Co-Organizing the Collective Journey of Inquiry with Idea Thread Mapper

This research integrates theory building, technology design, and design-based research to address a central challenge pertaining to collective inquiry and knowledge building: How can student-driven, ever-deepening inquiry processes become socially organized and pedagogically supported in a community? Different from supporting inquiry using predesigned structures, we propose reflective structuration as a social and temporal mechanism by which members of a community coconstruct/reconstruct shared inquiry structures to shape and guide their ongoing knowledge building processes. Idea Thread Mapper (ITM) was designed to help students and their teacher monitor emergent directions and co-organize the unfolding inquiry processes over time. A study was conducted in two upper primary school classrooms that investigated electricity with the support of ITM. Qualitative analyses of classroom videos and observational data documented the formation and elaboration of shared inquiry structures. Content analysis of the online discourse and student reflective summaries showed that in the classroom with reflective structuration, students made more active and connected contributions to their online discourse, leading to deeper and more coherent scientific understandings

Eye on Collaborative Creativity : Insights From Multiple-Person Mobile Gaze Tracking in the Context of Collaborative Design

Early Career WorkshopNon peer reviewe

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs