Self-efficacy is critical for improving computational thinking in gaming. However, conventional assessment methods like questionnaires can produce disengaged responses or require additional work to understand the questions and have limitations in the assessment of dynamic self-efficacy. To address this challenge, this study proposes a method using in-game features to assess self-efficacy, which can change over the playtime. The employed in-game feature is the Planning Stage, in which learners strategize and prepare their approach before engaging with challenges. We collect data on the time spent in this stage, which is newly proposed in this study. The experiment was conducted with twenty-five students in the Multimedia Technology and Animation Major playing Tenacity-P to accomplish tasks from a computer programming course. To validate the method, we compared self-efficacy results from the game-based approach with those from conventional questionnaires. The findings highlight the need for strategic planning in managing difficult tasks. As challenges increased, performance measures varied more, underscoring the importance of strong problem-solving skills. Efficient strategic planning directly improved self-efficacy, as participants with better planning skills maintained higher self-efficacy despite increasing task difficulty.
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Nobnop et al. (2024) studied this question.