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Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19.

Ahajjam Tarik¹, Haidar Aissa¹, Farhaoui Yousef¹

¹L-STI,T-IDMS, University of Moulay Ismail, Faculty of Science and Technics, Errachidia, Morocco.

Procedia Computer Science

|May 24, 2021

Summary

This study uses artificial intelligence (AI) to predict student performance in Morocco during COVID-19. An AI-powered recommendation system was developed to identify factors influencing academic success.

Keywords:

COVID-19 Data Analysis Data Science Machine Learning Recommendation artificial intelligent high school prediction

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Area of Science:

Educational Technology
Artificial Intelligence
Machine Learning

Background:

The COVID-19 pandemic significantly impacted education globally, affecting student performance.
Assessing and predicting student academic performance is crucial for timely interventions.
Moroccan higher education institutions face challenges in monitoring student progress, especially in remote regions like Guelmim Oued Noun.

Purpose of the Study:

To develop and evaluate an intelligent recommendation system for predicting the academic performance of students in the Guelmim Oued Noun region.
To leverage artificial intelligence techniques to identify key factors influencing student success during the COVID-19 pandemic.
To provide insights for educators and policymakers to support student learning outcomes.

Main Methods:

Utilized artificial intelligence algorithms to build a predictive model.
Developed a recommendation system to analyze student data and performance indicators.
Applied machine learning techniques to identify significant variables affecting academic results.

Main Results:

The AI-based recommendation system demonstrated effectiveness in predicting student performance.
Identified specific factors correlating with academic success and challenges during the pandemic.
The system offers personalized recommendations for student support.

Conclusions:

Artificial intelligence offers a viable solution for predicting and improving student academic performance in educational settings.
The developed system can assist educational institutions in proactively addressing student needs.
Further research can refine the AI model for broader application in educational contexts.