Shaping and Evaluating a System for Affective Computing in Online Higher Education using a Participatory Design and the System Usability Scale

Online learning's popularity has surged. However, teachers face the challenge of the lack of non-verbal communication with students, making it difficult to perceive their learning-centered affective states (LCAS), leading to missed intervention opportunities. Addressing this challenge requires a system that detects students' LCAS from their non-verbal cues and informs teachers in an actionable way. To design such a system, it is essential to explore field experts' needs and requirements. Therefore, we conducted design-based research focus groups with teachers to determine which LCAS they find important to know during online lectures and their preferred communication methods. The results indicated that confusion, engagement, boredom, frustration, and curiosity are the most important LCAS and that the proposed system should take into account teachers' cognitive load and give them autonomy in the choice of content and frequency of the information. Considering the obtained feedback, a prototype of two versions was developed. The prototype was evaluated by teachers utilizing the System Usability Scale (SUS). Results indicated an average SUS score of 80.5 and 74.5 for each version, suggesting acceptable usability. These findings can guide the design and development of a system that can help teachers recognize students' LCAS, thus improving synchronous online learning.