A Systematic Review on Student Performance Analysis Using Machine Learning
DOI:
https://doi.org/10.18486/ijcsnt/14.2.008Keywords:
EDM, Student Performance, Machine Learning, CNN, Early PredictionAbstract
In educational system, the student performance is a core component which pays a significant role in the development of any school/college. The student performance handling is very crucial in learning process and it is one of the significant factors of learning. In the field of research educational data mining (EDM), by using the data knowledge education system can be improved. Advanced machine learning (ML) methods can predict student’s performance with key features based on academic, behavioral, and demographic data. Significant works have predicted the student’s performance based on the primary and secondary data sets derived from the student’s existing data. These works have accurately predicted student’s performance but did not provide the metrics as suggestions for improved performance. In this paper, we present the review of literature on the field of early student performance prediction using machine learning and also discuss machine learning techniques with their advantages and disadvantages.
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