Chapter 3

Physical Models Support Active Learning as Efective Tinking Tools

Cassidy R. Terrell,*,1 Margaret A. Franzen,2 Timothy Herman,2 Sunil Malapati,3 Dina L. Newman,4 and L. Kate Wright4

1Center for Learning Innovation, University of Minnesota, 111 S. Broadway, Rochester, Minnesota 55904, United States 2Center for BioMolecular Modeling, Milwaukee School of Engineering, 1025 N. Broadway, Milwaukee, Wisconsin 53202, United States 3Chemistry, Clarke University, 1550 Clarke Drive, Dubuque, Iowa 52001, United States 4Tomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, 85 Lomb Memorial Drive, Rochester, New York 14623, United States *E-mail: [email protected].

From the of a novicestudent, the molecular biosciences are inherently invisible. A challenge facing bioscienceeducators isto helpstudents create detailed mental models of the thatmake up a livingcelland how they all work togethertosupport life. With the advancement of rapid-prototyping, also knownas 3D (three dimensional)-printing, physical models of biomolecules are entering undergraduate classroomsastoolsto aid in constructing mental models of biological phenomenaat the molecular-level. Tisrelatively new pedagogical tool requires evidence-based practices for optimal use in aidingstudentconceptual and Downloaded via Cassidy Terrell on December 15, 2019 at 13:31:49 (UTC). visual development. Tis chapter presents currentevidence for the use of physical modelsas learningtools, while also introducingcasestudies on howphysical models of biomolecules are designedand assessed in undergraduate molecular

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Introduction

In this chapter, we focus on development, useand assessment of biomolecularphysical models in undergraduate molecular bioscienceeducation. Here “molecular biosciences” encompasses any course utilizingconcepts featuring biomolecules, rangingfrom monomers tomacromolecules that support life on the molecular level (e.g. introductory biology; general, organic, and biochemistry (GOB); biochemistry, molecular biology, and cellular biologycourses). At the undergraduate level, VisionandChange identifes modelingand simulation ascorecompetencyand disciplinary practices

© 2019 American Chemical Society Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. (1). Te Next Generation ScienceStandards (NGSS) Framework defnition of models includes , physical replicas, mathematical representations, analogies, and computer simulations that aretools for the studenttoengage in “developing questions, making predictionsand explanations, analyzing and identifying faws in systems, and communicating ideas (2).” Moreover, models provide an opportunity for the studenttoengage in an iterative process of “comparing their predictionswith the real world and thenadjusting themtogain insights into the phenomenon being modeled (2).” Tisis the vein in which the potential power of models lies, as manyauthors suggest that learningbarriers, particularly thoserelatedtoabstract concepts, arisefrom unchallenged incorrect ideas, fawed mental modelsand the inabilitytorelate new concepts to other knowledge (3, 4). Here, we predict usingphysical modelswillfurther the student’s development of arobust mental model thatisable toovercome misconceptionsand further the student’s learning progression in the molecular biosciences. Since no modelis identical to the concept it represents (else it would cease being a model), students needto be trainedto be skeptical in analyzingany model (5–7). Students who can examine a modeland explain how the modelis both likeand unlike the real thing it represents demonstrate aconceptual understanding of what the modelrepresents. Furthermore, like the Hindu fable of the blind menand the elephant, each model only represents a part of the whole, and it is through transitioningamong multiple representations thatwegain a true sense of what modelsrepresent (8). Assuch, physical models oferanavenue to develop students’ visual literacy skills, arecognized compounding variable in the abstract nature of the molecular biosciences wherein students are inundatedwith a variety of representationscontaining difering levels of abstraction (1, 9–11). Several studies suggest that theserepresentationscan leadtostudent learning difculties and propagate misconceptions (4, 12–16). For example, spectacularanimations of molecular processes helptoconvey difcult concepts, yet they ofen provide a “wow” factor to the expert, while moving through the information too quickly for a noviceto process (17). Molecularvisualization sofware allowseducators and students aliketorotateand spin structures in virtual 3D space, but our assumption thatstudents areable to “see” the objects in 3D may be a false one, especially if they have never experienced similar, tangible structures in the real world (18). One possible explanation for these difculties is thatcurrent molecular biosciencecurricula include litle to no explicit instruction on interpreting, evaluatingand moving through levels of representations (4, 9, 19, 20). Physical models of abstract concepts can aid in developingvisual literacy skills that alsosupport the overall aim here – to develop robust learner mental models that enable students to think like scientists. Untilrecently, students’ primary exposuretophysical models was limitedto the use of small molecule modelingkits in a chemistrycourse. Advances in structural biology begantoreveal the 3D structure of macromolecules, but it was impossible for students toconstruct physical models of these complex structures. During thissame time, the development of molecularvisualization sofware made it possible for students to experiencevirtual representations of macromolecules in a computer environment. Today, advances in anadditivemanufacturing process knownasrapid prototyping –commonly referredtoas 3D-printing–havemade it possible toconstruct physical models of complex molecularstructures (Figure 1) (21). As 3D-printingtechnologycontinues toevolve, it is becoming possible to create modelswith complex color schemes in a variety of materialsfrom hard plastertofexible plastic and rubber. Te recent explosion of low-costflament-based 3D printers nowmakes it possible for molecular bioscienceeducators toacquire this modelingtechnology for as litle as several thousand dollars.

44 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. Figure 1. Physical models of molecular structures. A. An amino acid – constructed by students using a small molecule kit. Models B-E are “assemblies of ,” constructed by 3D printing. B. ATP with magnet- docked phosphate groups. C. A fnger (PDB ID: 1ZAA) D. A four-subunit channel (PDB ID: 1J95). E. Te 70S E. coli ribosome (PDB ID: 4V5D) with magnet-docked large and small subunits, three tRNAs and a short stretch of mRNA.

Awide variety of modelshave been used in allscientifc disciplines torepresentcomplex, ofen abstract concepts. Moststudies on physical modelshave beenconducted in organic courses or K-12 education, with relatively few investigatingstudent learningand behavior with biomolecularphysical modelsat the undergraduate level. Among thesestudies, however, exists evidence for student learninggains in diferent molecular biosciencesetings. Intworelatedstudies, Oliver-Hoyoand authors report not only increasedstudentengagement but also integration of knowledgeacross biochemistryconcepts afer usingaseries of macromolecules with small molecules (22, 23). Another investigation demonstrated beter learninggains for students participating in a molecular dissection activity involving a 3D physical DNA modelcomparedto the comparative buildingactivity (24). In biology, higher learninggains for females arecitedafer usingaphysical model in one classsession (18), while anotherretroactivestudy usingseveral physical models relatedto the fow of genetic information demonstrates learninggains independent of gender (25). In fact, lowerachievingstudents demonstrated the highestabsolutegains (25). Tislaststudy is discussed in greater detaillater in this chapter. A few studies have examined the impact of combining physical modelswith virtual activities, and, although thereissome disagreement on the degree of impact, thesestudies do conclude with higher learninggainsassociatedwith the complementary use of thesevisual tools (26–28). Although theseresults are promising, moreresearch is needed on best practices with physical modelsand on howand whenstudents learn using thesetools. Much of this, however, will hinge on increased educator and student access to, and training with, physical models. Herein, we introduce threecasestudies involvingphysical models of biomolecules in anactive learning undergraduate classroom seting. Particularconsideration for the use of physical models for students requiringaccessibilityservices is presented in the fnal casestudy. Each casestudy stemsfrom and works closely with the MilwaukeeSchool of Engineering’s (MSOE) Center for BioMolecularModeling (CBM), and assuch we begin with an introductorycasestudy on the CBM’s history of engaging communities of students, educators and researchers in physical modeling.

45 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. Case Study I: Te CREST Project

Modeling Projects

Te CBM explores the use of tactile, physical models of macromolecules and their building blocks – createdby 3D printingtechnology–as a novel way in which to introducestudents to the invisible world of molecular bioscience. Initiated in 1999, the CBM was originally focused on the creation of accuratecustom models of for usebyresearchers. But in 2001, the development of new designsofware (RP-RasMol) made it possible for the frsttime to involveteachers and their students in the design of thesephysical models. Aswe beganto incorporate model design into our professional development programs for high schoolscienceteachers, we quickly realized that not only were the models efectiveteachingtools, but that the process of modeling wasaneven more powerful experience. Tisrealization ledto the development of the student modeling projects, in which a smallteam of students and their teacherwork closely with a research labto createaphysical model of a protein that is central to the work of the lab. Although our initial insight into the power of modelingasasuccessful pedagogical approach involved high schoolteachers and their students in the SMARTTeam program (Students Modeling AResearch Topic), wesoon began exploring the use of thisapproach at the undergraduate level (29). Welaunchedaseries of CRESTProjects (ConnectingResearchers, Educators and STudents) resulting in additional insights into the power of modelingat the undergraduate level. ACREST modeling project combines (i) astudent-centeredmodeling project involvinganactiveresearch lab with (ii) a collaborativeinstructionalmaterials development project in which collaborativeteams create materials thatengage classroom students in an inquiry-driven exploration of the research project (30). Te mostrecent iteration of CREST explores the value of engaging undergraduates in the community of science through meaningful conversationswith researchers at a professional meeting (31). Te program identifes anawardeewhoseresearch will be presentedat the annual American Society for Biochemistryand MolecularBiology (ASBMB) meeting. Teams of undergraduates exploresome aspect of the research topic and designand build a physical 3D modeltotell the molecularstory of the structureand function of one of the proteins involved in thisresearch. Teams tell their molecularstoryat a postersession at the ASBMB meeting, atend the award lecture, then meet with the researcherand colleagues in a “CRESTConversation.” Tesephysical models become the shared mental modelamongresearchers and students and allowstudents toengage in authentic scientifc conversations with the researchers (32). Although a number of scientifc professional societies encourage undergraduateparticipation, both in atending meetings and in presenting posters, unless thereisaspecifc lecture that pertains directly tostudents’ research, undergraduates can feeloverwhelmedand inadequateasscientists whenatendingscientifc sessions. Te CRESTProgram allowsteams of students to designaphysical modeltotell a molecularstory, atend a scientifc session on thatspecifc topic, then meet with their peers and researchers to discuss the topic in depth within the context of a professional meeting. Wehypothesize that this meaningful engagementwill helpstudents identifyasscientists, one of the key afective traits required for retention in STEMfelds, especially for groups underrepresented in the sciences (33, 34). Indeed, undergraduates who participated in extracurricularCREST projects expressedgreaterconfdenceand identityasscientists and viewed their facultyadvisor asa collaborator more than a mentor asaresult of participation in CREST (30). Te greatestgains in

46 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. theseafective domains wasseen in undergraduates from primarily undergraduate institutions (PUI) which lacked research opportunities for undergraduates (30).

Instructional Materials Development Recentcalls for reform in deliveringscience instruction emphasize the needto shiffrom lecturingtostudent-centered learning (1). Yet educators are slowtoadopt beter learningstrategies (35, 36). Terearenumerous obstacles toovercome toadopt new methods. Changeis difcult (37). Educators needto be dissatisfedwith the current methods, and there needsto be a viable alternative thatis clearly advantageous (38, 39). Even the best methods, when executed poorly, yield less than ideal results. Furthermore, few things are done perfectly the frsttime they are tried. Terefore, educators needappropriate training in using new innovationsand a management of expectations on their frstatempt at implementation (40). Perhaps of the greatest signifcanceis the need for time –time torefect, time to fail, time toanalyze progress, time torevise, and time to sharesuccesses and challenges with others. Educators beneft from a community of peersupport, aswellas ongoing professional developmentsupportand administrativebackingtoencourage persistencetosuccess (40–42). Inanswerto these challenges to implementation of best practices, asecond aspect of the CREST Project engages collaborativegroups (researchers, educators, students) in creatingstudent-centered instructional materials thatmakecurrentresearch accessible toearly career trainees. Te CBM ofers asummer facultyworkgroup meeting that allowseducators toget awayfrom their routines and focus on developing innovativematerials for their classrooms. Tese “micro-sabbaticals” provide focusedtime for educators tocollaborate in developing new ideas, and ongoing interactions during implementation provide the peersupport neededtowork through obstacles toadoption. Since most teachingis done in isolation, educators value cross-disciplinarycollaboration and recognition from peers that their ideasare valuable (30). Participants alsoreported thathavingaset time dedicatedto working on projects, aswellascollaborators with whom towork, allowed themto dedicate the time needed to develop materials and implement new teaching strategies in their classes (30). Te threecasestudies belowwereseededbyCRESTcollaborationsand highlight a progression from the design or choice of physical modelto the learningassessment for molecular bioscience courses. In all, thesecasestudies provide molecular bioscienceeducators with information on the design/choice of physical models, design of correlatingassessment of student learning, and types of evidence from assessment analysis.

Case Study II: Carbohydrate Models Of the four major macromolecules (proteins, lipids, nucleic acids), the structure-function relationship of carbohydrates isrelatively unexplored in molecular bioscienceeducation, with greater instruction time devotedto metabolismand laboratory exercises focused on chemical reactivity diferences (43, 44). Assuch litle to no evidence exists on student learningwith these biomolecules - even though they possessarange of structure-function properties and oferan excellent opportunity to engage students in several threshold concepts in biochemistry. Carbohydratecurriculum typically beginsby introducingvocabularyrelatedto the structures of monosaccharides; this foundational scafoldingis then usedto introducestructure-function concepts relatedto disaccharides and fnally endingwith structure-function comparison among various polysaccharides. Evenat the start, students haveahardtime understandingand visualizing

