Online learning and technostress for STEM and non-STEM undergraduates

• Main Author: Sim, Jo Lin
• Other Authors: Xu Hong
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Social sciences::Psychology
• Online Access: https://hdl.handle.net/10356/168263
• Institution: Nanyang Technological University

With the COVID pandemic, much learning has shifted online. As the younger generations are generally considered to be more tech-savvy than their older counterparts, the former may experience challenges in using online learning technologies but are unable to receive adequate help from their peers or family. This means that youths often need to be more proactive when looking for solutions in navigating their own online learning. Hence, this study aims to investigate the relationships among university students’ technostress, burnout, and online learning self-efficacy (OLSE). It seeks to examine if OLSE moderates the link between the technostress and academic burnout students may experience and whether there is any meaningful difference in the technostress and OLSE of Science, Technology, Engineering, and Mathematics (STEM) versus non-STEM students. A final sample of 137 responses from Nanyang Technological University students who grew up in Singapore was analyzed using bivariate correlations, the moderation model, and Welch’s t-test. It was found that technostress was positively and OLSE was negatively linked to burnout while OLSE had a positive moderating effect on the relationship between technostress and burnout. Meanwhile, STEM students had non-significantly lower technostress and higher OLSE than non-STEM students. Besides further research targeting this study’s limitations, results suggest that institutions should launch holistic measures which target the individual, administrative, and technical aspects of online learning to improve students’ experiences. Keywords: technostress, burnout, online learning self-efficacy, university students, STEM, non-STEM, bivariate correlations, moderation model, Welch’s t-test, COVID pandemic