The Impact of Scaffold Interactions on Learning: A Learning Analytics Approach using Multimodal Data

Scaffolds are widely used to support self-regulated learning (SRL), and research has shown that students’ interactions with scaffolds impact their effectiveness. Prior research has primarily classified scaffold interaction as a binary construct (e.g., compliance vs non-compliance), which oversimplifies complex scaffold interaction patterns and limits understanding of the effectiveness of scaffolds. The present study addressed this limitation by adopting a more nuanced learning analytic perspective to examine the range and quality of scaffold interaction using multimodal data. In a pre-post-test experiment, university students (n = 67) learned with adaptive scaffolds—differentiated as just-in-time or on-demand—in a 45-minute learning session using a technology-enhanced learning environment. Combining logfile and eye-tracking data, we identified distinct interaction profiles through hierarchical agglomerative clustering of students’ scaffold interactions over the course of learning. Learners who engaged more consistently and actively with scaffolds demonstrated higher levels of cognitive activity changes and achieved stronger performance in essay tasks than peers with minimal or purely visual interaction (i.e., sighting but not acting on scaffolds). The process model comparison highlighted differences in the temporal structure of learning and scaffold interaction activities between groups with varying interaction profiles. These findings highlight the shortcomings of binary classifications and provide new insights into how adaptive scaffolds can be designed to respond to diverse interaction patterns and better foster SRL.