The Consume - Create Spectrum : Balancing Convenienceand Computational Thinking in STEM Learning

Ashok BasawapatnaUniversity of Colorado Boulder

Department of ComputerScience

Boulder, CO. 80303 USAbasawapa@colorado.edu

Alexander RepenningUniversity of Colorado Boulder

Department of ComputerScience

Boulder, CO. 80303 USAralex@cs.colorado.edu

Kyu Han KohUniversity of Colorado Boulder

Department of ComputerScience

Boulder, CO. 80303 USAkohkh@colorado.edu

Mark SavignanoUniversity of Northern

Colorado, Dept. of Education1868 Ute Creek Dr

Longmont, ColoradoSavi6521@bears.unco.edu

ABSTRACTFuture school science standards, such as the Next Genera-tion Science Standards (NGSS), emphasize the integrationof simulation and modeling activities in the classroom envi-ronment. The extremes of these activities have two vastlydifferent implementations. On one hand, a teacher can havestudents experiment on a pre-made simulation associatedwith the material. On the other hand, students can use, forexample, an end-user programming tool to create the sim-ulation from scratch. This allows students to not only ex-periment on, but also, to model the real world phenomenonbeing studied– a key component of computational thinking.However, the greater amount of time necessary for studentauthoring of simulations can make such an approach infea-sible in the classroom environment. This paper presents aspectrum of strategies for integrating simulations into class-rooms emphasizing our research at the Scalable Game De-sign Lab, University of Colorado Boulder as well as researchfrom other entities. Starting at consuming simulations andadding more user interaction and authoring elements beginsto provide a gentle slope from consumption towards simula-tion creation. Results indicate that many of these strategiesare quite effective.

Categories and Subject DescriptorsK.3.2 [Computing Milieux]: Computers and Education—Computer Uses in Education

General TermsAlgorithms, Design, Experimentation, Human Factors

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KeywordsHigh School Computer Education, Middle School ComputerEducation, Simulation and Modeling, Computational Think-ing, Next Generation Science Standards

1. INTRODUCTIONClassrooms across the United States are looking for ways

to integrate aspects of computational thinking into their cur-riculum [15]. As of now, computational thinking is definedas a problem solving process that includes the following:Formulating Problems such that they can be solved by acomputer, Logically Organizing and Analyzing Data, Repre-senting Data Through Abstractions such as models and sim-ulations, Automating Solutions through algorithmic think-ing, Identifying Analyzing and Implementing Solutions Effi-ciently, and Generalizing and Transferring This Process tosolve a wide variety of problems [3].

Activities related to simulation authoring offer a uniqueopportunity to integrate computational thinking into theclassroom environment. Future standards, such as the NextGeneration Science Standards (NGSS) and the CommonCore Curriculum Standards (CCC) emphasize the use ofsimulations. For example, the NGSS states in its Struc-ture, Function, and Information Processing section that, ingrades 9-12, ”students who demonstrate understanding canuse modeling to explain the function of positive and nega-tive feedback mechanisms in maintaining homeostasis thatis essential for organisms [2].”

Simulation authoring enables classrooms, such as Life Sci-ence and Biology classes for example, to meet upcomingscience standards and effectively integrate computationalthinking. However, at present time, very few teachers acrossthe United States are employing simulation or modeling ac-tivities [10]. One major barrier to integrating simulationsauthoring activities is finding the class time as well as ap-propriate activities [6].

In our experiences, at the University of Colorado Scal-able Game Design Lab, we have students successfully au-thor simulations employing the end-user agent-based pro-gramming tool AgentSheets and its subsequent 3D version

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AgentCubes. AgentSheets and AgentCubes is meant for stu-dents with little or no prior programming experience en-abling them to make their first visual game or simulationwithin five hours [18]. Though it may seem like a relativelysmall amount of time, five hours equals a 1 week projectwhich can be prohibitive in classrooms.

There are alternatives to authoring simulations from scratchthat are time efficient, and begin to integrate computationalthinking concepts. For a particular classroom’s constraints,one might wonder if one of these alternatives yields a ”sweetspot” between convenience and computational thinking effi-cacy. In this paper we attempt to outline a variety of optionsthat create a gentle-slope path, from consuming pre-madesimulations to authoring simulations, taken from both ourresearch experiences and others. It is our aim to give teach-ers, curriculum designers, and researchers insight into thespectrum of simulation related classroom activities.

