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Causal mechanistic reasoning is a thinking strategy that can help students explain complex phenomena using core ideas commonly emphasized in separate undergraduate courses, as it requires students to identify underlying entities, unpack their relevant properties and interactions, and link them to construct mechanistic explanations. As a crossdisciplinary group of biologists, chemists, and teacher educators, we designed a scaffolded set of tasks that require content knowledge from biology and chemistry to construct nested hierarchical mechanistic explanations that span three scales (molecular, macromolecular, and cellular). We examined student explanations across seven introductory and upper-level biology and chemistry courses to determine how the construction of mechanistic explanations varied across courses and the relationship between the construction of mechanistic explanations at different scales. We found non-, partial, and complete mechanistic explanations in all courses and at each scale. Complete mechanistic explanation construction was lowest in introductory chemistry, about the same across biology and organic chemistry, and highest in biochemistry. Across tasks, the construction of a mechanistic explanation at a smaller scale was associated with constructing a mechanistic explanation for larger scales; however, the use of molecular scale disciplinary resources was only associated with complete mechanistic explanations at the macromolecular, not cellular scale.

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.

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.

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.

Specialized and emerging fields of research infrequently cross disciplinary boundaries and would benefit from frameworks, methods, and materials informed by other fields. Document co-citation analysis, a method developed by bibliometric research, is demonstrated as a way to help identify key literature for cross-disciplinary ideas. To illustrate the method in a useful context, we mapped peer-recognized scholarship related to systems thinking. In addition, three procedures for validation of co-citation networks are proposed and implemented. This method may be useful for strategically selecting information that can build consilience about ideas and constructs that are relevant across a range of disciplines.

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., (© 2014 Wyse et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).)

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.

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.