Positive Affective Feedback Mechanisms in an Online Mathematics Learning Platform

This research aims to investigate the allocation mechanisms of written positive affective feedback (PAF) in online mathematics assignments provided by teachers, employing multimodal learning analytics. We extract mathematical text features and readability indicators from teacher comments, utilizing collinearity matrices for linear feature selection. Student multimodal response patterns are obtained through clustering. To analyze the teacher comment strategies under different student response patterns, Mann-Whitney U tests were employed to investigate differences in student scores and feedback readability between scenarios with and without PAF. Our findings uncover the linguistic characteristics of teacher-provided PAF and the corresponding strategies they adopt. Teachers are more inclined to offer PAF to K-12 students with higher scores, challenging assignments, and younger ages. The study points out potential imbalances in the allocation of teacher PAF and emphasizes key factors that teachers need to consider when providing PAF. The findings offer new insights for educators to contemplate on designing and implementing more effective PAF strategies.