A framework for understanding the characteristics of complexity in biology

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

Epistemic Modals and Contextual Projection

The last few years have seen a growing interest in the semantic analysis of epistemic modal claims. By my lights, the most appealing analysis of epistemic modals is the relativistic approach. However, in their paper, “CIA Leaks”, Kai von Fintel and Anthony Gillies present some problems they think the relativistic approach must explain. I aim to defend a variation on the relativistic analysis of epistemic modals. I argue that when we determine the truth-value of sentences containing epistemic modals, we are free to evaluate modal claims from contexts other than the standard context of utterance. This freedom to evaluate the modal claims from different contexts is what I call contextual projection. When contextual projection takes place the sentence can be either true or false, appropriate or inappropriate. Furthermore, I will argue that the general phenomenon that is contextual projection is a common occurrence observable in ordinary language use

Elucidating the Population Dynamics of Japanese Knotweed Using Integral Projection Models

Plant demographic studies coupled with population modeling are crucial components of invasive plant management because they inform managers when in a plant’s life cycle it is most susceptible to control efforts. Providing land managers with appropriate data can be especially challenging when there is limited data on potentially important transitions that occur belowground. For 2 years, we monitored 4 clonal Japanese knotweed (Polygonum cuspidatum) infestations for emergence, survival, shoot height until leaf senescence, dry shoot biomass after senescence, and rhizome connections for 424 shoots. We developed an integral projection model using both final autumn shoot height and shoot biomass as predictors of survival between years, growth from year to year, and number of rhizomes produced by a shoot (fecundity). Numbers of new shoots within an infestation (population growth rate λ) were projected to increase 13-233% in a year, with the greatest increase at the most frequently disturbed site. Elasticity analysis revealed population growth at 3 of the 4 sites was primarily due to ramet survival between years and to yearto- year growth in shoot height and shoot biomass. Population growth at the fourth site, the most disturbed, was due to the large production of new rhizomes and associated shoots. In contrast to previous studies, our excavation revealed that most of the shoots were not interconnected, suggesting rhizome production may be limited by the size or age of the plants, resource availability, disturbance frequency, or other factors. Future integration of plant population models with more data on belowground growth structures will clarify the critical stages in Japanese knotweed life cycle and support land managers in their management decisions

Sexual Selection as a Tool to Improve Student Reasoning of Evolution

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

Convergence and transdisciplinary teaching in quantitative biology

The United States National Science and Technology Council has made a call for improving STEM (Science, Technology, Engineering, and Mathematics) education at the convergence of science, technology, engineering, and mathematics. The National Science Foundation (NSF) views convergence as the merging of ideas, approaches, and technologies from widely diverse fields of knowledge to stimulate innovation and discovery. Teaching convergency requires moving to the transdisciplinary level of integration where there is deep integration of skills, disciplines, and knowledge to solve a challenging real-world problem. Here we present a summary on convergence and transdisciplinary teaching. We then provide examples of convergence and transdisciplinary teaching in plant biology, and conclude by discussing limitations to contemporary conceptions of convergency and transdisciplinary STEM

Quantitative modelling biology undergraduate assessment

The Quantitative Modelling Biology Undergraduate Assessment (QM BUGS Version II) assesses undergraduate biology students’ quantitative modelling abilities and confidence. The assessment is intended to be given in undergraduate biology courses where instructors are engaging students in quantitative modelling within biological contexts. The assessment consists of 36 questions: 25 multiple choice questions addressing four subcategories within quantitative modelling understanding (Quantitative Act, Quantitative Interpretation, Quantitative Modelling, and Meta-Modelling) and 11 Likert questions addressing student confidence about modelling in biology within the four subcategories. QM BUGS assessments were piloted in multiple undergraduate biology courses at both a research intensive university and regional university in fall 2017 (QM BUGS I) and spring 2018 (QM BUGS II). Here we present the development and theoretical framework for the assessment, focusing upon reliability and validity evidence with respect to measures of student understanding and student confidence following administration of the QM BUGS II

Undergraduate Students’ Accuracy & Confidence in Detecting Errors in Biological Models Related to GPA

Research Questions 1. Does students’ abilities to Accurately detect errors relate to their GPA? 2. Which concepts affect student ability to Accurately and Confidently detect errors? GPA was positively related to accuracy but was unrelated to confidence (Fig. 1) Subject areas affected students’ accuracy and confidence § Students were more accurate on ecology & evolution models (Fig. 2) § Students were more confident in ecology models and less confident in physiology models (Fig. 2) Variation in student ability and subject area competency can provide teachers with places to focus and improve science understanding. Similar to the work of Clark et al 2020, this study gives merely a foundation on how certain principles affect the neural aspects of how students engage in reasoning about biology

Establishing Students’ Abilities to Reason with Relationships in the Context of Cellular Respiration

This study aims to establish the level at which University of Nebraska-Lincoln students reason with simple relationships in the context of cellular respiration at the levels of glycolysis, Krebs cycle, and electron transport chain. These processes are component processes of cellular respiration and each has multiple inputs and outputs. 633 student consented for this study, from which 18 student models were randomly selected, processed, and analyzed. Classroom observations were used to determine structures and relationships that were inputs and outputs to the three processes. In their models, students did not include different numbers of input or outputs when describing glycolysis, Krebs cycle and the electron transport On average, students had about one input and more than one output per process. The correctness was high for all three processes, however, relationships associated with Krebs cycle were significantly lower than relationships associated with ETC. Relationships associated with glycolysis were intermediate in quality. It was concluded that student\u27s still have a limited understanding of all processes, despite high correctness, because only one input and output for each process was included when three to four should be expected. Students must develop their system thinking skills to comprehend the smaller components at a high level, before they can consider the entire system. Current research suggests that computational modeling is one approach the University can implement as an activity to develop students\u27 system thinking skills