Automatic Mind Wandering Detection in Educational Settings: A Systematic Review and Multimodal Benchmarking

TL;DR

This paper systematically reviews and benchmarks multimodal approaches for detecting mind wandering in online education, analyzing datasets, models, and preprocessing methods to improve adaptive learning systems.

Contribution

It introduces a comprehensive benchmarking framework with a generalizable preprocessing pipeline and evaluates multiple models across diverse datasets, highlighting current limitations and future opportunities.

Findings

Multimodal signals like EEG, facial video, and eye tracking show varying effectiveness in detecting mind wandering.

Traditional machine learning and neural network models have different strengths depending on the modality.

Re-engagement behaviors post-mind wandering can be detected from post-probe data, offering new detection avenues.

Abstract

Detecting mind wandering is crucial in online education, and it occurs 30% of the time, as it directly impacts learners' retention, comprehension, and overall success in self-directed learning environments. Integrating automated detection algorithms enables the deployment of targeted interventions within adaptive learning environments, paving the way for more responsive and personalized educational systems. However, progress is hampered by a lack of coherent frameworks for identifying mind wandering in online environments. This work presents a comprehensive systematic review and benchmark of mind wandering detection across 14 datasets covering EEG, facial video, eye tracking, and physiological signals in educational settings, motivated by the challenges in achieving reliable detection and the inconsistency of results across studies caused by variations in models, preprocessing approaches, and evaluation metrics. We implemented a generalizable preprocessing and feature extraction pipeline tailored to each modality, ensuring fair comparison across diverse experimental paradigms. 13 traditional machine learning and neural network models, including federated learning approaches, were evaluated on each dataset. In a novel ablation study, we explored mind wandering detection from post-probe data, motivated by findings that learners often re-engage with material after mind wandering episodes through re-reading or re-watching. Results highlight the potential and limitations of different modalities and classifiers for mind wandering detection, and point to new opportunities for supporting online learning. All code and preprocessing scripts are made openly available to support reproducibility and future research.