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Although the gut and the brain vastly differ in physiological function, they have been interlinked in a variety of different neurological and behavioral disorders. The bacteria that comprise the gut microbiome communicate and influence the function of various physiological processes within the body, including nervous system function. However, the effects of social experience in the context of dominance and social stress on gut microbiome remain poorly understood. Here, we examined whether social experience impacts the host zebrafish (Danio rerio) gut microbiome. We studied how social dominance during the first 2 weeks of social interactions changed the composition of zebrafish gut microbiome by comparing gut bacterial composition, diversity, and relative abundance between socially dominant, submissive, social isolates and control group–housed communal fish. Using amplicon sequencing of the 16S rRNA gene, we report that social dominance significantly affects host gut bacterial community composition but not bacterial diversity. At the genus level, Aeromonas and unclassified Enterobacteriaceae relative abundance decreased in dominant individuals while commensal bacteria (e.g., Exiguobacterium and Cetobacterium) increased in relative abundance. Conversely, the relative abundance of Psychrobacter and Acinetobacter was increased in subordinates, isolates, and communal fish compared to dominant fish. The shift in commensal and pathogenic bacteria highlights the impact of social experience and the accompanying stress on gut microbiome, with potentially similar effects in other social organisms. 

This article builds on the work of Scott et al. (Scott EE, Cerchiara J, McFarland JL, Wenderoth MP, Doherty JH. J Res Sci Teach 1: 37, 2023) and Shiroda et al. (Shiroda M, Fleming MP, Haudek KC. Front Educ 8: 989836, 2023) to quantitatively examine student language in written explanations of mass balance across six contexts using constructed response assessments. These results present an evaluation of student mass balance language and provide researchers and practitioners with tools to assist students in constructing scientific mass balance reasoning explanations. 

This paper details the development of the first reasoning framework to describe how students’ reasoning about biological bulk flow pressure gradients develop toward scientific, mechanistic reasoning. 

These findings are the first empirical evidence to support the claim that using Physiology Core Concept reasoning supports transfer of knowledge across different physiological systems. 

Recent calls in biology education research (BER) have recommended that researchers leverage learning theories and methodologies from other disciplines to investigate the mechanisms by which students to develop sophisticated ideas. We suggest design-based research from the learning sciences is a compelling methodology for achieving this aim. Design-based research investigates the “learning ecologies” that move student thinking toward mastery. These “learning ecologies” are grounded in theories of learning, produce measurable changes in student learning, generate design principles that guide the development of instructional tools, and are enacted using extended, iterative teaching experiments. In this essay, we introduce readers to the key elements of design-based research, using our own research into student learning in undergraduate physiology as an example of design-based research in BER. Then, we discuss how design-based research can extend work already done in BER and foster interdisciplinary collaborations among cognitive and learning scientists, biology education researchers, and instructors. We also explore some of the challenges associated with this methodological approach. 

Abstract We systematically compared two coding approaches to generate training datasets for machine learning (ML): (i) a holistic approach based on learning progression levels and (ii) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for undergraduate physiology, each targeting five levels of a developing flux learning progression in an ion context. Human-coded datasets were used to train two ML models: (i) an 8-classification algorithm ensemble implemented in the Constructed Response Classifier (CRC), and (ii) a single classification algorithm implemented in LightSide Researcher’s Workbench. Human coding agreement on approximately 700 student responses per item was high for both approaches with Cohen’s kappas ranging from 0.75 to 0.87 on holistic scoring and from 0.78 to 0.89 on analytic composite scoring. ML model performance varied across items and rubric type. For two items, training sets from both coding approaches produced similarly accurate ML models, with differences in Cohen’s kappa between machine and human scores of 0.002 and 0.041. For the other items, ML models trained with analytic coded responses and used for a composite score, achieved better performance as compared to using holistic scores for training, with increases in Cohen’s kappa of 0.043 and 0.117. These items used a more complex scenario involving movement of two ions. It may be that analytic coding is beneficial to unpacking this additional complexity. 

Vision and Change challenged biology instructors to develop evidence-based instructional approaches that were grounded in the core concepts and competencies of biology. This call for reform provides an opportunity for new educational tools to be incorporated into biology education. In this essay, we advocate for learning progressions as one such educational tool. First, we address what learning progressions are and how they leverage research from the cognitive and learning sciences to inform instructional practices. Next, we use a published learning progression about carbon cycling to illustrate how learning progressions describe the maturation of student thinking about a key topic. Then, we discuss how learning progressions can inform undergraduate biology instruction, citing three particular learning progressions that could guide instruction about a number of key topics taught in introductory biology courses. Finally, we describe some challenges associated with learning progressions in undergraduate biology and some recommendations for how to address these challenges. 

Two organizations found ways to be more intentional about encouraging participation by a diverse spectrum of attendees at scientific meetings—the scientific community can learn from their experiences. 

Constructed responses can be used to assess the complexity of student thinking and can be evaluated using rubrics. The two most typical rubric types used are holistic and analytic. Holistic rubrics may be difficult to use with expert-level reasoning that has additive or overlapping language. In an attempt to unpack complexity in holistic rubrics at a large scale, we have developed a systematic approach called deconstruction. We define deconstruction as the process of converting a holistic rubric into defining individual conceptual components that can be used for analytic rubric development and application. These individual components can then be recombined into the holistic score which keeps true to the holistic rubric purpose, while maximizing the benefits and minimizing the shortcomings of each rubric type. This paper outlines the deconstruction process and presents a case study that shows defined concept definitions for a hierarchical holistic rubric developed for an undergraduate physiology-content reasoning context. These methods can be used as one way for assessment developers to unpack complex student reasoning, which may ultimately improve reliability and validation of assessments that are targeted at uncovering large-scale complex scientific reasoning.