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

Sina Rismanchian, Shayan Doroudi

TL;DR

This paper tackles the conflict among ICAP, KLI, and CLT by introducing Procedural ABICAP, an executable agent-based model that encodes procedural knowledge as a weighted knowledge graph and simulates four engagement modes with mode-specific learning dynamics. The model extends ABICAP to procedural domains, incorporating cognitive load differences and a constructive/interactive edge-reinforcement mechanism to reconcile predictions from the three theories. Through simulations, the authors demonstrate that Procedural ABICAP can reproduce ICAP patterns (including the expertise reversal effect) and explain conditions under which higher engagement may or may not yield better learning, depending on prior knowledge and task structure. The work offers a tool for generating novel hypotheses and guiding adaptive instruction while bridging conceptual and procedural learning, with implications for educational design and future replication studies.

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.

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

TL;DR

This paper tackles the conflict among ICAP, KLI, and CLT by introducing Procedural ABICAP, an executable agent-based model that encodes procedural knowledge as a weighted knowledge graph and simulates four engagement modes with mode-specific learning dynamics. The model extends ABICAP to procedural domains, incorporating cognitive load differences and a constructive/interactive edge-reinforcement mechanism to reconcile predictions from the three theories. Through simulations, the authors demonstrate that Procedural ABICAP can reproduce ICAP patterns (including the expertise reversal effect) and explain conditions under which higher engagement may or may not yield better learning, depending on prior knowledge and task structure. The work offers a tool for generating novel hypotheses and guiding adaptive instruction while bridging conceptual and procedural learning, with implications for educational design and future replication studies.

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.
Paper Structure (19 sections, 5 equations, 2 figures)

This paper contains 19 sections, 5 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. ICAP
  4. KLI
  5. Cognitive Load Theory
  6. Procedural ABICAP
  7. Knowledge
  8. Learning
  9. Passive
  10. Active
  11. Constructive
  12. Interactive
  13. Simulations and Results
  14. Simple Procedural KCs
  15. ICAP Hypothesis and Expertise Reversal Effect
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Passive learner's mastery over a node through steps
  • Figure 2: Simulation with 20 nodes that demonstrates the expertise reversal effect.