A systematic literature review on student performance predictions

Nawang, Hasnah; Makhtar, Mokhairi; Hamza, Wan Mohd Amir Fazamin Wan

doi:10.19101/ijatee.2021.874521

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…In Nawang et al (2021) systematic literature review, the authors discussed the use of the EDM, LA, and ML to predict student's academic performance in secondary schools and higher educational institutions. This review provided an overview of the methods and algorithms that are used in the prediction and identified the features that have the most significant effects on their performance.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

Alnasyan,

Basheri,

Alassafi

2024

Preprint

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With advances in Artificial Intelligence (AI) and the increasing volume of online educational data, Deep Learning techniques have played a critical role in predicting student’s academic performance. Recent developments have assisted instructors in determining the strengths and weaknesses of student’s academic achievement. This understanding will benefit from adopting the necessary interventions to assist students in improving their performance, helping at-risk students, and preventing dropout rates. In this review, 46 studies between 2019 and 2023 that apply one or more Deep Learning (DL) techniques, either alone or in combination with Machine Learning (ML) or Ensemble Learning techniques, have been reviewed and analyzed. Moreover, the review has examined utilizing datasets from public (MOOCs), private (LMSs), and other platforms. Four categories were used to group the features: demographic, previous and current academic performance, and learning behavior/activity features. Therefore, the analysis has demonstrated that the DNNs and CNN-LSTM models were the most commonly used techniques. Moreover, it has been noted that the studies that have used DL techniques such as CNNs, DNNs, and LSTMs, performed well by achieving high prediction accuracy above 90%; other studies achieved accuracy ranging between 60% and 90%. For datasets used within the reviewed studies, even though 44% of the studies have used LMSs datasets, it is found that OULAD was the most used dataset from MOOCs. For the grouped features, the results of the analysis indicate that the best features for prediction are the learning behavior and activity features, which outperform other feature categories.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

Alnasyan,

Basheri,

Alassafi

2024

Preprint

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“…On topic of student performance predictions which includes predicting students grades (tests, exams, quizzes, assignments, GPA) and attendance, (Nawang et al, 2021) analyzed 40 papers and found that this application of ML has been widely adopted in higher education. Regarding the most widely used techniques by researchers, the authors found that while Decision Tree, Random Forest, SVM, Artificial Neural Network (ANN) and Naïve Bayes (NB) were the most widely used techniques and it was Random Forest that proved to be the most accurate technique in predicting student performance.…”

Section: Theoritical Framework and Related Studiesmentioning

confidence: 99%

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

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In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.

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“…A wide range of data mining techniques has been employed for this purpose, each with its own strengths and limitations. A survey was conducted to provide a comprehensive overview of the intelligent models and paradigms used in education [12]. The survey identifies various challenges in predicting student performance, such as the high dimensionality of educational data, class imbalance, and the lack of labelled data.…”

Section: Related Workmentioning

confidence: 99%

Deep Biased Matrix Factorization for Student Performance Prediction

Huynh-Ly

Nguyen

2023

EAI Endorsed Trans Context Aware Syst App

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In universities that use the academic credit system, selecting elective courses is a crucial task that can have a significant impact on a student's academic performance. Students who perform poorly in their courses may receive formal warnings or even face expulsion from the university. Thus, a well-designed study plan from a course recommendation system can play an essential role in achieving good academic performance. Additionally, early warnings regarding challenging courses can help students better prepare and improve their chances of success. Therefore, predicting student performance is a vital component of both the course recommendation system and the academic advisor's role. To this end, numerous studies have addressed the prediction of student performance using various approaches such as association rules, machine learning, and recommender systems. More recently, personalized machine learning approaches, particularly the matrix factorization technique, have been used in the course recommendation system. However, the accuracy of these approaches in predicting student performance still needs improvement. To address this issue, this study proposes an approach called Deep Biased Matrix Factorization, which carries out deep factorization via multi-layer to enhance prediction accuracy. Experimental results on an educational dataset have demonstrated that the proposed approach can significantly improve the accuracy of student performance prediction. By using this approach, universities can better recommend elective courses to their students as well as predict student performance, which can help them make informed decisions and achieve better academic outcomes.

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A systematic literature review on student performance predictions

Cited by 11 publications

References 41 publications

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Deep Biased Matrix Factorization for Student Performance Prediction

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