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| Title: | 以相空間分割與階層序列合成預測學生表現
Predicting Student Performance with Phase Space Partition and Hierarchical Sequence Synthesis |
| Authors: | 許甄珉
Hsu, Chen-Min |
| Contributors: | 郁方
Yu, Fang
許甄珉
Hsu, Chen-Min |
| Keywords: | 相空間
預測
階層序列
合成
GHSOM
Phase Space Partition
Hierarchical Sequence |
| Date: | 2020 |
| Issue Date: | 2020-09-02 11:46:23 (UTC+8) |
| Abstract: | 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. |
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| Description: | 碩士
國立政治大學
資訊管理學系
107356019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107356019 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001449 |
| Appears in Collections: | [資訊管理學系] 學位論文
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