Computer Simulations of Laboratory Experiences

Roy B. Cloriana

During the last five years. a strong interest in computersimulations has emerged in computer education journals andmagazines. This echoes a request or desire on the part ofteachers for computer simulations. probably as a reactionagainst the preponderance of "poor" software available formicro-computers. A simulation according to McGuire (1976)is "placing the individual in a rcalislic selling where he isconfronted bya problematicsilUalionwhich requires his activeparticipation in initialing and carrying through sequences ofinquires., decisions, and actions." Lunetta and Hofstein (1981)state that a "simulation is the process of interacting with amodel that represents reality:' Are computer simulations thevchicle thaI can unlock the promised but not yet achievedpotential of the computer in our classrooms?

Gagne (1962) identified the following characteristics ofasimulation:1. A simulation represents a real situation in which operationsare carried oul.2. Asimulation provides the user with certain controls over theproblem or situation.3. A simulation omits certain distracting variables which areirrelevant or unimportant for the particular instructionalgoals.

Simulation = (Reality) - (Task Irrelevant elements)

Do these qualities of simulations imply any inherent improve­ment over current computer use in the classroom setting? Inour minds, we must be clear to separate the appeal of a "good"(well designed) computer simulation from a "bad" (poorlydesigned) computer drill and practice program (computerdrill and practice can and should be well designed). By theirvery nature. simulations are harder and more expensive todesign and so will tend to be better than a run-of-the-millprogram.

The purpose of this paper is to consider the aspects andqualities of computer simulations in the simulation of sciencelaboratory experiments specifically, and relate this to generalsimulation applications. This paper will attempt to answer thefoUowing questions:1. Is there evidence to indicate that computer simulations ofscience laboratory experiments com pared to aetual science labexperiences will result in improved achievement?

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2. What recommendations for computer simulation designand application are suggested by current research? (transfer.feedback, learner control•...).

When ShoUd SirTUations Be Used?

In a study by Cavin and Lagowski (1978) of students usinga computer simulated chemistry experiment compared tostudents actually doing the experiment; the students madereadings from a spectrophotometer, recorded this in a datatable, then performed the necessary calculations to determinethe results and form conclusions. The computer simulationgroups achieved as well or better than tbe other groups. Also.the simulation took significantly less time. (In some eases.,simulation students took more observations and so more timewas used.) No relation was shown between the amount of timetaken and aptitude. Computer simulations were effective forboth low and high aptitude students. In a study by Boblick(1972) of students using a computer simulated physics experi­ment compared to students actually doing the experiment. thestudents made time measurements and observations of theresults of a one dimensional elastic collision (both werediscovery type activities with the student choosing values forthe independent variables). Both groups showed learninggains, but the computer simulation group bad significantlyhigher scores on the pasllest. It was suggested that thesimulation more closely represented a real clastic collision andalso was easier Lo use than the real experiment.

"The computer simulation provided an environmentwhich focused on the essential aspects of this experiment,eliminated the unnecessary consideration of variables nOIrequired in the study of momentum. provided a greater nexi­bility in the selection and employment of values for theessential variables, operated with sufficient speed to enablethe student to gather large amounts of data in a short periodof time, and furnished a means for rapid analysis of the datacoUeeted" Boblick (1972).

In these studies and also in Hedlund & Casolara (1986),Johnson and Johnson (1986). McGuire (1976), Sherwood &Hasselbring (1984), and Vockell & Rivers (1984); studentsachieved as well and usually better on the posuests throughcomputer simulations_ The point is not that computer simula­tions are better than any particular instructional approach, but

Roy B. Clarialla is Q graduate Stlldeflf, DepaTtmCJt of Cunicu­lwn Qnd Instruction, Memphis State University, Memphis, TN38152

Journal of Computers in Mathematics and Science Teaching

Declarative knowledge, on the other hand, is probably storedwith the fund of other similar declarative knowledge andwould be less context bound. This knowledge can be general­ized to new situations and would be available for heuristicsdevelopment. This idea can be written as:

First, simulations are more or less context bound by theirvery nature. This variable can be manipulated by the designerto increase either far or near transfer depending upon theinstructional objectives.

Second, rule use and practice in different contexts is themain emphasisof most types ofsimulations, implying tbat neartransfer can be expected when using most computer simula­tions, ho....'Cver certain types ofsimulationscan incorporate useof analogies allowing for far transfer.

