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.
