Kabdo Choi
KAIST, Daejeon, Korea, Republic of
Designing solution plans before writing code is critical for successful algorithmic problem-solving. Novices, however, often plan on-the-fly during implementation, resulting in unsuccessful problem-solving due to lack of mental organization of the solution. Research shows that subgoal learning helps learners develop more complete solution plans by enhancing their understanding of the high-level solution structure. However, expert-created materials such as subgoal labels are necessary to provide learning benefits from subgoal learning, which are a scarce resource in self-learning due to limited availability and high cost. We propose a learnersourcing workflow that collects high-quality subgoal labels from learners by helping them improve their label quality. We implemented the workflow into AlgoSolve, a prototype interface that supports subgoal learning for algorithmic problems. A between-subjects study with 63 problem-solving novices revealed that AlgoSolve helped learners create higher-quality labels and more complete solution plans, compared to a baseline method known to be effective in subgoal learning.
https://dl.acm.org/doi/abs/10.1145/3491102.3501917
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)