Behavior-based Feedback Loop for Attentive E-reading (BFLAe): A Real-Time Computer Vision Approach

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Abstract

This study is built upon a behavior-based framework for real-time attention evaluation of higher education learners in e-reading. Significant challenges in AI model developments for learning analytics have been 1) defining valid indicators and 2) connecting the analytics results to interventions, balancing the generalization and personalization needs. To address this, we utilized a public multimodal WEDAR dataset and trained a neural network model based on real-time features of learners, aiming at predicting learners’ moment-to-moment distractions. Real-time features for model training include 30 learners’ attention regulation behaviors annotated every second, reaction times to blur stimuli, and page numbers indicating various reading phases. Our preliminary model based on a neural network has achieved 66.26% accuracy in predicting self-reported distractions. Based on the model, we suggest a framework of a Behavior-based Feedback Loop for Attentive e-reading (BFLAe). It has text blur as feedback, a mechanism responsive to learners’ distractions that also works as data for next-round feedback. The general feedback implementation rules are established on a statistical analysis conducted on all learners. In addition, we propose a strategy for personalizing feedback using a quartile analysis of individual data, promoting learner-specific feedback. Our framework addresses the high demand for an automated e-learning assistant with non-intrusive data collection based on real-world settings and intuitive feedback provision. The feedback system aims to help learners with longer attention spans and less frequent distractions, leading to more engaging e-reading.
Original languageEnglish
Title of host publicationProceedings of IJCAI-2023 Workshop&Challenge on Micro-gesture Analysis for Hidden Emotion Understanding (MiGA 2023)
EditorsGuoying Zhao, Björn W. Schuller, Ehsan Adeli, Tingshao Zhu, Haoyu Chen
Publication statusPublished - 2023
EventMicro-gesture Analysis for Hidden Emotion Understanding 2023 - Macau, China
Duration: 21 Aug 202322 Aug 2023
Conference number: 1

Workshop

WorkshopMicro-gesture Analysis for Hidden Emotion Understanding 2023
Abbreviated titleMiGA 2023
Country/TerritoryChina
CityMacau
Period21/08/2322/08/23

Keywords

  • Behavior-based Learning Analytics
  • Neural Networks
  • E-reading Application
  • Multimodal Feedback Loop

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