Applied Teaching and Learning Analytics: A Functional Prototype for Matching-to-SampIe Cognitïve Assessment

Building upon the theoretical framework established in "Correlation analysis using teaching and learning analytics" (Heliyon, 2021), this paper presents the development of a functional prototype that operationalizes Teaching and Learning Analytics (TLA) through Matching-to-Sample (MTS) cognitive assessment paradigms. While traditional educational metrics often rely on subjective self-reported data, this solution introduces a robust framework for collecting objective, real-time behavioral data. The system integrates five MTS variations-Identity (IMTS), Arbitrary (AMTS), Categorical (CMTS), Delayed (DMTS), and Constructed Response (CRMTS)integrated with digital phenotype computation and peer-to-peer collaborative environments. Developed using React, TypeScript, and Supabase, the prototype supports immersive VR/AR scenarios and provides a granular data collection pipeline for future non-linear correlation analyses. Preliminary results demonstrate the prototype's capacity to identify unique learning profiles, bridging the gap between theoretical analytics and practical pedagogical intervention.