Journal Article

Deep Learning for Predicting Attrition Rate in Open and Distance Learning (ODL) Institutions

Ukwandu, Elochukwu, Ezeanya, Christiana Uchenna, Garki, Farida Shehu, Onyeakazi, Jude Chukwuma, Oyewola, David Opeoluwa, Ndunagu, Juliana Ngozi

2024

Overview

Student enrollment is a vital aspect of educational institutions, encompassing active, registered and graduate students. All the same, some students fail to engage with their studies after admission and drop out along the line; this is known as attrition. The student attrition rate is acknowledged as the most complicated and significant problem facing educational systems and is caused by institutional and non-institutional challenges. In this study, the researchers utilized a dataset obtained from the National Open University of Nigeria (NOUN) from 2012 to 2022, which included comprehensive information about students enrolled in various programs at the university who were inactive and had dropped out. The researchers used deep learning techniques, such as the Long Short-Term Memory (LSTM) model and compared their performance with the One-Dimensional Convolutional Neural Network (1DCNN) model. The results of this study revealed that the LSTM model achieved overall accuracy of 57.29% on the training data, while the 1DCNN model exhibited lower accuracy of 49.91% on the training data. The LSTM indicated a superior correct classification rate compared to the 1DCNN model.

• Publisher: MDPI AG
• Subject: active and inactive student / Artificial neural networks / attrition rate / Colleges & universities / Datasets / Deep learning / Distance education / Distance learning / Dropouts / Education / long short-term memory / Machine learning / Neural networks / one-dimensional convolutional neural network / School enrollment / Social networks / student enrollment / Students