47 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. the small diferences in structures of monosaccharides which then impedes understanding di- and polysaccharides. Te conceptual and skills neededto understand chiralitymay be a signifcant learningbarrier in understandingcarbohydrates’ structure-function relationships. For example, a simple hexose like glucosehas four chiral centers and anadditional chiral center on the anomeric upon ring formation. Upper levelstudents who havecompleted the organic chemistrycurriculum cansuccessfully build correct ringstructures using organic modelkits, such as Prentice-Hall, Darling or Maruzensets, due to the familiaritywith cyclohexane. However, forming α- and β-linkages from those glucose modelsis ofen beyond their skillset, and, evenwhen they manageto do it, they cannot make the connections between the linkages and resultant polysaccharide properties. Te linkages as shown in most line representations (e.g. Haworth projections or chair conformations) are meant to focus atention on identifying the linkages. Te line representations, however, do not convey how α- and β-linkages impose the steric restrictions in three dimensions that leadto the diferentstructural properties of amyloseand cellulose, respectively. Te 3D structure of amylose (PDB ID: 1C58) shows the helical nature of amyloseaswellashydrogen bonding possibilities. While the structure of cellulosestraight chain polymeris not available directly on the RCSBProtein DataBank site, structures of enzymes bound to diferentcellodextrinscontaining threeto nine β-D-glucose units areavailable (PDB IDs: 4C4C, 3QXQ, 4TF4, etc.). Instructors may utilizevirtual visualization tools, such asJmol, to showstudents the end results of repeated α- and β-linkages for amyloseand cellodextrins, or skilledstudents may evenmanipulate the virtual structures themselves. However, the path from linkagetofnal structure stillremainsmuddy for moststudents. Physical modelswherestudents canmake diferent linkages and manipulate structures to “see” steric restrictions for themselves would be useful. Based on theseobservations, a simplifed glucose model was designedwith –OH groups on C2 and C3 fxedto the ring. (-O‑, redcolor) and (-H, whitecolor) atomswere designedas in most modelkits with holes and linkers. Te hydroxylgroups could be atachedto C4 (belowring), C6 (abovering, awayfrom C1) and either in the α- and β-positions on the anomeric C1. Te anomeric C1 wascoloredgrey while all otheratoms including the fxedhydroxylgroups weretancolored. Clearly visible numbers were printed for each carbon, which is proposedto aid students in proper orientation of the model during instruction. Te dihedral angles were chosen bycomparison with available coordinates for amyloseand cellodextrinsand the model built using Spartan 14 modelingsofware. Te choice not to useCPK coloring throughout was explicitly made to focus atention on thosehydroxylgroups forming linkages; asstudents using glucose molecules built with organic kits ofen struggled with fnding the correct –OH groups for linkages. Te modelswere used in an upper levelBiochemistrycourse (6-8students) and a lower level introductoryGOB (General, Organic and Biochemistry) course for pre-nursingstudents (20-25 students) overtwo class periods. Each studenthadat least one glucose modeltowork with and frst understood the α- and β-positions on C1. Tey thenworkedwith partners to build maltose and cellobiose models. One unexpected beneft of workingwith physical models was the explicit connection betweencondensation and removal of water, since they physically hadtoremove water tomake the glycosidic linkage. Students thenworked in largergroups to extend maltosetoamylose and cellobiosetocellulose. Numberedcarbons helpedwith building1,4 linkages. Evenwith fve glucoseresidues linkedtogether, the diferences between the polysaccharide structures becomes unmistakable. Te α-linkages result in a fexible chain thatnaturally curvedand showed the beginnings of a helix. Te β-linkages result in a rigid and linear fbril. (Figure 2)

48 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. Figure 2. Amylose and cellulose models (on the lef) formed fom repeated α- and β- linkages with glucose monomers. A single glucose monomer (on the right) shows the number and color scheme selected for the 3D printed models.

Students were then instructedto locateand compare the linkages within the polymer, and with the model in-hand the diferences wereobvious. Te α-linkages were exposedand readily seenwhile the β-linkages werehardertofnd evenwith numberedcarbons. Students were directedtomake the connection betweenavailability of linkages and function of polysaccharides. Glucose in storage polysaccharides likeamylose should be readily cleavable, and a helix exposes the linkages easily. Cellulose “hides” its linkages and prevents easy accessand thus isagood structural polysaccharide. Diferentgroups could come togetherto form longer chains or in the case of amylose, make1,6 linkages tomakeamylopectin or glycogen. Additionally, the presence of a single freeanomeric carbon with multiple branches illustrates howreducingendsare “used up” in storage polysaccharides. Te glucose modelswere simple enough to be usedwith lowerand upper levelstudents in diferentcapacities. Pre-nursingstudents appreciatedhaving their own glucose modeltostudy before they started forming linkages. Some students were a litle confusedbytancoloring for mostatoms, however this became a teachable momentabout the limitednature of anygiven model. Biochemistry students were more usedtoworkingwith multiple modelsrepresenting a single structureand had no such difculty. Aferworkingwith both GOB and biochemistrystudents, the modelswere simplifed. Te –OH group on C6 wasfxedto the ringand coloredtan. Only a few glucose models needed the –OH group on C6 to create branchingand this focused the use of modelssolely on 1,4 linkages. Te absence of CPK coloring does preventstudents from visualizinghydrogen bonds betweenresidues. Te potential for usingfexible linkers to simulateH-bondsiscurrently being investigated. In all, this set of physical modelsis designedtoengagestudents in building mental models that integrate the unique and varied structure-function concepts of carbohydrate chemistry.

Case Study III: Serine Protease Active Site Models Educators allot a signifcant portion of molecular biosciencecurriculum to proteins, compared to lipids, nucleic acids or carbohydrates. Protein status in thesecurriculais not surprisingconsidering that this ofers anavenue tocoverand integrate four of the fve biochemistry threshold concepts:“physical basis of interactions, thermodynamics of macromolecularstructure formation, freeenergy, and biochemical pathway dynamicsand regulation (45).” Assuch, aplethora of intervention activities, from POGILtovirtual basedtools, with corresponding learning outcomes and gains, arereported in the literature. Evenwith this, litle research has explicitly identifedstudent misconceptionsrelatedto this biomolecule. Compounding on the extensivetime spentcovering topicsrelatedto proteinsare the vastarray of representationsstudents are exposedto throughout

49 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. the course of protein education. At the outset of thisstudy we wantedto develop a series of physical modelingactivities with accurate features totarget student understanding of protein structure- function concepts while concurrently buildingstudents’ visual literacy skills. We alsosoughtto usea validated instrument for assessmenttotest the impact of these models. Herewe describe the design of serine proteasephysical models that intentionally address the three primary misconceptions identifed through the Enzyme SubstrateInteractionsConcept Inventory (ESICI): electronics, stereochemistry and geometric complementary (46–48). We choseto createaset of serine protease models for chymotrypsin, trypsin and elastaseas these enzymes, their substrates and inhibitors are not only common examples in biochemistrytextbooks but also because theseenzymes are exceptional examples of: substratespecifcity, transition state stabilization, acid-baseand covalentcatalysis, geographical diferences betweencatalytic and binding residues, alteration of pKa values of active siteresidues to facilitatecatalysis, and integration with enzyme kineticsand inhibitor concepts. Current instruction with the proteases usually focuses on one, primarily chymotrypsin, and then mentions others (typically elastaseand trypsin) for comparison of binding pockets that highlightenzyme specifcity. Te visual representations of these enzymes in textbooks usually highlight eitherelectronics or geometric complementary. Additionally, with 3D virtual modelingstudents are likely only analyzing one proteaseatatime. Hereaset of proteases enables students tomake direct comparison among all threeenzymes. Inparticularafer years of teachingwith proteases we note thatstudents struggle to build a mental model for geometric complementary, thereforewe proposeaset of physical modelswith several diferentrenderings will best target this misconception. While students demonstrate misconceptionsrelatedtoeach phenomenon separately, a complete understanding of howenzymes interact with substrates requires synthesis of all three concepts. Te set of serine protease models was designedwith respect toeach targeted misconception. Tese modelswere developedwith a team of undergraduates in conjunction with the CBM. Asateamwe decided on threemajor components for each proteaseas described in 1, an example of which is shown in Figure 3. Tesephysical modelsare used in an undergraduate biochemistrycoursetaughtwith an explicit focus on increasingvisual literacy skills through the use of modelsand modeling. Te course follows afipped classroom design in which students watch a concept-basedvideo prior to classand complete a pre-classassignment. During the 50 minutecoursetime students engage in active learningactivities in groups of two or threestudents. Te proteasephysical modelsare usedacrosstwocoursedays. During thesedays a physical modelset is shared betweentwogroups, with each group completing their ownactivity. During the frstdaystudents aregivenan exploratoryactivity designedtohave students identifyand compare the enzymes, then identifyand compare the substratebased on electronic, geometric complementaryand stereochemical interactionsamong the pieces. During the second daywith the protease modelset, each group works through a problem-based learning (PBL) activity while having the models available for reference. To measurewhether these models impact student learningand the targeted misconceptions, several assessments were used in controland intervention semesters. In the controlsemesters students worked through the PBL activitywithout the use of models. In both the controland intervention semesters students completed the ESICIat the startand end of the semester; this instrumentis usedas a pre/post measure of student learningand misconceptions. Additionally responses tostudentanswers on the in-classactivities wererubric-scoredwith each itemtaggedwith acorresponding misconception (electronics, stereochemistry, geometric complementary or some

50 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. combination). Te same analysis was performed on correlating questions for students on individual exams on this material. Analysis of these data is forthcoming. Morerecently the serine proteasekit isemployed in the NSF-fundedModeling for the Enhancement of LearningChemistry (ModEL-C) longitudinal study aimingto defne how models impact the learning processand cognitive load for students. In thisstudy, biometric dataiscollected usingelectroencephalographic (EEG) and eye trackingtoolsand voicerecordings from simulated learningenvironments where biochemistrystudents complete the serine proteaseactivity described above. Inaddition, observational and rubric analyses of studentresponses toactivities from the simulated learningenvironmentand real classroom sessions provide furtherdata for assessing student learning. From thesefndings, an iterative process for modeland assessment designwill be proposed for educators interested in designing 3D physical modelswith correspondingactive learning assessments that optimize the cognitive load and target student misconceptions.

