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The purpose of this paper is to share the iterative process we used to design a task that elicits causal mechanistic reasoning and how the subsequent student responses can be analyzed. Our goal in this task is to strike a balance between eliciting as much student knowledge as possible without providing so much structure that the answer becomes obvious. The task development was approached using (1) a resources perspective of learning, (2) principles of scaffolding, and (3) evidence-centered design, for which we specified evidence that would be considered a fully causal mechanistic explanation. That is, an explanation which pays explicit attention to the properties, interactions, and behaviors of entities that are involved at a scalar level below the phenomenon under consideration. Since our eventual goal is to characterize how students use knowledge across disciplinary boundaries, the phenomenon of protein--ligand binding was chosen as the context for this task, because it requires students to apply ideas learned in chemistry courses to a biological phenomenon. After three rounds of iterative refinement, a final task was developed. To characterize students' responses to this task, we developed a coding scheme which can be used to code explanations based on the presence or absence of three key ideas relevant to this phenomenon. In this paper, we share the detailed processes and approaches used in task development, which we hope will provide insight into instructors and researchers as they, too, develop such tasks to explore student reasoning.

As an instructional tool, models can transform the student experience from the static to the dynamic, the flat to the 3D, and the siloed to the integrated. Few practical resources exist to help instructors transition toward model-based classroom practices. The "Modeling in the Classroom" evidence-based teaching guide provides instructors with a tool kit for incorporating models and modeling into their classrooms (https://lse.ascb.org/evidence-based-teaching-guides/modeling-in-the-classroom). The guide discusses the underpinnings of modeling as a core scientific practice, one that can enable student development of systems thinking skills and understanding of biological concepts. The guide describes a variety of model types, including phylogenetic trees, simulations, animations, diagrams, conceptual models, concept maps, and tactile models supported by summaries of and links to articles and resources. In this paper, we will introduce key findings describing why and how to use models in the classroom. We also describe open research questions needed to address classroom implementation, instructional design, and development of students' knowledge and skills. It is our hope that the guide will provide a suitable combination of research-based findings and practical suggestions that instructors will be supported and encouraged to thoughtfully incorporate modeling to support learning goals.

In this study, we propose an 'Approach to Modeling' (AtM) framework for examining how undergraduates approach tasks that require modelling scientific phenomena. Our framework is adapted from Approach to Learning (AtL) theories and consists of three observable behavioural constructs: metacognition, generative thinking, and causal reasoning. Twenty students participated in two think-aloud interviews, one year apart, in which they engaged in a total of five modelling tasks. We used AtM to analyse and score students' modelling behaviours. We concluded that: (1) students' AtM is associated with, but cannot be predicted by their previous academic achievement, and (2) students' AtM is not an inherent characteristic of the individual, but differs as modelling tasks change. We discuss the influence of prompt construction on AtM; specifically the presence of cueing words, the type and amount of background information provided, and familiarity with the contexts and concepts addressed in the prompts. In addition, we discuss connections between AtM and broader notions of science learning. We conclude with recommendations for constructing modelling prompts that may encourage diverse learners to engage more deeply in modelling tasks.

Since 2009, the U.S. National Science Foundation Directorate for Biological Sciences has funded Research Coordination Networks (RCN) aimed at collaborative efforts to improve participation, learning, and assessment in undergraduate biology education (UBE). RCN-UBE projects focus on coordination and communication among scientists and educators who are fostering improved and innovative approaches to biology education. When faculty members collaborate with the overarching goal of advancing undergraduate biology education, there is a need to optimize collaboration between participants in order to deeply integrate the knowledge across disciplinary boundaries. In this essay we propose a novel guiding framework for bringing colleagues together to advance knowledge and its integration across disciplines, the "Five 'C's' of Collaboration: Commitment, Collegiality, Communication, Consensus, and Continuity." This guiding framework for professional network practice is informed by both relevant literature and empirical evidence from community-building experience within the RCN-UBE Advancing Competencies in Experimentation-Biology (ACE-Bio) Network. The framework is presented with practical examples to illustrate how it might be used to enhance collaboration between new and existing participants in the ACE-Bio Network as well as within other interdisciplinary networks.

One of the goals of college-level introductory biology is to establish a foundation of knowledge and skills that can be built upon throughout a biology curriculum. In a reformed introductory biology course, we used iterative model construction as a pedagogical tool to promote students' understanding about conceptual connections, particularly those linking genetic variation to organismal fitness. In interviews conducted 2.5 years later, we examined students' retrieval of conceptual connections emphasized during the course. Students constructed a model similar to those practiced during the course, reviewed their models with the interviewer, and answered questions about how they retrieved this knowledge conceptual understanding. Student proficiency on this task was evaluated based on the quality of their modeling and responses to questions about their models. Three distinct groups emerged: students that had an inadequate cognitive structure for the biological concepts (absent), and students that had incomplete or complete cognitive structures. Students in the Complete group were better able to verbally link genetic variation to phenotypic variation and differential fitness and successfully used relationships stored in their cognitive structure to explain gaps present in their drawn model. Students in the Incomplete group had fragmented knowledge where some concepts were connected to each other, but not to the whole model. Students in the Absent group had extensive gaps in knowledge and were unable to connect their conceptual ideas. Students who were most proficient in the task differed in their ability to access and search within their cognitive structure and verify the quality of their conceptual relationships. This allowed the most proficient students to fill in knowledge gaps and transfer their conceptual understanding to new contexts.

Graduate teaching assistants (TAs) are increasingly responsible for instruction in undergraduate science, technology, engineering, and mathematics (STEM) courses. Various professional development (PD) programs have been developed and implemented to prepare TAs for this role, but data about effectiveness are lacking and are derived almost exclusively from self-reported surveys. In this study, we describe the design of a reformed PD (RPD) model and apply Kirkpatrick's Evaluation Framework to evaluate multiple outcomes of TA PD before, during, and after implementing RPD. This framework allows evaluation that includes both direct measures and self-reported data. In RPD, TAs created and aligned learning objectives and assessments and incorporated more learner centered instructional practices in their teaching. However, these data are inconsistent with TAs' self-reported perceptions about RPD and suggest that single measures are insufficient to evaluate TA PD programs.

Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational tool for instruction and assessment in an Introductory Biology course on genetics, evolution, and ecology. Over the course of one semester, students iteratively constructed, evaluated, and revised "Gene-to-Evolution" (GtE) models intended to promote understanding about the connections linking molecular-level processes with population-level outcomes. In practice, a successful GtE model contextualizes information provided in a case study and explains how genetic-level variation originates at the molecular level, is differentially expressed among individuals, and is acted upon by the environment to produce evolutionary change within a population. Our analyses revealed that students' ability to construct biologically accurate models increased throughout the semester. Model complexity peaked near mid-term then subsequently declined. This suggests that, with time, students were building more parsimonious models, shedding irrelevant information, and improving in their ability to apply accurate and appropriate biological language to explain relationships among concepts. Importantly, we observed the greatest relative gains in model correctness among students who entered the course with lower mean GPA. In an analysis comparing performance among achievement tritiles, lower-performing students effectively closed the achievement gap with the highest performing students by the end of the semester. Our findings support the effectiveness of model-based pedagogies for science teaching and learning, and offer a perspective on pedagogical application of modeling strategies to foster systems thinking and knowledge structuring in college-level biology. (Contains 4 tables and 6 figures.)

Introductory biology courses are widely criticized for overemphasizing details and rote memorization of facts. Data to support such claims, however, are surprisingly scarce. We sought to determine whether this claim was evidence-based. To do so we quantified the cognitive level of learning targeted by faculty in introductory-level biology courses. We used Bloom's Taxonomy of Educational Objectives to assign cognitive learning levels to course goals as articulated on syllabi and individual items on high-stakes assessments (i.e., exams and quizzes). Our investigation revealed the following: 1) assessment items overwhelmingly targeted lower cognitive levels, 2) the cognitive level of articulated course goals was not predictive of the cognitive level of assessment items, and 3) there was no influence of course size or institution type on the cognitive levels of assessments. These results support the claim that introductory biology courses emphasize facts more than higher-order thinking. (Contains 3 figures and 2 tables.)

Evolution is foundational to understanding biology, yet learners at all stages have incomplete and incorrect ideas that persist beyond graduation. Contextual features of prompts (e.g., taxon of organism, acquisition vs. loss of traits, etc.) have been shown to influence both the learning process and the ideas students express in explanations of evolutionary processes. In this study, we compare students' explanations of natural selection for humans versus a nonhuman animal (cheetah) at different times during biology instruction. We found "taxon" to be a significant predictor of the content of students' explanations. Responses to "cheetah" prompts contained a larger number and diversity of key concepts (e.g., variation, heritability, differential reproduction) and fewer naïve ideas (e.g., need, adapt) when compared with responses to an isomorphic prompt containing "human" as the organism. Overall, instruction increased the prevalence of key concepts, reduced naïve ideas, and caused a modest reduction in differences due to taxon. Our findings suggest that the students are reasoning differently about evolutionary processes in humans as compared with nonhuman animals, and that targeted instruction may both increase students' facility with key concepts while reducing their susceptibility to contextual influences.

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills.