The practice of implementing developmental teaching evaluation in information technology courses under the network environment using neural networks

Higher education field is flourishing and refinement of certain aspects in this regard is critical for the development of the society in the digital age. Implementation of exploratory teaching evaluation in information technology courses can enhance the clarity, diversity, and dynamism of assessment processes, thereby improving teaching quality. This work develops a comprehensive index system for developmental teaching evaluation using the AHP hierarchical analysis. It uses fuzzy comprehensive evaluation to estimate teachers' performance, considering feedback from school leaders, students, self-evaluation, and peers. The proposed work uses Neural Networks (NN) machine learning algorithm for predictive assessment of teaching effectiveness. Results indicate the capability of this evaluation system to enhance practical teaching. Real-world strategies and measures are proposed for implementing developmental teaching evaluation in information technology courses within the networked environment, aiming to provide valuable information for educational institutions. Further, the experimentation on NN shows better performance on various metrics compared to existing works.