Table 1. Serine Protease Model Design Features Features of the physical model Coloring and rendering Proposed misconception targeted by the physical model Active site Each backbone model shows Te key side chains are Electronics backbone the catalytic triad side chains rendered in spheres with CPK model and one - two key binding coloring. Te rest of the pocket side chains. protein is rendered as alpha carbon backbone. Active site Each surface plate model Te frst iteration rendering Electronics and geometric surface shows the surface topology used CPK coloring. In a complementary of the active site. second iteration, the surface plates were constructed of clear plastic so students could see the underlying atoms. Substrates and Each designed substrate was All substrate and inhibitor Electronics, inhibitors docked using AutoDock in molecules are rendered in stereochemistry, geometric Chimera. One small spheres with CPK coloring. complementary molecules is a chymotrypsin inhibitor, Tosyl phenylalyl chloromethyl ketone (TPCK).

Figure 3. Serine protease physical model. A. Backbone, surface place, substrate epimers, and inhibitor for chymotrypsin. B. Surface plate snapped on top of backbone with the substrate epimers and inhibitor on the lef side. C. Substrate bound in the active site. Photos courtesy of Cassidy Terrell.

51 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. In all, thisset of physical modelsis designedtoengagestudents in building mental models that decreasestudent misconceptionswhile also increasingstudentvisual literacyand conceptual understanding. Additionally, we proposeanavenue for usingphysical modelsto investigatecognitive load and engagement to beter understand how students learn with this pedagogical tool.

Case Study IV: Flow of Genetic Information Models Te “Central Dogma of MolecularBiology” describes information fow in a cell, from storage in molecules of DNA through expression asfunctional products (proteins) (49). A thorough conceptual understanding of the purposes and underlying processes of genetic information fowis a crucial foundation on which numerous molecular biologytopicsare built. Hence, genetic information fow is one of fveCoreConcepts of VisionandChange, which has beenfurtherarticulated in the Biocore Guide for interpreting the CoreConcepts (2a) (1, 50). Genetic information fowis also one of the four “BigIdeas” in the AdvancedPlacementBiologyCurriculum Framework, and is described in the frstobjective in the Next Generation ScienceStandards for life sciences in high school (HS‐LS1‐1) (2, 51). Typical undergraduatestudents, however, struggle with many ideasassociatedwith genetic information fowand focus on superfcial termsand representationssuch as “transcription” and “Punnetsquares” but cannot visualize or articulate the underlying molecular processes behind the terminology (52–59). Many undergraduate biology instructors haverecognized the learningstruggles and are interested in fnding ways to improvestudent learning on topicsrelatedtogenetic information fow. And while active-engagement pedagogies, whencomparedto lecture-only strategies, result in higher learninggains in STEM disciplines, our recentwork has highlighted thatphysical modelsare beter active-learningtools for helpingstudents graspcertain topicsrelatedtogenetic information fow (25, 60–63). Westudied the use of physical models in a sophomore-levelCelland MolecularBiologycourse atalarge, private university in the northeasternU.S. Many of the models used in thiscoursewere diferentfrom the 3D-printed, atomic-level models describedabove, but insteadwerebased on more stylized, manipulable pieces made of craffoam, presenting a molecular levelview of interaction of the macromolecules. For example, the Flow of Genetic Information Kit (FGIK) uses foam pieces to demonstrate how DNA is built by DNA polymerases in the process of replication, howRNA is built byRNA polymerases in the process of transcription, and how proteinsare built byribosomes in the process of translation (see Figure 4). Trough a retrospectiveanalysis of studentdata on the validatedCentral DogmaConcept Inventory (CDCI) tool (55), the authors demonstrated signifcantly higher learninggains on CDCI questions thatwereassociatedwith a physical model‐based in-classactivitycomparedwith questions thatwereassociatedwith clicker questions or peer discussion problems (25). Te CDCItoolemploys amultiple-select format, which helped the authors unearth interestingand useful paterns of student responses. While allstudents learnedfrom engagingwith the model-basedactivities, the researchers found that higher performingstudents improved their overallscorebyrefning their almost-fully- correct responses. For example, ifacorrect question response wasABD, the higherpatern of responses from the higher performingstudents wentfrom AB (pre) toABD (post). Lower performingstudents entered the coursewith lesscontent knowledge, overall. Te authors found that lower performingstudents improvedbyrecognizing morecorrect vs incorrect responses afer engagingwith the model-basedactivities. For example, ifacorrect question responsewith ABD, lower performingstudents wentfrom choosing (ACE) to (AB). Lower performingstudents may

52 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. not have lefthe coursewith complete expert-like mental models, but thisgroup made some of the greatest learning gains, strongly suggesting they had created new (and correct) knowledge.

Figure 4. Te Flow of Genetic Information Kit (3DMD). Students explore the process of protein translation using a model-based activity. Photo courtesy of Leslie Kate Wright.

Figure 5. Model based activities showed dramatic learning gains for all students, including low performers (botom quartile) and deaf/hard of hearing (D/HH). In this analysis, each multiple-select question was scored as right or wrong (no partial credit). Students were ranked in quartiles by their scores on the entire CDCI at the beginning of the semester, although their performance is shown only for questions related to model-taught concepts. Doted lines show that on average, D/HH students fell between the 2nd and 3rd quartile, both pre and post instruction. Error bars are SEM, n=426 with 34 D/HH.

