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研究生:李書雅
研究生(外文):Shu-ya Li
論文名稱:應用資料探勘技術分析學生程式碼
論文名稱(外文):Using Data Mining Techniques to Analyze Students'' Source-Code Data
指導教授:許中川許中川引用關係
指導教授(外文):Chung-Chian Hsu
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
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:2009
畢業學年度:97
語文別:中文
論文頁數:50
中文關鍵詞:自組映射圖決策樹分析階層式分群程式語言學習程式碼相似度
外文關鍵詞:Source Code SimilarityHierarchical ClusteringDecision TreeSelf-organizing mapProgramming Languages Learning
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傳統程式語言教學課程,通常是一位教師指導多位學生,並透過作業或測驗等方式得知學生的學習成效,但這兩種方法,仍難以觀察學生真正的學習情況。雖然網路教學系統已能夠記錄學生的學習資料,但教師仍難以從龐大的記錄中了解學生的學習特徵,並且無法透過視覺化方式呈現程式碼特徵,以協助教師分析學生的學習狀況,以及程式測驗過程中可能發生抄襲行為,而老師難以從中得知。因此本研究目的旨在探討如何分析學生的作答特徵,並提出一套架構透過多種資料探勘技術分析測驗資料,包含決策樹分析、階層式分群、自組映射圖、特徵選選等方法,並以特徵屬性及程式結構兩種不同角度分析,以協助教師能夠從程式測驗資料中,透過資料探勘的分群方法,找出學生學習過程的隱含資訊。
In traditional programming language courses, the relationship of the teacher and students are one-to-many. A teacher knows students’ learning outcomes from their source codes of homework or testing, but both of the sources are difficult for the teacher to observe the students’ real learning situation. Although an Internet teaching system is able to record the learning information of students, but the teacher still has difficulty to understand the learning characteristics of students due to the large amount of raw data, and can’t visually observe the characteristics of source codes to analyze student learning outcomes. It is also difficult to know whether the programming test or homework occurs plagiarism.
The purpose of this study is to explore how to analyze the characteristics of students’ programming source codes. We propose a framework for analyzing programming source code data by using data mining techniques, including decision tree, hierarchical clustering, self-organizing map, and feature selection methods. We also use two different points of views which include source codes features and program structure to help teachers to identify implicit information in the learning process by using data mining techniques.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究限制 2
1.4 論文架構 2
二、 文獻探討 3
2.1 背景知識 3
2.1.1 決策樹歸納法 3
2.1.2 自組映射圖 4
2.1.3 階層式分群 5
2.1.4 程式設計教學 6
2.2 相關研究 6
2.2.1 文件相似度比對 6
2.2.2 程式碼相似度比對 7
2.2.3 資料探勘應用於程式語言教學 9
三、 研究架構 10
3.1 以特徵屬性分析 10
3.1.1 程式碼特徵選取 11
3.1.2 分析屬性與分數間的關係 12
3.2 以程式結構分析 17
3.2.1 階層式分群 17
3.2.2 自組映射圖 23
四、 實驗 27
4.1 實驗資料集 27
4.2 實驗結果 28
4.2.1 以特徵屬性為基礎之實驗結果 28
4.2.2 以程式碼結構為基礎之實驗結果 33
4.3 實驗匯整 44
五、 結論與未來工作 46
5.1 結論 46
5.2 未來工作 46
參考文獻 47
附錄 49
附錄1、類別對應結果 49
附錄2、相似度矩陣 50
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