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.
Learning problem decomposition-recomposition with data-driven chunky parsons problem within an intelligent logic tutor.
Problem decomposition into sub-problems or subgoals and
recomposition of the solutions to the subgoals into one complete
solution is a common strategy to reduce difficulties in
structured problem solving. In this study, we use a datadriven
graph-mining-based method to decompose historical
student solutions of logic-proof problems into Chunks. We
design a new problem type where we present these chunks
in a Parsons Problem fashion and asked students to reconstruct
the complete solution from the chunks. We incorporated
these problems within an intelligent logic tutor
and called them Chunky Parsons Problems (CPP). These
problems demonstrate the process of problem decomposition
to students and require them to pay attention to the
decomposed solution while they reconstruct the complete
solution. The aim of introducing CPP was to improve students’
problem-solving skills and performance by improving
their decomposition-recomposition skills without significantly
increasing training difficulty. Our analysis showed
that CPPs could be as easy as Worked Examples (WE).
And, students who received CPP with simple explanations
attached to the chunks had marginally higher scores than
those who received CPPs without explanation or did not
receive them. Also, the normalized learning gain of these
students shifted more towards the positive side than other
students. Finally, as we looked into their proof-construction traces in posttest problems, we observed them to form identifiable
chunks aligned with those found in historical solutions
with higher efficiency.
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- Award ID(s):
- 2013502
- NSF-PAR ID:
- 10525859
- Publisher / Repository:
- Springer
- Date Published:
- Format(s):
- Medium: X
- Location:
- In Proceedings of the 16th International Conference on Educational Data Mining (EDM)
- Sponsoring Org:
- National Science Foundation
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