Reconciling Different Theories of Learning with an Agent-based Model of Procedural Learning

TL;DR

This paper introduces Procedural ABICAP, a computational model that unifies different learning theories to better understand procedural learning and resolve conflicting claims in educational frameworks.

Contribution

The study presents a novel computational model that reconciles ICAP, KLI, and CLT theories for procedural learning, addressing their conflicting claims.

Findings

Model demonstrates how different theories can be integrated.

Simulations show the model's ability to reconcile conflicting results.

Provides a tool for testing educational interventions.

Abstract

Computational models of human learning can play a significant role in enhancing our knowledge about nuances in theoretical and qualitative learning theories and frameworks. There are many existing frameworks in educational settings that have shown to be verified using empirical studies, but at times we find these theories make conflicting claims or recommendations for instruction. In this study, we propose a new computational model of human learning, Procedural ABICAP, that reconciles the ICAP, Knowledge-Learning-Instruction (KLI), and cognitive load theory (CLT) frameworks for learning procedural knowledge. ICAP assumes that constructive learning generally yields better learning outcomes, while theories such as KLI and CLT claim that this is not always true. We suppose that one reason for this may be that ICAP is primarily used for conceptual learning and is underspecified as a framework for thinking about procedural learning. We show how our computational model, both by design and through simulations, can be used to reconcile different results in the literature. More generally, we position our computational model as an executable theory of learning that can be used to simulate various educational settings.