Abstract Collaborative learning (CL) 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 CL 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 CL. Practitioner Notes What is already known about this topic Collaborative learning occurs when team members systematically activate, sustain and regulate their cognition, motivation, emotions and behaviors towards the attainment of their goals. Socially shared regulation in learning contributes to success in collaborative learning. What this paper adds “Shared” processes in collaborative learning are hard for researchers to study and understand them, for learners to recognize them and for teachers to support them. Multimodal data collection provides opportunities to study multiple aspects of student behaviors and regulation processes. With the aid of advanced technologies multidisciplinary collaboration between the learning sciences, affective computing and machine learning can help to study these complex phenomena. Implications for practice and/or policy The case examples demonstrate how multidisciplinary collaboration can meet the challenges in understanding and facilitating collaborative learning. Multidisciplinary efforts with multimodal data will contribute to collaborative learning practice by providing theoretically informed feedback and personalized support.