Deep Learning towards Expertise Development in a Visualization-based Learning Environment
With limited problem-solving capability and practical experience, novices have difficulties developing expert-like performance. It is important to make the complex problem-solving process visible to learners and provide them with necessary help throughout the process. This study explores the design and effects of a model-based learning approach implemented in a web-based learning environment that not only allows learners to capture and reflect on their problem-solving process in visual formats but also helps them to identify the gap between their performance and that of the expert for effective reflection and improvement. The proposed approach attempts to utilize expert knowledge to transform open-ended problem-solving experience into a systematic and deliberate effort towards expertise development. Twenty-five medical students participated in the study by using the proposed learning environment to complete a number of diagnostic problem-solving tasks. The results show that the approach positively affects students’ achievements in subject knowledge, problem-solving performance, and perceptions and motivation for learning in the proposed learning environment.
