Tracing Students’ Learning Behaviors in Statistical Practice Sessions: What Do Visualizations of Learning Logs Tell Us?

Author: Hsiu-Ping Yueh (Department of Psychology/ Department of Bio-Industry Communication and Development, National Taiwan University), Chia-Yu Hsu (Graduate Institute of Informatics, Kyoto University), Weijane Lin (Department of Library and Information Science, National Taiwan University)

Vol.&No.：Vol. 69, No. 3
Date：September 2024
Pages：171-194
DOI：https://doi.org/10.6209/JORIES.202409_69(3).0006

Abstract：
Observing the learning behaviors of large groups of students during hands-on statistical learning activities is challenging for instructors and calls for radical new measures. This study adopted an online learning platform that recorded student behaviors as learning logs while they completed drills and practices. A visualization approach was used to analyze the time-series behavioral data with direct and detailed measurements in a real-time classroom setting. A total of 7,869 learning logs from 58 college students in a statistical practice session were collected and visualized to map student behaviors to the classroom environment, instruction, and phases of the course. The key findings suggested that students with higher test scores exhibited better strategic learning behaviors by referencing course materials frequently during class and tests. High performers actively followed lectures and instructors and strategically located and used appropriate learning resources during tests. In contrast, low performers were reluctant to use lectures and handouts referentially but relied more on the instructor’s in-person guidance. The results supported the idea that learning logs tracking student movements, trajectories, and annotations could complement the assessment of quantitative test scores. Visualized analytics allowed instructors to understand students’ learning and test-taking strategies at a group level, and visualizing student behaviors as assessment results could guide instructors in providing timely learning resources and support to enhance teaching interactions. This study filled an important research gap by connecting the abundant behavioral information to its practical applications in statistics teaching and learning by bridging the divide between the vast quantities of educational data available and its meaningful use in instructional settings. The findings also provided empirical support for the feasibility and importance of implementing learning analytics based on the paradigm of portfolio assessment, with specific implications for teaching statistics and related practice courses.

Keywords：learning analytics, statistical practices, visualization, portfolio assessment

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