The remainder of this paper will outline and discuss thisspace in-depth along with data and observations from novelapproaches, contained within this spectrum.

2. GENTLE SLOPE APPROACH TOWARDSSIMULATION AUTHORING

Figure 1 is a depiction of how computational thinking re-lates to simulation creation; the basic problem solving pro-cess outlined involves model development, computationalexpression of ideas, running the simulation code, visualiz-ing the consequences of this implementation, and possiblyrevising the model based on this visualization. The subtextof this figure is that the student develops a strategy to gaininsight into a real world problem through the use of sim-ulations in the representational domain. The ability of astudent to create a simulation enables the investigation de-picted in Figure 1. Furthermore, Figure 1 begins to give usa criteria to evaluate the extent a simulation related activitycontains computational thinking concepts.

Figure 2 depicts the Consume-Create spectrum of sim-ulation activities that range from convenient activities tothose that heavily integrate computational thinking. Theseinclude animations, interactive simulations, collective simu-lations, construction set simulations, pattern based author-ing, end-user programming, and traditional programminglanguages.

Figure 1: How creating simulations relates to com-putational thinking. Users start with a question, de-velop a model, express the model computationally,run the model, visualize the consequence of theirthinking and possibly revise the model

It should be noted that Figure 2 is by no means an exhaus-tive list but serves as a good beginning point for a discussionof simulation use in the classroom environment. Startingfrom the left and going to the right of Figure 2, we see ac-tivities that begin to offer the user greater control over thesimulation mechanics itself by enabling higher degree of ex-pressive freedom. Furthermore, with each item there existslimitations that are overcome by traveling to the right. Forexample, one way an animation, located on the far left, islimited is that the animation does not enable changing aparameter associated with the animated phenomenon. Bygiving the user an interactive simulation instead, located tothe right of animations, eliminates this limitation providinga more open-ended exploration of the phenomenon. Con-versely, for any activity in Figure 2, we see that this activitycontains the option of doing all the activities to the left of it.For example, by using a programming language, like C++or Java, the user hypothetically has the ability to do every-thing offered through end-user programming all the way tocreating a simulation animation that can be displayed to en-hance student understanding (though such a tact might beinfeasible for students with little programming experience).What follows is an analysis of each of the activities listed inFigure 2 emphasizing their benefits as an in-class activity,including supporting science learning goals, and a focusedappraisal of their relationship to computational thinking.

2.1 AnimationsScientific animations are not simulations per se, but sim-

ilar to simulations in that they create a representation ofa scientific phenomena. Animation activities entail a stu-dent watching a movie depicting a representation of a givenscientific system, often times accompanied by a narrative.Examples of these activities include the suite of animationson the BrainPop1 website and The Institute for ChemistryLiteracy through Computational Science (ICLCS) 2. Figure3 depicts screenshots of example animation activities.

In the left activity depicted in Figure 3, students watch ananimated model of ice melting on a molecular level. Theseactivities typically take minutes to complete (depending onthe length of the animation) and are often accompanied bytext that describes the phenomenon being depicted. This ac-tivity is usually accompanied by a worksheet for students tohelp synthesize the academically one way lesson. This ped-agogy of learning regards to Bloom’s Taxonomy revised oflearning ranks on the pyramid– Factual knowledge/Remember[14]. However useful for efficiency when navigating the cur-riculum, the students do not get a chance to interact andsynthesize learning.

By running an animation, students are not using any com-putational thinking skills– students do not have to create analgorithm or abstract the real world into the representationaldomain. However, there is good evidence that these visualrepresentations do yield better student understanding. Onelarge scale study, for example, claims that students in classesthat used BrainPOP increased their science scores on theSAT 10 test by one to two grade levels, when compared tocontrol classrooms [1].

2.2 Interactive Simulations1www.brainpop.com2iclcs.illinois.edu/

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Animation

InteractiveSimulation

CollectiveSimulation

Construction SetSimulation

Pattern-BasedAuthoring

End-UserProgramming

TraditionalProgramming

convenience

computational thinkingConsume Create

collide

Figure 2: Gentle slope spectrum of options from using (convenient) to authoring (computational thinking)simulations.

