On the Prevalence of Multiple-Account Cheating in Massive Open Online Learning : A Replication Study / Yingying Bao, Guanliang Chen and Claudia Hauff.
Massive Open Online Courses (MOOCs) are a promising form of online education. However, the occurrence of academic dishonesty has been threatening MOOC certificates' effectiveness as a serious tool for recruiters and employers. Recently, a large-scale study on the log traces from more than one h...
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| Format: | eBook |
| Language: | English |
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2017.
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MARC
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| 100 | 1 | |a Bao, Yingying. | |
| 245 | 1 | 0 | |a On the Prevalence of Multiple-Account Cheating in Massive Open Online Learning : |b A Replication Study / |c Yingying Bao, Guanliang Chen and Claudia Hauff. |
| 264 | 1 | |a [Place of publication not identified] : |b Distributed by ERIC Clearinghouse, |c 2017. | |
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| 500 | |a Abstractor: As Provided. |5 ericd. | ||
| 500 | |a Educational level discussed: Higher Education. | ||
| 500 | |a Educational level discussed: Postsecondary Education. | ||
| 516 | |a Text (Speeches/Meeting Papers) | ||
| 516 | |a Text (Reports, Research) | ||
| 520 | |a Massive Open Online Courses (MOOCs) are a promising form of online education. However, the occurrence of academic dishonesty has been threatening MOOC certificates' effectiveness as a serious tool for recruiters and employers. Recently, a large-scale study on the log traces from more than one hundred MOOCs created by Harvard and MIT has identified a specific cheating strategy viable in MOOCs: "Copying Answers using Multiple Existences Online" (CAMEO). In essence, learners create several accounts on a MOOC platform, request assessment solutions via some of the accounts, and then submit these "harvested" solutions in their main account to receive credit. In our work, we "replicate" the CAMEO implementation and apply it to ten edX MOOCs created by the Delft University of Technology. Our results show that in those MOOCs, 1.9% of certificates were likely earned through CAMEO cheating, a number comparable to the fraction of cheating observed in Harvard and MIT MOOCs. [For the full proceedings, see ED596512.] | ||
| 524 | |a International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017). |2 ericd. | ||
| 650 | 0 | 7 | |a Large Group Instruction. |2 ericd. |
| 650 | 0 | 7 | |a Online Courses. |2 ericd. |
| 650 | 0 | 7 | |a Cheating. |2 ericd. |
| 650 | 0 | 7 | |a Incidence. |2 ericd. |
| 650 | 0 | 7 | |a Computer Assisted Testing. |2 ericd. |
| 650 | 0 | 7 | |a Tests. |2 ericd. |
| 650 | 0 | 7 | |a Identification. |2 ericd. |
| 650 | 0 | 7 | |a Student Characteristics. |2 ericd. |
| 650 | 0 | 7 | |a College Students. |2 ericd. |
| 650 | 0 | 7 | |a Foreign Countries. |2 ericd. |
| 650 | 0 | 7 | |a Student Motivation. |2 ericd. |
| 650 | 0 | 7 | |a Data Collection. |2 ericd. |
| 650 | 0 | 7 | |a Introductory Courses. |2 ericd. |
| 650 | 0 | 7 | |a Replication (Evaluation) |2 ericd. |
| 700 | 1 | |a Chen, Guanliang, |e author. | |
| 700 | 1 | |a Hauff, Claudia, |e author. | |
| 856 | 4 | 0 | |u http://files.eric.ed.gov/fulltext/ED596613.pdf |z Full Text (via ERIC) |
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