What Attention Regulation Behaviors Tell Us About Learners in E-Reading?: Adaptive Data-Driven Persona Development and Application Based on Unsupervised Learning

Different individual features of the learner data often work as essential indicators of learning and intervention needs. This work exploits the personas in the design thinking process as the theoretical basis to analyze and cluster learners’ learning behavior patterns as groups. To adapt to the learning practice, we develop data-driven personas...

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

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...

Role of Multimodal Learning Systems in Technology-Enhanced Learning (TEL): A Scoping Review

Technology-enhanced learning systems, specifically multimodal learning technologies, use sensors to collect data from multiple modalities to provide personalized learning support beyond traditional learning settings. However, many studies surrounding such multimodal learning systems mostly focus on technical aspects concerning data collection...

Can We Empower Attentive E-reading with a Social Robot? An Introductory Study with a Novel Multimodal Dataset and Deep Learning Approaches

Reading on digital devices has become more commonplace, while it often poses challenges to learners' attention. In this study, we hypothesized that allowing learners to reflect on their reading phases with an empathic social robot companion might enhance learners' attention in e-reading. To verify our assumption, we collected a novel dataset ...

WEDAR: Webcam-based Attention Analysis via Attention Regulator Behavior Recognition with a Novel E-reading Dataset

Human attention is critical yet challenging cognitive process to measure due to its diverse definitions and non-standardized evaluation. In this work, we focus on the attention self-regulation of learners, which commonly occurs as an effort to regain focus, contrary to attention loss. We focus on easy-to-observe behavioral signs in the real...

Developing AI into explanatory supporting models: An explanation-visualized deep learning prototype

Using Artificial Intelligence (AI) and machine learning technologies to automatically mine latent patterns from educational data holds great potential to inform teaching and learning practices. However, the current AI technology mostly works as "black box"-only the inputs and the corresponding outputs are available, which largely impedes...