Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits)

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

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Analysis of Student Academic Performance and Social Media Activities by Using Data Mining Approach

Proceedings of the 2020 The 6th International Conference on E-Business and Applications ◽

10.1145/3387263.3387279 ◽

2020 ◽

Author(s):

Enda Esyudha Pratama ◽

Eva Faja Ripanti

Keyword(s):

Data Mining ◽

Social Media ◽

Academic Performance ◽

Data Mining Approach ◽

Student Academic Performance ◽

Using Data

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An Analysis on Student Academic Performance by Using Decision Tree Models

The Journal of Social Sciences Research ◽

10.32861/jssr.spi6.615.620 ◽

2018 ◽

pp. 615-620

Author(s):

Jastini Mohd. Jamil ◽

Nurul Farahin Mohd Pauzi ◽

Izwan Nizal Mohd. Shahara Nee

Keyword(s):

Data Mining ◽

Academic Performance ◽

Decision Tree ◽

Large Volume ◽

Educational Data Mining ◽

Data Mining Techniques ◽

Student Academic Performance ◽

Tree Models ◽

Student’S Performance ◽

Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

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