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A Systematic Review of Big Data Driven Education Evaluation

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  • Lin Lin
  • Danhua Zhou
  • Jingying Wang
  • Yu Wang

Abstract

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.

Suggested Citation

  • Lin Lin & Danhua Zhou & Jingying Wang & Yu Wang, 2024. "A Systematic Review of Big Data Driven Education Evaluation," SAGE Open, , vol. 14(2), pages 21582440241, April.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:2:p:21582440241242180
    DOI: 10.1177/21582440241242180
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    References listed on IDEAS

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    1. Kanadpriya Basu & Treena Basu & Ron Buckmire & Nishu Lal, 2019. "Predictive Models of Student College Commitment Decisions Using Machine Learning," Data, MDPI, vol. 4(2), pages 1-18, May.
    2. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    3. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
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