Inproceedings,
Exploring Adaptive Social Comparison for Online Practice
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, page 374–379. New York, NY, USA, Association for Computing Machinery, (Jun 28, 2024)
DOI: 10.1145/3631700.3664899
Abstract
Students experience motivational issues during online learning which has led to explorations of how to better support their self-regulated learning. One way to support students uses social reference frames or social comparison in student-facing learning analytics dashboards (LADs) and open learner models (OLMs). Usually, the social reference frame communicates class averages. Despite the positive effects of class-average-based social comparison on students’ activity levels and learning behaviors, comparison to class average can be misleading for some students and offer an irrelevant reference frame, motivating only low or high performers. Such conflicting findings highlight a need for an investigation of social reference frames that are not based on the “average” student. We extend the research on social comparison in education by conducting two complementary classroom studies. The first explores the effects of different fixed social reference frames in a non-mandatory practice system, while the second introduces an adaptive social reference frame that dynamically selects the peers who serve as a comparison group when students are engaged in online programming practice. We reported our analyses from both studies and shared students’ subjective evaluations of the system and its adaptive comparison functionality.
