Using machine learning techniques for early identification of at-risk students in a course at the intermediary levels of education
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Date
2023-05
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Abstract
This thesis focuses on the use of machine learning techniques to identify at-risk students at
intermediary levels of education. The goal of this project is to develop a predictive model that
can identify students who may be struggling in the course before they reach a critical point that
leads to lower grades or withdrawal. Based on the analysis of related literature, this study
selected students’ personal characteristics and academic performance as input attributions at the
intermediary level of education. Prediction models were developed using Artificial Neural
Network (ANN), Decision Tree (DT) and Linear regression. A sample of 785 students was
utilized in the procedures of model training and testing. The results of each model were
presented in a confusion matrix and were analyzed by calculating the rates of accuracy,
precision, recall, and F-measure. The results suggested all three machine learning methods were
effective for student at-risk prediction, but DT presented a better performance. By identifying at
risk students early, teachers can provide additional support and resources to prevent students
from falling behind and dropping out. Ultimately, this research could have significant
implications for improving student retention rates in intermediary education and helping students
achieve their academic goals.