The aviation industry's reliance on automation raises concerns about pilot complacency, necessitating continuous pilot proficiency measures. To that end, real-time pilot skill feedback is vital—through alerts on declining skill levels or scalable levels of autonomy. Current cybernetic methods are limited as they assume linearity and time-invariance of human behavior and lack real-time capability. Neural Networks (NNs) offer a solution but face challenges such as high computational costs and limited generalization capability. To overcome these issues, this paper introduces a new and compact Residual Network for eXplainable Convolutional MTS Classification (RN-XCM) designed explicitly to classify pilot skill levels. Results demonstrate RN-XCM's ability to accurately classify skill levels based on 1.2 seconds of dual-axis control data, achieving a test accuracy of up to 93.50%, while requiring 50% less training time than competing NN models. It also achieves a test accuracy of 80.16% for previously unseen subjects, signifying its competence as a one-size-fits-all classifier. Notably, RN-XCM performs 17.88% better when classifying dual-axis tracking tasks over single-axis tracking tasks. Overall, the possibility of real-time feedback provided by the RN-XCM can enable quantitative evaluation of pilot control behavior, therefore enhancing safety and facilitating smoother interactions between pilots and aircraft.