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.

Abstract Virtual reality technologies in educational settings have demonstrated their potential to improve understanding, engagement, motivation and learning outcomes. However, there are multiple technical, pedagogical, and institutional challenges on the way of technology adoption in the education sector. In this group-concept-mapping study within the CloudClass project we aim at identifying the requirements for implementing a desktop VR tool (CloudClass) for education in the university context. Teachers, multimedia experts and managers from a Spanish and a Dutch university (a face-to-face and a distance learning one) were asked to complete the focus prompt “ To use/implement CloudClass in education it is required/ needed that.… ”. The generated statements were classified thematically and rated for importance and feasibility. 95 unique statements were generated and sorted statistically into 5 clusters: Evaluation, Institutional Requirements, Maintenance and Training, Student Requirements, Affordances and infrastructure. A strong correlation was identified between the importance and feasibility of the identified clusters. To ensure a sustainable implementation of a desktop VR tool like CloudClass in a university setting a holistic approach considering all identified clusters is needed. Clusters Maintenance and Training and Institutional requirements are the low-hanging fruits to invest in, as both clusters scored highest on importance and feasibility.

Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively creates a robust, sustainable, and continuous machine learning training process. We evaluated Facegame with an experiment that revealed several contributions to the field of affective computing. First, the gamified data collection approach allowed us to access a rich variation of facial expressions of each basic emotion due to the natural variations in the players' facial expressions and their expressive abilities. We report improved accuracy when the collected data were used to enrich well-known in-the-wild facial emotion datasets and consecutively used for training facial emotion recognition models. Second, the natural language prescription method used by the Facegame constitutes a novel approach for interpretable explainability that can be applied to any facial emotion recog-nition model. Finally, we observed significant improvements in the facial emotion perception and expression skills of the players through repeated game play.