An Improved Early Student’s Academic Performance Prediction Using Deep Learning

Aslam, Nida; Khan, Irfan Ullah; Alamri, Leena H.; Almuslim, Ranim S.

doi:10.3991/ijet.v16i12.20699

Cited by 31 publications

(16 citation statements)

References 23 publications

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“…Also, the performance of our approach improves substantially as we combine more features to predict student performance. The reason of the threshold causes increasing of the accuracy of the model is, as we see in Figures 12,13,14 and 15, all students' grades in the same category are very close to the grades in the next category.…”

Section: H Experimenting With Dimesionality Reduction By T-snementioning

confidence: 86%

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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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Section: H Experimenting With Dimesionality Reduction By T-snementioning

confidence: 86%

“…Alsalm et al [14] proposed an approach to predict the studentâs grades using the mathematics and Portuguese language course grades data set and applied Deep learning model. The model of this work is only validated using two datasets and need to be validated on other large size balanced datasets.…”

Section: Related Workmentioning

confidence: 99%

Early Predicting of Students Performance in Higher Education

Alhazmi

Sheneamer

2023

IEEE Access

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“…It enables the network to correctly discover hidden patterns and extract insightful knowledge, thus achieving better and more reliable results. Similarly, Aslam et al [75] proposed SMOTE into DNN based on a dense model using eight hidden layers to overcome the imbalance problem. TABLE 7 summarizes all approaches, balancing methods and algorithms regarding their strengths and weaknesses in handling imbalanced classification, especially for the student grade prediction domain.…”

Section: ) Hybrid Approachmentioning

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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“…Aslam et al [15] separate the significance of 2 blooming techniques, ML and Blockchain, in the education domain. Blockchain technique, with data immutability as its major benefits, was employed in the miscellaneous field for security factors.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Hamza¹,

Hassine²,

Abunadi³

et al. 2022

Computers, Materials &Amp; Continua

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Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique to choose an optimal subset of features. Moreover, the swallow swarm optimization (SSO) with the SSAE model is derived to perform the classification process. To showcase the enhanced outcomes of the ISOFS-OSSAE model, a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine (UCI) Machine Learning Repository. The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

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An Improved Early Student’s Academic Performance Prediction Using Deep Learning

Cited by 31 publications

References 23 publications

Early Predicting of Students Performance in Higher Education

Early Predicting of Students Performance in Higher Education

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

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

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