Using Data Mining Applications to Analyze the Grades of Students in the Department of Construction and Civil Engineering: A Case Study at the Higher Institute of Science and Technology – Kufra

Abstract

This study presents to analyze the grades of students in the department of construction and civil engineering in the department of construction and civil engineering, specializing in architectural drawing and surveying, from the Spring semester (2006-2007) until the Fall semester (2010-2011). The goal was to understand the factors influencing students' grades, as well as the lack of interest in the department due to the difficulty in obtaining grades of previous graduates. To address the issue of student data accumulation. The study also sought to explore various methods to achieve specific results and establish a digital repository of student data to support decision-makers in understanding student outcomes. The significance of the study lies in predicting and analyzing students' grades in the department of construction and civil engineering, as well as understanding students' lack of interest and identifying weaknesses in academic courses. The present study employed an analytical research methodology. The K-Means Clustering Algorithm was used for data mining to identify factors leading to academic underachievement. the study results showed that a repetition of subjects within the curriculum for architectural drawing and surveying for more than one academic semester or course. The study results also showed that the differences in students' grades between the architectural drawing and surveying branches, with the architectural drawing branch having higher grades at 6.65 compared to 5.56 for the surveying branch. Then the study recommended the necessity of building a database for the institute's students and conducting a balance between the subjects and courses for the students of the architectural drawing department, as well as conducting workshops to update the study materials.