Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao, Ariel O.

doi:10.30534/ijatcse/2019/122862019

Cited by 9 publications

(4 citation statements)

References 44 publications

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“…We also found that 16 studies proposed an improved academic prediction performance. When compared with others it turned out to have better accuracy than this one (Gamao and Gerardo, 2019;Yang and Li, 2018;Czibula et al, 2019;Popescu and Leon, 2018;Mansouri et al, 2021).…”

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 86%

“…Most methods or algorithms for predicting academic performance are ML-based classification algorithms because they provide higher accuracy against other proposals (Rimadana et al, 2019;Gamao and Gerardo, 2019;Xu et al, 2019;Vora and Kamatchi, 2019;Tsiakmaki et al, 2020;Jayaprakash et al, 2020). These classification algorithms are mainly: Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN).…”

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

“…Techniques that applied methods and algorithms for the prediction of academic performance Rimadana et al (2019)Shanthini et al (2018);Gamao and Gerardo (2019);Xu et al (2019);Vora and Kamatchi (2019);;…”

mentioning

confidence: 99%

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A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

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The prediction of students' academic performance is an area of great concern for universities and educational institutions since academic performance is one of the most important aspects of the learning process. To analyze this behavior, this study makes a critical analysis of the topic of interest and aims to review, analyze and summarize the latest research advances related to the prediction of academic performance. The systematic literature review method is applied to answer three questions: (1) What factors are determinants in predicting students' academic performance? ( 2) what methods are used to predict students' academic performance? and(3) what are the objectives and interests in predicting students' academic performance? After conducting the study of 50 outstanding articles, as results, we found that academic factor is the guideline for predicting academic performance; supervised machine learning is the most used technique, highlighting support vector machine, random forests, and neural networks; the most outstanding objectives for the application of prediction were: Student performance with 53%, risk of failure with 14%, search for student knowledge with 12%, avoid dropout with 12%, and decision making with 10%.

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Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 86%

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Since its inception almost 15 years ago, FA and its modified variants have demonstrated significant success in various fields of application. For example, in multilevel image segmentation [3], as a way to reduce the number of dimensions [4], optimizing convolutional neural networks [5], solving course timetabling problems [6], and dealing with complex engineering tasks [7], [8], among other things. Knowing that FA has a universal application makes it a fascinating subject to pursue.…”

Section: Introductionmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

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Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

Fahd

Venkatraman²,

Miah

et al. 2021

Educ Inf Technol

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Recently, the field of machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, atrisk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a narrowed down dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results not only show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics but also provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.

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Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Cited by 9 publications

References 44 publications

A Systematic Review Regarding the Prediction of Academic Performance

A Systematic Review Regarding the Prediction of Academic Performance

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

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