Pdf Using Data Mining To Predict Secondary School Student Performance

Pdf Using Data Mining To Predict Secondary School Student Performance Through extensive search of the literature and discussion with experts on student performance, a number of factors that are considered to have influence on the performance of a student were identified. We have distributed the data set into 5 other datasets to predict the result for scholarship, students’ performance, students’ behavior, aptitude skills and overall performance.

Pdf Student Performance Prediction Using Data Mining Techniques The present work intends to approach student achievement in secondary education using bi dm techniques. recent real world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The proposed study investigates the role of some key demographic factors in addition to academic factors as well as the academic performance of high school students to predict success by means of utilizing data mining techniques. Recent real world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and ques tionnaires. There are several studies published up to the date that used data mining techniques for predicting the students’ exam performance. the main goal of these studies is to classify the entire students into two classes, i.e., "pass" or "fail".

Pdf Data Mining Approach For Predicting Student Performance Recent real world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and ques tionnaires. There are several studies published up to the date that used data mining techniques for predicting the students’ exam performance. the main goal of these studies is to classify the entire students into two classes, i.e., "pass" or "fail". In this paper, we have addressed the prediction of secondary student grades of two core classes (mathematics and portuguese) by using past school grades (first and second periods), demographic, social and other school related data. In 2016, mueen et al. [5] developed a model using data mining techniques for predicting the academic performance of students. they used three classification techniques i.e. decision tree multilayer perception, and naïve bayes using weka tool.