Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

Collaborative learning can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion, and motivation) is key. Decades of research on self-regulated learning has advanced our understanding about the need for and complexity of those mediating processes in learning. Recent research has shown that it is not only the individual ’ s but also the group’s shared processes that matter and, thus, that regulation at the group level is critical for learning success. A problem here is that the “ shared ” processes in collaborative learning are invisible, which makes it almost impossible for researchers to study and understand them, for learners to recognize them, and for teachers to support them. Traditionally, research has not been able to make these processes visible nor has it been able to collect data about them. With the aid of advanced technologies, signal processing, and machine learning, we are on the verge of “ seeing ” these complex phenomena and understanding how they interact. We posit that technological solutions and digital tools available today and in the future will help advance the theory underlying the cognitive, metacognitive, emotional, and social components of individual, peer, and group learning when seen through a multidisciplinary lens. The aim of this paper is to discuss and demonstrate how multidisciplinary collaboration among the learning sciences, affective computing, and machine learning is applied for understanding and facilitating collaborative learning.