SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality
This work addresses adaptive learning for online education in VR, but it is incremental as it builds on existing monitoring and adaptive response technologies.
The paper tackles the problem of monitoring student understanding in virtual reality e-learning by introducing SMARTe-VR, a platform that collects facial biometrics and learning metadata, resulting in a dataset with over 25 hours of data from 10 users and preliminary experiments using Item Response Theory models for understanding detection.
This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR aims to collect data for adaptive learning, focusing on facial biometrics and learning metadata. The platform allows instructors to create customized learning sessions with video lectures, featuring an interface with an AutoQA system to evaluate understanding, interaction tools (for example, textbook highlighting and lecture tagging), and real-time feedback. Furthermore, we released a dataset that contains 5 research challenges with data from 10 users in VR-based TOEIC sessions. This data set, which spans more than 25 hours, includes facial features, learning metadata, 450 responses, difficulty levels of the questions, concept tags, and understanding labels. Alongside the database, we present preliminary experiments using Item Response Theory models, adapted for understanding detection using facial features. Two architectures were explored: a Temporal Convolutional Network for local features and a Multilayer Perceptron for global features.