Improving students’ data analysis skills through guided project-based learning: A case report from a technical university - Liao, Yung-Cheng; Chen, Mei-Su (Edelweiss Applied Science and Technology, 2025)
Business intelligence and big data analysis have garnered significant academic and practical attention. However, many educational institutions lack access to large databases or OLAP (Online Analytical Processing) software, which limits opportunities for hands-on practice and experiential learning. To bridge the gap between theoretical knowledge and practical application, this study designed a project-based learning model that simulates offline data analysis using Excel, grounded in the "learning-by-doing" educational theory. The research employed paired-samples Z-tests to evaluate performance across pretest, midtest, and posttest assessments. Additionally, the study examined the relationships between students’ gender, total hours absent, and the number of assignments submitted with their final grades through a multiple regression model. The findings indicated that the guided project-based learning model significantly improved students’ learning outcomes. Furthermore, the number of assignments submitted via e-learning software was positively associated with final grades. The development of project-based learning methods for practical e-learning lessons is crucial for many industries, emphasizing the importance of hands-on practice and completing practical projects. The instructional model proposed in this study offers a feasible solution to existing challenges by utilizing available software and resources to enhance practice opportunities for students.