No abstract

News feeds in virtually all social media pla orms include engagement metrics, such as the number of mes each post is liked and shared. We find that exposure to these signals increases the vulnerability of users to low-credibility informa on in a simulated social media feed. This finding has important implica ons for the design of social media interac ons in the post-truth age. To reduce the spread of misinforma on, we call for technology pla orms to rethink the display of social engagement metrics. Further research is needed to inves gate how engagement metrics can be presented without amplifying the spread of low-credibility informa on.

No abstract

Psychology researchers have long attempted to identify educational practices that improve student learning. However, experimental research on these practices is often conducted in laboratory contexts or in a single course, which threatens the external validity of the results. In this article, we establish an experimental paradigm for evaluating the benefits of recommended practices across a variety of authentic educational contexts—a model we call ManyClasses. The core feature is that researchers examine the same research question and measure the same experimental effect across many classes spanning a range of topics, institutions, teacher implementations, and student populations. We report the first ManyClasses study, in which we examined how the timing of feedback on class assignments, either immediate or delayed by a few days, affected subsequent performance on class assessments. Across 38 classes, the overall estimate for the effect of feedback timing was 0.002 (95% highest density interval = [−0.05, 0.05]), which indicates that there was no effect of immediate feedback compared with delayed feedback on student learning that generalizes across classes. Furthermore, there were no credibly nonzero effects for 40 preregistered moderators related to class-level and student-level characteristics. Yet our results provide hints that in certain kinds of classes, which were undersampled in the current study, there may be modest advantages for delayed feedback. More broadly, these findings provide insights regarding the feasibility of conducting within-class randomized experiments across a range of naturally occurring learning environments.

Smart Meters are a key component of increasing the power efficiency of the Smart Grid. To help manage the grid effectively, these meters are designed to collect information on power consumption and send it to third parties. With Smart Metering, for the first time, these cloud-connected sensing devices are legally mandated to be installed in the homes of millions of people worldwide. Via a multi-staged empirical study that utilized an open-ended questionnaire, focus groups, and a design probe, we examined how people characterize the tension between the utility of Smart Metering and its impact on privacy. Our findings show that people seek to make abstract Smart Metering data accountable by connecting it to their everyday practices. Our insight can inform the design of usable privacy configuration tools that help Smart Metering consumers relate abstract data with the real-world implications of its disclosure.