About this Research Topic
In this article collection, we invite papers that address questions relevant to the pursuits of designing, implementing, and evaluating affect-aware educational technologies. We also welcome papers that advance theoretical understanding of the role emotions play in learning, or that address closely related contexts, such as health care or professional training.
In the last several decades, significant advances have been made in understanding the various roles emotion plays in decision-making, problem-solving, social interaction, learning, and more. Contrary to earlier views of cognition and memory that emphasized information processing, modern theories more appropriately position emotions as central, even critical to developing an understanding of human behavior. Technological developments in physiological sensing, such as facial expression recognition, heart-rate monitoring, and brain imaging have enabled researchers to develop fine-grained models to predict emotional states and how they change over time. These models have fueled further “affect-aware” technological developments in a number of fields, and very prominently in education. Furthermore, given the challenges associated with classifying emotional states and building models of how systems should respond appropriately, much of this work also leverages artificial intelligence techniques to a high degree. Additionally, advances in ‘big data’ and deep learning, in particular, allow for a different level of insight into various areas; nevertheless, the work relating this with emotions is scarce on the ground. For these reasons, we encourage articles on the topics as above.
Articles can take various forms:
• literature reviews: an extended and thorough discussion of a topic relevant to affect-aware learning technologies (e.g., detection of emotional states through facial expressions, techniques for detecting emotional engagement, strategies for mitigation of frustration)
• research studies: papers that report on specific studies, that advance theoretical understanding and suggest future research.
• position papers: shorter articles that provide background in some aspect of affect-aware learning technologies and propose a new direction or change in the field.
While a great deal of research exists in the field, big questions remain unanswered. In this special collection, we hope to capture the state of the field and articulate an agenda moving forward that will push the fields of educational technology, AI, and the learning sciences in new directions.
Keywords: human learning, affect-aware, educational technologies, role of emotion, social interaction, cognition, information processing, human behaviour, physiological sensing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.