Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

Hernández, Carlos Felipe Rodríguez; Musso, Mariel; Kyndt, Eva; Cascallar, Eduardo

doi:10.1016/j.caeai.2021.100018

Cited by 49 publications

(32 citation statements)

References 58 publications

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“…Machine learning algorithms were found to be relevant in exploring student learning behavior, solving students' academic problems, optimizing the educational environment, and enabling data-driven decision making [20]. Moreover, machine learning algorithms are also used to identify key factors that influence students' academic success in schools and explore the relationship between these key factors [21], [22]. For example, predicting student learning outcomes by combining various aspects of student life, namely student personality; behavior and learning styles as well as lifestyles such as sleep patterns, exercise patterns and others [23].…”

Section: Discussion Of the Findingsmentioning

confidence: 99%

“…[18] Long Short-Term Memory (LSTM) [19], [22] Logistic regression [21], [18], [20], [12], [24], [14] Support Vector Machine (SVM) [14], [15], [18], [20], [19], [24] Multilayer perceptron (MLP) neural network [12], [16], [21] Artificial Neural Network (ANN) [16], [17], [15], Algorithm Reference [22], [14], [24] Principal Component Analysis (PCA) [14] Feature Agglomeration (FA) [14]…”

Section: Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

Sandra¹,

Lumbangaol²,

Matsuo³

2021

TEM Journal

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One of the ultimate goals of the learning process is the success of student learning. Using data and students' achievement with machine learning to predict the success of student learning will be a crucial contribution to everyone involved in determining appropriate strategies to help students perform. The selected 11 research articles were chosen using the inclusion criteria from 2753 articles from the IEEE Access and Science Direct database that was dated within 2019-2021 and 285 articles that were research articles. This study found that the classification machine learning algorithm was most often used in predicting the success of students' learning. Four algorithms that were used most often to predict the success of students' learning are ANN, Naïve Bayes, Logistic Regression, SVM and Decision Tree. Meanwhile, the data used in these research articles predominantly classified students' success in learning into two or three categories which are pass/fail; or fail/pass/excellent.

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Section: Discussion Of the Findingsmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

Sandra¹,

Lumbangaol²,

Matsuo³

2021

TEM Journal

View full text Add to dashboard Cite

show abstract

“…Student at risk [37] 11 variables include socio-economic background, university entrance examination results, and CGPA. CGPA [38] 123 variables, including prior academic achievement, tuition fees, students' socioeconomic status, students' home characteristics, students' household status, students' background information, high school characteristics, working status, university background, and academic performance in higher education.…”

Section: Refmentioning

confidence: 99%

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

et al. 2022

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Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

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“…It also helps in counseling students in alarming situations that can positively impact their academic achievements, i.e., COVID-19. Thus, during the literature survey, we have found many students' prediction systems which are interesting; nevertheless, they are failed to mathematical model emotional attributes and synchronized them with institutional attributes, study schedules, and family attributes [ 31 , 37 , 83 – 98 ]. The objective of this study is to identify the relationship between extracurricular activities and students' performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Potential Future Directions in Optimization of Students’ Performance Prediction System

Ahmad

ElAffendi

Anwar

et al. 2022

Computational Intelligence and Neuroscience

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Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students’ performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems’ dependency on real-world datasets. It also delays the mathematical modeling of students’ emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students’ performance and various influential factors. Second, it critically evaluates new students’ performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.

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Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

Cited by 49 publications

References 58 publications

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Potential Future Directions in Optimization of Students’ Performance Prediction System

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