Out of the 426 students included in the study, 34 of the students identifedasDeaf or Hard- of-Hearing (D/HH), a population that faces signifcant challenges in post-secondarysetings and lags behind hearing peers in B.S. college degreeatainment (64). D/HHstudents, who are also underrepresented in STEM, may experiencecommunication barriers in undergraduate classes and may not be able tofully participate in and learnfrom typical lectures and discussions (65). Research also shows that D/HHstudents mayoverestimate their own understanding of the material which maycontributeto the overallgap in B.S. degreeachievementcomparedto undergraduate hearing peers (64, 66). For example, in 2015 33% of all hearing individuals (ages 25-64) hadcompleteda B.S. degreecomparedto only 18% of D/HH individuals of the same agerange. SimilartoEnglish LanguageLearners (ELLs), D/HHstudents may faceadditional challenges when tryingto understand a spoken lecture (or spoken discussion) while also tryingtotake notes during class (65).

53 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. Tus, strategies thatactivate information channels, other thanspokenlanguage, such asmanipulation of modelsand model-basedactivities, may be especially useful for learners who are D/HH or have diversecommunication styles (Figure 5) (65, 66). Dissecting out the D/HHstudents from the above analysis showed that D/HHmade the same dramatic learninggainsas hearingstudents on model- based questions. While futurework is neededto probe more deeply intowhy modelsaresuch efective learning tools, several cognitive theories such asconstructivism, zone of proximal development, reduction of cognitive loadand shared mental models, support the notion that modelsand model-basedactivities are efectivetools for deep learning. Herewe briefy describe each of these theories and suggestwhy they may be particularly important in the context of D/HH, English LanguageLearners (ELL), or other special populations.

Constructivism Tis theory postulates thatstudents learn bestwhen they construct their own explanations through guidedactivities (67). Te dynamic, physical model‐basedactivities allow learners to explore and build (through manipulation of the actual models) but also allow for refnementand reorganization of students’ mental models of molecular processes. Aconstructivistapproach may be especially benefcial for D/HH learners because thesestudents may be supported during classes by signlanguage interpreters or real-time captionists. In a traditional lecture-basedcourse D/HHmay not be able towrite things down or take notes for themselves because they havetosplit their atention between the board (or PowerPoint slide) and watching the interpreter or captioningscreen. Using a model-basedactivityisveryhands-on with D/HHstudents working directly with the model, and not relying on interpreters or captionists as the conduit for information.

Zone of Proximal Development

Learners ofenrequirescafoldingto learn new things; in otherwords, it is difcult to incorporate ideas thataretoo farawayfrom their prior knowledge (68). Physical modelsmay ofera“bridge” to learners with a shaky/incomplete mental model of transcription, when they are tryingto learnabout gene expression. Ofering this bridgeis benefcial to allstudents but may be especially helpful for deafstudents because, comparedto hearing peers, they enter post-secondary institutionswith greater diferences in academic preparation and educational experiences (69).

Reduction of Cognitive Load

Learners can be expectedto hold only 5-9 pieces of information in their working memoryat atime (70). Tus, the cognitivestructure of humansrestricts the types of environments thatare ideal for deep learning (71, 72). A lecture that incorporates 15 new vocabulary or technical terms probably is not ideal for deep learning! Physical modelsserveasan extension of cognitivespace for learners; instead of havingtoremember detailsand terms of a process, learners can lookat, pointtoand manipulateaphysical 3D structure. Aswith hearing peers, D/HHstudents ofenmust balancecognitive loadwith new learning opportunities. Unlike hearingstudents, D/HHstudents who communicate usingAmericanSignLanguage (ASL) mayhaveanother challenge; when interpreters encounteran unfamiliarterm or a word thatis not connectedwith a standardASL sign (or a sign they areaware of), they may invent a brand new sign on the fy tocommunicateaterm or idea (73). Tisphenomenon may increasecognitive load in D/HH learners as they mayhaveto

54 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. keep track of anotherASL sign during a class. Physical modelsmay alleviatesome of the cognitive load facedby D/HH learners since they willhavetorely less on ASL interpreters and canhandle the model themselves.

Shared Mental Model As learners manipulate the physical model they alter their existing mental modelsto align more closely with the physical model. Students working in a group haveastructuretowhich toreferas they share ideasand engage in discussion. Tus, physical models become the embodiment of a shared group mental model, improving the learning experience for all. Tereisacorrelation with students’ sense of belongingtoaSTEMcommunityand college persistence, but deafcollegestudents, among others, maystruggle toachieve thatsense of belonging. Including model-basedactivities during class may help D/HH students form connections with their peers (74, 75). Active learning classrooms that incorporatephysical models of molecular biological processes improve learning for allstudents, but particularly for low performers and individualswith communication difculties. Wesuggest that modelsare helpful beyond simply encouragingactive engagement because they 1) make the abstract moreconcrete, 2) provide a shared mental model for discussion, 3) do not depend on jargon or vocabulary, and 4) promote dynamic rather thanstatic conceptions.

