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Online learning is becoming increasingly popular, whether for convenience, toaccommodate work hours, or simply to have the freedom to study from anywhere.Especially, during the Covid-19 pandemic, it has become the only viable optionfor learning. The effectiveness of teaching various hard-core programmingcourses with a mix of theoretical content is determined by the studentinteraction and responses. In contrast to a digital lecture through Zoom orTeams, a lecturer may rapidly acquire such responses from students' facialexpressions, behavior, and attitude in a physical session, even if the listeneris largely idle and non-interactive. However, student assessment in virtuallearning is a challenging task. Despite the challenges, different technologiesare progressively being integrated into teaching environments to boost studentengagement and motivation. In this paper, we evaluate the effectiveness ofvarious in-class feedback assessment methods such as Kahoot!, Mentimeter,Padlet, and polling to assist a lecturer in obtaining real-time feedback fromstudents throughout a session and adapting the teaching style accordingly.Furthermore, some of the topics covered by student suggestions include tutorsuggestions, enhancing teaching style, course content, and other subjects. Anyinput gives the instructor valuable insight into how to improve the student'slearning experience, however, manually going through all of the qualitativecomments and extracting the ideas is tedious. Thus, in this paper, we propose asentiment analysis model for extracting the explicit suggestions from thestudents' qualitative feedback comments.

A literature survey on student feedback assessment tools and their usage in sentiment analysis