Prediction of Students’ Academic Performance using Machine Learning
Alkaf, Dhekra Bajammal, Faigah Asrar, Manal
Alkaf, Dhekra
Bajammal, Faigah
Asrar, Manal
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Abstract
Education is an important factor of a civilization and has been of importance throughout history. The right education results in a fruitful future of change, development, and globalization for coming generations. Thus, it is of great importance to constantly re-evaluate the techniques used in modern education systems with the aim of continually improving the provision and quality of education. In a time where technology thrives to support human development in numerous ways, machine learning can be a great asset to the education system. Through the application of machine learning on educational data through prediction, universities and educational institutions will be able to improve teaching and learning outcomes, as well as provide the right support for the different types of students at the institution. The main objective of this project is to provide an accurate machine learning model that can predict students’ performance early on to support them on their journeys. This project will be fulfilled by continuous research, working closely with large numbers of data, and experimenting with different algorithms to reach the best results possible. In this paper Random Forest resulted in being the best performing algorithm for the intended model.