Exploring the Design and Impact of Interactive Worked Examples for Learners with Varying Prior Knowledge

2026-02-18

AI Summary

This study addresses the moderating role of learners’ prior knowledge in the effectiveness of example-based interventions within traditional tutoring systems, reflecting a competence–processing interaction. Grounded in the ICAP (Interactive, Constructive, Active, Passive) framework, the research designs two types of interactive examples—Buggy (error-correction) and Guided (rule-completion)—within an intelligent tutoring system for logical problem solving. Through a controlled experiment, it investigates how these interventions differentially affect learners with varying levels of prior knowledge. This work represents the first application of the ICAP framework to logic tutoring and proposes tailored intervention strategies aligned with learners’ prior knowledge. Results indicate that both interactive example types significantly outperform passive examples; Buggy examples particularly benefit high-prior-knowledge learners by fostering an exploration–correction cycle, whereas Guided examples better support low-prior-knowledge learners by reducing errors and promoting effective help-seeking.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where low prior knowledge learners benefit more. We applied the ICAP learning theory to design two new types of worked examples, Buggy (students fix bugs), and Guided (students complete missing rules), requiring varying levels of cognitive engagement, and investigated their impact on learning in a controlled experiment with 155 undergraduate students in a logic problem solving tutor. Students in the Buggy and Guided examples groups performed significantly better on the posttest than those receiving passive worked examples. Buggy problems helped high prior knowledge learners whereas Guided problems helped low prior knowledge learners. Behavior analysis showed that Buggy produced more exploration-revision cycles, while Guided led to more help-seeking and fewer errors. This research contributes to the design of interventions in logic problem solving for varied levels of learner knowledge and a novel application of behavior analysis to compare learner interactions with the tutor.

Problem

• worked examples
• prior knowledge
• aptitude-treatment interaction
• interactive learning
• logic problem solving

Innovation

• interactive worked examples
• ICAP theory
• aptitude-treatment interaction
• behavior analysis
• logic problem solving