# SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality

arXiv: https://arxiv.org/abs/2501.10977 · HTML: https://mlsanity.com/paper/2501.10977/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 19 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [MARLIN: Masked Autoencoder for facial video Representation LearnINg](https://mlsanity.com/paper/2211.06627/) (2022)
- [Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment](https://mlsanity.com/paper/2302.10908/) (2023)
- [mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation](https://mlsanity.com/paper/2006.05327/) (2020)
- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [edBB: Biometrics and Behavior for Assessing Remote Education](https://mlsanity.com/paper/1912.04786/) (2019)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education](https://mlsanity.com/paper/2305.12561/) (2023)
- [ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning](https://mlsanity.com/paper/2112.09165/) (2021)
- [Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB](https://mlsanity.com/paper/2006.00825/) (2020)
- [VAAD: Visual Attention Analysis Dashboard applied to e-Learning](https://mlsanity.com/paper/2405.20091/) (2024)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Biometrics and Behavior Analysis for Detecting Distractions in e-Learning](https://mlsanity.com/paper/2405.15434/) (2024)
- [mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection](https://mlsanity.com/paper/2309.07880/) (2023)
- [BeCAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors](https://mlsanity.com/paper/2002.00918/) (2020)
- [MATT: Multimodal Attention Level Estimation for e-learning Platforms](https://mlsanity.com/paper/2301.09174/) (2023)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)

## Built on by (most cited first, 6 of 6)

- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback](https://mlsanity.com/paper/2506.08634/) (2025)
- [EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations](https://mlsanity.com/paper/2602.15531/) (2026)
- [M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards](https://mlsanity.com/paper/2502.15363/) (2025)
- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)
- [AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations](https://mlsanity.com/paper/2607.25634/) (2026)

## Abstract (arXiv)

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.