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Learning to derive subgoals reduces the gap between experts and students andmakes students prepared for future problem solving. Researchers have exploredsubgoal labeled instructional materials with explanations in traditionalproblem solving and within tutoring systems to help novices learn to subgoal.However, only a little research is found on problem-solving strategies inrelationship with subgoal learning. Also, these strategies are under-exploredwithin computer-based tutors and learning environments. Backwardproblem-solving strategy is closely related to the process of subgoaling, whereproblem solving iteratively refines the goal into a new subgoal to reducedifficulty. In this paper, we explore a training strategy for backward strategylearning within an intelligent logic tutor that teaches logic proofconstruction. The training session involved backward worked examples (BWE) andproblem-solving (BPS) to help students learn backward strategy towardsimproving their subgoaling and problem-solving skills. To evaluate the trainingstrategy, we analyzed students' 1) experience with and engagement in learningbackward strategy, 2) performance, and 3) proof construction approaches in newproblems that they solved independently without tutor help after each level oftraining and in post-test. Our results showed that, when new problems weregiven to solve without any tutor help, students who were trained with both BWEand BPS outperformed students who received none of the treatment or only BWEduring training. Additionally, students trained with both BWE and BPS derivedsubgoals during proof construction with significantly higher efficiency thanthe other two groups.

Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving