Simplicial reflection graph equivariant secretary bird quantum attention networks model for evaluating teaching quality in higher education

Evaluating teaching quality in higher education is vital to ensuring academic excellence and student learning outcomes. But the varied, subjective, and frequently variable nature of assessment indicators makes it a challenge to measure them effectively. In order to address these issues, this research proposes a novel model for evaluating the quality of instruction in higher education using simplicial reflection graph equivariant secretary bird quantum attention networks (SRGE-SBQAN). "Teaching Quality Evaluation in Higher Education," a dataset of 100 records that cover a wide range of teaching attributes from Indian universities, is used to train and evaluate the model. The data preprocessing phase utilizes Correlation Coefficients and Min–Max Normalization to normalize feature scales and minimize noise. Feature extraction is conducted via a Maximum-Entropy Regularized Decision Transformer, which detects the most impactful teaching characteristics. Reflection-Equivariant Quantum Neural Networks (REQNN) and Simplicial Graph Attention Transformers (SGAT) are used in the SRGE-SBQAN model to produce dependable predictions. The Secretary Bird Optimization Algorithm (SBOA) is used to adjust the model's parameters. The suggested model performs exceptionally well in terms of accuracy (99.9%), precision (99.5%), recall (99.7%), specificity (99.2%), F1-score (99.1%), Mean Absolute Error (MAE) (3.7), and Root Mean Square Error (RMSE) (3.3). According to these findings, the model is a powerful instrument for data-driven analysis and quality enhancement in higher education because of its strong generalization capacity, scalability to huge data, and superior interpretability.