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This research work, based on a bibliometric study, is carried out on the Assessment of Students’ Performance (ASP) using Machine Learning (ML). The study covers the period from 2004 to 2024 and the research was made on the Web of Science (WoS) database with the keywords: “student” and “performance” and “’machine learning”. A total of 1,452 files were found and scrutinized using Bibliometrix tool. The study highlights 7,392 authors who have been interested in the subject over the last twenty years, hence, the increasing interest to the topic. It also reveals that the three most relevant sources are undoubtedly “IEEE Access”, “Applied Sciences-Basel” and “Education and Information Technologies”. Moreover, the two most famous countries are USA and China with more than 1,400 popular documents. However, no African country appears among the twenty most popular countries. These results show that the ASP using ML is not sufficiently explored by African researchers affiliated to African universities.

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Assessment of Students’ Performance Using Machine Learning: A Bibliometric Study

  • 会议论文

  • 发表日期：

    2026年

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  • Michel Hountondji,
  • Pélagie Houngue,
  • Theophile Komlan Dagba

摘要

This research work, based on a bibliometric study, is carried out on the Assessment of Students’ Performance (ASP) using Machine Learning (ML). The study covers the period from 2004 to 2024 and the research was made on the Web of Science (WoS) database with the keywords: “student” and “performance” and “’machine learning”. A total of 1,452 files were found and scrutinized using Bibliometrix tool. The study highlights 7,392 authors who have been interested in the subject over the last twenty years, hence, the increasing interest to the topic. It also reveals that the three most relevant sources are undoubtedly “IEEE Access”, “Applied Sciences-Basel” and “Education and Information Technologies”. Moreover, the two most famous countries are USA and China with more than 1,400 popular documents. However, no African country appears among the twenty most popular countries. These results show that the ASP using ML is not sufficiently explored by African researchers affiliated to African universities.