Open access
Empirical Research Article
Published: 23 October 2026
Adaptive Knowledge Tracing and Real-Time Cognitive Load Balancing in High School Algorithmic Problem Solving
Hendrik van der Linden, Zhi-Qiang Xiao, Sarah L. Miller
1. Dept. of Informatics & EdTech, Universitas Tadulako; 2. AI in Education Lab, Cambridge; 3. Department of Cognitive Computing.
Abstract
Deploying dynamic Bayesian Knowledge Tracing with EEG-informed cognitive load thresholds across 640 computer science students. Students in the adaptive intervention achieved 28% higher mastery rates in dynamic programming concepts.
Keywords:
Knowledge Tracing, Cognitive Load, Algorithmic Problem Solving, Computer Science Education, Adaptive Learning Systems
Publication Details
| Journal | Journal of Intelligent Pedagogy |
| Issue | Volume 1, Issue 1 (2026), pp. 1–12 |
| ISSN (Online) | 3025-8891 |
| Publisher | Global Innovation & Academic Press (GIA Press) |
| Peer Review | Double-blind peer reviewed in accordance with COPE ethical guidelines |
| License | Creative Commons Attribution 4.0 International (CC BY 4.0) |
How to Cite (APA 7th Edition)
Hendrik van der Linden, Zhi-Qiang Xiao, Sarah L. Miller (2026). Adaptive Knowledge Tracing and Real-Time Cognitive Load Balancing in High School Algorithmic Problem Solving. Journal of Intelligent Pedagogy, 1(1), 1–12. https://doi.org/10.59653/joip.v1i01.2026