USER AUTHENTICATION IN MOBILE LEARNING: BEHAVIORAL ANALYSIS WITH GYROSCOPE AND ACCELEROMETER DATA

2025

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.58805/kazutb.v.4.29-1231
Data confidence 70 medium
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Abstract

With rising popularity of mobile devices and increasingly voluminous learning data, securing individual information has become a matter of urgency. This work proposes a behavioral authentication model that reinforces access security on smart learning platforms through learning user behavior from accelerometer and gyroscope data. Designed to suit mobile learning environments where voluminous user interaction with interactive content necessitates trustworthy authentication processes, this model reinforces access security on smart learning platforms through learning user behavior from accelerometer and gyroscope data. Beyond taking into consideration accessible authentication processes, this paper further presents an innovative solution designed to suit mobile environments. Experiment results prove the model to be viable in upkeeping data integrity as well as securing user access to school systems.