Teaching about complex systems is no simple matter: building effective professional development for computer-supported complex systems instruction

The recent next generation science standards in the United States have emphasized learning about complex systems as a core feature of science learning. Over the past 15 years, a number of educational tools and theories have been investigated to help students learn about complex systems; but surprisingly, little research has been devoted to identifying the supports that teachers need to teach about complex systems in the classroom. In this paper, we aim to address this gap in the literature. We describe a 2-year professional development study in which we gathered data on teachers’ abilities and perceptions regarding the delivery of computer-supported complex systems curricula. We present results across the 2 years of the project and demonstrate the need for particular instructional supports to improve implementation efforts, including providing differentiated opportunities to build expertise and addressing teacher beliefs about whether computational-model construction belongs in the science classroom. Results from students’ classroom experiences and learning over the 2 years are offered to further illustrate the impact of these instructional supports.     

Exploratory Mediation Analysis via Regularization

Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment.     

Nonlinear Mixed-Effects Modeling Programs in R

In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each.     

Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings

This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy.     

Clifton Forge and Back Again

Abstract

Coming from small-town Appalachia, Josh Howard reflects on his career as a public historian with a focus on sport. He maps out his recent work at the National Baseball Hall of Fame, Lamar University, and other sport history work in explaining a journey from a background in statistics to one in public sport history. He concludes by offering some suggestions to better develop scholarship on the intersection of public history and sport history specifically through partnerships with professional sport franchises.     

Theme A: the engineer's role

Before rendering an account of the different sessions, I feel constrained to make some general remarks on the main theme, namely “The engineer's role”.     

Winning Is(n't) Everything: The Paradox of Excellence and the Challenge of Organizational Epideictic

Encompassing both the controlled messages of values advocacy and less explicit rhetorical actions such as philanthropy, community programs, and volunteerism, organizational epideictic affirms common values. This essay argues that such common values are problematized by the presence of paradox even in seemingly innocuous epideictic subjects. Through a case study of the National Collegiate Athletic Association's “Stay in Bounds” program teaching children to be good sports both on and off the field, the essay demonstrates the challenges of organizational epideictic through the paradox of excellence. It also provides suggestions for the management—but not elimination—of paradox in organizational epideictic, particularly directed at external publics.