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Providing feedback to students as they learn to integrate individual concepts into complex systems is an important way to help them to develop robust understanding, but it is challenging in large, undergraduate classes for instructors to provide feedback that is frequent and directed enough to help individual students. Various scaffolds can be used to help students engage in self-regulated learning and generate internal feedback to improve their learning. This study examined the use of enhanced answer keys with added reflection questions and instruction as scaffolds for engaging undergraduate students in self-regulated learning within an introductory biology course. Study findings show that both the enhanced answer keys and reflection questions helped students to engage in metacognition and develop greater understanding of biological concepts. Further, students who received additional instruction on the use of the scaffolds changed how they used them and, by the end of the semester, were using the scaffolds in significantly different ways and showed significantly higher learning gains than students who did not receive the instruction. These findings provide evidence for the benefit of designing scaffolds within biology courses that will support students in engaging in metacognition and enhancing their understanding of biological concepts., (© 2017 J. L. Sabel et al. CBE—Life Sciences Education © 2017 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).)

There is an emphasis on survival-based selection in biology education that can allow students to neglect other important evolutionary components, such as sexual selection, reproduction, and inheritance. Student understanding of the role of reproduction in evolution is as important as student understanding of the role of survival. Limiting instruction to survival-based scenarios (e.g., effect of food on Galapagos finch beak shape) may not provide students with enough context to guide them to complete evolutionary reasoning. Different selection forces can work in concert or oppose one another, and sexual selection can lead to the selection of trait variants that are maladaptive for survival. In semistructured interviews with undergraduate biology students (n = 12), we explored how leading students through a sequence of examples affected student reasoning of evolution. When presented with an example where sexual selection and survivability favored the same variant of a trait, students emphasized survival in their reasoning. When presented with a scenario where sexual selection selected for trait variants that were maladaptive for survival, more students described how two different selection forces contributed to evolutionary outcomes and described reproductive potential as a part of fitness. Moreover, these students considered how the maladaptive traits were inherited in the population. Scenarios where sexual selection and survival-based selection were opposed improved student ability to reason about how factors other than survival impact evolutionary change. When instructors introduce students to scenarios where survival-based selection and sexual selection are opposed, they allow students to change their reasoning toward inclusion of reproduction in their evolutionary reasoning.

Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students' understanding and their ability to use and reason with models. To maximise the effectiveness of computational modelling, this study examined an improved cognitive scaffolding and its impact on student learning of cellular respiration. This scaffolding proposes the "predict-observe-revise-explain" (PORE) sequence of tasks that explicitly challenge students to revise their predictions and computational models to resolve cognitive conflict. Based on revision work in a CMB activity, a sample of n = 362 undergraduate biology students were categorised into three groups -- not expected to revise (NR, n = 109), required-revised (RR, n = 179), and required-did not revise (RDNR, n = 74). Students' performance in "predict," "revise," and "explain" tasks were significantly associated with post-test performance. RR students were more than twice as likely to demonstrate a positive learning gain in the post-test ("odds ratio" = 2.47) compared to RDNR students. While science education has implicitly acknowledged revision as a critical cognitive process in modelling, this study presents evidence that making revision an explicit cognitive task in a CMB activity supports student learning of a complex biological system.

Quantitative modelling plays an important role as biology increasingly deals with big data sets, relies on modelling to understand system dynamics, makes predictions about impacts of changes, and revises our understanding of system interactions. An assessment of quantitative modelling in biology was administered to students (n = 612) in undergraduate biology courses at two universities to provide a picture of student ability in quantitative reasoning within biology and to determine how capable those students felt about this ability. A Rasch analysis was used to construct linear measures and provide validity evidence for the assessment and to examine item statistics on the same scale as student ability measures. Students overall had greater ability in quantitative literacy than in quantitative interpretation of models or modelling. There was no effect of class standing (Freshmen, Sophomore, etc.) on student performance. The assessment showed that students who participated felt confidence in their ability to quantitatively model biological phenomena, even while their performance on ability questions were low. Collectively modelling practices were correlated with students' metamodelling knowledge and not correlated with students' modelling capability confidence. Biology instructors who incorporate the process of modelling into their courses may see improved abilities of students to perform on quantitative modelling tasks.

