Objective assessment of academic performance using virtual reality and machine learning in early adolescents

Early identification of academic deficits facilitates timely and effective interventions that are essential for individual development and societal progress. Conventional assessment methods provide insights through demographic and prior academic records and subjective measures; however, there is an increasing demand for objective, ecologically valid indicators that reflect real-world learning processes. Extending these approaches to younger learners, particularly those aged 11-12 years, addresses critical research gaps. This study presents an exploratory investigation of a Virtual Reality (VR)-based academic assessment tool designed to capture behavioral and performance metrics from learners. A cohort of 120 typically developing students participated, with teacher evaluations serving as the ground truth. In addition to raw VR metrics, confirmatory factor analysis (CFA) was employed to derive latent cognitive dimensions that provided a structured representation of students' behavioral performance. Multiple machine-learning algorithms were tested to classify performance levels, with the Random Forest classifier achieving the highest accuracy of 95%. The findings demonstrate that VR-captured behavioral indicators have strong feasibility and predictive validity for educational assessment. The study also identifies a minimal, interpretable set of tasks and features that enhances practical deployment. These results provide foundational evidence for integrating VR-based assessment, latent cognitive factors, and ML methods to generate dynamic, ecologically valid insights and to support targeted early interventions for improved educational outcomes.