Model Human Learners: Computational Models to Guide Instructional Design

TL;DR

This paper introduces the Model Human Learner, a computational framework that predicts learning outcomes and guides instructional design by simulating human learning processes, demonstrated through successful prediction of experimental results.

Contribution

It presents the first successful implementation of a computational model that predicts human learning outcomes and supports instructional design decisions.

Findings

Accurately predicted outcomes of two human A/B experiments

Generated learning curves without human data

Provided theoretical insights into intervention effectiveness

Abstract

Instructional designers face an overwhelming array of design choices, making it challenging to identify the most effective interventions. To address this issue, I propose the concept of a Model Human Learner, a unified computational model of learning that can aid designers in evaluating candidate interventions. This paper presents the first successful demonstration of this concept, showing that a computational model can accurately predict the outcomes of two human A/B experiments -- one testing a problem sequencing intervention and the other testing an item design intervention. It also demonstrates that such a model can generate learning curves without requiring human data and provide theoretical insights into why an instructional intervention is effective. These findings lay the groundwork for future Model Human Learners that integrate cognitive and learning theories to support instructional design across diverse tasks and interventions.