Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

Şekeroğlu, Boran; Abiyev, Rahib H.; İlhan, Ahmet; Arslan, Murat; Idoko, John Bush

doi:10.3390/app112210907

Cited by 45 publications

(16 citation statements)

References 70 publications

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“…Their goal was to provide a step-by-step guide for researchers and practitioners interested in applying data mining techniques to predict student success. Sekeroglu et al (2021) present a systematic literature review of student performance prediction studies between 2010 and 2020. They identified 297 relevant articles from three citation databases.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Individual Feature Importance in Student Persistence Prediction

2023

JHETP

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Student persistence is of great importance for all stakeholders in higher education. There have been numerous studies using data mining and machine learning tools to predict student persistence. However, very little research has explored individual feature importance and their distinctive roles in predicting individual outcomes. In this study, we compare the predictive performance of two widely used machine learning models, logistic regression, and random forest, and use the SMOTE to improve the model performance. We analyze the feature importance in both aggregated form and their varied impact on individual predictions using a real-world student persistence dataset. In the discussion section, we propose practical approaches for monitoring and predicting student persistence.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Individual Feature Importance in Student Persistence Prediction

2023

JHETP

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“…Student grade prediction is one of the essential areas that can determine and monitor student performance in higher educational institutions (HEI). This area has gained great attention in the education sector over the years as many studies have been interested and proven the reliability of student grade prediction with many help of the existing machine learning algorithms to enhance student success [1]- [3]. The aim is to facilitate the educational sector to evaluate the risk of academic failure and provide feedback to improve student outcomes for each semester.…”

Section: Introductionmentioning

confidence: 99%

“…Provide a taxonomy of current imbalanced classification methods used for predicting student grades to highlight the most applied algorithms in the education field that will ease the professionals, practitioners, and academic researchers to understand the significance of this technique. 3. A comparative study of existing balancing methods with their classifiers in both aspects (binary and multiclass) and accuracy scores more comprehensively can be used for future educational research.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

et al. 2023

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Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students' performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy of the predictive model is a challenge due to the imbalanced nature of the dataset, which caused to bias results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybridlevel. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field.

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“…Nevertheless, the research focused on predicting student learning performance in superiority on one of these issues rather than considering them in their entirety. For instance, previous studies proposed a hybrid sampling strategy to improve the model performance with an imbalanced VLE data set [9,11,12], while the others focused on machine learning performance by investigating various model architectures such as the decision tree, naïve Bayes, and MLP on student learning performance [8,13,14].…”

Section: Introductionmentioning

confidence: 99%

Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence

et al. 2022

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Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students’ activities’ data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students’ activities’ maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models.

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Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

Cited by 45 publications

References 70 publications

Exploring Individual Feature Importance in Student Persistence Prediction

Exploring Individual Feature Importance in Student Persistence Prediction

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence

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