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Creators/Authors contains: "Kim, Nam"

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  1. Abstract Objective

    Collisions between bicycles and motor vehicles are one of the leading risk factors for injury and death in childhood and adolescence. We examined longitudinal and concurrent effortful control (EC) as predictors of risky bicycling behavior in early- to mid-adolescence, with age and gender as moderators. We also examined whether EC was associated with parent-reported real-world bicycling behavior and all lifetime unintentional injuries.

    Methods

    Parent-reported EC measures were collected when children (N = 85) were 4 years old and when they were either 10 years (N = 42) or 15 years (N = 43) old. We assessed risky bicycling behavior by asking the adolescents to bicycle across roads with high-density traffic in an immersive virtual environment. Parents also reported on children’s real-world bicycling behavior and lifetime unintentional injuries at the time of the bicycling session.

    Results

    We found that both longitudinal and concurrent EC predicted adolescents’ gap choices, though these effects were moderated by age and gender. Lower parent-reported early EC in younger and older girls predicted a greater willingness to take tight gaps (3.5 s). Lower parent-reported concurrent EC in older boys predicted a greater willingness to take gaps of any size. Children lower in early EC started bicycling earlier and were rated as less cautious bicyclists as adolescents. Adolescents lower in concurrent EC were also rated as less cautious bicyclists and had experienced more lifetime unintentional injuries requiring medical attention.

    Conclusion

    Early measures of child temperament may help to identify at-risk populations who may benefit from parent-based interventions.

     
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  2. Abstract

    Mastering the concept of distributed forces is vital for students who are pursuing a major involving engineering mechanics. Misconceptions related to distributed forces that are typically acquired in introductory Physics courses should be corrected to increase student success in subsequent mechanics coursework. The goal of this study was to develop and assess a guided instructional activity using augmented reality (AR) technology to improve undergraduate engineering students' understanding of distributed forces. The AR app was accompanied by a complementary activity to guide and challenge students to model objects as beams with progressively increasing difficulty. The AR tool allowed students to (a) model a tabletop as a beam with multiple distributed forces, (b) visualize the free body diagram, and (c) compute the external support reactions. To assess the effectiveness of the activity, 43 students were allocated to control and treatment groups using an experimental nonequivalent groups preactivity/postactivity test design. Of the 43 students, 35 participated in their respective activity. Students in the control group collaborated on traditional problem‐solving, while those in the treatment group engaged in a guided activity using AR. Students' knowledge of distributed forces was measured using their scores on a 10‐item test instrument. Analysis of covariance was utilized to analyze postactivity test scores by controlling for the preactivity test scores. The treatment group demonstrated a significantly greater improvement in postactivity test scores than that of the control group. The measured effect size was 0.13, indicating that 13% of the total variance in the postactivity test scores can be attributed to the activity. Though the effect size was small, the results suggest that a guided AR activity can be more effective in improving student learning outcomes than traditional problem‐solving.

     
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