Print Email Facebook Twitter Detecting Multiple-Accounts Cheating in MOOCs Title Detecting Multiple-Accounts Cheating in MOOCs Author Bao, Y. Contributor Hauff, C. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Software Technology Programme Web Information Systems Date 2017-02-21 Abstract Massive Open Online Course (MOOC) is a course designed for unlimited participation and can be accessed by anyone through the Web. As a promising education form, it has attracted lots of attentions from institutions, learners and employers. However, the effectiveness and fairness of MOOC have been encroaching by academic dishonesty. Academic dishonesty is defined as using dishonest means to gain an undeserved reward or to get rid of an embarrassing situation in relation to an academic exercise. It is a widespread occurrence in different levels of education and various education forms. In this thesis, we focus on a cheating strategy, Copying Answers using Multiple Existence Online (CAMEO), in MOOCs. The strategy involves learners who use fake accounts for harvesting solutions that they later submit in different accounts. On the basis of user logs, we identify potential CAMEO users in 10 MOOCs provided by Delft University of Technology (TU Delft) on edX with three different detection methods. Besides, we analyze the characteristics of the detected users. Our results reveal that among the 8171 certificates issued in the 10 MOOCs, an estimated 2% of the certificates are earned by CAMEO users. We find that the CAMEO users are more likely to cheat at the midterm of a MOOC than the other periods of the course. The research makes contributions to understanding the popularity of cheating especially CAMEO in MOOCs and getting the knowledge of cheaters’ behaviors preferences in MOOCs. With the knowledge, at the end of the thesis, targeting at CAMEO, we purpose preventions to MOOC platforms and instructors to defend the effectiveness of MOOCs and the value of MOOC certificates. To reference this document use: http://resolver.tudelft.nl/uuid:64ee5526-8c9e-4013-9019-c63a63413ca2 Part of collection Student theses Document type master thesis Rights (c) 2017 Bao, Y. Files PDF Thesis__YingyingBao.pdf 4.06 MB Close viewer /islandora/object/uuid:64ee5526-8c9e-4013-9019-c63a63413ca2/datastream/OBJ/view