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Educational Anomaly Analytics: Features, Methods, and Challenges.

Teng Guo1, Xiaomei Bai2, Xue Tian3

  • 1School of Software, Dalian University of Technology, Dalian, China.

Frontiers in Big Data
|January 31, 2022
PubMed
Summary
This summary is machine-generated.

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This study reviews data-driven methods for analyzing educational anomalies, such as predicting course failure and student dropout. It highlights the potential of educational data analytics to improve student outcomes and institutional quality.

Area of Science:

  • Education
  • Computer Science
  • Data Analytics

Background:

  • Educational anomalies impact student careers and university retention.
  • Traditional data collection methods (e.g., questionnaires) are inefficient for large-scale analysis.
  • The rise of digital education and educational management systems offers vast data resources.

Purpose of the Study:

  • To provide a comprehensive review of data-driven educational anomaly analytics.
  • To focus on key research areas: course failure, dropout, mental health, graduation difficulty, and employment difficulty.
  • To identify challenges and inform educational policymaking.

Main Methods:

  • Systematic review of data-driven methodologies in educational anomaly analytics.
  • Categorization of research into five key areas: prediction of course failure, dropout, mental health issues, graduation difficulty, and employment difficulty.
Keywords:
anomaly analyticsanomaly detectiondata scienceeducational big datamachine learning

Related Experiment Videos

  • Analysis of methodological approaches and challenges in current research.

    • Main Results:

    • Educational anomaly analytics is an emerging interdisciplinary field.
    • Data-driven approaches offer a more scalable and efficient alternative to traditional methods.
    • Significant research attention is focused on predicting student outcomes and challenges.

    Conclusions:

    • Data-driven educational anomaly analytics holds great promise for improving education.
    • Addressing current research challenges is crucial for advancing the field.
    • This review provides a foundation for future research and policy development.