Student Academic Performance Prediction using Supervised Learning Techniques

Abstract

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model.
 
 Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. 
 
 This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

“…Outcomes reveal better accuracy due to the reference of family expenditure, such as a natural gas, electricity, telephone, water, and accommodation, and students' personal data, such as gender, marital status, and employment, etc. To enhance engineering students' performance, a study by [1] identified the factors that can affect student success in this tough major. The study focused on the use of J48 and REP Tree algorithms to elicit the type of relationship between social parameters and student performance and predicting students' performance in their third semester.…”

“…The prediction of student academic performance helps in identifying weak students who will struggle with their studies. Science and IT majors are among the hardest at college level [1], [2]. Therefore, the management of computer and IT related institutions take essential steps to detect and correct the way for weak students.…”

Multi-Class Assessment Based on Random Forests

“…As we have just seen, the studies seem to focus on the same algorithms: the SVM, the Naïve Bayes, the decision trees, as well as the logistic regression [1,[4][5][6]8]. The study of existing works has led, on many occasions, to note the points which make this task difficult [8]. These difficulties are intrinsically linked to the nature of the data to be considered.…”

“…Another point that can greatly reduce the performance of predictions is the fact that the datasets considered are generally poorly balanced. It is only very recently that some studies are interested in Random Forests (RF) to solve this problem [2,[8][9][10].…”

Effects of COVID-19 Pandemic on University Students’ Learning

“…Various supervised learning algorithms and neural networks from the first group are applicable for educational data: for example, J48, Non-Nested Generalisation (NNge) and Multilayer Perceptron (MLP) [15], Random Forest [16], ensemble [17], Deep Learning [18,19]. Clustering methods like k-means [20,21], document frequency method and support vector machines [22] for students' data also belong to this group.…”