Abstract
This paper addresses the cold start problem in personalized learning by proposing an adaptive assessment strategy that integrates motivation, self-assessment, and test-based validation. By dynamically linking interest and competency to assessment length, the approach minimizes cognitive burden. Simulations across three digital competency frameworks demonstrate its potential to enhance learner autonomy and engagement. Future work will focus on empirical validation and integration into learning platforms.
Recommended Citation
Zinke-Wehlmann, Christian and Barth, René, "AN ADAPTIVE ASSESSMENT STRATEGY FOR PERSONALIZED LEARNING" (2025). Proceedings of the 2025 AIS SIGED International Conference on Information Systems Education and Research. 4.
https://aisel.aisnet.org/siged2025/4