Abstract

Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources. Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques. Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model. Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.

Highlights

  • The growth of data in research and education communities has shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis

  • Historical data are collected from Hawassa University Student Information Systems Portal (HUSIS) URL: https://sis.hu.edu.et/ with relevant credentials assigned to the School of Informatics registrar

  • The information indicated above (Table 3) contains metrics related to the prediction model dropout

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Summary

Introduction

The growth of data in research and education communities has shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. (Huang et al, 2020) Early prediction of students' academic status helps to intervene early and act to improve learning outcomes It helps increase graduation rates by appropriately helping students, helping higher education policymakers, monitoring the efficiency and effectiveness of teaching-learning activities, giving critical feedback to students and teachers, and modifying learning activities. A practical prediction algorithm results in a high prediction accuracy of the students' achievement; identify the low-performing students at the beginning of the learning process. To achieve these objectives, a large volume of student data must be analyzed and predicted using various machine learning models. This helps to reduce the underlying problem by implementing rapid and consistent intervention mechanisms

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