Data Mining Approach to Detect Student's Exams Performance Factors.

Document Type : Original full papers (regular papers)

Authors

1 Instructor

2 Computer Science Department, Faculty of Computers and Information, Fayoum university, Egypt, Fayoum

3 Information system Department, Faculty of Computers and Information, Fayoum university, Egypt, Fayoum

Abstract

The valuable procedures and evaluation couldn't reveal the valuable information concealed within the student’s feedback on their achievements. Also, the rapid proliferation of academic failure has become the systemic challenge of the modern educational system. Therefore, identifying the variables that enhance student achievement and fulfilment was given top priority. Consequently, statistical analysis and data mining techniques were widely applied in a variety of disciplines, including education. Gender, father's occupation, student location, and school attendance may all have a role in this study's findings. This research set out to look into how these factors affected student performance. One hundred and ninety-nine Fayoum high school students were randomly selected for this descriptive correlational study. The information was gleaned via student surveys and the participants' own self-reported demographics. The information was gleaned via student surveys and the participants' own self-reported demographics. Descriptive statistics were calculated using mean and standard deviation; inferential statistics, such as Pearson correlation and linear regression analysis, were computed using SPSS v26. The outcomes indicated a statistically significant correlation (p 0.05) between a student's father's occupation and where he lived with the student's academic performance. A substantial (p 0.05) relationship was found between parent employment and student location, as well as academic performance. According to the results, there was a significant correlation between students' success and the occupation and location of their fathers.

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