Abstract
Online learning platforms give learners unprecedented control over pacing, yet this flexibility often leaves students without support at the moments when they need it most—during transitions between activities, cognitive states, or levels of engagement. Existing self-regulated learning (SRL) interventions tend to rely on fixed schedules or simplistic triggers, overlooking how learners’ trajectories unfold and where they begin to diverge toward productive or unproductive pathways. This paper introduces a framework for trajectory-aware interventions, an approach that integrates temporal design with real-time behavioral diagnostics to identify and support critical transition moments. Through a comparative analysis of major online learning platforms and a synthesis of SRL, trajectory theory, and learning analytics literature, we propose a taxonomy outlining five categories of trajectory-aware interventions. The framework clarifies when, how, and why interventions should be deployed to help learners develop self-regulation skills, maintain engagement, and navigate online learning environments more effectively.
Keywords
Learning Design, Learning Trajectory, Online education, Temporal Design
DOI
https://doi.org/10.21606/drs.2026.2714
Citation
Kim, S., Pino, Z., Safyer, P., Neyrey, A., Sharma, R., Huang, T., and Sekyi, M. (2026) Designing for learning rhythms: A taxonomy of trajectory-aware interventions in online learning, in Simeone, L., Gray, C. M., Verhoeven, A., de Götzen, A., Bakırlıoğlu, Y., Zohar, H., Stead, M., and Buwert, P. (eds.), DRS2026: Edinburgh, 8–12 June, Edinburgh, United Kingdom. https://doi.org/10.21606/drs.2026.2714
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Included in
Designing for learning rhythms: A taxonomy of trajectory-aware interventions in online learning
Online learning platforms give learners unprecedented control over pacing, yet this flexibility often leaves students without support at the moments when they need it most—during transitions between activities, cognitive states, or levels of engagement. Existing self-regulated learning (SRL) interventions tend to rely on fixed schedules or simplistic triggers, overlooking how learners’ trajectories unfold and where they begin to diverge toward productive or unproductive pathways. This paper introduces a framework for trajectory-aware interventions, an approach that integrates temporal design with real-time behavioral diagnostics to identify and support critical transition moments. Through a comparative analysis of major online learning platforms and a synthesis of SRL, trajectory theory, and learning analytics literature, we propose a taxonomy outlining five categories of trajectory-aware interventions. The framework clarifies when, how, and why interventions should be deployed to help learners develop self-regulation skills, maintain engagement, and navigate online learning environments more effectively.