Figure 3: Two animation examples. The left im-age depicts an ICLCS simulation animation of icemelting and the right image depicts a BrainPOPanimation on diffusion

Interactive simulations allow users to alter parameters andrun the simulation to see the effect of these modifications;unlike animations, students can run multiple experiments tobetter understand the effect of various parameter changes.Interactive simulations are usually centered around a spe-cific concept, in a limited domain, and can take very littleclass time [16] . Examples of Interactive simulations are thePhysics Education Technology (PhET)3 Buoyancy simula-tion and the suite of simulations offered at National TaiwanNormal University’s (NTNU) Virtual Physics Laboratory4.Figure 4 depicts a screenshot of the Phet simulation.

The NTNU wave simulation allows users to vary the di-rection and period of a plotted electromagnetic wave. ThePhET buoyancy simulation in Figure 4, allows user to puttwo different blocks in water or oil and displays the buoy-ancy forces on the blocks. The blocks can be either the samemass, the same volume, or the same density.

PhET reports that students play with simulations for hours,find the activities engaging, and show a greater degree ofconceptual learning as compared to control groups [19]. How-

3phet.colorado.edu4www.phy.ntnu.edu.tw/ntnujava/

Figure 4: The image depicts PhET simulation thatenables students to explore buoyancy

ever, other research points to the idea that these simulationsmight give students an oversimplified view of scientific in-quiry actually hindering their scientific literacy and abilityto do science outside of the pre-made simulation environ-ment; for example, many of these simulations assume idealsimulation environments with no experimentation flaws [7].If students were to author these simulations, they could atleast be aware of these ideal conditions.

Compared to animations, interactive simulations allow fora deeper inquiry of a given concept. However, students areexposed to little, if any, computational thinking concepts.In fact, much of the computational thinking, such as the ab-straction necessary in creating this representational systemand what parameters to include and ignore in this represen-tational system, is done for the students by the simulationauthor.

2.3 Collective SimulationsCollective simulations (also called ”participatory simula-

tions”) are similar to interactive simulations with the addi-tion of a collaborative social element. In collective simu-lations, instead of controlling every parameter value, eachstudent takes on a specific role and adjusts parameters as-sociated with that role in real time. Often these simulationsrely on students communicating with each other to develop

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an overarching strategy to achieve a specific goal. Theseinteractions also begin to introduce students to the idea oflocal decisions yielding global consequences through peer-student feedback.

In Mr. Vetro, a National Institute of Health sponsoredproject that focused on how the body reacts to various ac-tivity, students played the role of lungs, heart, and dictatedthe amount of physical activity (exercise) applied to a vir-tual human named Mr. Vetro [11]. Each role has a setof parameters students can control. Students playing therole of the heart , for example, could define things like Mr.Vetro’s heart rate and stroke volume. In Gridlock, writtenin HubNet, a collective simulation environment based off ofNetLogo, students each control the state of a traffic lightand and attempt to optimize traffic flow through a city [20].Figure 5 depicts Mr. Vetro collective simulation.

Figure 5: Students working on Mr. Vetro.

In terms of computational thinking, collective simulationsenable students to embody the representation of the system.In this respect, students are the abstraction between the realworld and the representational domain. Furthermore, stu-dents interact with classmates who represent other aspectsof the real world. This necessitates peer organization andreal-time integration of data among students to arrive at acollective algorithm through each moment in time. Like in-teractive simulations, students are varying parameters, butstudents are modifying these parameters constantly in re-sponse to peer feedback in order to achieve the collectivegroup goals.

The results of Mr. Vetro show a strong indication ofenhanced student understanding. For example, using Mr.Vetro for 1-2 periods in highschool classrooms indicated stronglearning gains, as compared to control instruction coveringthe same material without the use of a collective simulation,over a diverse group of students [11]. Evidence also suggestsstrong student engagement during the activity with somestudents referring to it as one of the most intense classroomexperiences [11].

The limitations of collective simulations mirror those ofinteractive simulations in that students can only change theparameters provided to them. Collective simulations beginto incorporate elements of computational thinking; however,students still do not get to set up the simulation or partic-ipate in simulation parameter selection deciding which as-pects of the real world to emphasize and ignore.