Conclusions and Future Directions

Although each of the casestudies detailedabovehas unique features, they demonstrate that physical modelscan be employed in a variety of ways to create shared mental modelsamong researchers, educators and students. Tereareseveral common threads sharedbytwo or more of these cases:

• Tefrsttwocases engage undergraduates in designing models. Making decisionsabout which structures to depict totell a molecularstory, or to bestcompare similarstructures, or howtoresolvetwoconficting pieces of data in the literature, requires students to think like scientists (76). • Cases IIand IIIemploybackward designbyfrst identifying learningobjectives, then targetingstudent misconceptions in the design of instructional materials (77). Te physical modelsserveas mental modelsand thinkingtools, optimizingcognitive loadso that students can develop a deeper conceptual understanding. • Cases I-III utilizeaccuratephysical modelsto explore molecular interactionsand connectivity. Case IV, on the otherhand, employs models that focus on a molecular process (DNA replication, transcription, translation). Tereis value in using both types of models, aswellas in transitioningamongmultiple models of the same structure (78–84). • Tefrsttime students use a new tool (ie models), they needto learn HOW to use the tool. Students experiencefrustration when they are expectedto simultaneously learn howto use a new tooland master the concepts. Students needtime to explore models before they are expectedtomaster the concepts the modelconveys. Educators can bridge the gapby orientingstudents to the models, discussing the use of color and renderings of the models aswellas limitations of models. Justas in observingfne art, novices must be guidedtoan

55 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. understanding of what they areviewingbyan expert in ordertoappreciate the nuances of models they explore. • To be the most efective, students should be exposedto models throughout a course/ curriculum. Multiple representations of the same molecularstory allowstudents tolayer details for greatercomplexity. Models of similarstructures allowstudents tocompareand contrast, growing their conceptual understanding. Multiple types of models (physical vs. virtual, schematic vs. accurate) allowstudents to develop skills in transitioningamong models.

As discussed in Case IV, incorporatingphysical models in the molecular biosciences classroom may be especially benefcial for D/HHstudents. Te goal of universal design for learningisto optimize learning experiences for all students, recognizing that allstudents learn diferently (85). Indeed, each of the cognitive theories discussed in Case IV asapplyingto D/HHstudents is applicable to all students. Careful design of physical modelsas instructional toolswillmake molecularvisualization accessible toawide variety of learning diferences. Physical modelsserveasatactile embodiment of mental models, eliminating the need for cumbersome vocabulary in developingaconceptual understanding. Tisis valuable for both D/HHstudents and those for whom English isasecond language. Careful color selection and the addition of tactile distinctionsmake the molecularworld accessible to color blind and visually impaired students, respectively. Along these lines, other limitationsand challenges exist for educators usingphysical models in the classroom and/or laboratoryenvironment. Monetarycosts of acquiring the modelsremain abarrier for use. Evenifsome modelsare purchased theremay not be enough models for every student, and sharingmay impede the models’ intended use. Te models also ofenrequire the educator to provide information on orientation, color and proper use. However, thereareseveral options for educators who are interested in incorporatingphysical models in the classroom. Te MilwaukeeSchool of EngineeringhasaModelLendingLibrarywith a variety of models (86). Borrowers schedule online for a threeweek loan period (one week for shipping out, one week for classroom use, and a thirdweek for return shipping) and pay only return shippingcosts. Educators interested in building their own modelscantakeadvantage of a free designs available online (87, 88). Guidanceisavailable for thosewishingto build models using their own tabletop extrusion printers, or purchased inexpensively through 3D printing services (89–91). Asphysical models become increasingly utilizedas a pedagogical tool, educators and researchers mayconsideravenues for investigatingstudent learningand optimal designelements to both the modeland assessment. Such studies could investigate the impact of color, sizeand scale, types of renderings to usetogether, relevance/impact of spatial reasoningand visual literacy skills, and best practices for classroom use. Additionally, comparativestudies using 3D printed modelsand traditional organic kits from Prentice-Hall, Darling or Maruzencould ofer insights intowhich approach bestsupports student learning. Along these lines no evidence on student learning exists to determine the impact of students creating 3D physical modelscomparedwith using pre-made physical models.

Acknowledgments Te CRESTProject issupportedby the National ScienceFoundation underawardnumbers DUE-1022793, DUE-1323414 and DUE-1725940.

56 Bussey et al.; Biochemistry Education: From Theory to Practice ACS Symposium Series; American Chemical Society: Washington, DC, 2019. Te serine proteases kit, datacollection and analysisissupportedby the National Science Foundation under the ModEL-C project with award numbers: IUSE 1711402 and 1711425. Any opinions, fndings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily refect the views of the National Science Foundation.

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