Survey of Machine Learning Techniques for Student Profile Modeling

Authors

  • Touria Hamim University Hassan II, Casablanca, Morocco
  • Faouzia Benabbou University Hassan II, Casablanca, Morocco
  • Nawal Sael University Hassan II, Casablanca, Morocco

DOI:

https://doi.org/10.3991/ijet.v16i04.18643

Keywords:

Profile modeling, Student profile, Machine learning

Abstract


Developments in information technology have led to the emergence of several online platforms for educational purposes, such as e-learning platforms, e-recommendation systems, e-recruitment system, etc. These systems exploit advances in Machine Learning to provide services tailored to the needs and profile of students. In this paper, we propose a state of art on student profile modeling using machine learning techniques during last four years. We aim to analyze the most used and most efficient machine learning techniques in both online and face-to-face education context, for different objectives such as failure, dropout, orientation, academic performance, etc. and also analyze the dominant features used for each objective in order to achieve a global view of the student profile model. Decision Tree is the most used and the most efficient by most of research studies. And academic, personal identity and online behavior are the top characteristics used for the student profile. To strengthen the survey results, an experiment was carried out, based on the application of machine learning techniques extracted from the state of art analysis, on the same datasets. Decision tree gave the highest performance, which confirms the survey results.

Author Biographies

Touria Hamim, University Hassan II, Casablanca, Morocco

Laboratory of Modeling and Information Technology, Faculty of sciences Ben M’SIK

Faouzia Benabbou, University Hassan II, Casablanca, Morocco

Laboratory of Modeling and Information Technology, Faculty of sciences Ben M’SIK

Nawal Sael, University Hassan II, Casablanca, Morocco

Laboratory of Modeling and Information Technology, Faculty of sciences Ben M’SIK

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Published

2021-02-26

How to Cite

Hamim, T., Benabbou, F., & Sael, N. (2021). Survey of Machine Learning Techniques for Student Profile Modeling. International Journal of Emerging Technologies in Learning (iJET), 16(04), pp. 136–151. https://doi.org/10.3991/ijet.v16i04.18643

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Section

Papers