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SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

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  • Zeynab (Artemis) Mohseni

    (Department of Computer Science and Media Technology, Linnaeus University, 351 95 Växjö, Sweden)

  • Rafael M. Martins

    (Department of Computer Science and Media Technology, Linnaeus University, 351 95 Växjö, Sweden)

  • Italo Masiello

    (Department of Computer Science and Media Technology, Linnaeus University, 351 95 Växjö, Sweden)

Abstract

Visual learning analytics (VLA) tools and technologies enable the meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow a better understanding of students’ educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher’s purpose. In this study, we conducted a user study and improved our web-based similarity-based grouping VLA tool, (SBGTool) to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms, and improvement in some visualizations. To counteract color blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the numbers of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students’ engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student–system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool’s efficacy. Finally, we provide the outcomes of the user study that evaluated the tool’s effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.

Suggested Citation

  • Zeynab (Artemis) Mohseni & Rafael M. Martins & Italo Masiello, 2022. "SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes," Data, MDPI, vol. 7(7), pages 1-18, July.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:7:p:98-:d:865081
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    References listed on IDEAS

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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
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