Third, all analogous prior knowledge should nOl be gen­eralized to every new situation. That is why this negativetransfer is part of our normal cognitive processing as viewedfrom a natural selection viewpoint. For example, a prehistoricman that waves a stick at one type of predator frightens thatpredator away, but waving tbat same stick at a differentpredator may invite a quick snack. The key to survival isimmediate negative transfer based upon discrimination of thecontext, the learned task of stick waving should nol be gener­alized to every similar situation.

So in developing simulations, the analogies chosen mustbe similar to the near transfer task. and one of the key iearningoutcomes must be discrimination of the context of when thislearningshould be used as well as performanceof the learningitself.

Fourth, procedural objectives of this type are probablystored in long term memory in a different way than thedeciarath'C knowledge and as such are nearly an automaticprocess. This implies that these procedural objectives wouldbe stored in a way that allows for a more rapid recall and anintuitive action or reaction based upon the context, along withinformation about that specific context, almost as an auto­matic system. These may best be learned through rule use wilhpractice until it is overlearned (automated). The learning mayinvolve simple or complex behaviors. This idea may besummarized as:

lbe Ireatment described is probably not a guided discovery.However, either position supports the use of some form oflearner guidance and a reduction of learner control in com­puter simulations, especially wben instruction Lime is limited.

Near and Far Transfer

Transfer, both ncar and far, may be a forte of computersimulations......there is considerable evidence that much ofwhat is learned can only be applied to problems that aresimilar to those tbat are experienced in training (transferfailures)." Clark and Vogel (1985). As mentioned earlier,simulations simulate a reality, preferably the reality in whichthe learning wiU be used later. In tbe VockeU and Rivers(1984) study mentioned previously, the learning transferred tosubsequent simulations (near transfer). Thorndike andWoodworth's (1901) identical element hypothesis suggeststhat transfer is best when training is Like the reality for whichthe learning is intended and such obvious logic is easy toaccept. Gagne's (1962) definition ofsimulatioru;as previouslymentioned is represented in this formula:

Simulation = (Reality) - (Task irrelevant elements)

In fact, a computer simulation can go at least one step further.Reality can be an enhanced reality that highlights the mostimportant cues while removing the task irrelevant cues. Theformula might then become:

Simulation ,... (Enhanced reality) - (Task irr~evant ~ements)

Presumably, the more like reality the simulation is, theeasier or more efficient the ncar transfer. This may becomplex because it can involve the context of the reality, pastexperiences, attitudes about self, feelings of self worth, andother factors that arc not part and parcel of a computersimulation design. Clark and Vogel's (1985) general recom­mendations on transfer arc helpful:

1. The extcnt of transfer is dctermined, in part, by the amountofdecontextualization achieved during instruction (amount ofreality included in the design through the removal of irrelevantcues).2. Simulations that employ relevant analogies will promotefarther transfer than instruction employing rule use and prac­tice in differcnt contexts. (Assumes a large quantity of previ­ously learned meaningful examples as the analogy base.)3. If analogous prior knowledge used in transfer containsoperations that are nOl permitted in the application task,negative transfer may occur.4. Other things beingequal, procedural objectives will producenearer transfer learning and declarative objectives will resultin far transfer learning.5. Other things being equal, far transfer is achieved at theexpense of ncar transfer.

Learning experience(near transfer)

Learning experience(far transfer)

Content of procedure+

Context of procedure(spec.ic)

Content of declarative info+

Analogous context(general)

16 Journal of Computers in Mathematics and Science Teaching

Last, when you go for far transfer, you sacrifice neartransfer. This may be due again to the way each would bestored in long term memory. lnstruction designed for fartransfer would lack the context specific discriminators thatwould allow for an automatic algorithmic selection of thelearning associated with it, and so this learning would be medin the broad field in memory with which it is most closelylinked. This field contains many other general experiences,and when this learning is called upon, only a generalized "bestanswer with other infonnation attached to it" isavailable fromthe long term memory store, rather than the specific learningonly. This then would be far transfer at the expense of neartransfer.

In summary, we suggest that transfer is a function of thereality or specificity of context built into the computer simula­tion, and that both ncar and far transfer can be achieved mosteffectively through computcr simulation.

Levels d Questioning

Some simulations depend upon questioning for the devel­opment of cognitive processes in the learner. The currentsuggestion is that higher level questions (Bloom's CognitiveTaxonomy: Analysis, Synthesis, Evaluation, ...) are more ef­fcaive because they may cause deeper level processing in thelearner.

