Doniyorbek Ahmadaliev
Researcher • Adaptive Learning • Educational Data
Publication Spotlight
Adaptive recommendation of student-created micro-lessons via learning-style and knowledge-level modeling
What this paper contributes
We present Lesslet, an adaptive e-learning platform that recommends student-created micro-lessons (lesslets) using learner modeling based on learning style and knowledge level.
Key idea: learners both consume and create micro-lessons,
and the system adapts recommendations to support engagement and performance.
Context: implemented in an “Introduction to Algorithms” course and evaluated with multiple learner groups.
Highlights
- UGC as adaptive material: micro-lessons created by students become recommendation resources.
- Dual modeling: learning style + knowledge level guide personalization.
- Transparent evaluation: report effects with/without difficult outlier topics.
- Reusable assets: supplementary materials and code for replication.