The creation and analysis of models is integral to all scientific disciplines, and modeling is considered a core competency in undergraduate biology education. There remains a gap in understanding how modeling activities may support changes in students' neural representations. The aim of this study was to evaluate the effects of simulating a model on undergraduates' behavioral accuracy and neural response patterns when reasoning about biological systems. During brief tutorials, students (n = 30) either simulated a computer model or read expert analysis of a gene regulatory system. Subsequently, students underwent functional magnetic resonance imaging while responding to system-specific questions and system-general questions about modeling concepts. Although groups showed similar behavioral accuracy, the Simulate group showed higher levels of activation than the Read group in right cuneal and postcentral regions during the system-specific task and in the posterior insula and cingulate gyrus during the system-general task. Students' behavioral accuracy during the system-specific task correlated with lateral prefrontal brain activity independent of instruction group. Findings highlight the sensitivity of neuroimaging methods for identifying changes in representations that may not be evident at the behavioral level. This work provides a foundation for research on how distinct pedagogical approaches may affect the neural networks students engage when reasoning about biological phenomena.

Meaningful learning occurs by relating new information to and revising prior knowledge, making it essential to understand student knowledge before helping them move toward a more scientific understanding. In this study, we characterise prior knowledge about cellular respiration in undergraduate students enrolled in introductory biology by analysing student-constructed concept maps (N = 182) and interviews (N = 9). Students were instructed to create concept maps from a bank of 20 concepts with the purpose of interconnecting the processes of cellular respiration, showing how pools of ATP are generated and used, and identifying where the events of cellular respiration occur. Student maps were analysed for content, quality and organisation of knowledge. Interviews were used to corroborate inferences made from concept maps. Students had a simplified understanding of cellular respiration and its processes as evident by cognitive structures with limited quantities of schemas that were vaguely connected and linearly organised. Furthermore, students had a better understanding of glycolysis than fermentation. Instructors can use these findings to help students build better knowledge of cellular respiration by focusing on incorporating relevant schemas, creating quality connections among schemas, and organising their knowledge of cellular respiration to reflect biological complexity.

Background: Computational modeling is an increasingly common practice for disciplinary experts and therefore necessitates integration into science curricula. Computational models afford an opportunity for students to investigate the dynamics of biological systems, but there is significant gap in our knowledge of how these activities impact student knowledge of the structures, relationships, and dynamics of the system. We investigated how a computational modeling activity affected introductory biology students' mental models of a prokaryotic gene regulatory system ("lac" operon) by analyzing conceptual models created before and after the activity. Results: Students' pre-lesson conceptual models consisted of provided, system-general structures (e.g., activator, repressor) connected with predominantly incorrect relationships, representing an incomplete mental model of gene regulation. Students' post-lesson conceptual models included more context-specific structures (e.g., cAMP, "lac" repressor) and increased in total number of structures and relationships. Student conceptual models also included higher quality relationships among structures, indicating they learned about these context-specific structures through integration with their expanding mental model rather than in isolation. Conclusions: Student mental models meshed structures in a manner indicative of knowledge accretion while they were productively re-constructing their understanding of gene regulation. Conceptual models can inform instructors about how students are relating system structures and whether students are developing more sophisticated models of system-general and system-specific dynamics.

Model-based instruction offers numerous benefits to students, including increased content knowledge and critical thinking. This study explored the differences in the knowledge outcomes and reasoning processes employed by undergraduate students in an introductory biology lab as they constructed, revised, and simulated a computational model of a prokaryotic gene regulatory system. We analysed post-lesson conceptual models (n = 335) and audio-recorded small group discussions (n = 10) of students that either constructed and revised a computational model and then investigated a pre-constructed computational model or only investigated a pre-constructed computational model to determine the impact of constructing and revising a computational model. No significant differences were found between the mental models of students who constructed and revised a computational model and those who only simulated a pre-constructed computational model. However, this study demonstrated that constructing and revising a model before simulating it offered additional benefits to students by fostering deeper cognitive reasoning processes. Constructing and revising a model elicited more explanatory and evaluative reasoning and prompted students to discuss the underlying mechanisms of the biological system.

Understanding the functioning of natural systems is not easy, although there is general agreement that understanding complex systems is an important goal for science education. Defining what makes a natural system complex will assist in identifying gaps in research on student reasoning about systems. The goal of this commentary is to propose a framework that explicitly defines the ways in which biological systems are complex and to discuss the potential relevance of these complexity dimensions to conducting research on student reasoning about complexity in biology classrooms. We use an engineering framework for dimensions of complexity and discuss how this framework may also be applied to biological systems, using gene expression as an example. We group dimensions of this framework into "components," "functional relationships" among components, "processes," "manifestations," and "interpretations" within biological systems. We explain four steps that discipline-based education researchers can use to apply these dimensions to explore student reasoning about complex biological systems.

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.

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.)