Effort is key in learning, evidenced by its omnipresence in both empirical findings and educational theories. At the same time, students are consistently found to avoid effort. In this study, we investigate whether limiting effort avoidance improves learning outcomes, and explore for whom this would be the case. In a large-scale computer adaptive practice system for primary education, over 150,000 participants were distributed across four conditions in which a problem-skipping option was delayed for 0, 3, 6, or 9 seconds. The results show that after a 14 week period, no average treatment effects in learning outcomes can be found between conditions. A substantive typology of students, based on the expected target mechanisms of the intervention, neither shows consistent conditional average treatment effects. Nevertheless, the substantive typology is shown to be meaningful, as the different types—toilers, skippers, and rushers—differ with respect to their learning outcomes. We argue that although the scale of the experiment suggests a precise null finding, the cumulative nature of the effect of problem skipping cautions against generalizing this finding to sustained intervention.

Adolescents show more risk taking behavior than children and adults. Most adolescents do not experience adverse consequences of this increased risk taking behavior. However, excessive risk taking can result in long term adverse consequences. To better target prevention efforts at those adolescents who are at risk for excessive risk taking, these adolescents should be identified early. Here we first test which statistical approach is best suited to predict the likelihood of risk taking behavior. We use data from a large, three wave longitudinal sample with 298 participants between the ages of 8-25 at the first measurement. We compare out-of-sample prediction performance of three different forms of Ordinary Least Squares (OLS) regression models and a Least Absolute Shrinkage and Selection (LASSO) model. Results show that the LASSO model outperforms all three OLS regression models on out-of-sample prediction for prediction of risk taking two years later. Furthermore, we show how LASSO can be used to determine a criterion value of who is at risk for specific future behavior, in this case likelihood of excessive risk taking. This criterion value can be used for early identification of individuals at risk and can provide guidance on decisions about prevention efforts.