Humans adopt various problem-solving strategies depending
on their mastery level, problem type, and complexity. Many of
these problem-solving strategies have been integrated within intelligent
problem-solvers to solve structured and complex problems efficiently.
One such strategy is the means-ends analysis which involves comparing
the goal and the givens of a problem and iteratively setting up subgoal(s)
at each step until the subgoal(s) are straightforward to derive from the
givens. However, little is known about the impact of explicitly teaching
novices such a strategy for structured problem-solving with tutors. In
this study, we teach novices a subgoal-directed problem-solving strategy
inspired by means-ends analysis using a problem-based training intervention
within an intelligent logic-proof tutor. As we analyzed students’
performance and problem-solving approaches after training, we observed
that the students who learned the strategy used it more when solving
new problems, constructed optimal logic proofs, and outperformed those
who did not learn the strategy.
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Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving
Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.
more » « less- PAR ID:
- 10443974
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- International Journal of Artificial Intelligence in Education
- Volume:
- 34
- Issue:
- 3
- ISSN:
- 1560-4292
- Format(s):
- Medium: X Size: p. 825-861
- Size(s):
- p. 825-861
- Sponsoring Org:
- National Science Foundation
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