Current insights

The Current Insights feature is designed to introduce life science educators and researchers to current articles of interest in other social science and education journals. In this installment, I highlight three recent studies from the fields of psychology and higher education that can inform practices in the life sciences. The first is a synthesis paper that builds a unifying framework for the diverse activities that fall under the umbrella term “active learning.” This paper emphasizes a novel aspect of the active-learning classroom: student agency. The second paper employs an underutilized framework in biology education research, quantitative critical theory, to explore why faculty–student interactions may not be universally beneficial. The third paper explores how valuing relationships can keep first-generation college students from reaching out for help when they need it. Together, these last two papers help researchers understand the perceived costs and benefits of seeking help from faculty

Recent Research in Science Teaching and Learning

The Current Insights feature is designed to introduce life science educators and researchers to current articles of interest in other social science and education journals. In this installment, I highlight three diverse research studies: one addresses the relationships between active learning and teaching evaluations; one presents an observation tool for documenting metacognition in the classroom; and the last explores things teachers can say to encourage students to employ scientific reasoning during class discussions

Recent research in science teaching and learning

The Current Insights feature is designed to introduce life science educators and research-ers to current articles of interest in other social science and education journals. In this in-stallment, I highlight three that explore how different types of stress can produce different educational outcomes, how studying by writing questions can improve performance, and how faculty beliefs about intelligence can influence students’ interest in and evaluation of a course

The Trade-off between Graduate Student Research and Teaching: A Myth?

Many current faculty believe that teaching effort and research success are inversely correlated. This trade-off has rarely been empirically tested; yet, it still impedes efforts to increase the use of evidence-based teaching (EBT), and implement effective teaching training programs for graduate students, our future faculty. We tested this tradeoff for graduate students using a national sample of life science PhD students. We characterize how increased training in EBT impacts PhD students\u27 confidence in their preparation for a research career, in communicating their research, and their publication number. PhD students who invested time into EBT did not suffer in confidence in research preparedness, scientific research communication, or in publication number. Instead, overall, the data trend towards a slight synergy between investing in EBT and research preparation. Thus, the tension between developing research and teaching skills may not be salient for today\u27s graduate students. This work is proof of concept that institutions can incorporate training in EBT into graduate programs without reducing students\u27 preparedness for a research career. Although some institutions already have graduate teaching programs, increasing these programs at scale, and including training in EBT methods could create a new avenue for accelerating the spread of evidence-based teaching and improved teaching across higher education

Caution, Student Experience May Vary: Social Identities Impact a Student's Experience in Peer Discussions

In response to calls for implementing active learning in college-level science, technology, engineering, and mathematics courses, classrooms across the country are being transformed from instructor centered to student centered. In these active-learning classrooms, the dynamics among students becomes increasingly important for understanding student experiences. In this study, we focus on the role a student prefers to assume during peer discussions, and how this preferred role may vary given a student's social identities. In addition we explore whether three hypothesized barriers to participation may help explain participation difference in the classroom. These barriers are 1) students are excluded from the discussion by actions of their groupmates; 2) students are anxious about participating in peer discussion; and 3) students do not see value in peer discussions. Our results indicate that self-reported preferred roles in peer discussions can be predicted by student gender, race/ethnicity, and nationality. In addition, we found evidence for all three barriers, although some barriers were more salient for certain students than others. We encourage instructors to consider structuring their in-class activities in ways that promote equity, which may require more purposeful attention to alleviating the current differential student experiences with peer discussions.National Science Foundation NSF DUE 1244847Science and Mathematics Educatio

Beyond linear regression: A reference for analyzing common data types in discipline based education research

[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling—generalized linear models (glms)—and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher’s identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work

Hyperpolarized Helium 3 MRI in Mild-to-Moderate Asthma: Prediction of Postbronchodilator Reversibility

Background: Longitudinal progression to irreversible airflow limitation occurs in approximately 10% of patients with asthma, but it is difficult to identify patients who are at risk for this transition. Purpose: To investigate 6-year longitudinal changes in hyperpolarized helium 3 (3He) MRI ventilation defects in study participants with mild-to-moderate asthma and identify predictors of longitudinal changes in postbronchodilator forced expiratory volume in 1 second (FEV1) reversibility Materials and Methods: Spirometry and hyperpolarized 3He MRI were evaluated in participants with mild-to-moderate asthma in two prospectively planned visits approximately 6 years apart. Participants underwent methacholine challenge at baseline (January 2010 to April 2011) and pre- and postbronchodilator evaluations at follow-up (November 2016 to June 2017). FEV1 and MRI ventilation defects, quantified as ventilation defect volume (VDV), were compared between visits by using paired t tests. Participants were dichotomized by postbronchodilator change in FEV1 at follow-up, and differences between reversible and not-reversible groups were determined by using unpaired t tests. Multivariable models were generated to explain postbronchodilator FEV1 reversibility at follow-up. Results: Eleven participants with asthma (mean age, 42 years ± 9 [standard deviation]; seven men) were evaluated at baseline and after mean 78 months ± 7. Medications, exacerbations, FEV1 (76% predicted vs 76% predicted; P = .91), and VDV (240 mL vs 250 mL; P = .92) were not different between visits. In eight of 11 participants (73%), MRI ventilation defects at baseline were at the same location in the lung at follow-up MRI. In the remaining three participants (27%), MRI ventilation defects worsened at the same lung locations as depicted at baseline methacholine-induced ventilation. At follow-up, postbronchodilator FEV1 was not reversible in six of 11 participants; the concentration of methacholine to decrease FEV1 by 20% (PC20) was greater in FEV1-irreversible participants at follow-up (P = .01). In a multivariable model, baseline MRI VDV helped to predict postbronchodilator reversibility at follow-up (R 2 = 0.80; P \u3c .01), but PC20, age, and FEV1 did not (R 2 = 0.63; P = .15). Conclusion: MRI-derived, spatially persistent ventilation defects predict postbronchodilator reversibility 78 months ± 7 later for participants with mild-to-moderate asthma in whom there were no changes in lung function, medication, or exacerbations

Normalisation of MRI ventilation heterogeneity in severe asthma by dupilumab

Ventilation heterogeneity in asthma could be due to many reasons. Luminal obstruction due to inflammatory cells or mucus, smooth muscle constriction and airway wall thickness could all contribute individually or collectively to ventilation heterogeneity. Interleukin-4 and interleukin-13, acting through the common interleukin-4 receptor, have the potential to modulate all of these features of asthma.1 Inhaled hyperpolarised gas MRI provides a way to regionally visualise and quantify the functional consequence of these features.2 Dupilumab is a fully human monoclonal antibody directed against the alpha-subunit of the interleukin-4 receptor.3 Here, we report a severe asthmatic who showed significant improvement and normalisation of MRI ventilation heterogeneity and associated clinical and physiological variables with dupilumab treatment, suggesting that dupilumab modulated various aspects of luminal airway obstruction

Closing the Achievement Gap in a Large Introductory Course by Balancing Reduced In-Person Contact with Increased Course Structure

Hybrid and online courses are gaining attention as alternatives to traditional face-to-face classes. In addition to the pedagogical flexibility afforded by alternative formats, these courses also appeal to campuses aiming to maximize classroom space. The literature, however, reports conflicting results regarding the effect of hybrid and online courses on student learning. We designed, taught, and assessed a fully online course (100% online) and a hybrid-and-flipped course (50% online 50% face-to-face) and compared those formats with a lecture-based face-to-face course. The three formats also varied in the degree of structure; the hybrid course was the most structured and the face-to-face course was the least structured. All three courses were taught by the same instructor in a large Hispanic-serving research university. We found that exam scores for all students were lowest in the face-to-face course. Hispanic and Black students had higher scores in the hybrid format compared with online and face-to-face, while white students had the highest performance in the online format. We conclude that a hybrid course format with high structure can improve exam performance for traditionally underrepresented students, closing the achievement gap even while in-person contact hours are reduced

Getting Under the Hood: How and for Whom Does Increasing Course Structure Work?

The authors explore the transferability of an active-learning intervention and expand upon the original studies by 1) disaggregating student populations to identify for whom the intervention works best and 2) exploring possible proximate mechanisms (changes in student behaviors and perceptions) that could mediate the observed increase in achievement.At the college level, the effectiveness of active-learning interventions is typically measured at the broadest scales: the achievement or retention of all students in a course. Coarse-grained measures like these cannot inform instructors about an intervention's relative effectiveness for the different student populations in their classrooms or about the proximate factors responsible for the observed changes in student achievement. In this study, we disaggregate student data by racial/ethnic groups and first-generation status to identify whether a particular intervention—increased course structure—works better for particular populations of students. We also explore possible factors that may mediate the observed changes in student achievement. We found that a “moderate-structure” intervention increased course performance for all student populations, but worked disproportionately well for black students—halving the black–white achievement gap—and first-generation students—closing the achievement gap with continuing-generation students. We also found that students consistently reported completing the assigned readings more frequently, spending more time studying for class, and feeling an increased sense of community in the moderate-structure course. These changes imply that increased course structure improves student achievement at least partially through increasing student use of distributed learning and creating a more interdependent classroom community