An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods

Evangelista, Edmund; Sy, Benedict Descargar

doi:10.11591/ijece.v12i5.pp5226-5235

Cited by 3 publications

(2 citation statements)

References 25 publications

(40 reference statements)

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“…The scatter plot's linear relationship indicates that the model effectively captures the underlying patterns in the data. Additionally, the nature of this relationship, whether positive or negative, provides insights into the direction and strength of the association between the variables being considered [50]. The use of scatter plots as a visualization tool provides valuable insights into the predictive accuracy and performance of the machine learning models used in our analysis.…”

Section: Resultsmentioning

confidence: 99%

Students Final Academic Score Prediction Using Boosting Regression Algorithms

Muhammady,

Nugraha,

Nastiti

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.

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Section: Resultsmentioning

confidence: 99%

Students Final Academic Score Prediction Using Boosting Regression Algorithms

Muhammady,

Nugraha,

Nastiti

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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“…Evangelista and Descargar [17] provided a method for improving the performance prediction of several single classification algorithms by employing them as base classifiers of heterogeneous ensembles and homogeneous ensembles (bagging and boosting) (voting and stacking). Their model needs to perform optimization techniques to find out the algorithm parameters and configuration.…”

Section: Related Workmentioning

confidence: 99%

Early Predicting of Students Performance in Higher Education

Alhazmi

Sheneamer

2023

IEEE Access

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Students learning performance is one of the core components for assessing any educational systems. Students performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability to use data knowledge to improve education systems has led to the development of the field of research known as educational data mining (EDM). EDM is the creation of techniques to investigate data gathered from educational settings, allowing for a more thorough and accurate understanding of students and the improvement of educational outcomes for them. The use of machine learning (ML) technology has increased significantly in recent years. Researchers and teachers can use the measurements of success, failure, dropout, and more provided by the discipline of data mining in education to predict and simulate education processes. Therefore, this work presents an analysis of students performance using data mining methods. The paper presents both clustering and classification techniques to identify the impact of students performance at early stage with on the GPA. For the clustering technique, the paper uses dimensionality reduction mechanism by T-SNE algorithm with various factors at early stage such as admission scores and first level courses, academic achievement tests (AAT) and general aptitude tests (GAT) in order to explore the relationship between these factors and GPA's. For the classification technique, the paper presents experiments on different machine learning models on predicting student performance at early stages using different features including courses' grades and admission tests' scores. We use different assessment metrics to evaluate the quality of the models. The results suggest that educational systems can mitigate the risks of students failures at the early stages.

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