In astudyby Merrill (1985), the effects oflow or high levelquestions with corrective feedback (CF) or attribute isolationfeedback (AIF) were examined. AlF informs learner ofcorrectness or incorrectness and isolates the attributes of theconcepts being studied, thus focusing attention on the criticaland variable attributes of a concept. Four groups wereeslabl~hcd, LO/CF, HI/CF, LO/AIF, HI/AlF. All wc<cJuniorcbemistrystudents, n= 154. Time on task wasthesame.HI cognitive level questions were beller than LQ level ques­tions in causing higher scores on posttests (at the .001 level).Effects of CF versus AIF were nOl established. It was sug­gested that the concepts measured by the posttests were nothard enough nor the instructional time long enough to show 3.nadvantage for AIF.

Generally, the results of this study support the use ofhigher level questions in computer simulations, but not AlF.

Feedbad<

The amount and type of feedback in a computer simula­tion is a central question that must be answered. Content is thecritical factor because the content will determine the presen­tation mode, and the presentation mode will require a specificfeedback approach. Gredler (1986) has suggested the follow­ing taxonomy of computer simulations:

1. Structured questions with associated graphics1 Assignmcnt of variables with results exercises

Winter 1988/89

3. Diagnostic simulations4. Grou)J'"Interactive

Structured questions with associated graphics would pres­ent content, the images would represent the reality (context)portion of the simulation. This category would probably bebest for developing the cognitive processes related to analysis/deductive reasoning and would require observation, interpre­tation, interpolation, extrapolation and other skills. Thecontent might be explained by the graphics. The presentationmode would be episodic, each question would represent alearning episode and immediate feedback would be inherentin the episode, there is probably one right answer. Eachadditional episode mayor may not build upon previous epi­sodes. This model is fairly structured. Assignment ofvariables exercises describe many or most science laboratorycomputer simulations. Basically, an independent variable isset by the learner and the results on the dependent variable areobserved through several trials, much like a real laboratoryexperiment. This category of simulation would be best fordeveloping inductive or synthesis processes. The nature of thissimulation is cyclic, feedback is related to variables effectingother variables. There maybe one right answer, but it is a largeconcept (declarative knowledge) that must be induced aftermanyvariable.response episodes, also incidental learning mayoccur involving efficient selection of values for the independ­ent variable (a process skill).

Diagnostic simulations could be considered to be a formof expert system. This type of simulation usually involvesapplied decision making in a professional content area (a largefield of related information within the professional allows forgeneralized decision making as well as a large body of proce·dural or algorithmic information in specific context allows forspecific decision making; the crux is to decide which is calledfor and then act). A decision variable is selected by the learnerafter several episodes, and thc episodes tcnd to blend into onelarge scenario (definition of context). The results of thedecision may be immcdiate or delayed and mayor may nOI beeasy to interpret (again calling upon previously learned knowl­edge). The purpose of the simulation may be to interpret thesituation or context as well as to make the appropriate deci­sions. Normally, one right answer is impossible to define. Theresults or many scenario decision making events will eventu­ally develop in the learner super-concepts (algorithms andheuristics) that may be considered "expert knowledge".

The group interactive simulation would be applied deci­sion making in a real social context. Each social context isdifferent. Distinguishing the correct approach in context is theprinciple learning outcome. There is not asuggestion of a rightor wrong answer but rather the question "is the solutionappropriate in this context?". As in diagnostic simulations, theepisodes blend together into a scenario that has occasionaldecision making points. Results or decisions made are oftenquite difficult to determine because these are effecting (heemotions of the individuals involved. Feedback in both calC-

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Individ'-'llization I Grouping

2. describe what happened (FEEDBACK)3. describe how it made you feel4. what would you do differently next time? (return to :# 1

gories is variable at best, and approaches the form orreedbackthat occurs in a real life situation.

In experiential type computer simulations (Diagnosticsimulations and Group Interactive simulations), it may oftenbe difficult or inappropriate to "break-in to"the simulation toprovide feedback in the form of intrusive questions. Often, ifthe simulation is of short enough duration, questions andfeedback may be provided at the conclusion following thetraditional adult experiential learning cycle:

This model assumes thaI timely feedback is best. Also,tobe most effective, feedback should be solicited or requested bythe learner.

The timing of feedback in these experiential simulationsthen., seems to be a function of the amount of reality built intothe simulation. Sometimes reality gives instant feedback andsometimes reality requires solicitation of feedback after per­formance, any other approach would seem to be intrusive andartificial.

