臺灣博碩士論文加值系統

為了提升學生的學習成效，提早並準確識別高風險學生，使得教師能夠早期介入輔導，是許多相關研究關注的議題。
混成式課程是一種結合線上與線下學習的課程，有別於傳統的線下學習，學生亦能夠透過線上學習平台，來進行多方面的學習。然而，學生在學習過程當中，會留下許多紀錄，例如學生的作業成績、影片瀏覽行為、線上活動頻率、線上測驗成績等等。因此，本論文透過資料探勘與機器學習技術，收集一門混成式微積分課程的學生學習活動資料，使用多元線性迴歸來預測學生的期末成績。
相關研究指出，預測模型的準確率容易受到離群值的影響。因此，本論文使用RANSAC演算法，作為離群值偵測的方法，將離群值從資料中去除。為了在移除離群值後更進一步改善預測模型的準確率，本論文以T檢定作為特徵選取的方法，保留對期末成績有顯著影響的關鍵特徵，來進一步改善預測模型的準確率。
根據研究結果顯示，透過本論文提出的離群值偵測與特徵選取流程，預測誤差由15.516分降低至4.571分，改善了約70%的預測誤差。

In order to improve students’ learning performance, early and accurately identify at-risk students, so that teachers can early intervention, is the focus topic of many related research.
Blended course is a course which combine online and offline learning, different from traditional offline learning, students are also able to learn through the online learning platform. However, students will leave a lot of records in the learning process, such as students' homework grade, video viewing behavior, online activity frequency, online test grade etc. Therefore, this paper based on data mining and machine learning technologies, collects students’ learning activity data from a blended calculus course, uses multiple linear regression to predict students’ final grade.
Related researchs point out the accuracy of the prediction model is easily affected by outliers. Therefore, this paper uses RANSAC algorithm as outlier detection method to remove outliers from data. In order to futher improve accuracy of prediction model after remove outliers, this paper uses T-Test as feature selection method, retains the key features that have a significant impact on the final grade, to futher improve accuracy of prediction model.
According to the results of research, through the outlier detection and feature selection process proposed in this paper, prediction error from 15.516 down to 4.571 points, improving the prediction error about 70 percent.

摘要 i
ABSTRACT ii
圖目錄 v
表格目錄 vi
一、 緒論 1
二、 文獻探討 3
2.1 隨機抽樣一致（RANdom SAmple Consensus） 3
2.2 特徵選取 3
2.3 學習風險預測 4
2.4 總結 5
三、 混成式微積分課程 7
四、 方法 8
4.1 資料收集 8
4.2 資料前處理 8
4.2.1 填充缺失值 8
4.2.2 資料整合 9
4.2.3 資料聚合 10
4.3 離群值偵測 10
4.4 特徵選取 11
4.5 殘差分析 11
4.6 迴歸分析 12
4.7 交叉驗證 12
4.8 資料標準化 13
五、 結果及討論 14
5.1 研究問題一 14
5.1.1 未移除Outlier流程-結果 14
5.1.2 移除Outlier流程-結果 15
5.1.3 結果總結 19
5.2 研究問題二 19
5.2.1 加入特徵選取流程-結果 20
5.2.2 結果總結 22
5.3 研究問題三 23
5.3.1 「week 1 ~ week 3」資料集流程-結果 24
5.3.2 結果總結 28
六、 結論 29
參考文獻 31

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