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.

[1] Garg, K., Verma, K., Patidar, K., & Tejra, N. (2020, May). Convolutional neural network based virtual exam controller. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 895-899). IEEE.
[2] Clark, T. M., Callam, C. S., Paul, N. M., Stoltzfus, M. W., & Turner, D. (2020). Testing in the time of COVID-19: A sudden transition to unproctored online exams. Journal of chemical education, 97(9), 3413-3417.
[3] Chua, S. S., Bondad, J. B., Lumapas, Z. R., & Garcia, J. D. L. (2019, October). Online examination system with cheating prevention using question bank randomization and tab locking. In2019 4th International Conference on Information Technology (InCIT) (pp. 126-131). IEEE.
[4] Jalali, K., & Noorbehbahani, F. (2017). An automatic method for cheating detection in online exams by processing the student’s webcam images. In Proc. 3rd Conf. Elect. Comput. Eng. Technol.(E-Tech) (pp. 1-6).
[5] Shruti, M. I., Pratiksha, M. K., Jyoti, M. M., Snehal, M. N., & Vaibhav, D. Designing Security Framework for Secure Exam System based on QR Code.
[6] Hearn Moore, P., Head, J. D., & Griffin, R. B. (2017). Impeding Students' Efforts
to Cheat in Online Classes. Journal of Learning in Higher Education, 13(1), 9-23.
[7] Fask, A., Englander, F., & Wang, Z. (2014). Do online exams facilitate cheating? An experiment designed to separate possible cheating from the effect of the online
test taking environment. Journal of Academic Ethics, 12(2), 101-112.
[8] Cluskey Jr, G. R., Ehlen, C. R., & Raiborn, M. H. (2011). Thwarting online exam cheating without proctor supervision.Journal of Academic and Business
Ethics, 4(1), 1-7.
[9] Ladyshewsky, R. K. (2015). Post-graduate student performance in ‘supervised in- class’ vs.‘unsupervised online’multiple choice tests: implications for cheating and test security. Assessment & Evaluation in Higher Education, 40(7), 883-897.
[10] Siyao, L., & Qianrang, G. (2011, August). The research on anti-cheating strategy of online examination system. In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)(pp. 1738-1741). IEEE.
[11] Badge, J. L., Cann, A. J., & Scott, J. (2007). To cheat or not to cheat? A trial of the JISC plagiarism detection service with biological sciences students. Assessment & Evaluation in Higher Education, 32(4), 433-439.
[12] London, M. (2017). 5 Ways to cheat on online exams. available at: www. insidehighered. com/digital-learning/views/2017/09/20/creative-ways-students- try-cheat-onlineexams (accessed 27 September 2018).
[13] Cerimagic, S., & Hasan, M. R. (2019). Online exam vigilantes at Australian universities: Student academic fraudulence and the role of universities to counteract. Universal Journal of Educational Research, 7(4), 929-936.
[14] Bilen, E., & Matros, A. (2021). Online cheating amid COVID-19. Journal of Economic Behavior & Organization, 182, 196-211.
[15] Nguyen, J. G., Keuseman, K. J., & Humston, J. J. (2020). Minimize online cheating for online assessments during COVID-19 pandemic.Journal of Chemical Education, 97(9), 3429-3435.
[16] Balderas, A., & Caballero-Hernández, J. A. (2020, October). Analysis of Learning Records to Detect Student Cheating on Online Exams: Case Study during COVID- 19 Pandemic. In Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 752-757).
[17] Goldberg, D. (2021). Programming in a pandemic: Attaining academic integrity in online coding courses.Communications of the Association for Information Systems, 48(1), 6.
[18] Li, H., Xu, M., Wang, Y., Wei, H., & Qu, H. (2021, May). A Visual Analytics Approach to Facilitate the Proctoring of Online Exams. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-17).
[19] Tiong, L. C. O., & Lee, H. J. (2021). E-cheating Prevention Measures: Detection of Cheating at Online Examinations Using Deep Learning Approach--A Case Study. arXiv preprint arXiv:2101.09841.
[20] Rehman, N. A. Online Examination System in the Times of COVID-19: A Case Study of Pakistan.
[21] Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81- 106.