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Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System

Author

Listed:
  • Younyoung Choi

    (Department of Adolescent Coaching Counseling, Hanyang Cyber University, Seoul 04763, Korea)

  • Young Il Cho

    (Division of Police Administration, Dongguk University, Seoul 04620, Korea)

Abstract

The sustainable computer-based evaluation system (SCE) is a scenario-based formative evaluation system, in which students are assigned a task during a course. The tasks include the diversity conditions in real-world scenarios. The goals of this system are learning to think as a professional in a certain discipline. While the substantive, psychological, instructional, and task developmental aspects of the assessment have been investigated, few analytic methods have been proposed that allow us to provide feedback to learners in a formative way. The purpose of this paper is to introduce a framework of a learning analytic method including (1) an assessment design through evidence-centered design (ECD), (2) a data mining method using social network analysis, and (3) an analytic method using a Bayesian network. This analytic framework can analyze the learners’ performances based on a computational psychometric framework. The tasks were designed to measure 21st century learning skills. The 250 samples of data collected from the system were analyzed. The results from the social network analysis provide the learning path during a course. In addition, the 21st century learning skills of each learner were inferred from the Bayesian network over multiple time points. Therefore, the learning analytics proposed in this study can offer the student learning progression as well as effective feedback for learning.

Suggested Citation

  • Younyoung Choi & Young Il Cho, 2020. "Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System," Sustainability, MDPI, vol. 12(19), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7950-:d:419633
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    References listed on IDEAS

    as
    1. Jeffrey Mo, 2017. "Collaborative problem solving," PISA in Focus 78, OECD Publishing.
    2. Russell G. Almond & Joris Mulder & Lisa A. Hemat & Duanli Yan, 2009. "Bayesian Network Models for Local Dependence Among Observable Outcome Variables," Journal of Educational and Behavioral Statistics, , vol. 34(4), pages 491-521, December.
    3. Patrick Howard & Catherine O’Brien & Brent Kay & Kristin O’Rourke, 2019. "Leading Educational Change in the 21st Century: Creating Living Schools through Shared Vision and Transformative Governance," Sustainability, MDPI, vol. 11(15), pages 1-13, July.
    4. Robert Mislevy, 1994. "Evidence and inference in educational assessment," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 439-483, December.
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    Cited by:

    1. Younyoung Choi & Jigeun Kim, 2021. "Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis," Sustainability, MDPI, vol. 13(18), pages 1-13, September.
    2. Chung Kwan Lo & Gaowei Chen, 2021. "Improving Experienced Mathematics Teachers’ Classroom Talk: A Visual Learning Analytics Approach to Professional Development," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    3. Younyoung Choi & Hyunwoo Lee, 2022. "Psychometric Properties for Multidimensional Cognitive Load Scale in an E-Learning Environment," IJERPH, MDPI, vol. 19(10), pages 1-12, May.

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