Leveraging Social Norms to Encourage Online Learning: Empirical Evidence from a Blended Learning Course

The growing role of digital learning environments in education poses new challenges to individuals’ self-regulated learning which may exacerbate self-control problems (i.e., procrastination) if not addressed. The present study explores the role of social norms in information systems to support university students by encouraging online learning. Based on a field experiment with 58 participants of an eighteen-week blended learning course, we investigate the impact of descriptive and injunctive normative feedback on participants’ online learning behavior. Specifically, we find that a small modification to the learning environment increases participants’ course-specific online learning time by 25.4%. The study provides additional evidence that this effect largely stems from a time segment in which the participants of the control group were disproportionately less active, potentially due to self-control problems. Ultimately, our findings have important implications for the design of digital learning environments to effectively support individuals in achieving their learning goals

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations