臺灣博碩士論文加值系統

在教育的領域中，學生就如同顧客一般，如何主動提供資訊，輔助學生們能適當且方便地選擇其所需的課程，是一個令許多學校困擾的問題。目前陸續有研究提出各種推薦系統以解決學生學習上的問題，但大多數皆應用於同一門課程中不同章節之間學習路徑的安排，相對於應用在跨課程之間的推薦研究較為缺乏。
本研究提出一個以資料探勘與協同過濾法為基礎之課程推薦方法，研究對象為某科技大學資管系四技部與二技部學生。首先將學生分為新生和舊生二類，針對新生，本研究採用「協同過濾法」，四技生以「一年級必修成績表現」，而二技生則以「學籍資料」定義為學生的偏好（preference），以K最近鄰居分類法找出與新生偏好最相似的群組之修課紀錄中，前N項被最多人選擇的課程於予推薦；舊生則採用資料探勘中的「序列型樣」，依學生先前的選課紀錄以及修課成績表現，推薦此學期適合的課程；再將序列推薦的結果與「關聯法則」結合，找出序列推薦課程的相關課程，提供學生個人化的選課推薦。
最後在推薦品質的衡量上，本研究採用F1-measure做為評估標準，並以二技部94學年入學學生以及四技部92學年入學學生之選課資料做為測試資料，以檢驗本研究之推薦結果。

In the field of education, students play the role of customer. How to actively offer information and assist them in choosing suitable elective courses are agonizing problems in many universities. Although various recommender systems have been proposed to solve these problems, many studies focus on the arrangement of the learning path between different chapters in the same course, such as e-learning, but only few studies for recommending different courses are proposed.
In this study, we proposed a methodology to provide personalized recommendations on elective courses based on Data Mining and Collaborative Filtering techniques. The research objects are the 4-year and 2-year technological program students in the Department of Information Management in a National University of Science and Technology. Firstly, students were classified into two groups: freshmen and seniors. Secondly, for freshmen, we used the Collaborative Filtering approach to find the top-N recommendation courses of each cluster of students. For seniors, according to students’ enrolling records and scores, we used the Sequential Patten approach to find the preferred sequential courses and then used the Association Rule approach to find the related courses based on the recommendation results of the Sequential Patten approach. Finally, we used the F1-measure to measures the quality of our recommendation results.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究範圍與限制 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 資料探勘 6
2.1.1 資料探勘的定義 6
2.1.2 資料探勘的功能 7
2.2 推薦系統 9
2.2.1 內容導向過濾法（Content-Based Filtering） 9
2.2.2 協同過濾法（Collaborative Filtering；CF） 10
2.2.3 混合式過濾法（Hybrid Filtering ） 14
2.3 課程推薦之相關研究 15
第三章 研究方法 17
3.1 研究對象 18
3.2 資料收集階段 19
3.3 資料前處理階段 19
3.4 課程探勘階段 22
3.4.1 K最近鄰居導向協同過濾法 22
3.4.2 序列型樣 26
3.4.3 關聯法則 29
3.5 課程推薦階段 31
3.6 研究評估 36


第四章 實驗結果與分析 37
4.1 新生推薦 37
4.1.1 四技 37
4.1.2 二技 40
4.1.3 與其他方法的比較 43
4.2 舊生推薦 47
4.2.1 四技 47
4.2.2 二技 49
4.2.3 與其他方法的比較 50
第五章 結論 53
5.1 結論 53
5.2 研究貢獻 54
5.3 研究限制 54
5.4 未來研究方向 55
參考文獻 56
附錄一 新生各課程修課人數統計表 60
附錄二 新生各群組Top-N課程 62
附錄三 舊生各學年學期熱門課程 65
附錄四 四技序列型樣規則集 66
附錄五 二技序列型樣規則集 76
附錄六 四技關聯法則規則集 87
附錄七 二技關聯法則規則集 99

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