Manuscript received August 7, 2024; revised August 28, 2024; accepted October 9, 2024; published December 13, 2024
Abstract—To scientifically evaluate and optimize the blended teaching model in vocational colleges, this article integrates the Context, Input, Process, Product (CIPP) model and the Artificial Neural Network (ANN) model to conduct an empirical study on students majoring in Information Management at a vocational college. The study collected and analyzed various indicators, including student engagement, academic performance, and teacher-student interactions, using methods such as surveys and platform data analysis. The results demonstrated that the blended teaching model significantly enhanced student engagement and learning outcomes while also exhibiting excellent resource utilization efficiency. The CIPP model provides a comprehensive evaluation of the teaching model from a macro perspective, while the ANN model, through deep learning algorithms, offers more precise predictions and assessments of its effectiveness. This study provides scientific evidence for optimizing the blended teaching model in vocational colleges and offers important guidance for improving educational quality and resource allocation efficiency.
Keywords—higher vocational colleges, blended teaching mode, teaching evaluation
Cite: Dingfu Luo, Myungsoo Kim, Haijun Qian, and Zhuolai Liang, "Evaluation and Optimization of Blended Teaching Mode in Higher Vocational Colleges: A Comparative Study of CIPP Model and Artificial Neural Network Evaluation Model," International Journal of Information and Education Technology vol. 14, no. 12, pp. 1734-1742, 2024.