Integrating Lesson Study and Talking Stick Model for Enhancing Student Activity and Conceptual Understanding of Inverse Functions: Implications for AI-Supported Higher Mathematics Education
Keywords:
Lesson Study, Talking Stick, Inverse Functions, Student Activity, Conceptual understanding, Collaborative Action Research, Computational Mathematics EducationAbstract
This paper reports on an effort to improve student learning in a tertiary mathematics course by combining two approaches: Lesson Study (LS) and the Talking Stick (TS) model. The focus was inverse functions, a topic where students at UNIROW Tuban consistently struggled — pre-study data showed 63% low participation and 68% misconception rates around domain and codomain. We used a Collaborative Action Research (CAR) design with two plan-do-see cycles and 30 third-semester students. Data came from observation, activity questionnaires, and pre/post-tests, analyzed both quantitatively and qualitatively. Results were encouraging. Participation climbed from 63% to 86%. Conceptual understanding scores rose from 68.3 to 84.7, a statistically significant gain (t(29) = 11.45, p < 0.001, Cohen’s d = 1.35, N-Gain = 0.74). Domain/codomain errors dropped sharply, from 68% to just 12%. A strong correlation between participation and learning gains (r = 0.82) suggested the two were genuinely connected. We also discuss how the habits of reasoning developed through TS may prepare students to engage more critically with AI-assisted mathematical tools. The LS-TS combination worked here as a two-level model — improving teaching through reflection and improving learning through structured accountability. While AI tools were not directly implemented in this study, the findings highlight foundational cognitive practices — verbal reasoning, self-monitoring, and conceptual accountability — that are necessary for effective and critical engagement with AI-supported mathematical systems
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