Data-Driven Student Interest Identification for Intelligent Teaching Management Systems in Engineering Colleges.

This protocol presents a quantum-inspired computational framework for identifying engineering students' interests using multidimensional educational and behavioral data. The objective is to provide a systematic and reproducible workflow that integrates data preprocessing, behavioral pattern analysis, feature selection, and hybrid neural learning to support educational decision-making and personalized learning. The workflow begins with Box-Cox Robust IQR Scaling (BCRIS) to preprocess and normalize educational data by reducing semantic noise and improving data quality. Student learning behaviors are subsequently analyzed using the Quantum Cognitive State Model (QCSM), which represents behavioral transitions and evolving learning preferences under uncertain conditions. Relevant features are then selected using the Quantum-Enhanced Swarm-Whale Optimization Feature Selection (QES-WOFS) algorithm to reduce feature redundancy and identify informative behavioral characteristics. Finally, the Meta-Learning Hybrid Quantum-Classical Neural Classifier (ML-HQNC) integrates quantum-inspired learning with deep neural networks to classify student interests. The protocol was validated using a publicly available educational dataset. Model performance was evaluated using conventional metrics, including accuracy, F1-score, and the area under the receiver operating characteristic curve (AUC), together with Interest Detection Precision (IDP), Quantum Pattern Stability Index (QPSI), and Cross-Domain Adaptability Gain (CDAG). The framework achieved an overall classification accuracy of 96.17% on the evaluated dataset. This protocol provides a reproducible computational workflow for analyzing multidimensional educational data to support intelligent educational management and personalized learning applications.