دانلود رایگان مقاله شناسایی و جلوگیری از تقلب "حساب متعدد" در دوره های آموزشی عمومی اینترنتی

Abstract

We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The strategy, Copying Answers using Multiple Existences Online (CAMEO), involves a user who gathers solutions to assessment questions using a “harvester” account and then submits correct answers using a separate “master” account. We use a small-scale experiment to verify CAMEO and estimate a “lower bound” for its prevalence among 1.9 million course participants in 115 MOOCs from two universities. Using conservative thresholds, we estimate CAMEO prevalence at 1237 certificates, accounting for 1.3% of the certificates in the 69 MOOCs with CAMEO users. Among earners of 20 or more certificates, 25% have used the CAMEO strategy. CAMEO users are more likely to be young, male, and international than other MOOC certificate earners. We identify preventive strategies that can decrease CAMEO rates and show evidence of their effectiveness in science courses.

5. Conclusion

The CAMEO detection algorithm uses three strategies that hold general promise for the analysis of clickstream data. First, time difference analysis is a tool to infer relationships among students. Second, Bayesian criteria allow appropriately conservative classification when data are limited. Third, transitive closure is a technique for robust consideration of possible CAMEO users. Beyond cheating detection in MOOCs, these tools may aid more generally in identification of collaboration and interaction among online users. There is continued interest in the potential for MOOCs to increase efficiency and spur innovation in higher education. Four features of CAMEO severely undermine this potential. First, unless prevented, this cheating strategy allows students to earn certificates in open online courses without any understanding of the domain material. Second, the strategy is highly convenient, requiring no interactions with external resources, either animate or inanimate. Third, it is unrestricted, employable in a nonselective, open admission setting. Fourth, whereas cheating is traditionally considered with respect to individual assessments or portions thereof, CAMEO is a course-level strategy. It is less cheating than the wholesale falsifi- cation of a certificate. In this paper, we have demonstrated the prevalence of the CAMEO cheating strategy in a large sample of MOOCs, and we have argued that it poses a serious threat to interpretations of their certifications. Protecting certification requires CAMEO prevention, and we have shown that preventive strategies hold promise. Yet, CAMEO is only one of many possible cheating strategies. Sophisticated detection algorithms should be a part of a general approach to protect the validity of online course certification. We recommend and look forward to future interventions that increase and encourage honest behavior in online learning environments while disallowing and discouraging cheating in all its forms.