A Literature Review of Student Performance Prediction in E-Learning Environment

Chweya, Ruth; Shamsuddin, Siti Mariyam; Ajibade, Samuel-Soma M.; Moveh, Samuel

doi:10.46820/jsetm.2020.1103

Cited by 2 publications

(5 citation statements)

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“…The selected studies in this analysis with learning behaviours as input factors found that student engagement levels or participation frequencies positively influenced students' academic performance. This also aligns with the findings of Hussain et al (2018) and Chweya et al (2020).…”

Section: Alignment Of the Thematic Analysis And Hedm Frameworksupporting

confidence: 91%

“…This student behavioural data includes student engagement data, including the frequency of uploading assignments, accessing online material, and solving online quizzes. These factors were crucial determinants of students' academic performance, this also aligns with Hussain et al (2018) and Chweya et al (2020) findings.…”

Section: Alignment Of the Thematic Analysis And Hedm Frameworksupporting

confidence: 85%

“…The discussion explores the EDM process within these systems, highlighting its significance in extracting meaningful insights from educational data. Chweya et al, 2020;Coronel & Morris, 2016;Du et al, 2020;Hasan et al, 2020;Khedr & El Seddawy, 2015;Li & Xu, 2023;Liang et al, 2016;Lottering et al, 2020;Matsebula & Mnkandla, 2016;Meghji et al, 2018, p. 2;Raghavjee et al, 2021;Ray & Saeed, 2018;Romero & Ventura, 2013;Roy & Singh, 2017;Singh & Pal, 2020;Thakar, 2015;Vambe & Sibanda, 2016;Wanjau & Muketha, 2018;Zohair & Mahmoud, 2019) Justification for the Focus on EDM over Learning Analytics…”

Section: Educational Data Miningmentioning

confidence: 99%

“…The most generally used LMS open-source is Modular Object-Oriented Developmental Learning Environment (Moodle) (Li & Xu, 2023). It allows for creating powerful, engaging, flexible online experiences (Chweya et al, 2020). LMS collects massive amounts of data useful for analysing student behaviour (Raghavjee et al, 2021).…”

Section: Edm In Educational Systemsmentioning

confidence: 99%

“…This method categorises new cases through a similarity metric and maintains all available cases. The advantage of K-Nearest Neighbours is its robustness regarding the search space, as classes do not have to be separated linearly, including its usability in both classification and regression prediction problems (Chweya et al, 2020). Furthermore, a few studies used custom or ensemble methods such as bagging or gradient boosting to achieve high prediction effectiveness when the other traditional classification algorithms were not performing well.…”

Section: Alignment Of the Thematic Analysis And Hedm Frameworkmentioning

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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Section: Alignment Of the Thematic Analysis And Hedm Frameworksupporting

confidence: 91%

Section: Alignment Of the Thematic Analysis And Hedm Frameworksupporting

confidence: 85%

Section: Educational Data Miningmentioning

confidence: 99%

Section: Edm In Educational Systemsmentioning

confidence: 99%

Section: Alignment Of the Thematic Analysis And Hedm Frameworkmentioning

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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Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

Xiao¹,

Shanthini

Thilak

2021

J. Inter. Net.

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Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

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A Literature Review of Student Performance Prediction in E-Learning Environment

Cited by 2 publications

References 0 publications

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

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

Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

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