Sentiments Analysis and Feedback among Three Cohorts in Learning Software Engineering Modules

Chia, Y. X., Loh, K. H., Ong, Z. Y. B., Lim, J. X., Ooi, J. K., Cao, Q. , Yau, P. C.Y. and Lim, L. H. I. (2022) Sentiments Analysis and Feedback among Three Cohorts in Learning Software Engineering Modules. In: 2022 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Hong Kong, 04-07 Dec 2022, (Accepted for Publication)

Abstract

Computing Science (CS) and software programming skillsets are widely used in many fields. The acquisition of software engineering skills is important and crucial to prepare students for the software industry. Learning of professional software development (PSD) knowledge involves various educational approaches. This study examines the feedback in students' learning journey and preparedness for the industry across three years of the CS degree programme. The research is carried out to evaluate the current bottlenecks of learning in the PSD course. Surveys are conducted to collect feedback of 72 CS students from three different cohorts, namely freshmen, penultimate and final study year. Their sentiments are identified and analyzed using the quantitative and qualitative data gathered. Observed from the results obtained, most of the final year students have perceived attainment of favourable skills needed for the industry, after their internship experience where they applied their skillsets. A significant rise in positive sentiments from graduating students suggests that the PSD module taught is relevant and applicable for the software industry. However, the sentiments among the penultimate year students are different and show that there might be an issue with the current course delivery, which needs to be improved. In addition, this paper suggests possible improvement opportunities that could be explored by eliciting current challenges faced in the current syllabus. These include the specification variances for projects, timely introduction of topics, flexibility in marking rubrics, and introduction of in-demand practices.

• Item Type: Conference Proceedings
• Status: Accepted for Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi and Yau, Dr Peter C Y and Lim, Dr Li Hong Idris
• Authors: Chia, Y. X., Loh, K. H., Ong, Z. Y. B., Lim, J. X., Ooi, J. K., Cao, Q., Yau, P. C.Y., and Lim, L. H. I.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Engineering