Evaluating Learning Progress and Self-Efficacy in Statistical Data Presentation through the Application of GPT Integrated with Google Colab
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Abstract
In the digital era driven by data, statistical data presentation has become a fundamental skill in analytical thinking and data communication. This is especially crucial for pre-service mathematics teachers who are expected to transfer such competencies to the next generation. However, the complexity of statistical software often creates a technical barrier to developing this competency. This study aimed to design and evaluate learning activities that integrate GPT with Google Colab to reduce such obstacles. The participants were 28 third-year pre-service mathematics teachers who engaged in five learning activities. Their performance was evaluated through pre- and post-tests and a self-efficacy questionnaire. The results revealed a medium level of learning gain (mean n-gain = 0.63) and a high level of self-efficacy (mean = 3.65). These findings highlight the potential of GPT and Google Colab as effective tools for overcoming technical barriers, enhancing content understanding, and preparing future educators for the demands of the digital age. This study specifically evaluated both the learning progress (n-gain) and the self-efficacy of participants, which are central to the research findings.
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