Predicting Student Performance using Data Mining Techniques in Libyan High Schools - Saleh, Mahjouba Ali; Palaniappan, Sellappan; Abdalla, Nasaraldeen Ali Alghazali (Universitas Negeri Semarang, 2021)
Student performance in schools have been always the key factor for the teacher ability to teach and what brings good reputation to the school. Recently schools in Libya are facing an issue trying to figure out why students perform poorly in certain subjects and how can they know how they will perform next in the future in coming semesters in perspective subject. There are several methods proposed to predict the student’s performance, using data mining. This paper proposes using Math and English as key factors to predict the performance of the students. results and findings of the presented method in terms of predicting students’ performance based on their grades in Math and English. The results are divided in to three main sections clustering analysis using k-mean algorithm, classification analysis was done using two rounds first using Gain Ratio Evaluations to find out the top attributes that used by J84 algorithm in second round of classification, and rule association analysis using A priori algorithm. Rule association analysis is applied for the clusters generate by clustering analysis to generate the rules associated with each cluster. For each section, a list of inputs is presented with the scale used for the values followed by the results of the algorithm and explanation for the finding.