Recognizing Student Emotions Using an Agent-Based Emotion Engine

We investigate the significance of emotions in agent-based systems that support student-lecturer performance evaluation. Emotions are defined as discrete and consistent responding processes that are changed by internal or external events and stimulus. We begin the research by conducting questionnaires on the student-lecturer domain, from which the emotions of stakeholders in the domain are studied. We then implement an agent-based emotion engine to compute student’s emotions while evaluating lecturers. The emotion engine outputs are fed into an agent’s analysis function for making recommendations to the lecturer and the administrative officer, which include teaching strengths and weaknesses from students’ perspectives, with suggestions for areas of improvement. The paper concludes with a discussion on the effective use of emotions in the domain to uphold the performance of the lecturers.