Translated Abstract
In this study, we propose an evaluation system that utilizes natural language processing to detect stumbling points among individuals learning dementia care, with the aim of contributing to the development of expertise in dementia care education. Thirteen skilled occupational therapists and eleven occupational therapy students organized clinical pictures of three virtual dementia cases based on the ICF framework. Utilizing the descriptions obtained and corresponding ICF codes for training, we screened for 'deficit descriptions' and 'extra descriptions' among novices. The identification of features among novices, such as a lack of focus on residual functions in dementia patients and insufficient recognition of the importance of social connections, proved to be incredibly valuable. Capturing these stumbling points of novices forms the foundation for interface design connecting learners and educators, aiming to optimize both and achieve an enhanced learning support environment, with an expectation for improving the quality of dementia care at the field level. Additionally, the scheme of detecting stumbling points using the ICF and extracting the thinking features of professionals demonstrated its potential applicability to the existing educational content.
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