In recent years, there has been a strong push to transform STEM education at K-12 and collegiate levels to help students learn to think like scientists. One aspect of this transformation involves redesigning instruction and curricula around fundamental scientific ideas that serve as conceptual scaffolds students can use to build cohesive knowledge structures. In this study, we investigated how students use mass balance reasoning as a conceptual scaffold to gain a deeper understanding of how matter moves through biological systems. Our aim was to lay the groundwork for a mass balance learning progression in physiology. We drew on a general models framework from biology and a covariational reasoning framework from math education to interpret students' mass balance ideas. We used a constant comparative method to identify students' reasoning patterns from 73 interviews conducted with undergraduate biology students. We helped validate the reasoning patterns identified with >8000 written responses collected from students at multiple institutions. From our analyses, we identified two related progress variables that describe key elements of students' performances: the first describes how students identify and use matter flows in biology phenomena; the second characterizes how students use net rate-of-change to predict how matter accumulates in, or disperses from, a compartment. We also present a case study of how we used our emerging mass balance learning progression to inform instructional practices to support students' mass balance reasoning. Our progress variables describe one way students engage in three dimensional learning by showing how student performances associated with the practice of mathematical thinking reveal their understanding of the core concept of matter flows as governed by the crosscutting concept of matter conservation. Though our work is situated in physiology, it extends previous work in climate change education and is applicable to other scientific fields, such as physics, engineering, and geochemistry.

The basis for mastering neurophysiology is understanding ion movement across cell membranes. The Electrochemical Gradients Assessment Device (EGAD) is a 17-item test assessing students' understanding of fundamental concepts of neurophysiology, e.g., electrochemical gradients and resistance, synaptic transmission, and stimulus strength. We collected responses to the EGAD from 534 students from seven institutions nationwide, before and after instruction. We determined the relative difficulty of neurophysiology topics and noted that students did better on what questions compared to how questions, particularly those integrating concentration gradient and electric forces to predict ion movement. We also found that, even after instruction, stu-dents selected one incorrect answer, at a rate greater than random chance for nine questions. We termed these incorrect answers attractive distractors. Most attractive distractors contained terms associated with concentration gradients, equilibrium, or anthropomorphic and teleological reasoning, and incorrect answers containing multiple terms were more attractive. We used v2 analysis and alluvial diagrams to investigate how individual students moved or did not move between answer choices on the pre-and posttest. Interestingly, students selecting the attractive distractor on the pretest were just as likely as other incorrect students to move to the correct answer on the posttest. In contrast, of students incorrect on both the pre-and posttest, students who selected the attractive distractor on the pretest were more likely to stick with this answer on the posttest than students choosing other incorrect answers. Combining the EGAD results with alluvial diagrams can inform neurophysiology instruction to address points of student confusion.NEW & NOTEWORTHY Investigating students' alternative reasoning in neurophysiology, this research is the first to investigate how analyzing the most common incorrect answer can shed light on the concepts students struggle with when reasoning about neurophysiological problems, especially those dealing with both chemical and electrical driving forces to predict ion movement across cell membranes.

Mass balance (MB) reasoning offers a rich topic for examination of students' scientific thinking and skills, as it requires students to account for multiple inputs and outputs within a system and apply covariational reasoning. Using previously validated constructed response prompts for MB, we examined 1,920 student-constructed responses (CRs) aligned to an emerging learning progression to determine how student language changes from low (1) to high (4) covariational reasoning levels. As students' abilities and thinking change with Context, we used the same general prompt in six physiological contexts. We asked how Level and Context affect student language and what language is conserved across Contexts at higher reasoning Levels. Using diversity methods, we found student language becomes more similar as covariational reasoning level increases. Using text analysis, we found context-dependent words at each Level; however, the type of context words changed. Specifically, at Level 1, students used context words that are tangential to MB reasoning, while Level 4 responses used words that specify inputs and outputs for the given Item Context. Further, at Level 4, students shared 30% of language across the six contexts and leveraged context-independent words including rate, equal, and some form of slower/lower/smaller. Together, these data demonstrate that Context affects undergraduate MB language at all covariational reasoning levels, but that the language becomes more specific and similar as Level increases. These findings encourage instructors to foster context-independent, comparative, and summative language during instruction to functionally build MB and covariational reasoning skills across contexts.

The Physiology Core Concept of flow down gradients is a major concept in physiology, as pressure gradients are the key driving force for the bulk flow of fluids in biology. However, students struggle to understand that this principle is foundational to the mechanisms governing bulk flow across diverse physiological systems (e.g., blood flow, phloem sap flow). Our objective was to investigate whether bulk flow items that differ in scenario context (i.e., taxa, amount of scientific terminology, living or nonliving system) or in which aspect of the pressure gradient is kept constant (i.e., starting pressure or pressure gradient) influence under-graduate students' reasoning. Item scenario context did not impact the type of reasoning students used. However, students were more likely to use the Physiology Core Concept of flow down [pressure] gradients when the pressure gradient was kept constant and less likely to use this concept when the starting pressure was kept constant. We also investigated whether item scenario context or which aspect of the pressure gradient is kept constant impacted how consistent students were in the type of reasoning they used across two bulk flow items on the same homework. Most students were consistent across item scenario contexts (76%) and aspects of the pressure gradient kept constant (70%). Students who reasoned using flow down gradients on the first item were the most consistent (86, 89%), whereas students using pressures indicate (but don't cause) flow were the least consistent (43, 34%). Students who are less consistent know that pressure is somehow involved or indicates fluid flow but do not have a firm grasp of the concept of a pressure gradient as the driving force for fluid flow. 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.NEW & NOTEWORTHY 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.

Pressure gradients serve as the key driving force for the bulk flow of fluids in biology (e.g., blood, air, phloem sap). However, students often struggle to understand the mechanism that causes these fluids to flow. To investigate student reasoning about bulk flow, we col-lected students' written responses to assessment items and interviewed students about their bulk flow ideas. From these data, we constructed a bulk flow pressure gradient rea-soning framework that describes the different patterns in reasoning that students express about what causes fluids to flow and ordered those patterns into sequential levels from more informal ways of reasoning to more scientific, mechanistic ways of reasoning. We obtained validity evidence for this bulk flow pressure gradient reasoning framework by collecting and analyzing written responses from a national sample of undergraduate bi-ology and allied health majors from 11 courses at five institutions. Instructors can use the bulk flow pressure gradient reasoning framework and assessment items to inform their instruction of this topic and formatively assess their students' progress toward more scien-tific, mechanistic ways of reasoning about this important physiological concept.

We novelly applied established ecology methods to quantify and compare language diversity within a corpus of short written student texts. Constructed responses (CRs) are a common form of assessment but are difficult to evaluate using traditional methods of lexical diversity due to text length restrictions. Herein, we examined the utility of ecological diversity measures and ordination techniques to quantify differences in short texts by applying these methods in parallel to traditional text analysis methods to a corpus of previously studied college student CRs. The CRs were collected at two time points (Timing), from three types of higher-ed institutions (Type), and across three levels of student understanding (Thinking). Using previous work, we were able to predict that we would observe the most difference based on Thinking, then Timing and did not expect differences based on Type allowing us to test the utility of these methods for categorical examination of the corpus. We found that the ecological diversity metrics that compare CRs to each other (Whittaker's beta, species turnover, and Bray-Curtis Dissimilarity) were informative and correlated well with our predicted differences among categories and other text analysis methods. Other ecological measures, including Shannon's and Simpson's diversity, measure the diversity of language within a single CR. Additionally, ordination provided meaningful visual representations of the corpus by reducing complex word frequency matrices to two-dimensional graphs. Using the ordination graphs, we were able to observe patterns in the CR corpus that further supported our predictions for the data set. This work establishes novel approaches to measuring language diversity within short texts that can be used to examine differences in student language and possible associations with categorical data.

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.

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.

The basis for understanding neurophysiology is understanding ion movement across cell membranes. Students in introductory courses recognize ion concentration gradients as a driving force for ion movement but struggle to simultaneously account for electrical charge gradients. We developed a 17-multiple-choice item assessment of students' understanding of electrochemical gradients and resistance in neurophysiology, the Electrochemical Gradients Assessment Device (EGAD). We investigated the internal evidence validity of the assessment by analyzing item characteristic curves of score probability and student ability for each question. and a Wright map of student scores and ability. We used linear mixed-effect regression to test student performance and ability. Our assessment discriminated students with average ability (weighted likelihood estimate: -2 to 1.5 Theta); however, it was not as effective at discriminating students at the highest ability (weighted likelihood estimate: >2 Theta). We determined the assessment could capture changes in both assessment scores (model r(2) = 0.51, P < 0.001, n = 444) and ability estimates (model r(2) = 0.47, P < 0.001, n = 444) after a simulation-based laboratory and course instruction for 222 students. Differential item function analysis determined that each item on the assessment performed equitably for all students, regardless of gender, race/ethnicity, or economic status. Overall, we found that men scored higher (r(2) = 0.51, P = 0.014, n = 444) and bad higher ability scores (P = 0.003) on the EGAD assessment. Caucasian students of both genders were positively correlated with score (r(2) = 0.51, P < 0.001, n = 444) and ability (r(2) = 0.47, P < 0.001, n = 444). Based on the evidence gathered through our analyses, the scores obtained from the EGAD can distinguish between levels of content knowledge on neurophysiology principles for students in introductory physiology courses.