MicroFlow: Advancing Affective States Detection in Learning Through Micro-Expressions

EasyChair Preprint 11376

Abstract

Gaining a deep understanding of student engagement is essential for designing effective learning experiences. In this study, we proposed the MicroFlow framework inspired by the concept of micro-expressions, to advance detecting learners’ affective states in learning. We collected data from 19 students (54 sessions) during Python programming. We found that microexpression features, Inter Vector Angles (IVA) combined models demonstrated the highest performance in detecting anxiety and flow state. The AUC for flow state improved by 10% (reaching 84%) compared to the AU model. For anxiety and boredom, we achieved AUC values of 71% and 70%, respectively. We highlighted the feasibility of our framework as a cost-effective tool that enable educators to create a more engaging learning environment by adjusting the complexity level of learners tasks, ultimately improve learning outcomes.

Keyphrases: Education, Flow Theory, Micro-expression Theory, emotion, facial expression, passive sensing

@booklet{EasyChair:11376,
  author    = {Mohammad Rahul Islam and Sang Won Bae},
  title     = {MicroFlow: Advancing Affective States Detection in Learning Through Micro-Expressions},
  howpublished = {EasyChair Preprint 11376},
  year      = {EasyChair, 2023}}