臺灣博碩士論文加值系統

由於 2019 年的 COVID-19 傳染病大流行的緣故，高等教育在一夜之間面臨 需要採用線上課程的方式來進行授課。在線上課程中，教學方式、考試評量與學 習評估等都與實體課程有非常大的差異，由於線上考試必須得使用電腦作答，但 作答電腦本身具有上網的功能，學生在考試期間隨時都可以透過網路去搜尋答案， 因此較難對學生的學習成效進行評估，教師也難以確認課程的教學成效。
在本篇研究中提出資料分析於輔助線上學習的即時線上考試系統(Real-time Online Assessment with Data Analyze System to Support E-Learning, ROAD)。首先 ROAD 可將一般題目進行隨機化讓每位考生的題目順序不同，並設置作答完無法 返回與較短的時間限制，轉變為線上考試的設置，以降低學生互相交流答案的可 能性;接著在線上考試中，ROAD 能夠紀錄學生在考試期間電腦上的行為模式， 並辨識可疑的作弊行為;最後通過決策樹演算法分析學生的線上行為模式資料與 考試的分數結果，我們能給予教師及學生回饋，讓師生瞭解該堂課的學習成效， 並幫助教師針對課程教學進行改善。

Due to the sudden outbreak of the COVID-19 in 2019, higher education institutions are facing the need to adopt online teaching. Online courses are very different from physical courses with teaching methods, test evaluation, and learning assessments. Considering that online assessment is usually done on a computer, students can search the answers from the internet during the assessment. Therefore, it is difficult to evaluate the effectiveness of both students and teachers for the online courses.
In this research, we proposed a Real-time Online Assessment with Data Analyze System to Support E-Learning (ROAD) to assist online learning assessment. First, ROAD can randomize general questions so that each student has a different sequential order of viewing questions, and set that cannot be returned after answering with shorter time limit, by these three part can reduce the possibility of students exchanging answers with each other and turn the questions into online assessments. Second, ROAD can record students on computer’s behavior patterns during the assessment and identify suspicious cheating behaviors. Finally, by analyzing student’s behavior patterns with online assessment score results through decision tree algorithm, we can give teachers and students feedback to improve the effectiveness of learning and teaching for the online courses.

第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 3
第二章 文獻探討 4
第一節 考試準備和設置 4
2.1.1 題庫隨機化 4
2.1.2 特殊題目設置 5
2.1.3 作答時間限制 5
第二節 監考線上考試 5
2.2.1 線上監考的必要性 6
2.2.2 攝像頭偵測 6
2.2.3 答案抄襲檢測 9
2.2.4 作答行為分析 9
第三章 研究方法 11
3.1 ROAD 題目設置 12
3.2 ROAD 線上監考 14
3.2.1 登入驗證 15
3.2.2 紀錄考生行為模式 17
3.2.3 作弊行為警示 19
3.3 ROAD 分析回饋 20
第四章 實驗分析 26
第一節 實驗環境 26
第二節 實驗結果分析 27
4.2.1 練習考 28
4.2.2 期末考 A 32
4.2.3 期末考 B 36
4.2.4 學生身分 39
第三節 實驗結果比較 43
第五章 結論與未來展望 46
第六章 參考文獻 47

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