Behind the scenes : unpacking students' experience during a collaborative AI workshop using multi-modal data

Abstract

Artificial Intelligence (AI) is playing a growing role in K-12 education. However, curricula often lack structure and proper assessment when paired with collaborative approaches like Design Thinking (DT). Here, behavioral and affective dynamics are overlooked, even though they are indicators of both performance and quality of the learning experience, warranting a more in-depth exploration through Multi-Modal Learning Analytics. Therefore, we engaged 63 students, divided into 29 groups (aged 11 to 15) in a DT workshop on AI, analyzing their performance across each stage of their experience, including their behavioral and affective (i.e., emotional) states, using data collected from physiological sensors, audio, and video recordings. Our results show that certain conditions (e.g., joint visual attention, boredom, and high stress) consistently predicted positive or negative performance across all stages of the workshop, while affective states such as confusion, frustration, high engagement, and low stress fluctuate with implications on the learning experience.