Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

TLDR

Course selection is a critical yet time‑consuming and subjective activity that can be improved by computational tools predicting student performance. The study investigates how displaying grade predictions through an interactive visualization affects students’ course‑selection decisions. The authors implemented an interactive visualization tool that presents grade predictions to students and examined its impact. Qualitative and quantitative analyses show that predictions can cause students to overemphasize performance at the expense of workload, while more specific predictions trigger a framing effect that increases effort in course selection.

Abstract

Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools.