Example Models

This list represents some common models that are frequently utilized in biology education but does not represent all.

  • The literature has mapped student skills that are necessary to be able to interpret phylogenetic trees (tree thinking skills). Students should be able to:
    • Describe how each phylogeny is a subset from the much larger tree of life
    • Determine evolutionary relationships (relatedness) from both a resolved or a polytomy structure and make inferences about phylogenetic relationships
    • Identify organisms’ characteristics (synapomorphy) and taxa that share those characters
    • Identify/evaluate clades and determine if a set of taxa comprise a clade
    • Recognize that characters can appear on multiple branches as an indication of convergent evolution
    • Recognize that the rotation of tree branches does not change relationship even if it does change adjacency
  • Students rely on a number of strategies to interpret taxa relatedness. In order to give appropriate feedback, it is important to gather students’ reasoning for their answer to a specific question. Student reasoning could be gathered using a short answer question asking them to explain their choices or providing a list of explanatory choices that are derived from previous student answers. Types of student reasoning strategies include:
    • Correct interpretation strategies like examining for most common recent ancestor and monophyletic groupings
    • Incorrect interpretation strategies that students utilize include examining branch tip proximity, counting nodes, and using contemporary decent
  • Student ability to interpret and construct trees that contain the same relationships is influenced by tree format.
    • Overall recommendation by multiple research groups is to exclusively use branch format trees especially for introductory students.
    • Diagonally formatted trees down-to-the-right are easier for students to interpret than the up-to-the right slants most often used in text books.
  • The formatting of phylogenetic trees can reinforce some misinterpretations of trees and these perceptions by students should be considered in instruction.
    • Evolution as progressive and directional meaning that the trees show primitive to advanced evolution with mammals or humans as the ‘goal’ of evolution.
    • Taxon and lineage ages flowing horizontally on vertically oriented trees.
    • Assuming that evolution only occurs at the nodes and that lines imply that there is no change from ancestral states.
  • Make your own phylogenetic trees using recourse websites to identify organismal relationships and synapomorphies using PowerPoint to make figures since many websites that are used to make phylogeny diagrams is for researchers instead of educators.

Dees J., Momsen J. L., Niemi J., & Montplaisir L. (2014) Student interpretation of phylogenetic trees in an introductory biology course. CBE Life Sci Educ 13, 666-676. This paper discusses both proper and improper reasoning strategies used by undergraduate biology students to determine the relationship between organisms on phylogenetic trees. They used homework and test questions across a semester to assess how instruction effects student reasoning and student choices. Each homework or test question asked students to identify closely related taxa and explain their reasoning. Correct reasoning strategies involved finding the most recent common ancestor because sharing a recent common ancestor indicates closer relatedness between taxa. The other correct reasoning strategy observed in this course was monophyletic grouping, which states that taxa in same monophyletic group are more closely related to each other than to taxa not in the monophyletic group. Incorrect reasoning strategies included looking at branch trip proximity (closeness of the nodes), counting nodes, contemporary decent (looking at branch tips instead of common ancestors), and external insights (reasoning not provided by the tree). Over the course of the semester, correct reasoning strategies increased dramatically from 8% on initial homework (pre-instruction) to 79% on final exam, but the questions remained difficult for students as evidenced by only approximately half getting questions correct on the final exam. There were no differences in performance between different grade levels or between majors. There was a correlation with class attendance and performance. This paper reviews common misconceptions and misinterpretations of phylogenetic trees.

Hobbs F. C., Johnson D. J., & Kearns K. D. (2013) A Deliberate practice approach to teaching phylogentic analysis. CBE Life Sci Educ 12, 676-686. The authors investigate deliberate practice to help students increase their ability to create and analyze phylogenetic trees. Instructors spaced the assignments throughout the class using little class time and debriefing with small groups and in whole class discussions. The paper’s supplements include the assignments and show how they are structured with increasing difficulty. The first assignment has students complete a character and taxon matrix. The second assignment has students map character traits onto a tree. The third assignment has them creating a phylogenetic tree and so forth. The increasingly demanding assignments allow students to develop skills throughout the semester. This paring of multiple sessions of practice with frequent feedback allowed the intervention group (deliberate practice) to do significantly better on phylogenetic questions than the comparison group despite similar performance on non-phylogenetic questions. Likewise, the intervention group improved significantly based on a pre-post concept assessment. These assignments and feedback procedures could be incorporated into courses to increase student phylogenetic modeling abilities.

Novick L. A., & Catley K. M. (2018) Teaching tree thinking in an upper level organismal biology course: testing the effectiveness of a multifaceted curriculum. J Bio Educ 52: 66-78. A research-based and iteratively-developed curriculum including instructional booklet, lectures, and lab activities for teaching tree thinking was delivered to an upper-level undergraduate biology course. This curriculum included a self-paced instructional booklet that included 2 problem sets, instruction on key terms (e.g., synapomorphy, most recent common ancestor, cladograms), and an assessment of evolutionary relatedness. The booklet included both rectangular and diagonally formatted trees. The curriculum included two innovative lab activities. Lab 1, entitled, ‘What You Ate for Dinner’ would be appropriate for a variety of classes including contributing to a phylogenetic tree with organsims in various foods. This activity allowed students to look at both obvious and ‘hidden’ taxa like pollinators and microorganisms that are necessary for food production to demonstrate the biodiversity of organisms necessary for dinner. The other laboratory activity students went on a field trip to collect evidence (specimens, drawings, photos) of evolution traits. Students were asked to describe taxa and to suggest putative adaptations such characteristics could confer. This research-based curriculum showed an increase in tree thinking skills from the pre-test to the post-test given 7 weeks following phylogeny instruction. The effect of the curriculum was especially strong in helping students determine relative evolutionary relatedness of organisms (in resolved structures) that in previous studies has been a difficult skill for students to attain. Like previous work, diagonal trees were harder for students and even though students were instructed and improved on how to translate between a diagonal and a branch diagram this did not improve interpretation of diagonal format. Supplemental materials describe how to access both the instructional booklet as well as the pre/post-test used to assess tree-thinking skills.

Novick L. R., & Catley K. M. (2016) Fostering 21st-century evolutionary reasoning: teaching tree thinking to introductory biology students. CBE Life Sci Edu 15: ar66. This paper compares a business-as-usual format for phylogeny instruction to an enhanced instruction in an introductory biology course. The enhanced instruction included a booklet and integrated or ‘unified’ learning experience where lectures reinforced and extended phylogenetic concepts by continually discussing them when talking about species diversity. Assessment was done by a pre-test and unannounced post-test that was analyzed for seven tree-thinking skills. The enhanced instruction had a much larger improvement in the composite score of the seven skills. The post-test was constructed so students not only answered questions but also had an opportunity to offer explanations by selecting from a list of 12 options derived from free responses in previous research. This multiple-choice selection of explanation made grading easier but still allowed students to present their reasoning. In the supplemental material, there is information on how to obtain the instructional booklet, course/lab materials, and the pre/post tree thinking assessment.

Dees J., Bussard C., & Momsen J. L. (2018) Further effects of phylogenetic tree style on student comprehension in an introductory biology course. CBE Life Sci Educ 17: ar17. This paper investigated the effect of tree style on student performance in an undergraduate introductory biology course examining how students read and build both diagonal or bracket phylogenetic trees. The instruction included both bracket and diagonal trees and looked at four sets of data across homework assignments, a unit exam, and a final exam. Before instruction, the bracket format had higher accuracy across a large range of tree tasks. After instruction, students were able to perform equally well using both diagonal and bracket formats to determine most recent common ancestor, monophyletic group, and contemporary descent. Students receiving the bracket format, however, constructed more accurate trees with fewer errors and outperformed the diagonal format on questions related to taxa relatedness. Determining taxa relatedness however, remains one of the weakest skills for students. Interestingly, ~80% students chose to construct the diagonal format on exams over the bracket format. Authors hypothesize that simplicity and speed are the primary reasons students preferred to construct the diagonal phylogenetic trees. Based on these data, the authors recommend that that instruction and assessment on trees should be exclusively using the bracket style.

Novick L. R., Stull A. T., & Catley K. M. (2012). Reading phylogenetic trees: the effects of tree orientation and text processing on comprehension. BioScience 62 (8), 757-764. Tree diagrams can be formatted in multiple ways to show evolutionary relationships. Both college and high school textbooks most often show a diagonal format slanted up and to-the-right. These tree diagrams can also be displayed with the same relationships using downward-slanted diagonals. Trees being informationally equivalent are not necessarily equivalent in how students can intercept or use the diagrams. Subjects viewed pairs of trees (1 diagonal; 1 rectangular) and were asked if they had the same relationships. Subject eye tracking were monitored and correct answers were collected. For native English-speaking college students who tend to track visual clues from left to right, the down and to-the-right orientation for tree diagrams was easier to interpret. They propose that reading the tree starting with the most derived sister group (one farthest from the root) decreases concentration on the branch tips, focusing instead on the pattern of nesting which corresponds to historical relationships. The down-to-the-right slant makes reading easier by following the natural left to right reading orientation. The authors propose changing diagrams to support tree thinking of diagonal trees to down-to-the right slant, and instruction should focus on how to scan and interpret trees. By showing students how to read the nested relationships they could be able to interpret convergent evolution, common ancestors, and patterns of character evolution.

Tree of Life Web Project Project that highlights biodiversity, organisms’ characteristics, and their evolutionary history that can be used as a source to help build phylogenies for practice, instruction, and assessments.

OneZoom Tree of Life Explorer Interactive map of evolutionary relationships that can be used as a source to help build phylogenies for practice, instruction, and assessments.

Encyclopedia of Life Collection of information of animals, plants, fungi, protists, and bacteria collected from books, journals, institutions (e.g. Smithsonian Institution’s National Museum of Natural History, and global bioinformatics projects).

Diagrams and Animations
  • Students routinely encounter diagrams and other visual representations in their biology textbooks and study materials. These visuals, along with text, are powerful learning resources. Not all diagrams, however, qualify as models; some diagrams are purely descriptive of the physical structure of biological systems.
  • Process diagrams can be considered as models because they describe the structure of a system and also explain how the system functions.
    • Process diagrams make structure and function of a biological system explicit by representing the structural components of the system (e.g., the photosynthetic thylakoid membrane, or a biogeochemical cycle) connected by arrows that represent mechanisms, processes and relationships.
    • Learners often need to infer the function of the system by interpreting the meanings of the arrows and integrating the visual information with the corresponding explanatory text.
    • Students may require training and scaffolding to become able to effectively use process diagrams to learn about complex biological systems as this interaction with complex process diagrams is not intrinsically simple.
  • Although visual representations and models are integral to biology discourse and classroom materials, the most common use of visuals in biology education is that of providing them to students for interpretation (i.e., embedded in instructional materials).
  • Student-generated models and diagrams have great potential for promoting learning with understanding. Asking students to develop models and diagrams in the context of learning activities and of formative or summative assessments, however, requires careful instructional design.
    • Instructors should explicitly formulate goals and expectations for drawing and modeling activities, share these goals with the students, and scaffold the activities appropriately.
  • Animations (dynamic visualizations of complex processes) have great potential to support learners as they build their mental models about biological processes. Technological advances have enabled creation and dissemination of animations for educational purposes. These products have become widespread long before research on how learners use animations could inform resource development and instructional design (Hegarty, 2004). Research on how learners use and cognitively process animations is still relatively new. This developing area of study promises to offer insights on the educational effectiveness of animations, and best practices for their design and use in instruction.

Quillin, K., & Thomas, S. (2015). Drawing-to-learn: a framework for using drawings to promote model-based reasoning in biology. CBE—Life Sciences Education, 14(1), es2. Visual model-based reasoning is grounded within students’ ability to convey their thinking through visual representations (drawings). This article defines drawing and provides a comprehensive categorization of types of visual representations and their uses. Affirming drawing as a generative activity that allows students to externalize their mental models and construct knowledge, the authors clearly emphasize that incorporating drawing into instruction needs to be explicitly aligned with course goals and assessments. In support of instructors wanting to incorporate drawing into their classroom practices, the article offers a collection of tools and best practices, aligned to possible end goals and skills. The goals of drawing to learn range from affective development to visual literacy, all the way to model-based reasoning; drawing activities can be designed and implemented to elicit all cognitive skill levels, from basic knowledge to analysis and synthesis. One of the most common challenges instructors face when using drawing- and model-based tasks as a form of assessment is that of evaluating student work; while the challenge is still open, the authors provide a useful starting point by proposing an array of possible solutions that teachers can adapt to their needs.

