Analysis of Off-Task Behaviors Among Health Science Students Participating in Metaverse-Based Anatomy Classes

Abstract

Objective: In this study, we aimed to analyze off-task behaviors among health science students participating in metaverse-basedanatomy classes and identify factors that hinder learning engagement by examining the relationship between generalcharacteristics and off-task behavior patterns. Design: Descriptive survey research. Methods: In this study, we included a total of 188 health science (i.e., 143 nursing and 45 physical therapy) students from oneuniversity with experience in metaverse-based anatomy classes. We collected data using a structured questionnaire comprising generalandmetaverse-related characteristics, and off-task behaviors measured using Kim's 40-item instrument on a 5-point Likert scale. Weperformed data analysis using SPSS/WIN 25.0, descriptive statistics, independent t-tests, and a one-way analysis of variance. Results: The most frequently performed off-task behavior was "searching on smartphone" followed by "using SNS onsmartphone" and "texting" (3.06, 2.90, and 2.88 points, respectively). Students with 2 semester of ≥ metaverse educationexperience displayed significantly higher off-task behavior scores (2.26±0.90 points) compared to those with ≤1 semesterexperience (2.00±0.90 points; p=0.048). Moreover, students who recognized the necessity of metaverse education exhibitedsignificantly higher scores (2.22±0.94 points) than those who did not (1.95±0.82 points; p=0.043). The primary reason for off-taskbehaviors was "because watching only video lectures is boring" in both departments. Conclusions: Off-task behaviors in metaverse-based anatomy classes involved predominantly smartphone-related activities. Students with more metaverse experiences and higher recognition of the necessity of metaverse educationdemonstrated increasedoff-task behaviors, suggesting a complex relationship between digital environment familiarity and learning engagement. Theresults of this study provide foundational data for designing effective metaverse educational strategies.