Procedia Computer Science· 2015· Review
A Review on Predicting Student's Performance Using Data Mining Techniques
- 834citations
- 2015year
Short summary
This review identifies data mining techniques as a promising approach to predict student performance and identify key factors affecting achievement in Malaysian educational institutions.
AI-generated from the title and abstract; the full text is not read.
Key points
- Data mining techniques can be used to predict student performance.
- Existing prediction methods are insufficient for Malaysian institutions.
- There is a lack of investigation into factors affecting student achievement in the Malaysian context.
- Educational data mining can improve student achievement effectively and efficiently.
AI-generated from the title and abstract; the full text is not read.
Abstract
Predicting students performance becomes more challenging due to the large volume of data in educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor the student progress and performance is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of students in Malaysian institutions. Second is due to the lack of investigations on the factors affecting students achievements in particular courses within Malaysian context. Therefore, a systematical literature review on predicting student performance by using data mining techniques is proposed to improve students achievements. The main objective of this paper is to provide an overview on the data mining techniques that have been used to predict students performance. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a students data. We could actually improve students achievement and success more effectively in an efficient way using educational data mining techniques. It could bring the benefits and impacts to students, educators and academic institutions.
The authors' abstract, as published at the source. Procedia Computer Science, 2015 · DOI ↗
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Field: Computer Science Applications
Computer Science ApplicationsComputer Science