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We investigate how inviting students to set task-based goals affects usage of an online learning platform and course performance. We design and implement a randomized field experiment in a large mandatory economics course with blended learning elements. The low-cost treatment induces students to use the online learning system more often, more intensively, and to begin earlier with exam preparation. Treated students perform better in the course than the control group: they are 18.8% (0.20 SD) more likely to pass the exam and earn 6.7% (0.19 SD) more points on the exam. There is no evidence that treated students spend significantly more time, rather they tend to shift to more productive learning methods. The heterogeneity analysis suggests that higher treatment effects are associated with higher levels of behavioral bias but also with poor early course behavior.
Personal data increasingly serve as inputs to public goods. Like other types of contributions to public goods, personal data are likely to be underprovided. We investigate whether classical remedies to underprovision are also applicable to personal data and whether the privacy-sensitive nature of personal data must be additionally accounted for. In a randomized field experiment on a public online education platform, we prompt users to complete their profiles with personal information. Compared to a control message, we find that making public benefits salient increases the number of personal data contributions significantly. This effect is even stronger when additionally emphasizing privacy protection, especially for sensitive information. Our results further suggest that emphasis on both public benefits and privacy protection attracts personal data from a more diverse set of contributors.