Improving Academic Performance Prediction by Dealing with Class Imbalance

2009 ◽  
Author(s):  
Nguyen Thai-Nghe ◽  
Andre Busche ◽  
Lars Schmidt-Thieme
Keyword(s):  
Academic Performance ◽  
Performance Prediction ◽  
Class Imbalance

scholarly journals Design and Optimization of a Fuzzy Logic System for Academic Performance Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Juan A. Rojas ◽  
Helbert E. Espitia ◽  
Lilian A. Bejarano
Keyword(s):  
Academic Performance ◽  
Performance Prediction ◽  
Teaching Methods ◽  
Continuous Monitoring ◽  
Educational Institution ◽  
Educational Institutions ◽  
Educational Support ◽  
Design And Optimization ◽  
Monitoring Process ◽  
Logic System

Currently, in Colombia, different problems in education exist; one of them is the inconvenience in tracing and controlling the learning trajectories that decide the topics taught in the country’s educational institutions. This work aims to implement a logic-based system that allows teachers and educational institutions to carry out a continuous monitoring process of students’ academic performance, facilitating early corrections of errors or failures in teaching methods, to promote educational support spaces within the educational institution.

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

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.

scholarly journals Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

2021 ◽  
Author(s):  
Shermain Puah
Keyword(s):  
Academic Performance ◽  
Educational Research ◽  
Performance Prediction ◽  
Predictive Accuracy ◽  
Lasso Regression ◽  
Machine Learning Methods ◽  
Large Numbers ◽  
Unseen Data ◽  
Research In Education ◽  
Science Performance

Predicting students’ academic performance has long been an important area of research in education. Most existing literature have made use of traditional statistical methods that run into the problems of overfitted models, inability to effectively handle large numbers of participants and predictors, and inability to pick out non-linearities that may be present. Regression-based ML methods that can produce highly interpretable yet accurate models for new predictions, are able to provide some solutions to the aforementioned problems. The present study is the first study that develops and compares between traditional MLR methods and regression-based ML methods (i.e. ridge regression, LASSO regression, elastic net, and regression trees) to predict students’ science performance in the PISA 2015. A total of 198,712 students from 60 countries, and 66 student- and school-related predictors were used to develop the predictive models. Predictive accuracy of the various models built were not that different, however, there were significant differences in the predictors identified as most important by the different methods. Although regression-based ML techniques did not outperform traditional MLR, significant advantages for using ML methods were noted and discussed. Moving forward, we strongly believe that there is merit for using such regression-based ML methods in educational research. Educational research can benefit from adopting ML practices and methods to produce models that can not only be used for explaining factors that influence academic performance prediction, but also for making more accurate predictions on unseen data.

An approach to academic performance prediction in tutoring systems based on fuzzy deformable prototypes

2015 ◽  
Vol 5 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Ma-Rosario Vázquez ◽  
Francisco P. Romero ◽  
José A. Olivas ◽  
Eduardo Orbe ◽  
Jesús Serrano-Guerrero
Keyword(s):  
Academic Performance ◽  
Performance Prediction ◽  
Tutoring Systems

scholarly journals Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

2021 ◽  
pp. 1-11
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Shamsul Alam ◽  
Md. Mizanur Rahman ◽  
Md. Abdul Matin
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Huge Amount ◽  
Novel Technique ◽  
Student Academic Performance ◽  
Academic Activities ◽  
Proposed Model ◽  
Prediction Systems

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

scholarly journals Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network

2019 ◽  
Vol 5 (4) ◽  
pp. 61
Author(s):  
Shamsuddeen Suleiman ◽  
Ahmad Lawal ◽  
Umar Usman ◽  
Shehu Usman Gulumbe ◽  
Aminu Bui Muhammad
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Academic Performance ◽  
Performance Prediction
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