A Practice-Oriented Computational Thinking Framework for Teaching Neural Networks to Working Professionals - Jing Tian (AI, 2025)
Background: Conventional machine learning courses are usually designed for academic learners, instead of working professionals. This study addresses this gap by proposing a new instructional framework that builds practical computational thinking skills for developing neural network models on business data. Methods: This study proposes a five-component computational thinking framework tailed for working professionals, aligned with the standard data science pipeline and an artificial intelligence instructional taxonomy. The proposed course instructional framework consists of mixed lectures, visualization-driven and coding-driven workshops, case studies, group discussions, and gamified model tuning tasks. Results: Across 28 face-to-face course iterations conducted between 2019 and 2024, participants consistently demonstrated satisfactions in gaining computational-thinking skills. Conclusions: The tailored framework has been implemented to strengthen working professionals’ computational thinking skills for neural-network work on industrial applications.