Applying game mechanics and student-generated questions to an online puzzle-based game learning system to promote algorithmic thinking skills

Abstract Algorithmic thinking is a core skill for constructing algorithms to solve problems and for understanding computer science. The purpose of this study was to examine the effects of using game mechanics and a student-generated questions strategy to promote algorithmic thinking skills in an online puzzle-based game learning system. An online puzzle-based game learning system, TGTS (Turtle Graphics Tutorial System), was developed to help students learn algorithmic thinking skills by allowing them to solve puzzles. A quasi-experiment was conducted to examine the effectiveness of using game mechanics alone and using game mechanics plus a student-generated questions strategy. Nine fourth-grade elementary classes (n = 242) were used to form three treatment groups, including one without game mechanics, one using game mechanics, and one using game mechanics plus a student-generated questions strategy. The results indicate that TGTS with game mechanics significantly enhanced algorithmic thinking skills and puzzle-solving performance. Furthermore, although TGTS with game mechanics plus the student-generated questions strategy is less effective than TGTS with only game mechanics in puzzle solving, it is in fact more effective in enhancing the algorithmic thinking skills. Additionally, this study demonstrated that TGTS with game mechanics plus the student-generated questions strategy can enhance students' engagement experiences and willingness to participate. This study can be a reference for designing learning activities and developing an online puzzle-based game learning system to promote students’ learning of algorithmic thinking skills.

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