Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan, Sarah; Aslam, Nida; Khan, Irfan Ullah

doi:10.1155/2022/8924028

Cited by 15 publications

(6 citation statements)

References 58 publications

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“…The approach aimed to predict student academic performance at a higher education level. Alwarthan et al's (2022) study identified an ensemble classification model that employed a Random Forest Ensemble with a dataset sourced from educational institutes' repositories. The study highlighted the scarcity of research predicting student academic performance in arts and humanities majors using students' data.…”

Section: Frequently Modeled Conventional Ensemble Classifiermentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

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Section: Frequently Modeled Conventional Ensemble Classifiermentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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“…A student's academic efficacy is contingent upon the calibre of the instruction and learning process. To enhance this process, it is possible to utilize learning outcomes to assess and improve [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Naseem

Mahmood

et al. 2023

Preprint

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Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X template outcomes. Educational Data Mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel Federated Learning (FL) standard that ensures the confidentiality of the dataset and allows predicting student grades, categorized into four levels: Low, Good, Average, and Drop. To enhance model precision, optimized features are incorporated into the training process. This study evaluates the optimized dataset using five machine learning (ML) algorithms , namely Support Vector Machine (SVM), Decision Tree, Nã A¯ve Bayes, K-Nearest Neighbours, and the proposed Federated Learning Model. The models’ performance is assessed regarding accuracy, precision , recall, and F1 score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of Federated Learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

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“…At the same time, the testing dataset evaluates the performance of the constructed model from the trained data. In most cases, data is split into 70% for training EDM models, while 30% is used for model testing, where the accuracy of models is calculated (Alwarthan et al, 2022).…”

Section: Student Development Environmentmentioning

confidence: 99%

“…If the evaluation metrics are unmet, the evaluation stage produces feedback for stakeholders to adjust the selected features to improve model performance. The accuracy, recall, precision, ROC area, and F-score are some of the evaluation metrics used in EDM (Alwarthan et al, 2022). If the evaluation criteria are met, a model is built and used to predict the outcomes from student data.…”

Section: Student Development Environmentmentioning

confidence: 99%

“…In contrast, students may use the transformed data to boost academic performance and manage study habits (Khedr & El Seddawy, 2015). Administrators or policymakers may also use the transformed data to respond to the demands of students and teachers, provide timely interventions, and improve education quality (Alwarthan et al, 2022).…”

Section: Student Development Environmentmentioning

confidence: 99%

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Using educational data mining to predict sub-Saharan African science, technology, engineering, and mathematics students’ academic performance: a systematic review.

Mhlongo

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moulded the research idea into reality, and their support and empathy also contributed to the continuation and completion of this research. I also want to thank Ms. Deborah Cunynghame for her continuous assistance and help with the administrative tasks of my research. I also want to thank my family, friends, and colleagues who provided the much-needed moral and technical support. Finally, I want to thank the almighty God for giving me the idea, will, and a constant supply of strength to complete my research.

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Cited by 15 publications

References 58 publications

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Using educational data mining to predict sub-Saharan African science, technology, engineering, and mathematics students’ academic performance: a systematic review.

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