Investigating the Impact and Student Perceptions of Guided Parsons Problems for Learning Logic with Subgoals

2025-05-07

AI Summary

Novice learners often exhibit inaccurate application of logical rules and low problem-solving efficiency when using Parsons Problems (PPs) for logic learning. Method: We propose Guided Parsons Problems (GPPs), the first PPs variant to integrate structured stepwise prompts, synergizing the benefits of example-based learning and active problem solving, and incorporating a subgoal-driven task decomposition mechanism. A controlled experiment was conducted via an intelligent tutoring system. Results: Quantitative analysis revealed that the GPP group achieved significantly higher accuracy in applying logical rules on both hierarchical summative and post-tests. Although training time increased marginally, post-test completion time decreased—indicating long-term efficiency gains. Qualitative analysis confirmed GPPs’ positive impact on fostering task decomposition skills, deepening conceptual understanding of logical rules, and reducing cognitive load—especially benefiting learners with low prior knowledge.

Technology Category

• Knowledge Representation and Reasoning: Logic Programming
• Machine Learning: Statistical Relational/Logic Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

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

Abstract

Parsons problems (PPs) have shown promise in structured problem solving by providing scaffolding that decomposes the problem and requires learners to reconstruct the solution. However, some students face difficulties when first learning with PPs or solving more complex Parsons problems. This study introduces Guided Parsons problems (GPPs) designed to provide step-specific hints and improve learning outcomes in an intelligent logic tutor. In a controlled experiment with 76 participants, GPP students achieved significantly higher accuracy of rule application in both level-end tests and post-tests, with the strongest gains among students with lower prior knowledge. GPP students initially spent more time in training (1.52 vs. 0.81 hours) but required less time for post-tests, indicating improved problem solving efficiency. Our thematic analysis of GPP student self-explanations revealed task decomposition, better rule understanding, and reduced difficulty as key themes, while some students felt the structured nature of GPPs restricted their own way of reasoning. These findings reinforce that GPPs can effectively combine the benefits of worked examples and problem solving practice, but could be further improved by individual adaptation.

Problem

• Improving logic learning with guided Parsons problems
• Enhancing rule application accuracy in students
• Balancing structured guidance and student reasoning freedom

Innovation

• Guided Parsons problems provide step-specific hints
• GPPs improve rule application accuracy significantly
• GPPs combine worked examples and problem solving