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Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

Author

Listed:
  • Younyoung Choi

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

  • Jigeun Kim

    (Institute of Psychological Innovation, Yonsei University, Seoul 03722, Korea)

Abstract

A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10149-:d:632980
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    References listed on IDEAS

    as
    1. 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.
    2. May Portuguez Castro & Marcela Georgina Gómez Zermeño, 2020. "Challenge Based Learning: Innovative Pedagogy for Sustainability through e-Learning in Higher Education," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    3. Jeongju Lee & Hae-Deok Song & Ah Jeong Hong, 2019. "Exploring Factors, and Indicators for Measuring Students’ Sustainable Engagement in e-Learning," Sustainability, MDPI, vol. 11(4), pages 1-12, February.
    4. Wahab Ali, 2020. "Online and Remote Learning in Higher Education Institutes: A Necessity in light of COVID-19 Pandemic," Higher Education Studies, Canadian Center of Science and Education, vol. 10(3), pages 1-16, September.
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    Cited by:

    1. Sébastien Jacques & Abdeldjalil Ouahabi & Zoe Kanetaki, 2023. "Post-COVID-19 Education for a Sustainable Future: Challenges, Emerging Technologies and Trends," Sustainability, MDPI, vol. 15(8), pages 1-4, April.
    2. Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches," Mathematics, MDPI, vol. 10(7), pages 1-19, April.

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