Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

Chaka, Chaka

doi:10.20944/preprints202108.0345.v1

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…About five Data Mining algorithms, including NB, DT, ANN, SVM, and RF, have been identified across all four study purposes. This finding contradicts a study done by Chaka [58] in South Africa, which has three purposes. The first purpose was about an educational Data Mining algorithm to predict student performance; the second purpose concerned a survey of educational Data Mining techniques and tools.…”

Section: Discussioncontrasting

confidence: 91%

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Shindo,

Mjahidi,

Waziri

2023

ITLT

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Poor ICT performance in teacher training colleges makes it more difficult for the majority of teachers to successfully use ICT resources in their teaching and learning. When teachers can efficiently utilize ICT resources, it empowers them to update their knowledge through online learning, consequently enhancing the overall quality of teaching and learning. This positive outcome can be observed through improved ICT performance. The aim of this article is to identify the appropriate Data Mining algorithms for predicting student teachers’ performance in ICT. The systematic literature review that was guided by the PRISMA statement 2020 served as the study methodology. It makes for clear reporting and offers a detailed checklist and flow diagram that direct the review procedure. On November 6, 2022, about 196 scholarly articles were downloaded from three digital libraries: Science Direct (38), ACM Digital Library (72), IEEE Xplore (51), and 35 from the Google Scholar search engine. After screening and eligibility checking, 28 scholarly articles were selected and analysed through content analysis in terms of the most commonly used algorithms, the year of publication, the study purposes, and the accuracy performance metrics. Considering the specific study findings represented quantitatively, Decision Trees and Naive Bayes were found to be the most commonly used Data Mining algorithms, with a count of 20.6% each. The most recently identified articles were published between 2014 and 2022. In terms of study purposes, a large number of studies focused on predicting student performance. Furthermore, about 6 out of 8 algorithms used in previous studies were found to score 80% or above in the average percentage of the highest and lowest accuracy metrics. Therefore, considering the general findings, the study identified five Data Mining algorithms as appropriate and most commonly used for prediction of student teachers’ performance in ICT. They are Naive Bayes, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Decision Tree. The findings of this study would assist the government, college tutors, and student teachers in making better decisions to improve ICT performance for pre-service and in-service teachers.

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Section: Discussioncontrasting

confidence: 91%

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Shindo,

Mjahidi,

Waziri

2023

ITLT

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“…It examined data from educational settings and employed data mining and machine learning approaches to identify the prediction pattern that represented students' performance and behaviour. Many techniques are used to mine educational data like K-Nearest Neighbour, Decision Trees, Machine Learning tools, Naive Bayes, and others [15,16]. Clustering, association rules, and classifications are examples of several types of knowledge that may be investigated.…”

Section: Comparative Studymentioning

confidence: 99%

Forecasting the Academic Performance by Leveraging Educational Data Mining

Saeed

2024

IASC

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The study aims to recognize how efficiently Educational Data Mining (EDM) integrates into Artificial Intelligence (AI) to develop skills for predicting students' performance. The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University. The first step's initial population placements were created using Particle Swarm Optimization (PSO). Then, using adaptive feature space search, Educational Grey Wolf Optimization (EGWO) was employed to choose the optimal attribute combination. The second stage uses the SVM classifier to forecast classification accuracy. Different classifiers were utilized to evaluate the performance of students. According to the results, it was revealed that AI could forecast the final grades of students with an accuracy rate of 97% on the test dataset. Furthermore, the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50% and could be categorized as having equal information ratio gain after the semester. While the random forest provided an accuracy of 28%. These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy (12%). The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance, reducing chances of failure, and taking appropriate steps at the right time to raise the standards of education. The study also motivates academics to assess and discover EDM at several other universities.

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“…Networks are probabilistic graphical algorithms with directed edges and nodes (Singh & Pal, 2020). The Naïve Bayes is a simple form of Bayesian networks with all the variables being conditionally class independent and is frequently used in EDM studies (Chaka, 2021). The Naïve Bayes method consists of algorithms such as Bernoulli, Gaussian, and Multinomial.…”

Section: Alignment Of the Thematic Analysis And Hedm Frameworkmentioning

confidence: 99%

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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Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

Cited by 5 publications

References 42 publications

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Forecasting the Academic Performance by Leveraging Educational Data Mining

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

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