The objective of this study was to determine the relative risk and reported symptoms of concussions in 11- to 13-year-old, female soccer players. For this, a survey to compare the reported incidence of concussion in age-matched female soccer players to nonsoccer players was performed. The survey included 342 girls between the ages of 11 and 13: 195 were involved in an organized soccer team and 147 were not involved in organized soccer but were allowed to participate in any other sport or activity. A total of 94 of the 195 soccer players, or 48%, reported at least one symptom consistent with a concussion. The most prevalent symptom for these girls was headache (84%). A total of 34 of the 147 nonsoccer players, or 23%, reported at least one symptom consistent with a concussion in the previous six months. These results determined that the relative risk of probable concussions among 11- to 13-year-old, female soccer players is 2.09 (p < .001, α = .05, CI = 95%). This demonstrates that the relative risk of probable concussions in young female soccer players is significantly higher than in a control group of nonsoccer players of the same sex and age.

Well-developed problem-solving skills are essential for any student enrolled in a science, technology, engineering or mathematics (STEM) course as well as for graduates in the workforce. One of the most essential skills is the ability to monitor one's own progress and understanding while solving a problem. Successful monitoring during the entire solution allows a solver to identify errors within a solution and make adjustments as necessary. To highlight this aspect of problem-solving, we have developed a framework and associated classroom activities that introduce students to monitoring (M) alongside the more traditional aspects of problem-solving models: analyzing the task (A), creating a plan (C), and executing the plan (E). This ACE-M framework has been successfully implemented in lower-division chemistry, mathematics and physics courses. Students enrolled in courses where ACE-M was used as the foundation for problem-solving instruction reported improved problem-solving self-efficacy, more monitoring while solving problems, and in many cases improved course grades. With this explicit instruction on self-monitoring, students are now introduced to expert problem-solving skills that will benefit them in their STEM careers.

It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines.

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