A Predictive Model for the Determination of Academic Performance in Private Higher Education Institutions

Makombe, Francis; Lall, Manoj

doi:10.14569/ijacsa.2020.0110949

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…To predict wither a student will pass or fail and to evaluate the effectiveness of the predictive strategies in the literature, the authors used (ANN) Artificial Neural Networks and five other algorithms. ANN performed better and the results showed that the students' performance6 is correlated with features like group assignments and if the student is bursary or not [28].…”

Section: Related Workmentioning

confidence: 99%

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

Oqaidi

Aouhassi

Mansouri

2022

Int. J. Emerg. Technol. Learn.

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Using machine learning to predict students’ dropout in higher education institutions and programs has proven to be effective in many use cases. In an approach based on machine learning algorithms to detect students at risk of dropout, there are three main factors: the choice of features likely to influence a partial or total stop of the student, the choice of the algorithm to implement a prediction model, and the choice of the evaluation metrics to monitor and assess the credibility of the results. This paper aims to provide a diagnosis of machine learning techniques used to detect students’ dropout in higher education programs, a critical analysis of the limitations of the models proposed in the literature, as well as the major contribution of this arti-cle is to present recommendations that may resolve the lack of global model that can be generalized in all the higher education institutions at least in the same country or in the same university.

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Section: Related Workmentioning

confidence: 99%

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

Oqaidi

Aouhassi

Mansouri

2022

Int. J. Emerg. Technol. Learn.

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“…Lottering et al (2020) applied predictive modelling techniques for 1,156 students to identify students at risk of dropping out of their registered qualification. Makombe and Lall (2020) worked on a computational predictive model using artificial neural networks to predict whether a student will pass or fail.…”

Section: Introductionmentioning

confidence: 99%

A data mining approach using machine learning algorithms for early detection of low-performing students

Khor

2022

IJILT

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PurposeThe purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance.Design/methodology/approachFor the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4).FindingsThe paper found that the decision trees algorithm outperformed other machine earning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.Originality/valueThe work meets the originality requirement.

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“…It should be mentioned that the values for these algorithms must be discrete values [27]. Among the classification algorithms is Kernel, which extends the regular logistic regression, used for binary classification, to deal with data that are not linearly separable [28].…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students

Chamorro-Atalaya¹,

Olivares-Zegarra²,

Paredes-Soria³

et al. 2021

IJACSA

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In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal and social attitudes of university students of professional engineering careers. For this, the Machine Learning Classification Learner technique is used by means of the Matlab R2021a software. The results reflect a predictive system capable of classifying the four satisfaction classes (1: dissatisfied, 2: not very satisfied, 3: satisfied and 4: very satisfied) with an accuracy of 91.96%, a precision of 79.09%, a Sensitivity of 75.66% and a Specificity of 92.09%, regarding the students' perception of their personal and social attitudes. As a result, the higher institution will be able to take measures to monitor and correct the strengths and weaknesses of each variable related to satisfaction with the quality of the educational service.

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A Predictive Model for the Determination of Academic Performance in Private Higher Education Institutions

Cited by 9 publications

References 11 publications

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms

A data mining approach using machine learning algorithms for early detection of low-performing students

Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students

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