Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Abstract

One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student's performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

Predicting academic performance: a systematic literature review

“…In particular, the bounds of the search performed in the review or survey were not always well defined. Of the 13 articles, eight [160,185,198,204,252,253,269,335] listed at least some of the sources, venues or fields that they had searched. Only six [198,204,252,253,269,335] listed the keywords or search terms used.…”

“…Of the 13 articles, eight [160,185,198,204,252,253,269,335] listed at least some of the sources, venues or fields that they had searched. Only six [198,204,252,253,269,335] listed the keywords or search terms used. Eight [99,160,185,198,252,253,269,335] described a year range or the absence thereof.…”

“…Only six [198,204,252,253,269,335] listed the keywords or search terms used. Eight [99,160,185,198,252,253,269,335] described a year range or the absence thereof. All surveyed a limited number or range of articles.…”

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

“…the developed methods show a high accuracy of the task of identifying the material class, which determines the possibility of their practical use for solving such tasks; the Method 2 should be used for solving classification tasks in the Material Science field in case that doesn't impose restrictions for their training time; the Method 1 shows the greatest accuracy of the solution of the classification task among all the considered ones. It only shows a slightly worse result compared to the basic method for the performance of the training procedure; based on the accuracy and speed of the Method 1 work, it can be used to solve applied classification tasks in the case of large dimensions of the input data; the proposed approach shows a high accuracy of calculating the Wiener polynomial coefficients, which enables its application in the fields of medicine [27], education [28], image processing [29], in particular, for solving tasks of both classification and regression.…”

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

“…Among the many predictive algorithms, the KNN is the simple, reliable, and one of the most commonly used algorithm for such prediction and classification purposes [6]. It has been used in crime mining [7], educational data mining [8], and healthcare services [9] and so on. However, the KNN algorithm is vulnerable to noise or irrelevant data features [10].…”