2.4 Construction Set SimulationsConstruction set simulations give users the freedom to set

up the physical placement of pre-programmed simulationobjects in addition to varying simulation parameters. Ex-amples include construction kits, that enable users to solve afocused problem in a limited domain. For example, the Pin-

ball Construction Kit allows users to prototype their ownpinball game by placing flippers, bumpers, and other ele-ments of a pinball game on a level [9]. Similarly, the PhETCircuit Construction Kit, depicted as an example of con-struction set simulations in Figure 2, allows users to placeresistors, bulbs, and other electrical components in a circuitand run it [19]. The CyberMOD project, funded by the Na-tional Institute of Health, involves classroom integration ofconstruction set simulations meant for the high school biol-ogy classroom. One CyberMOD construction set simulationallows students to investigate blood glucose homeostasis byplacing liver cell agents and pancreas cell agents in differentsections of the simulation world to achieve homeostatic reg-ulation of blood sugar. In doing this, users not only see andinteract with a simulation of how the human body works,but also, can experiment with different formations of cellsinvestigating things such as type 2 diabetes. Figure 6 is ascreenshot of the CyberMOD blood sugar regulation activ-ity.

Figure 6: The Blood Sugar Homeostasis CyberMODsimulation.

Construction set simulations enable a measure of compu-tational thinking in that students have to build the repre-sentational system world to solve or gain insight into a par-ticular problem. Data from CyberMOD indicates studentsnot only enjoyed the construction set simulation exercise,but after the exercise, the students were significantly morelikely to answer questions related to the human blood sugarregulation system correctly. Furthermore, the effect sizes for71 students between two pre and post test sections relatingto blood sugar regulation, were 0.881 and 1.242, which isquite large for such a small (1 hour) in-class intervention[17].

Construction set simulations give students an opportunityto be exposed to computational thinking and have the po-tential to greatly increase understanding. The inability toprogram simulation behaviors present a big limitation. In-vestigating how different behaviors of agents effect the sim-ulation, beyond parameter changes, necessitates a move to-wards simulation authoring.

2.5 Pattern Based AuthoringIn pattern based authoring, users begin to author the be-

haviors of simulation actors present in the simulation. In-stead of using low level behavior rules of end-user program-ming tools, pattern based authoring allows users to create

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interactions between simulation agents at a higher level. Ex-amples of pattern based authoring approaches include MagicPaper and the Simulation Creation Toolkit.

The Simulation Creation Toolkit, built on top of AgentCubes,allows users to apply high level Computational ThinkingPatterns, such as one agent absorbing another agent or oneagent tracking another agent, in order to create agent in-teractions [4, 5]. To accomplish this, users pick the inter-action they want present in their simulation from a paletteof generic objects acting out different behaviors– for exam-ple, one generic agent tracking another generic agent for thetracking pattern. Finally the user specifies how the patternshould work, i.e. how quickly the tracking should occur andthe background agents that the tracking agent can move onetc. Figure 7 is a screenshot of the Simulation CreationToolkit.

In Magic Paper, users can author physics simulations fromscratch by drawing high level physical formations and defin-ing things like where objects are anchored, the forces in-volved, and add wheels and friction to various objects [8].The force that one object exerts on another object can bedefined by outlining how one agent behaves in respect toanother agent. Magic paper assumes sketching in a limiteddomain wherein a rich graphical lexicon has already beenestablished– these symbols within the context of the sketch-ing environment make up the ”high level” patterns. Otherdomains in Magic Paper include a circuit construction en-vironment and a molecular chemical configuration environ-ment.

Figure 7: The Simulation Creation Toolkit

Both of these pattern based authoring examples rely onthe user’s ability to preserve interactions while abstractingout the specific agents involved in the interactions. In thissense, these approaches both force the user to think com-putationally to create these interactions. Research on theSimulation Creation Toolkit begins to show that this doesindeed occur as users not only transfer the use of Compu-tational Thinking Patterns to various contexts, but are alsoable to program simulations via analogy using these patterns[5, 4]. Pattern based authoring of simulations includes ev-ery mechanism outlined in Figure 1. Additionally, program-ming at the pattern level enables users to quickly createsimulations without having to implement time-consuminglower level code. For example, seventh grade students withno prior experience using the Simulation Creation Toolkit,completed and experimented on a predator/prey simulation

in four days or less of class time; this simulation, in previousexperiences, would take high school students over a week tocreate including the time it takes to learn the AgentCubesend-user programming tool [6].

