Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/137678| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 江玥慧 | zh_TW |
| dc.contributor.advisor | Chiang, Yueh-hui | en_US |
| dc.contributor.author | 林英儒 | zh_TW |
| dc.contributor.author | Lin, Ying-Ru | en_US |
| dc.creator | 林英儒 | zh_TW |
| dc.creator | Lin, Ying-Ru | en_US |
| dc.date | 2021 | en_US |
| dc.date.accessioned | 2021-11-01T04:00:56Z | - |
| dc.date.available | 2021-11-01T04:00:56Z | - |
| dc.date.issued | 2021-11-01T04:00:56Z | - |
| dc.identifier | G0108753211 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/137678 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊科學系 | zh_TW |
| dc.description | 108753211 | zh_TW |
| dc.description.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。 | zh_TW |
| dc.description.abstract | 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. | en_US |
| dc.description.tableofcontents | 第1章 緒論 1\n1.1 研究背景 1\n1.2 研究動機與目的 2\n1.3 研究問題 3\n1.4 論文架構 3\n第2章 文獻探討 4\n2.1 教育資料探勘 4\n2.2 深度學習 11\n2.3 循環神經網路 15\n2.4 動態時間校正應用於K-means 19\n2.5 序列分析 22\n第3章 研究方法 25\n3.1 資料來源 25\n3.2 實驗架構 25\n3.3 實驗（一）預測學生的學習成效 26\n3.3.1 實驗資料 26\n3.3.2 資料前處理 27\n3.3.3 建立深度神經網路模型 30\n3.3.4 評估指標 32\n3.4 實驗（二）繳交個人平時作業的特徵分群 32\n3.4.1 實驗資料 33\n3.4.2 資料前處理 33\n3.4.3 建立分群模型 34\n3.4.4 決定分群數 34\n3.5 實驗（三）小組作業的行為模式 35\n3.5.1 實驗資料 35\n3.5.2 課程活動 35\n3.5.3 行為編碼 38\n3.5.4 序列分析 41\n第4章 研究結果與討論 44\n4.1 預測學習成效之效果與成效不佳之學生 44\n4.2 分群結果與小組作業之行為模式 46\n4.3 個案討論 54\n4.4 小結 61\n第5章 結論與未來展望 65\n參考文獻 66\n附錄A 70 | zh_TW |
| dc.format.extent | 5224053 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0108753211 | en_US |
| dc.subject | 教育資料探勘 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 長短期記憶模型 | zh_TW |
| dc.subject | K-means | zh_TW |
| dc.subject | 動態時間校正 | zh_TW |
| dc.subject | 序列分析 | zh_TW |
| dc.subject | Education data mining | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Long short-term memory | en_US |
| dc.subject | K-means | en_US |
| dc.subject | Dynamic time warping | en_US |
| dc.subject | Sequential analysis | en_US |
| dc.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 | zh_TW |
| dc.title | Applying deep learning models, time series clustering methods and sequential analysis to exploring students` learning performance | en_US |
| dc.type | thesis | en_US |
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| dc.identifier.doi | 10.6814/NCCU202101631 | en_US |
| item.grantfulltext | restricted | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.fulltext | With Fulltext | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | thesis | - |
| Appears in Collections: | 學位論文 | |
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| 321101.pdf | 5.1 MB | Adobe PDF2 | View/Open |
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