Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions

Wanjau, Stephen Kahara; Okeyo, George; Rimiru, Richard

doi:10.7753/ijcatr0511.1004

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The test was conducted to anticipate student enrolment in Kenya's HEIs, including Engineering/ Mathematics disciplines and Science and Technologies (Wanjau, 2016). Nearly 18 traits were discovered based on a questionnaire.…”

Section: Literature Surveymentioning

confidence: 99%

Students' Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach

Nazir,

Noraziah,

Rahmah

2023

International Journal of Virtual and Personal Learning Environments

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An effective educational program warrants the inclusion of an innovative construction that enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational decision support system has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. In this literature survey, the authors have discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development. They explored the relationship between machine learning and multiagent intelligent systems in literature to conclude their effectiveness in student performance prediction paradigm. They used the PRISMA model for the literature review process. They finalized 18 articles published between 2014-2022 for the survey that match the research objectives.

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Section: Literature Surveymentioning

confidence: 99%

Students' Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach

Nazir,

Noraziah,

Rahmah

2023

International Journal of Virtual and Personal Learning Environments

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“…Wanjau, Okeyo, and Rimiru [14] proposed a model which predicts the choice of academic study field at higher education institutions. The data used in the research were collected by a survey at Dedan Kimathi University of Technology.…”

Section: Educational Data Mining -Edmmentioning

confidence: 99%

Predicting Study Programme Selection with Data Mining Classification Technique

Božić

2022

IJEEC

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The application of data mining in the field of education (Educational Data Mining - EDM) is becoming more and more popular. Predicting final grades during studies, measuring student and lecturer performance, targeting students, curriculum improvement, are just some of the examples that can support the development of this area. The focus of this article is on the prediction of the study programme that students will select during their higher education at the Faculty of Business Economics in Bijeljina. The analysis was conducted on the data of the faculty wherein the first two years students attend the same courses, while at the beginning of the third year they select a specific study programme. The aim of this paper is to use classification methods to predict the selected study programme based on the final grades achieved on courses during the first two years of study. The highest accuracy was obtained using random forest algorithm (59,94%). Model evaluation results show that choice of study programme does not depend only on the success achieved in all courses during the first two years of study. The analysis was performed using open-source WEKA mining tool, and the obtained results were presented and interpreted.

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“…The school has issues determining the variables that affect dropout events. While, by predicting the dropout factors, it can improve the performance of the school and help the academic system by giving early warning to students, by using a classification technique [7].…”

Section: Applying Data Mining Algorithmmentioning

confidence: 99%

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

Gil¹

2020

IJATCSE

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Currently, one of the challenges of academic institutions is dropout student issues. It is critical to recognize which students are at risk of losing in school and what are the underlying factors of dropout. The study of students' dropout in Lawigan National High School enables teachers to identify the influential factors of dropout cases in school preemptively, to respond to it immediately, and to assist prospective dropout students in continuing studies and gaining knowledge for a better future. Lawigan National High School is a public school supervised with the Department of Education. The said school holds all the records of the students. The study aims to identify the underlying factors of dropout students that need to have an intervention to lessen the value of dropouts. The Weka Experiment Environment platform used to run simulations with machine learning algorithms from ready datasets. Two classification algorithms C4.5 and Naïve Bayes (NB) tested on a dataset containing student academic and demographic details, e.g., age, gender, marital status, written, performance, and quarterly examination grade, attendance percentage, home distance, parents' income, mothers' education, fathers' education, and dropout status, using a 10-fold cross-validation to estimate generalization accuracy. The experiment result shows that the top indicator for students' dropout cases is the academic performance with 98.9474% accuracy as perceived by the C4.5 algorithm, as shown in Table 2.

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Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions

Cited by 7 publications

References 12 publications

Students' Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach

Students' Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach

Predicting Study Programme Selection with Data Mining Classification Technique

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

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