Development of a Predictive Model of Student Attrition Rate
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| Title: | Development of a Predictive Model of Student Attrition Rate |
|---|---|
| Authors: | Sani, Godwin, Oladipo, Francisca, Ogbuju, Emeka, Agbo, Friday J |
| Contributors: | Tietojenkäsittelytieteen laitoksen toiminta |
| Publisher Information: | Saba Publishing |
| Publication Year: | 2023 |
| Collection: | University of Eastern Finland: UEF Electronic Publications |
| Subject Terms: | machine learning, predictive model, random forest, random tree algorithm, student attrition, feature selection method |
| Description: | Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model. ; published version ; peerReviewed |
| Document Type: | article in journal/newspaper |
| File Description: | 1-12 |
| Language: | English |
| ISSN: | 2709-5908 |
| Relation: | Journal of Applied Artificial Intelligence; https://doi.org/10.48185/jaai.v3i2.601; https://erepo.uef.fi/handle/123456789/29111 |
| Availability: | https://erepo.uef.fi/handle/123456789/29111 |
| Rights: | CC BY 4.0 ; openAccess ; © 2022 Journal of Applied Artificial Intelligence ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.D5FC098F |
| Database: | BASE |
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