Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

TL;DR

This paper introduces a simulation-based framework to evaluate adaptive educational reading personalization, demonstrating its effectiveness across multiple subject ontologies with simulated learners.

Contribution

It presents a novel, theory-grounded simulation framework for assessing adaptive reading systems using open textbooks and learner models.

Findings

Adaptive reading improved outcomes in computer science.

Gains in inorganic chemistry were positive but inconclusive.

In general biology, adaptation was neutral or slightly negative.

Abstract

We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system builds a learning-objective and knowledge-component ontology from open textbooks, curates it in a browser-based Ontology Atlas, labels textbook chunks with ontology entities, and generates aligned reading-assessment pairs. Simulated readers learn from passages through a Construction-Integration-inspired memory model with DIME-style reader factors, KREC-style misconception revision, and an open New Dale-Chall readability signal. Answers are produced by score-based option selection over the learner's explicit memory state, while BKT drives adaptation. Across three sampled subject ontologies and matched cohorts of 50 simulated learners per condition, adaptive reading significantly improved outcomes in computer science, yielded smaller positive but inconclusive gains in inorganic chemistry, and was neutral to slightly negative in general biology.