Joint Attention Virtual Classroom: A Preliminary Study

Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search Game

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics.

Virtual reality technology in neuropsychological testing: A systematic review

Neuropsychological testing aims to measure individuals' cognitive abilities (e.g. memory, attention), analysing their performance on specific behavioural tasks. Most neuropsychological tests are administered in the so-called 'paper-and-pencil' modality or via computerised protocols. The adequacy of these procedures has been recently questioned, with more specific concerns about their ecological validity, i.e. the relation between test scores observed in the laboratory setting and the actual everyday cognitive functioning. In developing more ecological tasks, researchers started to implement virtual reality (VR) technology as an administration technique focused on exposing individuals to simulated but realistic stimuli and environments, maintaining at the same time a controlled laboratory setting and collecting advanced measures of cognitive functioning. This systematic review aims to present how VR procedures for neuropsychological testing have been implemented in the last years. We initially explain the rationale for supporting VR as an advanced assessment tool, but we also discuss the challenges and risks that can limit the widespread implementation of this technology. Then, we systematised the large body of studies adopting VR for neuropsychological testing, describing the VR tools' distribution amongst different cognitive functions through a PRISMA-guided systematic review. The systematic review highlighted that only very few instruments are ready for clinical use, reporting psychometric proprieties (e.g. validity) and providing normative data. Most of the tools still need to be standardised on large cohorts of participants, having published only limited data on small samples up to now. Finally, we discussed the possible future directions of the VR neuropsychological test development linked to technological advances.

Exploring Gender Differences in Computational Thinking Learning in a VR Classroom: Developing Machine Learning Models Using Eye-Tracking Data and Explaining the Models

Understanding existing gender differences in the development of computational thinking skills is increasingly important for gaining valuable insights into bridging the gender gap. However, there are few studies to date that have examined gender differences based on the learning process in a realistic classroom context. In this work, we aim to investigate gender classification using students’ eye movements that reflect temporal human behavior during a computational thinking lesson in an immersive VR classroom. We trained several machine learning classifiers and showed that students’ eye movements provide discriminative information for gender classification. In addition, we employed a Shapley additive explanation (SHAP) approach for feature selection and further model interpretation. The classification model trained with the selected (best) eye movement feature set using SHAP achieved improved performance with an average accuracy of over $$70\%$$ 70 % . The SHAP values further explained the classification model by identifying important features and their impacts on the model output, namely gender. Our findings provide insights into the use of eye movements for in-depth investigations of gender differences in learning activities in VR classroom setups that are ecologically valid and may provide clues for providing personalized learning support and tutoring in such educational systems or optimizing system design.