Empowering students through active learning in educational big data analytics

Abstract

Abstract 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.

Keywords

• Active learning
• Inquiry-based learning
• ChatGPT Python APIs
• Exploratory Data Analysis (EDA)
• Text mining
• Latent Dirichlet Allocation (LDA)