Improving Teaching and Learning Experience in Engineering Education Using Sentiment Analysis Techniques

Abstract

Students are the golden commodity when it comes to ringing in revenue for academic institution. Therefore, it is vital to ensure the opinions and feedback of students is taken seriously to ensure continuous improvement in the teaching and learning experience. In an era of digital information and rich opinionated text being easily available, it is crucial to look into different forms of data analysis from which vital information can be extracted. Sentiment and emotion analysis are one such area of research that looks to extract implicit information from written text and analyse data that would be able to provide a deeper insight compared to conventional measures. This paper is an extension of a previously conducted study of analysing emotion as well as sentiment of students’ feedback taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM). Supervised learning technique was adopted and data analysed revealed students were biased towards assignment and quizzes as these would help improve their carry forwards for the subject and the preference of chapters to the exam was more for conduction and convection compared to others which had more mathematical related calculation.