Pattern based authoring empowers users with the abil-ity to quickly add interactions to simulations without usinglower level code. However, a major limitation of patternbased authoring is that students are reliant upon the highlevel patterns offered; therefore, if a simulation creation ex-ercise does not lend itself to one of the patterns offered,creating that simulation may be impossible or may lead stu-dents to make faulty representational systems [5]. Overcom-ing this limitation requires students to master lower levelsimulation programming.

2.6 End User Programming and ProgrammingLanguages

Tools such as AgentCubes, Scratch, and Alice allow stu-dents, with little prior programming experience, to createsimulations using drag and drop rules instead of tedious syn-tax[12]. For example, in AgentCubes, agent behavior is dic-tated by dragging conditions and actions from a pre-madepalette to construct if/then conditionality statements. Thisstrategy has proven effective in enabling students to makevisual games more quickly and easily than in traditional pro-gramming languages like C++ or Java.

In creating their first game students begin to learn thetools necessary to create simulations. Students actually im-plement Computational Thinking Patterns by combining if/thenconditionality statements. Students begin to think com-putationally when they apply these learned ComputationalThinking Pattern implementations to create and experimenton simulations giving them insight into a real world phe-nomenon [18]. In terms of Figure 1, end-user programminggives students much more expressive ability to model the realworld than adding agent interactions at the pattern level.

The major barrier to integrating simulation end-user pro-gramming into the science classroom is finding the neces-sary time. As mentioned above, high school students cantake over a week to learn an end-user programming tool andsubsequently, create a simulation. Given enough time andresources, however, end-user programming of simulations al-lows students to accomplish everything outlined Figure 1.

2.7 Traditional ProgrammingTraditional Programming gives users the most expressive

power out of all the choices in Figure 2. For example, sinceAgentCubes is agent-based, it is hard to create a physicallyaccurate projectile motion simulation. However, in a tra-ditional language like C++ or Java, such a simulation ispossible, and in fact, has been done by authors of the PhETenvironment (which is written in Java)[19]. Other environ-ments such as Greenfoot and Starlogo are educational toolsthat enable students to begin learning programming con-structs and employ syntax akin to traditional programmingas opposed to graphical drag and drop rules employed byend-user programming tools. Greenfoot, for example, givesstudents and introduction to Java programming and Starl-ogo, based on Logo programming, enables agent based sim-ulation modeling [13, 12] .

Traditional programming languages have been used to cre-ate games and simulations in CS1 undergraduate classes [13].Like end-user programming, students apply every step in

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Figure 1. However, the time necessary to make studentsproficient at traditional programming is much greater thanend-user programming; therefore, outside of actual program-ming classes, this strategy is unrealistic.

3. DISCUSSION AND CONCLUSIONLooking at the options outlined Figure 2, we see tradeoffs

involved with every strategy presented that a teacher mightemploy to integrate simulation creation activities into theirclassroom environment. To the far left we see choices, suchas pre-made simulations, that are convenient, in terms ofpreparation and class time, but do not necessarily requirestudents to think computationally. Conversely, to the farright are choices, such as employing end-user and traditionalprogramming to create simulations, that do require studentsto think computationally but are extremely time consuming.In between these two extremes are choices teachers can makethat begin to introduce aspects of simulation authoring andcomputational thinking while preserving convenience. Forexample, teachers might substitute a construction set simu-lation or a collective simulation in place of a unit that cur-rently integrates an interactive simulation without adding asignificant amount of class time.

Figure 2 is an initial attempt to describe the space of sim-ulation related activities that teachers can do in the class-room. As mentioned above, this figure is by no means ex-haustive but is an interesting space to place any activitiescurrently being researched and developed for in-class use.Using this spectrum, we can begin to populate this spacewith more activities and this can be an important tool help-ing teachers meet upcoming science standards while equip-ping students with the computational and problem solvingskills necessary to be competitive in the 21st century.

4. ACKNOWLEDGMENTSPart of this work is supported by the National Science

Foundation under Grant Numbers 0833612, 0848962, 1138526and the National Institute of Health under Grant Numbers1R43 OD012081-01, 1R43 RR022008-0, and 1R43 RR022008-02 . Any opinions, findings, and conclusions or recommenda-tions expressed in this material are those of the authors anddo not necessarily reflect the views of the National ScienceFoundation or the National Institute of Health. AlexanderRepenning is the founder of AgentSheets Inc.

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