Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Sutapa Dey Tithi, Xiaoyi Tian, Min Chi, Tiffany Barnes

TL;DR

The paper addresses learning logic proofs with Parsons problems by introducing Guided Parsons problems (GPPs) that add subgoal-based chunking, step-specific hints, and a self-explanation module. A controlled study comparing GPPs against worked examples and problem solving shows that GPPs significantly improve rule application accuracy and reduce incorrect steps, especially for learners with low prior knowledge, albeit with higher initial training time. The findings support the idea that combining worked-example style scaffolding with active subgoal solving enhances cognitive engagement and learning efficiency, while also highlighting potential limitations due to fixed workflows and the need for adaptive support. Practically, GPPs offer a promising, more effective approach to logic education that can be extended with adaptive hints and validated across platforms and over longer-term retention.

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.

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

TL;DR

The paper addresses learning logic proofs with Parsons problems by introducing Guided Parsons problems (GPPs) that add subgoal-based chunking, step-specific hints, and a self-explanation module. A controlled study comparing GPPs against worked examples and problem solving shows that GPPs significantly improve rule application accuracy and reduce incorrect steps, especially for learners with low prior knowledge, albeit with higher initial training time. The findings support the idea that combining worked-example style scaffolding with active subgoal solving enhances cognitive engagement and learning efficiency, while also highlighting potential limitations due to fixed workflows and the need for adaptive support. Practically, GPPs offer a promising, more effective approach to logic education that can be extended with adaptive hints and validated across platforms and over longer-term retention.

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.
Paper Structure (10 sections, 1 equation, 2 figures, 4 tables)

This paper contains 10 sections, 1 equation, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Tutor Context
  4. Methods
  5. Results
  6. RQ1: Student Performance and Learning
  7. RQ2: Impact of GPP by Pretest Score
  8. RQ3: GPP Self-explanation Themes
  9. Discussion
  10. Conclusion and Future Work

Figures (2)

  • Figure 1: The tutor interfaces for three different problem types: (a) Guided Parsons Problems (GPP), where some steps are already done and hints are provided for students to complete missing steps, (b) Worked Example (WE), where the tutor performs each step for students, and (c) Problem Solving (PS), where students derive all the steps (nodes) of a proof.
  • Figure 2: Comparisons of incorrect steps and backward attempts across conditions during training level-end test and posttest phases.