ASU Scholarship Showcase

We recommend using backward design to develop course-based undergraduate research experiences (CUREs). The defining hallmark of CUREs is that students in a formal lab course explore research questions with unknown answers that are broadly relevant outside the course. Because CUREs lead to novel research findings, they represent a unique course design challenge, as the dual nature of these courses requires course designers to consider two distinct, but complementary, sets of goals for the CURE: 1) scientific discovery milestones (i.e., research goals) and 2) student learning in cognitive, psychomotor, and affective domains (i.e., pedagogical goals). As more undergraduate laboratory courses are re-imagined as CUREs, how do we thoughtfully design these courses to effectively meet both sets of goals? In this Perspectives article, we explore this question and outline recommendations for using backward design in CURE development.

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

Background: Worksites are important locations for interventions to promote health. However, occupational programs with documented efficacy often are not used, and those being implemented have not been studied. The research in this report was funded through the American Reinvestment and Recovery Act Challenge Topic 'Pathways for Translational Research,' to define and prioritize determinants that enable and hinder translation of evidenced-based health interventions in well-defined settings.

Methods: The IGNITE (investigation to guide new insights for translational effectiveness) trial is a prospective cohort study of a worksite wellness and injury reduction program from adoption to final outcomes among 12 fire departments. It will employ a mixed methods strategy to define a translational model. We will assess decision to adopt, installation, use, and outcomes (reach, individual outcomes, and economic effects) using onsite measurements, surveys, focus groups, and key informant interviews. Quantitative data will be used to define the model and conduct mediation analysis of each translational phase. Qualitative data will expand on, challenge, and confirm survey findings and allow a more thorough understanding and convergent validity by overcoming biases in qualitative and quantitative methods used alone.

Discussion: Findings will inform worksite wellness in fire departments. The resultant prioritized influences and model of effective translation can be validated and manipulated in these and other settings to more efficiently move science to service.

Women who start college in one of the natural or physical sciences leave in greater proportions than their male peers. The reasons for this difference are complex, and one possible contributing factor is the social environment women experience in the classroom. Using social network analysis, we explore how gender influences the confidence that college-level biology students have in each other’s mastery of biology. Results reveal that males are more likely than females to be named by peers as being knowledgeable about the course content. This effect increases as the term progresses, and persists even after controlling for class performance and outspokenness. The bias in nominations is specifically due to males over-nominating their male peers relative to their performance. The over-nomination of male peers is commensurate with an overestimation of male grades by 0.57 points on a 4 point grade scale, indicating a strong male bias among males when assessing their classmates. Females, in contrast, nominated equitably based on student performance rather than gender, suggesting they lacked gender biases in filling out these surveys. These trends persist across eleven surveys taken in three different iterations of the same Biology course. In every class, the most renowned students are always male. This favoring of males by peers could influence student self-confidence, and thus persistence in this STEM discipline.

Background: Counselor behaviors that mediate the efficacy of motivational interviewing (MI) are not well understood, especially when applied to health behavior promotion. We hypothesized that client change talk mediates the relationship between counselor variables and subsequent client behavior change.

Methods: Purposeful sampling identified individuals from a prospective randomized worksite trial using an MI intervention to promote firefighters’ healthy diet and regular exercise that increased dietary intake of fruits and vegetables (n = 21) or did not increase intake of fruits and vegetables (n = 22). MI interactions were coded using the Motivational Interviewing Skill Code (MISC 2.1) to categorize counselor and firefighter verbal utterances. Both Bayesian and frequentist mediation analyses were used to investigate whether client change talk mediated the relationship between counselor skills and behavior change.

Results: Counselors’ global spirit, empathy, and direction and MI-consistent behavioral counts (e.g., reflections, open questions, affirmations, emphasize control) significantly correlated with firefighters’ total client change talk utterances (rs = 0.42, 0.40, 0.30, and 0.61, respectively), which correlated significantly with their fruit and vegetable intake increase (r = 0.33). Both Bayesian and frequentist mediation analyses demonstrated that findings were consistent with hypotheses, such that total client change talk mediated the relationship between counselor’s skills—MI-consistent behaviors [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .02, .12] and MI spirit [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .01, .13]—and increased fruit and vegetable consumption.

Conclusion: Motivational interviewing is a resource- and time-intensive intervention, and is currently being applied in many arenas. Previous research has identified the importance of counselor behaviors and client change talk in the treatment of substance use disorders. Our results indicate that similar mechanisms may underlie the effects of MI for dietary change. These results inform MI training and application by identifying those processes critical for MI success in health promotion domains.

The U.S. scientific research community does not reflect America's diversity. Hispanics, African Americans, and Native Americans made up 31% of the general population in 2010, but they represented only 18 and 7% of science, technology, engineering, and mathematics (STEM) bachelor's and doctoral degrees, respectively, and 6% of STEM faculty members (National Science Foundation [NSF], 2013). Equity in the scientific research community is important for a variety of reasons; a diverse community of researchers can minimize the negative influence of bias in scientific reasoning, because people from different backgrounds approach a problem from different perspectives and can raise awareness regarding biases (Intemann, 2009). Additionally, by failing to be attentive to equity, we may exclude some of the best and brightest scientific minds and limit the pool of possible scientists (Intemann, 2009). Given this need for equity, how can our scientific research community become more inclusive?

The objective of this study was to identify physical, social, and intrapersonal cues that were associated with the consumption of sweetened beverages and sweet and salty snacks among adolescents from lower SES neighborhoods. Students were recruited from high schools with a minimum level of 25% free or reduced cost lunches. Using ecological momentary assessment, participants (N = 158) were trained to answer brief questionnaires on handheld PDA devices: (a) each time they ate or drank, (b) when prompted randomly, and (c) once each evening. Data were collected over 7 days for each participant. Participants reported their location (e.g., school grounds, home), mood, social environment, activities (e.g., watching TV, texting), cravings, food cues (e.g., saw a snack), and food choices. Results showed that having unhealthy snacks or sweet drinks among adolescents was associated with being at school, being with friends, feeling lonely or bored, craving a drink or snack, and being exposed to food cues. Surprisingly, sweet drink consumption was associated with exercising. Watching TV was associated with consuming sweet snacks but not with salty snacks or sweet drinks. These findings identify important environmental and intrapersonal cues to poor snacking choices that may be applied to interventions designed to disrupt these food-related, cue-behavior linked habits.

Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian missing data handling approach that can accommodate mediation analyses with any number of manifest variables. Computer simulation studies showed that the Bayesian approach produced frequentist coverage rates and power estimates that were comparable to those of maximum likelihood with the bias-corrected bootstrap. We share an SAS macro that implements Bayesian estimation and use 2 data analysis examples to demonstrate its use.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.