A Methodological Framework for Capturing Cognitive-Affective States in Collaborative Learning

TL;DR

This paper introduces a new methodological framework for capturing cognitive-affective states in collaborative learning, aiming to improve detection without disrupting natural interactions.

Contribution

It presents a novel approach combining retrospective recall and constrained reporting to analyze cognitive-affective states in group settings.

Findings

Participants' reports varied in frequency and timing.

Label distributions changed across different reporting methods.

The approach has implications for adaptive learning systems.

Abstract

Identification of affective and attentional states of individuals within groups is difficult to obtain without disrupting the natural flow of collaboration. Recent work from our group used a retrospect cued recall paradigm where participants spoke about their cognitive-affective states while they viewed videos of their groups. We then collected additional participants where their reports were constrained to a subset of pre-identified cognitive-affective states. In this latter case, participants either self reported or reported in response to probes. Here, we present an initial analysis of the frequency and temporal distribution of participant reports, and how the distributions of labels changed across the two collections. Our approach has implications for the educational data mining community in tracking cognitive-affective states in collaborative learning more effectively and in developing improved adaptive learning systems that can detect and respond to cognitive-affective states.