Kragten, M., Admiraal, W., & Rijlaarsdam, G. (2015). Students’ learning activities while studying biological process diagrams. International Journal of Science Education, 37(12), 1915-1937. Biological process diagrams are ubiquitous in textbooks, and instructors expect them to serve as study tools for learners to make sense of complex processes (such as photosynthesis and cellular respiration). This article highlights the complex interplay of cognitive and metacognitive activities that students must use when studying with process diagrams. The authors used eye-tracking and think-aloud procedures to monitor the learning activities of students engaging with three different process diagrams (representing how the stomach epithelium produce acid juices, how neurotransmitters and synaptic vesicles are recycled in neuron terminals, and how the same organism can use aerobic or anaerobic metabolic pathways). The findings of this study carry important implications for teaching and learning. Learning from biological process models is a cognitively and metacognitively complex task that not every student may be proficient in. Interventions that scaffold learning from process diagrams, aimed to train students on how to effectively unpack these representations, may be an important, and perhaps necessary, instructional investment.

Heiser, J., & Tversky, B. (2002, January). Diagrams and descriptions in acquiring complex systems. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 24, No. 24). Process diagrams are valuable models of complex systems, as they allow communicating simultaneously information about the structural properties and the function of systems. Typically, process diagrams are effective in conveying structural information about a system, through the spatial distribution and organization of visual elements. Functional information (the temporal and/or causal sequence of mechanisms and operations) is typically conveyed by arrows. Interestingly, novice learners seem to be able to extract structural, but not functional, information more readily from these types of diagrams. While structural information is explicit in process diagrams, functional information must be inferred by the user. This study explores college students’ interactions with process diagrams of complex mechanical (non-biological) systems. Participants (67 college psychology students) were asked to describe diagrams of mechanical systems with or without arrows, and their responses were decomposed into propositions, which were in turn coded as either “structural” or “functional”. The findings support the idea that, when studying with and from process diagrams, learners will readily extract information about the parts of a system and their immediate relationships, but will likely struggle with inferring how the system functions as a whole. One solution is that of ensuring that complex process diagrams are accompanied by explanatory text that focuses on the system function.

Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and instruction, 14(3), 257-274. Animations (dynamic visualizations) have become increasingly common in science education, due to the rapid advances in technologies that allow creating and disseminating these types of media. The author of this article, however, warns users that proliferation of readily available animations has long predated adequate research studies on the educational effectiveness of these resources. Importantly, animation designers have often created their products without knowledge of how learners cognitively process (and learn from) animations. While the first animations were typically videos that one needed to watch from beginning to end, without much control over it, more recently animations have begun to feature various degrees of interactivity. This article describes the cognitive strategies used by 12 undergraduate students tasked with learning from an interactive animation in an unfamiliar domain (a weather map). Students’ interactions with the animation were captured on video; after learning through interaction with the map, students replayed the video of their own interaction, adding a commentary to explain their process. In addition, students completed a follow-up application task about what they had learned. The results of this study indicated that learners primarily focused on isolated fragments of the temporal and spatial dimensions of the animation, essentially missing the broader, more integrative patterns. Learners’ deductions and predictions were relatively simplified (compared to the complexity depicted in the animation), and failed to integrate the meteorological markings across the entire map. The article discusses at length the implications of the findings, concluding that an animation for educational purposes should go beyond being a mere dynamic representation of a sequence of events, as they occur in nature. Design of animations for education should manipulate content presentation to highlight and emphasize important themes in ways that enable learners to extract the critical information.

  • Simulations allow students to manipulate a model to observe changes and make/test predictions of phenomena that are of temporal or spatial scales that are either too large or too small to be directly observed by students.
    • Goals for simulations include but are not limited to observing: molecular structure of a protein, changes in cellular concentrations of proteins or metabolites, population dynamics, observing anatomical differences, or participating in a high-fidelity health care simulation.
  • There is a wide range of modeling technologies that can facilitate student understanding and make/test predictions of phenomena.
    • These include Simbio, NetBioDyn, and CellCollective and also others like PyMol (molecular visualization), AutoDoc Vina (docking of molecules), Lig-Plot (protein-ligand interactions), and CytoScape (biological pathways).
    • Much of the work in this field has been to develop these technologies and enable their use in classes such that there is limited burden of requiring students to write computer code. Many of these technologies utilize a graphical user interface.
  • Instruction that uses simulations must carefully consider the instructional tasks that come before, during, and after the simulation, in order to best impact student learning.
    • Briefing before the simulation can acquaint students with the learning objectives, simulation features/operations, and/or any necessary content. Briefing can include assigned homework.
    • During the simulation, cues from activity or instructor can be used to help students complete the simulation and to check their understanding. Allowing students to check their understanding during the simulation allows them to repeat sections (for specific types of simulations).
    • After completion of a simulation it is essential for students to reflect upon performance and to assess their own acquisition of knowledge, skills, attitudes, or behaviors. The reflective process can include debriefing lead by an instructor. Debriefing after the simulation is a reflective process can be done as a group or individually. Debriefing is especially important in high-fidelity health care simulations where skills and interpersonal interactions are critical.
    • More work needs to be done to determine best strategies for using simulations in courses and conditions that can enhance learning.
System SimulationsHealth Science SimulationsMolecular Simulations

Pope D. S., Rounds C. M., & Clarke-Midura J. (2017) Testing the effectiveness of two natural selection simulations in the context of a large-enrollment undergraduate laboratory class. Evo Edu Outreach 10(3). Pope et al compare a physical simulation to a virtual simulation of natural selection to compare differences in student learning, enjoyment, and behavior. Both simulations deal with predator-prey interactions. The physical simulation was ClipBirds in which student use binder clips to forage for food. The virtual simulation was a SimBio lab looking at crab predation of snails based on shell thickness. The study was conducted in 36 different sections of an introduction to biology lab course taught by 20 different teaching assistants and lab sections were randomly assigned to the physical or virtual simulation group. Learning was assessed by a multiple choice pre/post testing followed by a delayed post-test one month later. There was no difference in student learning or retention between the physical and virtual simulation. Student enjoyment was self-reported by a Likert-scale survey question and students reported enjoying the physical simulation more. Student behavior was measured through classroom observations. Researchers observed differences in student behaviors in the two treatments; the virtual simulation had less time off-task and less negative affect. In addition to an excellent literature review, their data suggest there is no clear advantage to physical over virtual simulations. They do say, however, that the most useful and feasible simulation may be determined by classroom context. Benefits for the physical simulation are better for participants than bystanders, so maximizing the proportion of students that participate may help. A key advantage with the virtual simulation was the incorporation of instructions and feedback directly into the activity; however, students in a virtual simulation could benefit from whole-class discussions of the activity.

Ballet P., Rivière J., Pothet A., Theron M., Pichavant K., Abautret F., Frontville A., & Rodin V. (2017) Modelling and simulating complex systems in biology: introducing NetBioDyn- a pedagogical and intuitive agent based software. Multi-Agent-Based Simulations Applied to Biological and Environmental Systems. IGI Global. Ballet et al describe a freely-available software for agent-based modeling that has been used in middle school, high schools, and universities since 2010. NetBioDyn does not require any programming skills to use. The software helps students become more familiar with complex systems, particularly how a large number of system constituents (agents) can independently interact on a local level to yield complex systems outcomes that are often non-linear and unanticipated. The paper uses 3 examples to explain how instructors and students can use simulations in classroom settings, including bacteria predator-prey dynamics, oxygen exchange between gills and water, and blood coagulation in a vein. This essay will enable non-computer specialists to use modeling in their classroom with frequent screen shots of the simulations and detailed instructions including how to set up the NetBioDyn and steps for students to take during modeling. They say students can explore complex system examining the entities, behaviors, and the environment while focusing on the main mechanism of the system. It could enable instructors to discern and correct student misunderstandings.

Bergan-Roller H. E., Galt N. J., Chizinski C., Helikar T., & Dauer J. T. (2018) Simulated computational model lesson improves foundational systems thinking skills and conceptual knowledge in biology students. BioScience. 68 (8) 612-621. A simulation on cellular respiration was implemented in introductory biology labs for life-science majors using over 20 graduate teaching assistants spread across ~35 sections. Student-generated concept models were collected and analyzed from 142 students before the simulation exercise (pre) and after the exercise (post). The concept models were analyzed for the number of components and processes in their model, the number of relationships, the organization of the relationships, and the correctness of each relationship. These metrics reflected the lower levels of a system thinking hierarchy (STH). The simulation on cellular respiration had students engage in the scientific processes of making predictions about a perturbation in the system, testing their prediction with the simulation, and recording/evaluating the outcomes of the simulation. From pre- to post-simulation, researchers found a statistically significant increase in the quantity of components and relationships that students included in their models, an increase in model complexity, and an increase in correctness of relationships. Authors note that because of lab time limitations they were unable to assess more complex levels of the system thinking hierarchy. There is a need for assessments to address these higher-level skills associated with system-thinking, such as the ability to generalize/problem solve and to think temporally through predictions and retrospection.

Helikar T., Cutucache C. E., Dahlquist L. M., Herek T. A., Larson J. J., & Rogers J. A. (2015) Integrating interactive computational modeling in biology curricula. PLoS Comput Biol 11(3): e1004131. The authors created the Cell Collective to make simulation and modeling accessible to students without the need for students to learn a programming language or how to manipulate mathematical equations. Cell Collective can be used for classroom education using a set of pre-built models or for research purposes to build models using primary literature. These simulations then provide tools for understanding the system properties, generating new hypotheses, and performing in silico tests for pharmacology or emergent properties. In addition, these simulations show how biological systems change over time. The authors propose that instructors could incorporate pre-built models into their courses that students could 1) view the dynamics of a network looking at different components together and 2) observe real-time outputs that are consequences of changes to the molecular or cellular level. Additionally, Helikar et al taught a course where students ‘learn by modeling.’ Students read literature and constructed conceptual/qualitative models. Building models fostered curiosity from the students and increased motivation to understand and appreciate the diversity and complexity of biological systems. These models could be the first simulated model of a system and therefore could lead to students presenting at external conferences and the publication of a journal article.

Cell Collective This website includes simulations that can be used in a variety of different classes including introduction to biology, biochemistry, immunology, cellular biology, and cancer biology. The site includes a training module to help students understand how to build and utilize models. Each of the simulations has a PDF that includes an introduction, instructions, and classroom questions to take students through prediction and use of each of the activities. Each module also includes a list of learning objectives and references that were used to build the simulation.

Bergman-Roller H.E., Galt N. J., Dauer J. T., & Helikar T. (2017) Discovering cellular respiration with computational modeling and simulations. Course Source 4: 1-8. This is an example of an active learning computation model simulation that includes learning objectives, pre-class assignments, mini-lecture slides, and discovering cellular respiration activity. Answer keys are available from the authors upon request.

Pujol S., Baldwin M., Nassiri J., Kikinis R., & Shaffer K. (2016) Using 3D modeling techniques to enhance teaching of difficult anatomical concepts. Radiologic Education 23(4) 507-516. Pujol et al use senior medical students to build anatomical simulations from living patient CT scans for use with first year medical students. They targeted three areas of complex antimony including the pelvis, upper abdomen, and mediastinum. Many of the structures that were simulated are difficult to observe in a cadaver but can be seen in a simulation because it supports the ability to rotate, remove, vary transparency, and otherwise manipulate the models. Likewise, cadavers can have tissue contraction so these models based on living patients are closer to what students could observe in the operating room. Since multiple people can be scanned, they show a range of normal types from multiple patients and also show the anatomical variation that exists in the human population: every person is unique and anatomical structures differ slightly in shape. The software package is free (and open source), so in addition to didactic sessions, students could explore these models on their own. In addition, after a short introduction to the software students were able to easily use it. Student needs were assessed using anatomical quiz questions. Qualitative feedback was collected from students using online surveys follow the 3D anatomy workshops asking students to self assess proficiency understanding of 3D relationships. The most productive sessions occurred with students were given concrete tasks with models to guide manipulation of the data and to explore the information contained.

Sittner B. J., Aebersold M. L., Paige J. B., Graham L. M., Schram A. P., Decker S. I., & Lioce L. (2015) INACSL standards of best practice for simulation: past, present, and future. Nursing Educ Perspectives 36: 294-298. High fidelity simulations with mannequins in nursing education are an important part of developing and assessing health care competencies. This paper has a list of standards of practice for simulations that are useful for instructors beyond nursing education. The authors summarize the best practice standards for simulations as defined by International Nursing Association for Clinical Simulation and Learning (INACSL). The INACSL has a publication to define simulation terminology to enhance communication and to increase consistency in education, practice, and publications. This paper discusses the iterative process taken by the organization and leaders in nursing education to develop and revise standards. They review the role of facilitation, debriefing, and how simulation can address inter-professional education. Facilitation of simulation by the instructor includes activities and disposition before, during, and after simulation. One of the intriguing aspects of the learning objectives in nursing education simulations is the inter-professional education (allowing students to practice being a part of a health team and taking on the different roles of doctors and nurses). This can increase teamwork skills during a simulation by allowing students to practice cooperation, communication, and skills/knowledge sharing.

International Nursing Association for Clinical Simulation and Learning (INACSL) Society. Contains a set of resources for educators, including the a list of publications addressing assessment, clinical judgment, self-efficacy, debriefing, skill performance, and more.

