Predicting Postgraduate Student Engagement Using Artificial Intelligence (AI)

Abstract

The increasing number of international students (IS) enrolled in Australian higher education institutions, combined with the widespread adoption of online and hybrid learning, has significant implications for understanding the factors that influence engagement among this diverse student group. Early identification of students with low engagement facilitates academic success, prevents poor outcomes, optimizes resource allocation, improves teaching strategies, increases motivation, and supports long-term success. This study's main aim is to examine the use of AI to predict student engagement. Development of a theoretically informed survey that aimed to elicit postgraduate students' engagement was developed and validated by expert judgment. In total, 200 copies of the survey were distributed, 121 responses were received, and 96 were considered for this study representing a response rate of 48%. This study promotes a multidimensional approach, utilizing AI and ML methodologies, to determine the influence of social and cultural contexts on student engagement. This approach enables educators and institutions to create effective strategies for enhancing the learning experience of postgraduate students. Multiple AI and ML techniques have been utilized including synthetic data generation methods such GaussianCopula, triplet-based variational autoencoder, generative adversarial networks, CopulaGAN, and conditional tabular generative adversarial network. These techniques are specifically employed to predict various dimensions of engagement, including personal, academic, intellectual, social, and professional engagement. The performance of AI/ML algorithms, including support vector machine, K-nearest neighbors, decision trees, gradient boosting machine, random forest, Naive Bayes, logistic regression, and extra trees, was assessed using several metrics including F1 score, sensitivity, specificity, confusion matrix, and accuracy. The models used in this study achieved up to 85% accuracy, offering a solid foundation for guidelines and support to enhance decision making processes in higher education. These findings provide valuable insights for both academics and policy makers, laying the groundwork for evidence-based strategies to improve student engagement.