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Research on Reconstructing the Educational Value of Failure Experiences in Gamifed Learning

作者:Wang Lei

院校:Hubei University of Economics, Wuhan 430205, Hubei, China

摘要:This study explores the reconstruction of the educational value of failure experiences in gamifed learning, aiming to provide theoretical and practical guidance for optimizing learning processes. The research systematically analyzes the current status and problems of failure experiences in gamified learning, and clarifies their educational values in three dimensions: cognitive development, emotional attitudes, and social skills. Theoretically, failure experiences can stimulate learners’reflection, deepen knowledge understanding, cultivate critical thinking, enhance frustration tolerance, motivate learning, and improve social interaction abilities. Practically, strategies for reconstructing educational value are proposed, including goal-oriented design with contextual elements, cognitive guidance through failure education and role models, and interactive reinforcement via timely feedback and technological tools. Empirical results show that the experimental group using reconstructed strategies signifcantly outperforms the control group in learning motivation, academic performance, and attitudes toward gamifed learning. The study flls academic gaps in gamifed learning theories, ofers actionable insights for educators and developers, and highlights the need for expanded sample scope, enhanced qualitative research, and long-term strategy validation in future studies.

关键词

Gamified learning; Failure experiences; Educational value; Reconstruction strategy; Cognitive development; Emotional attitudes; Social skills

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参考

[1] Lutovac, S., & Kaasila, R. (2021). Towards conceptualising failure in mathematics as an autobiographical experience. European Journal of Teacher Education, 44(1), 35 - 49.
[2] Powers, F. E., & Moore, R. L. (2021). When Failure Is an Option: a Scoping Review of Failure States in Game-Based Learning. TechTrends, 65(3), 425 - 436.
[3] Hellerstedt,A., & Mozelius, P. (2019). Game - based learning - a long history.
[4] Chivukula,A., Yang, X., & Liu, W. (2020). Game TheoreticalAdversarial Deep Learning With VariationalAdversaries. IEEE Transactions on Knowledge and Data Engineering.

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