Examining the personal growth of college teacher educators through the lens of human development ecology: An approach utilizing artificial neural networks (ANNs) modeling

College teacher educators play a crucial role in improving teaching quality and have a significant impact on educational development. They must engage in a process of self-growth to achieve personal development, considering the interaction between individuals and their environment. This process involves deep exploration of their knowledge, skills, and attitudes, as well as an understanding of the broader institutional and cultural contexts. Through self-reflection and continuous learning, college teacher educators can foster personal growth by acquiring new teaching strategies, developing effective communication and interpersonal skills, and cultivating a reflective and adaptive teaching practice. Collaboration with colleagues, engagement with professional communities, and involvement in educational research and innovation are also essential aspects of personal growth. Creating supportive environments that facilitate the continuous development of college teacher educators is crucial for enhanced teaching quality and educational advancement. In this study, an artificial neural network (ANN) was employed to estimate the impact of changes in knowledge and skill development and reflection and continuous learning on the acquisition of new educational strategies, development of communication and interpersonal skills, and reflective and adaptive practice. The ANN's predictions indicate that increasing reflection and continuous learning has a more positive impact on acquiring new educational strategies compared to increasing knowledge and skill development alone. Additionally, when both knowledge and skill development and reflection and continuous learning are combined, it results in substantial growth in the development of communication and interpersonal skills and reflective and adaptive practice. The accuracy of the neural network's predictions was evaluated using linear regression, showing an acceptable level of error when compared to the experimental results.