Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior -the amount of cheating and learners' engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an 'analytically-verified' MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention. CCS CONCEPTS• Applied computing → E-learning; • Unsupervised learning → Anomaly detection.

Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior – the amount of cheating and learners’ engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an ‘analytically-verified’ MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention.

Human decision making is often prone to biases and irrationality. Group decisions add dynamic interactions that further complicate the choice process and frequently result in outcomes that are suboptimal for both the individual and the collective. We show that an implementation of a Blockchain protocol improves individuals' decision strategies and increases the alignment between desires and outcomes. The Blockchain protocol affords (1) a distributed decision, (2) the ability to iterate repeatedly over a choice, (3) the use of feedback and corrective inputs, and (4) the quantification of intrinsic choice attributes (i.e., greed, desire for fairness, etc.). We test our protocol's performance in the context of the Public Goods Game. The game, a generalized version of the Prisoner's Dilemma, allows players to maximize their own gain or act in ways that benefit the collective. Empirical evidence shows that participants' cooperation in the game typically decreases once a single player favors their own interest at the expense of others'. In our Blockchain implementation, "smart contracts" are used to safeguard individuals against losses and, consequently, encourage contributions to the public good. Across different tested simulations, the Blockchain protocol increases both the overall trust among the participants and their profits. Agents decision strategies remain flexible while they act as each other's source of accountability (which can be seen as formalized distributed "Ulysses contract"). To highlight the contribution of our protocol to society at large we incorporated an entity that represents the public good. This benevolent independent beneficiary of the contributions of all participants (e.g., a charity organization or a tax system) maximized its payoffs when the Blockchain protocol was implemented. We provide a formalized implementation of the Blockchain protocol and discuss potential applications that could benefit society by more accurately capturing individuals' preferences. For example, the protocol could help maximize profits in groups, facilitate democratic election that better reflect the public opinion, or enable group decision in circumstances where a balance between anonymity, diverse opinions, personal preferences and loss-aversion play a role.

Background Massive Open Online Courses (MOOCs) have touted the idea of democratizing education, but soon enough, this utopian idea collided with the reality of finding sustainable business models. In addition, the promise of harnessing interactive and social web technologies to promote meaningful learning was only partially successful. And finally, studies demonstrated that many learners exploit the anonymity and feedback to earn certificates unethically. Thus, establishing MOOC pedagogical models that balance open access, meaningful learning, and trustworthy assessment remains a challenge that is crucial for the field to achieve its goals. Objectives This study analysed the influence of an MOOC assessment model, denoted the Competency Exam (CE), on learner engagement, the level of cheating, and certification rates. At its core, this model separates learning from for‐credit assessment, and it was introduced by the MITx Biology course team in 2016. Methods We applied a learning analytics methodology to the clickstream data of the verified learners (N = 559) from four consecutive runs of an Introductory Biology MOOC offered through edX. The analysis used novel algorithms for measuring the level of cheating and learner engagement, which were developed in the previous studies. Results and Conclusions On the positive side, the CE model reduced cheating and did not reduce learner engagement with the main learning materials – videos and formative assessment items. On the negative side, it led to procrastination, and certification rates were lower. Implications First, the results shed light on the fundamental connection between incentive design and learner behaviour. Second, the CE provides MOOC designers with an ‘analytically verified’ model to reduce cheating without compromising on open access. Third, our methodology provides a novel means for measuring cheating and learner engagement in MOOCs.

No abstract