Abstract
Internet of things (IoT) field is quickly developing, with billions of devices deployed worldwide to provide IT solutions. It is an intersection point between computer science and electronic engineering, meaning that learning the fundamentals of electronic engineering is critical for computing students pursuing a career path in this field. This research aims to investigate how simulation-based learning affects computing students' confidence in developing IoT solutions. A mixed-methods approach was used, utilising pre and post-surveys with both closed and open-ended questions. Results indicate a significant increase in student confidence after the intervention, with a paired samples t-test showing a statistically significant improvement (p = 0.007). Data reliability was confirmed using Cronbach's alpha, yielding values of 0.924 and 0.859 for the pre and post-surveys, respectively. The findings have been validated using the triangulation approach. The findings suggest that the use of simulation-based learning significantly improved students' confidence and practical skills in IoT development. Reference to this paper should be made as follows: BenMubarak, M., Harinda, E. and Ihsan, M. (xxxx) 'Investigating the impact of simulation-based learning on computing students' confidence in developing internet of things solutions', Int.). His research interests include networks, wireless communication, IoT, AI and machine learning. He has international teaching experience in Yemen, Malaysia, Saudi Arabia, and the UK and has published in peer-reviewed journals and conferences. Eugen Harinda has a PhD and is a Lecturer at the University of Bolton, specialising in IoT, Data Science, and Artificial Intelligence. He is also a Fellow of Advance HE. His academic interests span applied machine learning, 2 M. BenMubarak et al. with a current focus on computer vision and deep learning techniques. His recent research explores intelligent systems capable of visual recognition and automated decision-making. He actively contributes to interdisciplinary projects that harness AI to solve real-world problems. With a background in both academic teaching and research, he is committed to advancing innovation in data-driven technologies and mentoring the next generation of AI professionals. Mansoor Ihsan is a Lecturer in Computing at the University of Bolton and holds a PhD degree in Computer Sciences and MSc in Data Telecom and Networks. He is also a Fellow of Advance HE. He is a computer networks researcher with expertise in sensor networks, cybersecurity and network security. He has experience in teaching a range of subjects including Linux OS, programming, cybersecurity and computer networks. He has a particular research interest in IoT, sensor networks, cybersecurity and cloud security. His current research area involves security in IoT and cloud leveraging machine learning technologies.