Machine Learning Based Student Academic Performance Prediction

Ram, Mange; Srija, V.; Bhargav, V Vinay; Madhavi, A.; Kumar, G. Sai Chaitanya

doi:10.1109/icirca51532.2021.9544538

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…With an accuracy of 87.5%, the proposed system could predict the student dropout rate in the MOOC course. Ram et al [26] proposed a machine learning system for the prediction of student academic performance using SVM, AdaBoost, LR, and RF classifiers. The study reported a 92% accuracy value using SVM and RF, and a 91% accuracy score using LR and AdaBoost.…”

Section: Related Workmentioning

confidence: 99%

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Sarwat

Ullah

Sadiq

et al. 2022

Sensors

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The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

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Section: Related Workmentioning

confidence: 99%

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Sarwat

Ullah

Sadiq

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Instructors and others seriously need to have relevant and timely information about higher Education's overall performance which is all about Students' academic activity in higher educations [2]. Researchers have recently proposed several machine learning-based algorithms for predicting academic achievement [3]. Michelle Richardson et al conducted a 13-year review, which focused on student performance based on the Grade Point Average (GPA).…”

Section: Introductionmentioning

confidence: 99%

Enticing Correlation between Motivation and Self-Regulation: A Novel Approach for Academic Excellence

Kharb¹,

Chahal²

2022

IJRASET

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Today, we can easily say that there is no limit to the size of the data that exists in the real world-it can be in tens, hundreds, thousands, billions or even trillions of dimensions. However, not all dimensions are vital or relevant for a more detailed analysis. The key objective of educational data mining is to analyze educational data to solve educational and research problems. Our research in this paper focuses primarily on identifying relevant characteristics that can help determine the academic performance of students. The selection of features carried out as data preprocessing techniques keeping in mind the key idea to eliminate irrelevant and redundant features and thus selecting the optimal characteristics that can improve the overall accuracy of the model. The characteristics selected through the selection of functions can help to predict the academic performance of the student in several colleges and universities, which is one of the most imperative contemplations by interested parties to build and maintain a quality system. Although there are several studies that determine the factors that affect the academic performance of students, there is still a gap in existing research that focuses on the psychological factors of a student. The document is structured as follows. Section two contains a brief explanation about the related work in this field. In section three, we have discussed the adopted methodology that presents the data set, the feature selection algorithms, and the characteristic evaluation approach. Section four presents the configuration and experimental results and section five focuses on the conclusion and future scope.

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