Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

Belachew, Ermiyas Birihanu; Gobena, Feidu Akmel

doi:10.23956/ijarcsse/v7i2/01219

Cited by 23 publications

(14 citation statements)

References 3 publications

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“…e results show that, in small datasets, Naïve Bayes and 1-NN can perform better than decision trees [6]. Support for better performance on Naïve Bayes on smaller datasets was also shown in a study by Belachew and Gobena in [7]. e study found that the size of the dataset and the imbalance and distribution of class values are the main challenges in work obtaining better accuracies.…”

Section: Related Workmentioning

confidence: 61%

Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Naicker

Adeliyi

Wing

2020

Mathematical Problems in Engineering

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Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

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Section: Related Workmentioning

confidence: 61%

Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Naicker

Adeliyi

Wing

2020

Mathematical Problems in Engineering

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“…They focus on the different models that previous papers have used and talked about the accuracy and process time these models had. Many papers had conflicting results as shown in how Hussain, Muhsin, Salal, Theodorou, Kurtoğlu, and Hazarika (2019) found that Neural Network models performed the best whereas Belachew and Gobena (2017) found Naïve Bayesian to be the best performing model. Belachew and Gobena's (2017) results were supported by the paper written by Jayaprakash, Balamurugan, and Chandar (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many papers had conflicting results as shown in how Hussain, Muhsin, Salal, Theodorou, Kurtoğlu, and Hazarika (2019) found that Neural Network models performed the best whereas Belachew and Gobena (2017) found Naïve Bayesian to be the best performing model. Belachew and Gobena's (2017) results were supported by the paper written by Jayaprakash, Balamurugan, and Chandar (2018). However, Obsie and Adem (2018) found that Linear Regression and Support Vector Regression were better than Neural Networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Correlation with Student Education Performance

Gupta

Gueneau

2021

J Stud Res

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The 21st century has seen the advent of the internet as well as the spread of increasingly powerful computer technologies. One of these new technologies is Artificial Intelligence and Machine Learning. These computer models assist in pattern recognition, task performance as well as prediction. One place where this technology can be used is Educational Data Mining. This study used these ML technologies on the Student Performance Dataset to see what features are correlated with high student academic performance. This study also utilized Feature Engineering to derive features that represent the interactions of different features from the original dataset in order to conduct further analysis.This study found that multiple different features such as parent relationship status, travel time between home and school, among others, had a positive correlation with student academic performance. Features such as past failures and increasing frequency of hanging out with friends after school was correlated with negative student academic performance. However, results with the ML models as well as Feature Engineering were inconclusive due to the results not having a high enough accuracy to merit analysis.

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“…A high accuracy of students" performance prediction is useful in identifying the low performing students at the start of the learning process. This objective is achieved by machine learning where techniques are employed to uncover patterns or models of data which is valuable in decision-making [12]. The study by Belachew and Gobena in [12] applied machine learning concepts to the dataset obtained from the college of www.ijacsa.thesai.org computing and informatics of Wolkite University registries office.…”

Section: Related Workmentioning

confidence: 99%

Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

Sam¹,

Naicker²,

Rajkoomar³

2020

IJACSA

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Ubiquitous learning (u-learning) refers to anytime and anywhere learning. U-learning has progressed to be considered a conventional teaching and learning approach in schools and is adopted to continue with the school curriculum when learners cannot attend schools for face-to-face lessons. Computer Science, namely the field of Artificial Intelligence (AI) presents tools and techniques to support the growth of u-learning and provide recommendations and insights to academic practitioners and AI researchers. Aim: The aim of this study was to conduct a meta-analysis of Artificial Intelligence works in ubiquitous learning environments and technologies to present state from the plethora of research. Method: The mining of related articles was devised according to the technique of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The complement of included research articles was sourced from the broadly used databases, namely, Science Direct, Springer Link, Semantic Scholar, Academia, and IEEE. Results: A total of 16 scientific research publications were shortlisted for this study from 330 articles identified through database searching. Using random-effects model, the estimated pooled estimate of artificial intelligence works in ubiquitous learning environments and technologies reported was 10% (95% CI: 3%, 22%; I 2 = 99.46%, P = 0.00) which indicates the presence of considerable heterogeneity. Conclusion: It can be concluded based on the experimental results from the sub group analysis that machine learning studies [18% (95% CI: 11%, 25%), I 2 = 99.83%] was considerably more heterogeneous (I 2 = 99.83%) than intelligent decision support systems, intelligent systems and educational data mining. However, this does not mean that intelligent decision support systems, intelligent systems and educational data mining is not efficient.

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Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

Cited by 23 publications

References 3 publications

Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Feature Correlation with Student Education Performance

Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

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