Publication

Abstract : Collaborative learning is a complex and multifaceted phenomenon which requires teachers to pay close attention to their students in order to understand the underlying learning process and to offer needed help. However, in authentic settings with multiple groups, it becomes extremely difficult for teachers to observe each group. This paper presents our current MMLA prototype, which allows the collection, analysis and visualization of two types of data from students: audio and logs. We showcase our idea using a Raspberry Pi-based prototype (named CoTrack) for capturing and understanding the students' behavior during face-to-face blended collaborative learning situations. More specifically, CoTrack captures audio data together with software logs captured from their activities using a digital tool Etherpad. Later on, the collected data collected is analyzed to extract the participation behavior across physical and digital spaces. CoTrack has been used in 2 lab and 2 authentic case studies. Preliminary results show that despite of manual set-up and accuracy problems which may emerge, practitioners have shown interest in using it in their (authentic) classroom practice.