Effects of Information Granularity on Health Education: An Artificial Intelligence-Based Situational R-Map Analysis

Abstract

Unintentional injury is the leading cause of death among children in Japan and around the world. Enforcement, engineering, and education-also known as the “three Es”-currently constitute the core approach to injury prevention, and education plays a critical role in school safety. Providing information tailored to learners is an essential factor allowing educators to provide effective education, and we believe that granularity is one of the key factors for tailored messages. The purpose of this study is 1) to propose a situational R-Map analysis method to manipulate the granularity of injury data and 2) to examine how granularity affects injury prevention education design using this method. In the situational R-Map analysis method, the words contained in each sentence of an injury situation description are transformed into 100-dimensional vectors using the distributed representation method. A situation vector is created as the average of the word vectors in each sentence. The dimension of the situation vector is reduced from 100 to 2 using the “t-SNE” method. Then, we reordered these clusters in order of severity. To examine how granularity affects injury prevention education design, we conducted a workshop to see whether information granularity affects the number of preventive strategies devised by caregivers. We created a list of five bar- or slide-related injury situations (coarse list) and a list of a list of 30 bar-related or 19 slide-related injury situations (fine list). All participants first read the coarse list to devise and write down preventive strategies for each type of playground equipment. Then, they read the fine list to see whether they had come up with any additional strategies after reading the fine lists, and if so, to write them down. A total of 131 caregivers participated in the study and the results suggest that the appropriate granularity depends on the type of equipment and the learner’s occupation and can be evaluated using our proposed method.