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Office, E.E. Machine Learning in Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/60075 (accessed on 05 October 2026).
Office EE. Machine Learning in Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/60075. Accessed October 05, 2026.
Office, Encyclopedia Editorial. "Machine Learning in Education" Encyclopedia, https://encyclopedia.pub/entry/60075 (accessed October 05, 2026).
Office, E.E. (2026, September 17). Machine Learning in Education. In Encyclopedia. https://encyclopedia.pub/entry/60075
Office, Encyclopedia Editorial. "Machine Learning in Education." Encyclopedia. Web. 17 September, 2026.
Machine Learning in Education
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Machine learning in education is the application of data-driven algorithms that learn patterns from educational data to support or automate tasks in teaching, learning, and educational management [1][2][3][4]. It includes methods such as classification, prediction, clustering, and recommendation applied to data from learning platforms, assessments, attendance, and interaction logs to identify learning needs, forecast performance, personalize content, and detect risks such as dropout [1][2]. Machine learning in education is characterized by model training on historical or real-time data, iterative performance evaluation, and integration into educational decision-making systems [2][3][4]. In research and practice, the term denotes the use of machine learning as an analytic and adaptive component of educational technology, distinct from rule-based systems that do not improve through data-driven learning.

machine learning in education educational data mining learning analytics adaptive learning

References

  1. Olaf Zawacki-Richter; Victoria I. Marín; Melissa Bond; Franziska Gouverneur; Systematic review of research on artificial intelligence applications in higher education – where are the educators?. Int. J. Educ. Technol. High. Educ. 2019, 16, 1-27. [CrossRef]
  2. Cristóbal Romero; Sebastián Ventura; Educational Data Mining: A Review of the State of the Art. IEEE Trans. Syst. Man Cybern. Part C (Applications Rev. 2010, 40, 601-618. [CrossRef]
  3. Mamdouh Alenezi; Saja Wardat; Mohammed Akour; The Need of Integrating Digital Education in Higher Education: Challenges and Opportunities. Sustainability 2023, 15, 4782. [CrossRef]
  4. Roberto Martinez-Maldonado; Vanessa Echeverria; Olga C. Santos; Augusto Dias Pereira Dos Santos; Kalina Yacef. Physical learning analytics; Association for Computing Machinery (ACM): New York, NY, United States, 2018; pp. 375-379. [CrossRef]
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Subjects: Computer Science, Information Systems
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Update Date: 17 Sep 2026
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