2 Discrete Sequence Synthesis . . . . . . . . . . 14
3.4 Sequence Prediction . . . . . . . . . . 16
4 Experiments . . . . . . . . . . 18
4.1 Logs of e-learning Platform . . . . . . . . . . 18
4.2 Predict Student Semester base on Midterm and Final Grade . . . . . . . . . . 21
4.3 Phase Space Partition on Student Weekly States . . . . . . . . . . 22
4.3.1 Effects of τ1 and τ 2 on GHSOM clustering . . . . . . . . . . 22
4.3.2 Comparison of partitioning methods. . . . . . . . . . 23
4.4 Symbolic Sequence on State Partitions . . . . . . . . . . 24
4.5 Student Partitions with Symbolic Label . . . . . . . . . . 25
4.6 State Partition Sequence Synthesis . . . . . . . . . . 25
4.6.1 Improve RNN Prediction with Synthesis Sequence . . . . . . . . . . 27 
4.7 Use Partial Sequence to Predict Student Partition . . . . . . . . . . . . . . 27 
4.8 Improve Accuracy in Predicting Semester Grades . . . . . . . . . . . . . . 31 
5 Conclusion . . . . . . . . . . 35 
6 Reference . . . . . . . . . . 36
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