Feasibility of Workstation-Level BLE Localization for Classroom Attendance: A Multi-Granularity Evaluation of Kalman-Filtered and Relational RSSI Features

Credential-based attendance mechanisms such as QR codes and RFID cards record who checked in but not whether that person is physically present, which makes them vulnerable to proxy attendance. Vision-based alternatives depend on line of sight and camera coverage, whereas Bluetooth Low Energy (BLE) indoor positioning can passively verify the presence of a student’s device. However, received signal strength indicator (RSSI) measurements are highly sensitive to multipath propagation, so neighboring workstations with similar signal characteristics are difficult to distinguish. This study evaluates the feasibility of workstation-level BLE localization in a classroom with 40 workstations and four beacons. RSSI was smoothed with a Kalman filter and combined with relational features, namely beacon ranks and pairwise RSSI differences, as inputs to Weighted K-Nearest Neighbor (WKNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) classifiers at 40, 20, 8, and 4 location classes. Kalman filtering improved accuracy for all four models at every granularity (+3.34 to +11.89 percentage points); for the two main models, WKNN and RF, all paired cluster-bootstrap 95% confidence intervals of this gain were above zero (+4.58 to +8.71 percentage points). The relational features improved tree-based ensembles at 40 classes (RF: +1.38 percentage points, 95% CI [+0.03, +2.88]; XGBoost: +1.35 percentage points, point estimate) but reduced WKNN accuracy at the finer granularities. RF reached 40.68% accuracy at 40 classes, 61.06% at 8 classes, and 73.37% at 4 classes, and all four classifiers performed similarly at 40 classes. These results show a clear trade-off between spatial resolution and classification reliability with four beacons; identity binding and session-level attendance decisions remain to be evaluated.