Behavior patterns characterize students’ choices and relate to cognitive load and performance in learner-controlled environments

Internet and Higher Education. 2026. Vol. 69. Article 101073.

Gorbunova A., Adamovich K., Savelyev A., Costley J.

Asynchronous online learning environments offer flexibility for students to navigate learning at their own pace, resulting in diverse behavioral patterns that can significantly impact cognitive load and academic performance. However, limited research has explored how learner-controlled environments shape these patterns and their relationship to learning outcomes and levels of cognitive load. This study investigates behavioral patterns in an online asynchronous graduate law class (n = 90) at a large university, analyzing data from a learning management system to categorize students into clusters based on their interactions with instructional components (e.g., video lectures, video-based examples, and problem-solving tasks). Cluster analysis revealed three distinct patterns: balanced learners, who achieved the highest performance; practice-oriented learners, who exhibited lower intrinsic cognitive load; and classic learners, characterized by comparatively lower extraneous cognitive load. However, these differences in extraneous load were not statistically significant between clusters, suggesting that the additional cognitive demands of learner control may have imposed similar baseline levels of extraneous load regardless of behavior pattern. Contrary to expectations, learner behavior patterns extended beyond example-based and problem-solving-first approaches, highlighting greater variability in learner strategies. These findings underscore the importance of understanding instructional paths in learner-controlled environments and how behavioral patterns can interact with both cognitive load and learning outcomes.

Keywords: problem-solving, asynchronous learning format, learner control, Example-based instruction, cognitive load theory

Publication based on the results of: Using digital traces to study learners' behavioral strategies (2025)