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Empowering students through active learning in educational big data analytics

Smart Learning Environments 2024年第1期11卷 14页

作者： Tsai, Yun-Cheng Department of Technology Application and Human Resource Development National Taiwan Normal University No. 162 He-Ping East Road Sec1 Taipei 10610 Taiwan

Purpose: This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students’ data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods: The study uses a teaching strategy that combines structured and unstructured data analysis using python tools and chatgpt apis. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results: The paper offers insights into how the teaching strategy effectively enhances students’ data analysis and critical thinking skills. It investigates the specific impact of active learning on students’ engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion: The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session’s effectiveness to accumulate in students’ personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era. © The Author(s) 2024.

关键词： Active learning chatgpt python apis Co-occurrence network Exploratory Data Analysis (EDA) Inquiry-based learning Latent Dirichlet Allocation (LDA) Minimum Viable Product (MVP) Principal Component Analysis (PCA) Text mining

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