 Abreu, P. A., de Lima Cavalho, K., Rabelo, V. W., Castro, H. C. (2019) Computational strategy for visualizing structures and teaching biochemistry. Biochemistry Molecular Biol Education 47(1): 76-84. Abreu et al have designed a set of tutorials for a biochemistry course to allow students to explore macromolecule structure-function relationships. They utilize the free software Swiss PDB viewer and files from the Protein Data Bank (PDB). This program allows colorization of amino acid residues by their chemical types and calculates hydrogen bond lengths. The tutorials cover the structure of the nicotine acetcholine receptor and ion transport through membrane, the hemoglobin structure and oxygen transport, the phosphoglycerate kinase enzyme inhibition and conformational changes, and DNA structure. These tutorials are included in the appendix. Using a pre/post-test that examined understanding of bonds that form macromolecule structure and information about conical examples. Student scores improved from 35% to 76% after completing the tutorials with undergraduate biology and pharmacy students (n =76). Students were also asked what they through of visualizing protein and DNA 3D structures. 89% of students mentioned that visualizing the structures made learning and understanding easier.

 Roche Allred, Z. D., Tai, H., Bretz, S. L., Page, R. C. (2017) Using PyMOL to explore the effects of pH on noncovalent interactions between Immunoglobulin G and protein A: A guided-inquiry biochemistry activity. Biochemistry Molecular Biol Education 45(6): 528-536. This activity uses protein simulation that allows students to identify hydrogen bonds and salt bridges between and within proteins at physiologic pH. Then they apply knowledge off pH/pKAa to understand the differences in protein structure at pH 4 and pH 11. Before the simulation students complete 3 pre-lab activities including a video on PyMOL, a vide on non-covalent interactions within myoglobin, and prefab questions to introduce IgG and Protein A. The activity first has students examine the secondary structures of both Protein A and the FC region of IgG in PyMOL. The second activity has students look at electrostatic interactions and the amino acids responsible for these interactions at pH7 using a combination of diagrams and simulation in PyMOL. The third activity has students examine rate contrasts between Protein A and IgG at 3 different pHs from the biolayer interferometry data. Activities take about 2 hours to complete. The pre-lab activities and all activity instructions are available in supplementary information. During the activity 86% of students could determine charges of amino acid and recognition of conditions necessary for salt bridge and 60% of students were able to identify, sketch, and describe non-covalent interactions (N=50). The majority of the students (68%) had difficulties understanding how pKa and pH affected Protein A and IgG interactions, showing students understanding of these topics is superficial. Students reported a better understanding of protein structure and intermolecular forces between proteins. This activity allowed students to explore the relationships between topics normally taught in isolation, looking at protein structure, pH, pKa, and non-covalent interactions.

 Roy, U. (2016) Structural biology of tumor necrosis factor demonstrated for undergraduates instruction by computer simulation. Biochemistry Molecular Biol Education 44(3): 246-255. This paper uses Accelrys DSV freeware that allows for advanced protein modeling with commercial grade graphics tools. The modeling task is for students in junior level courses like immunology or biochemistry courses to compare the structure of wild-type Tumor Necrosis Factor alpha (TNF-a) to its mutant (M3S). The paper contains proposed methods to assess students’ performance of these tasks.

Tactile Models
  • 3D-printing technology is becoming more and more available, as costs decrease. Many universities and public libraries are installing builders’ labs (makers’ spaces) that utilize fused filament fabrication (FFF). This provides an opportunity for educators and students to create their own tactile models for use in the classroom.
  • Tactile models are used across many disciplines of biology but the papers summarized here will focus on two areas where there their use has been studied in depth:
    • 3D realistic/accurate anatomical printed models, and
    • 3D-printed models to illustrate structure-function relationships with proteins and other small molecules.
  • Tactile models for anatomy and protein structure have outperformed 2D images and even computer simulations of structures.
    • In multiple comparison studies, student answers to factual knowledge (lower-level thinking) questions did not differ between subjects using the 3D model and the comparison group.
    • Use of tactile models resulted in increased student understanding of spatial relationships, increased student ability to make predictions, and increased student ability to integrate understanding.

Babilonia-Rosa M. A., Kouns H. K., & Oliver-Hoyo M. T. (2018) Using 3D printed physical models to monitor knowledge integration in biochemistry. Chem Edu Res Pract DOI: 10.1039/C8RP00075A. This paper describes a biochemistry course that incorporates a series of activities using 3D-printed models of small molecules, secondary/tertiary structure, enzyme/substrates, and a membrane protein to help students understand structure-function relationships. The goal was to encourage and monitor knowledge integration, defined as the process that students use to incorporate new ideas through reflection, linking, and reconciling with existing ideas. Knowledge integration was monitored using prompted student-created drawings after each activity. Coding of the drawings examined code frequency, code co-occurrence, and unprompted extension of ideas. Each of the physical models incorporated coloring for electron density. The prompt for the drawing following the first activity specifically required representing electron density, and 54% of students incorporated it into their drawing. The frequency with which students incorporated electron density in their drawings for the 4th and 5th activity was over 80%, although it was not specified in the drawing prompt. Knowledge integration was also seen in how students incorporated ideas not present in the activity into their model. For example, in their drawings of enzyme-substrate interactions, students included ideas like enzyme specificity (like seen in chymotrypsin), reaction rates, and allosteric regulation. This suggests that students were making relevant connections to concepts that had been discussed in other settings (e.g., the lecture portion of the course). Use of 3D models also increased student understanding of non-covalent interactions that are critical for macromolecule structure, and the influence of these interactions on macromolecule function.

Harris M. A., Peck R. F., Colton S., Morris J., Neto E. C., & Kallio J. (2009) A Combination of hand-held models and computer imaging programs helps students answer oral questions about molecular structure and function: a controlled investigation of student learning. CBE Life Sci Educ 8(1) 29-43. Authors investigated how students used computer imagery and/or tactile tools to help answer lower- and higher-level thinking questions about protein structure/function relationships in one-on-one interviews. The control group had access to the molecular imaging program, Protein Explorer, and the experimental group had access to both Protein Explorer and a 3D physical model. Students in the two groups group did not differ in typical course assessments, in 3D aptitude, or other demographic factors. A subset of students from both the control and experimental group participated in one-on-one interviews. When answering lower-level thinking questions, primarily about knowledge, the students generally chose not to use either type of model, and there was no difference in performance between control and experimental groups. An example of a knowledge question is: “Can you tell me what you learned about protein primary, secondary, tertiary, or quaternary structure?’. In contrast, when answering application or synthesis questions, students preferentially used the 3D model and the experimental group performed significantly better than the control group. An example of a synthesis question is: “Propose mutations which constitutively activate/deactivate this bio molecule.” Differences between control and experimental groups were not observed outside of the interview in the semester-long study, including presentations and papers, as both groups performed similarly in these tasks.

ReiBer S., Prock S., Heinzmann H., & Ulrich A. S. (2018) Protein ORIGAMI: a program for the creation of 3D paper models of folded peptides. Biochemistry & Molecular Biol Educ 46(4) 403-409. This paper describes a freeware program that can be used to build alpha-helixes and beta-pleated sheets from paper using an amino acid sequence. These paper models of peptides are a fast and inexpensive way to visualize secondary structural elements of proteins. The different amino acids are color-coded to enable recognition of the specific distribution of different types of side chains (charged, hydrophobic, or no side chain as for glycine). The paper includes a list of peptides that can be used to illustrate important structure-function relationships like protein-lipid interactions, dimerization, and protein folding motifs.

Yammie K., & Violato C. (2016) The effectiveness of physical models in teaching anatomy: a meta-analysis of comparative studies. Adv in Health Sci Educ 21(4) 883-895. This meta-analysis compares use of physical models in anatomy education with a variety of student populations, including medical or veterinary students, and undergraduates. The studies included in the meta-analysis compared physical models with either 2D images, cadaveric dissections, and 3D computer simulations. Overall, the 3D tactile models as compared to other models did not differ in students’ factual knowledge. In contrast, students that utilized 3D tactile models had to better spatial knowledge and long-term retention. The authors suggest that using these models could increase anatomical knowledge, especially of spatial relationships, at a low cost. They suggest that physical models have advantages as memory aids, reducing cognitive overload, facilitating problem solving, and increasing student participation. This is especially relevant since visualizing and understanding anatomical structures and their relationships imparts high cognitive load on learners.

Lin K. H. A., Loo Z. Y., Goldie S. J., Adams J. W., & McMenamin P. G. (2015) Use of 3D printed models in medical education: a randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy. Anat Sci Education 9, 213-221. This study examined first-year undergraduate students learning about cardiac anatomy using either cadaver materials, 3D-printed models, or a combination of 3D models and cadaver material. The participants received a short multiple-choice pre-test to assess baseline knowledge of cardiac anatomy. The participants received a 15-minute lecture before randomly being assigned to treatment condition. Each group needed to complete the same task, independent of the types of models they were provided, in 45 minutes, collaboratively and without instructor intervention. Following the task, the participants were given a short-answer post-test that was developed by a third-party instructor that did not know research questions or goals. Analysis revealed that the group using the 3D-printed models outperformed both the group using cadaver material and the group using a combination of 3D models and cadaver material. The authors suggested that novice students could experiences stress or anxiety in handling the cadaver material, and that could have accounted for the difference between the 3D model performance and the other groups. The authors suggest that 3D-printed models should be used in addition to cadaver material instead of a replacement because of the inherent value in learning to work with real specimens. They also suggest that 3D-printed accurate models do not disadvantage student learning, and are preferable to plastic models that are idealized caricatures lacking anatomical accuracy.

Beltram E. D. V. l., Tywhitt-Drake J., Shalaby R., Suckale J., & Krummel D. P. (2017) 3D printing of biomolecular models for research and pedagogy. J Vis Exp 121, e55427, doi:10.3791/55427. This link includes a video and paper to describe additive manufacturing to construct biomolecules using commercially available 3D printing Fused Filament Fabrication (FFF). The video and accompanying paper describe: (1) preparing the 3D model files for printing from structural data, and (2) process files for printing, slicing, and post-production processing of the model. The paper discusses different choices that need to be made when preparing the file for printing; for example, users can choose to print a surface representation or thickened ribbons to depict protein structure.

Concept Mapping
  • Concept maps are a type of knowledge representation that diagrams sets of concepts and the relationships among them as they relate to a particular knowledge domain.
    • Concept maps are comprised of concepts or ideas (generally enclosed in circles or boxes) linked by arrows that denote relationships. Arrows may or may not include phrases that describe relationships.
  • Structurally, concept maps are networks consisting of nested hierarchies, where larger, more encompassing concepts serve as umbrella categories for smaller, more specific concepts.
  • Concept maps primarily serve as a way to organize knowledge and map a knowledge domain, and are thereby distinguished from ‘true models’ (e.g., SBF) that serve to explain or predict phenomena.
  • Concept maps have frequently been used in research to approximate knowledge structure (aka, cognitive structure).
  • Instructionally, concept maps can be an effective tool for:
    • documenting change in student thinking, particularly as it relates to acquisition of new knowledge within a domain.
    • eliciting students’ prior knowledge in order to provide feedback to instructors who aim to design instruction that ‘meets students where they are’.
    • engaging students in activities that promote brainstorming and/or synthesizing topics, especially in collaborative groups.
    • enabling students’ to reflect on and organize their knowledge in a way that promotes consolidation of their knowledge structure.
  • Because knowledge structure is inherently unique to an individual, concept maps as representations of knowledge structure can be challenging to evaluate.

Novak, J. D. & Canas, A. J. (2007). Theoretical origins of concept maps, how to construct them, and uses in education. Reflecting Education 3(1):29-42. In a relatively short manuscript, Novak and Cañas, provide an overview of the origins, applications, and theoretical foundations of concept mapping as an assessment of knowledge structure. They explain how concept mapping fits within a cognitive psychological understanding of learning put forth by Ausubel (1963, 1968, and 1978) that “learning takes place by assimilation of new concepts and propositions into [an existing framework] held by the learner […] referred to as the individual’s cognitive structure.” The authors examine the psychological foundations of concept maps with particular attention to meaningful learning as a framework for understanding relationships among assessment, curriculum, and instructor roles, and link it to constructivist philosophies about knowledge creation. In doing so, they build a compelling argument for concept mapping as a valid assessment of knowledge structure.

Ruiz-Primo, M.A. & Shavelson, R.J. (1996). Problems and issues in the use of concept maps in science assessment. J Res Sci Teach 33(6):569-600. The central objective of this work is to put forth the idea that concept maps can be used as an assessment of students’ cognitive structure. The authors explicate how concept maps reflect three key features required of any assessment. Specifically, concept maps: (1) serve as a task for eliciting evidence of a knowledge within a domain; (2) provide an alternative (and more scientifically authentic) format in which students can reply to a prompt or problem (e.g., versus a multiple-choice option select); and, (3) can be scored in ways that enable consistent and accurate methods for evaluation. Based on their review of concept mapping research at the time, the authors noted that despite broad convergence on the assumption that concept maps measure a common construct (a student’s cognitive structure) there was tremendous variation across studies in how concept mapping techniques were being applied and evaluated. Despite this variation, the authors contend that concept maps can serve as a valid and reliable assessment of a learner’s knowledge structure, particularly from a cognitive theoretical perspective. However, the authors are careful to call for additional studies that provide empirical evidence to bolster that claim, particularly before concept maps become incorporated into large-scale assessments with potential to inform policy and the public about student learning and/or teacher efficacy.

Kinchin, I. M. (2000). Concept mapping in biology. J Biol Educ 34(2):61-68. Kinchin’s overview of concept mapping is structured as a series of practical needs or challenges for a typical instructor; he then explains how concept mapping can be used as a tool for addressing each concern and provides supporting evidence for his claim. As a specific example, he describes the instructional need for activating students’ prior knowledge in advance of new learning. Kinchin then describes how concept maps can serve as an ‘advance organizer’ in order to prime students’ for a lesson by first eliciting their prior knowledge. He also discusses alternative rationales for engaging students directly in the task of map building versus providing students with a map based on the instructor’s view of the lesson plan. Additional utilities of concept mapping discussed in this paper include identifying misconceptions, directing the focus of readings and discussions, organizing learning activities of collaborative groups, and providing a means for students to summarize and iteratively revise their understanding.

Stoddart, T., Abrams, R., Gasper, E. & Canaday, D. (2000). Concept maps as assessment in science inquiry learning – a report of methodology. International Journal of Science Education 22 (12): 1221–46. In this paper, the authors describe their personal experience using concept-mapping as an assessment strategy for a course that was designed to enable students to pursue topics of their choosing – thereby making it difficult to design a common assessment appropriate to everyone. As an assessment, the concept mapping tasks were linked to an on-line learning environment (virtual-canyon.org) that was used for instruction of students across different schools at elementary and high school levels. As such, a single reference rubric would be inadequate to capture the variation among learners. As a primary objective, the authors sought to establish whether concept mapping would prove a valid and appropriate approach for assessing learning across such diverse contexts. They employed a process of scoring concept maps that was achieved by breaking the maps down into propositions (pairs of concepts with linking phrases) and scoring for 3 features: accuracy, explanation, and proposition structure. Accuracy is represented by four levels – scientific (correct statements about scientific content); common knowledge (non-scientific, commonplace knowledge); inaccurate (scientifically inaccurate); and, affective (statements that convey emotion or personal thoughts). Explanations were classified as either ‘higher-order’ (meaning they described a function or purpose) or ‘basic descriptions’ (meaning they provided little more than statements of fact). Proposition structure was evaluated on the basis of whether it contained only one subject-object clause (simple) or one or more dependent clauses (complex). Subsequent tests of validity and reliability and comparisons across populations of interest led the authors to conclude that concept maps were capable of extracting meaningful components of learning in a manner that was practical, valid, and reliable. While this research-based approach is likely more than would be undertaken by an instructor in a classroom, elements of the scoring mechanism could certainly be drawn from to inspire a practical scoring scheme that would expedite grading. For example, evaluating the scientific merit of specific propositions, or evaluating whether the map adequately addresses an assigned biological function or process.

Hay, D., Kinchin, I., & Lygo-Baker, S. (2008). Making learning visible: the role of concept mapping in higher education. Studies in Higher Education 33(3):295-311. The primary contribution of this article is to make a case for how concept mapping can be used as a method for evaluating evidence of learning. The authors claim that a single, coherent definition of university-level learning is absent from the literature. In response, they put forth a version that borrows from learning theories contributed by Kolb, Jarvis, and Novak. Key aspects of their model of learning include conceptual change (where absence of change is equivalent to non-learning) and integration of new and prior knowledge. The authors develop an argument for how the architecture of concept maps measured at different times can provide evidence about both aspects of learning – change and integration of new and existing knowledge. Beyond that, the value of concept mapping is discussed with respect to its potential to make thinking visible (to both teacher and student) and to highlight the linearity that is inherent in most university instruction. They recommend four practices for university instructors that can each be achieved through the use of concept mapping: (1) establishing measures of students’ prior knowledge; (2) presenting new information/material in a way that deliberately considers students’ existing knowledge base; (3) facilitating meaning making in students by purposefully disclosing underlying knowledge structures that may be tacit to the instructor, but invisible to students; and, (4) measure change in students such that learning can be identified and causes of non-learning addressed.

Structure-Behavior-Function (SBF) Models
  • Structure-Behavior-Function (SBF) is a framework developed in systems engineering for model-based design of physical devices (Goel et al., 1996). SBF models of engineered devices explicitly represent (a) the function of a device, (b) its structure, and (c) the internal causal mechanisms and processes (behaviors) that explain how the function of the device as a whole emerges from the functions of its structural components.
  • The SBF framework has been adapted as a syntax for creating classroom models of biological systems. Using this approach, biological systems can be represented through models that emphasize their structure-function relationships which illustrate how the system works.
    • SBF models consist of boxes and arrows where structures (system components) are represented in boxes connected by arrows that signify the behaviors (mechanisms or causal relationships) that combine to explain the system’s function.
    • Although developed primarily for describing systems, application of the SBF syntax can be useful for describing and assessing diverse model types, including process diagrams, biochemical pathways, etc.
  • Students can construct SBF models of biological systems on paper or in digital form (e.g., with software like PowerPoint, or with concept mapping software like cMapTools).
  • SBF models are visually similar to concept maps in appearance (i.e., networks consisting of boxes and arrows), but differ in significant ways.
    • The primary purpose of a concept map is to document an individual’s knowledge domain, whereas the objective of an SBF model is to describe and explain how a system works;
    • Concept maps generally expand as a function of the learner’s domain knowledge (i.e., increase number of concepts represented). SBF models may expand as learners acquire new knowledge or they may become more parsimonious as learners shed extraneous information that does not contribute to the model’s explanatory function.
    • Concept maps are inherently hierarchical in nature, with groups of concepts logically nested under larger-scale. SBF models may have hierarchies, but these are not a required feature of their architecture.
  • SBF models can be an instructional tool to develop students’ systems thinking skills.
    • The process of creating an SBF model requires a student to identify and describe a focal system. This includes making explicit decisions about which system structures and behaviors to include and how to organize structures and behaviors to convey a particular function or outcome of the system.
    • Explicitly discussion and assessment of how mechanisms support the overall function of the system would support student development of mechanistic reasoning and allow students to map system properties across levels of organization (molecular, cell, tissue, etc.).
    • SBF models can also support students in developing connections between biological concepts. For example, SBF models have been used to connect basic genetics understanding to evolutionary outcomes.

Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16(3), 307-331. Complex systems are characterized by multiple interactions among system components that can occur at diverse spatial and temporal scales. Structure-Behavior-Function (SBF) theory provides a representational framework for examining thinking about complex systems at different biological levels, including the system’s constituent elements (structures), the behaviors of its structures, and contribution of structures to function. In this study, the authors apply the SBF framework to contrast models of complex systems constructed by experts and novices. Study participants included 7th-grade science students (novices) and preservice teachers (experts). The authors also conducted in-depth interviews to extract features of their holistic mental models of two systems – an aquarium and the human circulatory system. Findings from this study are consistent with others that find that novices tend to focus their understanding on visible structures of the system and struggle to comprehend system behaviors and functions. Expert perspectives varied qualitatively, often reflecting the nature and source of their expertise (e.g., ecologist vs. hobbyist; respiratory therapist vs. pulmonary physician). However, experts tended to use their understanding of system behaviors and functions as principles for organizing and representing their knowledge. As a result, the authors conclude that expertise in complex systems is fundamentally distinct from other types of expertise, owing to experts’ deep understanding about the role of interactions at multiple level as drivers of system properties.

Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., & Sinha, S. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Educational Technology & Society, 14(1), 66-81. Building from the premise that learners must construct their own knowledge, Vattam and colleagues describe an interactive learning environment that supports student understanding of complex systems through construction of SBF models. The Aquarium Construction Toolkit or ACT allows learners to build SBF models of classroom aquaria. In the interactive learning environment, students can work in one of three views: structural, behavioral, and functional. In the structural view, for example, the learner uses components/substances and their connections to build a graphical model. In contrast, in the behavioral view students focus on identifying and describing transitions between system states. The authors then piloted the ACT in three middle school classrooms where teachers were allowed complete freedom over how to integrate ACT into their curriculum. The researchers analyzed data pre- and post-instruction; data streams included student drawings of an aquarium system and an open-response item asking students to explain how given elements were related to the aquarium system. Metrics included the number of structures and components present in student models, and whether open-response items included a behavior (mechanism or process) or function (an outcome of a particular process). The researchers found significant gains in students’ understanding of structures, behaviors, and functions. The authors conclude the ACT is a promising tool for teaching about complex systems, though additional research is certainly necessary.

Dauer, J. T., Momsen, J. L., Bray Speth, E., Makohon-Moore, S. C., and Long, T. M. (2013). Analyzing change in students’ gene-to-evolution models in college-level introductory biology. Journal of Research in Science Teaching 50(6), 639-59. Across 3 sections of large-enrollment introductory biology, the authors investigated the efficacy of SBF modeling in helping students develop a deeper understanding of the connections between genetics and evolution, that is, how molecular-level change can ultimately result in change in the characteristics of a population. Using model-based instructional techniques, students learned to build, revise, and extend their models of biological systems. To enable comparison of pre- and post-instruction models, the authors identified two metrics to describe student models. First, proposition correctness describes the accuracy of any ‘box-arrow-box sequence’ and can be averaged across a student’s model for an average accuracy score. Second, the authors measured model complexity through the web-like causality index (WCI), which is a measure of the density of connections between model structures. The authors found that early models reflected an accretion of new knowledge and concepts (i.e., addition of structures, behaviors), as evidenced by models that were packed with propositions of varying correctness. Later models were more robust and parsimonious, shedding extraneous structures to better explain system function. This research demonstrates how SBF models can be integrated into classroom instruction and introduces metrics that may help instructors analyze student-generated models.

Bray Speth, E., Shaw, N., Momsen, J., Reinagel, A., Le, P., Taqieddin, R., & Long, T. (2014). Introductory biology students’ conceptual models and explanations of the origin of variation. CBE—Life Sciences Education, 13(3), 529-539. This article describes an instance of model-based learning about natural selection in an introductory biology course. Course topics, genetics and evolution, were conceptually connected in explicit ways. Students were engaged throughout the semester in model-building activities that required integrating concepts at multiple scales of biological organization (from genes to organisms and populations over time). Students iteratively constructed, extended and refined conceptual models of biological systems, framed as structure-behavior-function (SBF) box-and-arrow diagrams. Student gene-to-evolution models, produced on two course exams, were analyzed to reveal patterns of reasoning about the key biological processes underlying the origins and evolution of genetic variation in populations. Student models improved, from midterm to the final exam, in terms of their ability to convey the key causal processes that resulted in evolution by natural selection. Importantly, content analysis of models revealed that introductory biology students struggled connecting molecular genetic mechanisms with evolutionary processes. This was particularly evident for the process of mutation, which was prominently missing from students’ models at midterm. Despite a notable increase in the frequency of student models incorporating mutation as the origin of genetic variation, on the final exam, still 35% of students did not include this key process. Importantly, however, when students incorporated the concept of mutation in models, they conveyed that mutation is the key mechanism underlying the genetic origin of variation. Such clear mechanistic description of mutation emerged from students’ models but not from their short answers, suggesting that SBF models are more conducive than writing to representing mechanistic reasoning/understanding.

Lira, M.E., Gardner, S. M. (2017) Structure-function relations in physiology education: Where’s the mechanism? Adv Physiol Educ 41(2), 270-278. This paper uses the SBF framework as a lens to understand student learning in upper level physiology course. A semi-structured interview protocol to understand how seniors in an animal physiology course could describe and compare/contrast the terms: structure, process, mechanism, and function (n =10). Coding of transcripts used a constant comparative method looking at how students characterized the terms in levels of organization (molecular, cellular, organ, organismal, etc.) and how they differentiated between mechanism and function. Most students in this study characterized structures by relatively higher levels or organization (ie cells or organs) whereas mechanisms were lower levels of organization like molecules. Students characterized the function as a process (how the body should be working), a purpose (organisms need for something), or a product (end result or output of the process). Some students failed to articulate a diction between mechanism and process. These results are consistent with previous research that indicates that students have difficulty understanding the difference between the function of a system and the mechanism that causes the emergent function. Lira and Gardner recommend that explicitly discussion and assessment of how mechanisms support the overall function of the system. They postulate that this would support student development of mechanistic reasoning and allow students to map system properties across levels of organization (molecular, cell, tissue, etc.) and to encode memory using meaning functional relationships.


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Cite this guide: Wilson KJ, Long TM, Momsen JL, Bray Speth E. (2019) Evidence Based Teaching Guide: Modeling in Classroom. CBE Life Science Education. Retrieved from https://lse.ascb.org/evidence-based-teaching-guides/modeling-in-the-classroom/

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