In summary, feedback in computer simulations dependupon the conlent of the instruction which determines the formof the simulation.

Finally, we suggest that computer simulations simplify thecognitive processing tasks associated with learning both pro­cedural and declarative knowledge through removal of dis­traders to learning, and also provide practice in context thatwill allow for efficient near and far transfer of the learning.

Good simulations can be the vehicles that teachers arelooking for to unlock the promised potential of computers intheir classrooms. Further studies should be undertaken per­haps related to the most efficient depth of content and lengthof time for each type of simulation described, especiallyrelated to episodic and non-episodic computer simulations.Also, interactions of types of simulations with cognitive slyledimensions, and also with other forms of instruction for theenhancement of both may be viable areas of investigation.Finally, a third area would consider the amount of challenge toinclude in a simulation as related to variables such as studentachievement, or past experiences with the reality being simu­lated.

2. Cavin, Claudia S. & Lagowski, JJ. (1978). Effects ofComputer Simulated or Laboralory Experiments and StudentAptitude on Achievement and Time in a College General

1. Boblick, John M. (1972). Discovering the Conservation ofMomentum Through the Usc of a Computer Simulation of aOne-Dimensional ElasticCollision. Science Education, 56(3),337·344.

Refercflces

1. Support and learner guidance with some program controlare included in the design.2. Both near and far transfer of the learning is desired.3. Higher level questions are incorporated into the design.4. The type or structure of the simulation design is selectedbased upon the learning outcomes desired, which then definesthe amount and type of feedback.5. The general quality or the ability of each simulation toindividualize is considered before deciding on a group orindividualistic approach in its use.

There is some consistent evidence that computer simu­lated science experiments of the type described as "assign­ment of variables simulation" by Gred1er do result in im­proved learning both of specific content and also of moregeneralized cognitive processes.

We recommend that computer simulations are viablemeans of instruction especially when:

Summary / Condusion

In summarylhen, the general quality or tbe ability of eachsimulation to individualize should be considered before decid­ing on a group or individualistic approach in its use.

(SIMULATION)1. experience

Should learners use computer simulations in large groups,in small groups, or alone? There is no easy answer. Individu­alized instruction unlocks the instructional potential of thecomputer (which is in fact, the ability to individuaIi7..e), whilegroup co-operative instruction allows group processing of theinformation with a certain amount of peer tutoring.

Sherwood and Hasselbring (1984) in a study using O'dellWoods and O'dell Lakesimulations examined groups workingindividually, in small groups, and in whole class groups. Allgroups did equally well on the pasttest. Girls did better inlarger groups than individually.

A queslion arises over the quality of instruction deliveredby these simulations. Because computer simulations have thepotential to individualize instruction does not mean that everyprogram adually does. For simulations with low ability toindividualize, a group approach would seem best, particularlyif the content is difficult to grasp (allows peer tutoring). Asbetter simulations are developed, perhaps these will be indi­vidualized with resulting improvement in individual learnerachicvement over group instruction achievement. This indi­vidualization may include type,level, and number of examplesgiven and type and amount of other supporting material.

18 Journal of Computers in Mathematics and Science Teaching

Chemistry Laboratory Course. Journal 0/Research ill ScienceTeaching. 15(6),455-463.

3. Clark, Richard E. & Vogel, Alexander (1985). Transfer ofTraining Principles for Instructional Design. ECTJ, 33(2),113-123.

4. Gagne, Robert M. (1962). Psychological Principles inSystem Dt:!'elopment, Holt, Rinehart, & Winston, New York.

5. Gredler, Margaret Bell (1986). A Taxonomy of ComputerSimulalions. Educational Technology, 26(4),7-12..

6. Hedlund, Dalva E. & Casolara, William S. (1986). StudentReactions to the Use ofComputerized Experiments in Intro­ductory Psychology. EducatiOllQI Technology, 26(3), 4245.

7.Johnson, David W. & Johnson, Roger T. (1986). Computer­Assisted Cooperative Learning. Educational Technology,26(1),12-18.

8. Lunetta, Vincent N. & Hofstein,Avi (1981). Simulations inScience Education. Science Education, 65(3),243-252.

9. McGuire, Christine (1976). Simulation Technique in theTeaching and Testing of Problem-Solving Skills. Journal 0/Research in Science Teachin~13(~),_~-I00.

Wllller 1988/69

10. Merrill, John (1985). Levels o/Questioning and FornlS 0/Feedback: lnstnlctiollol Factors in Courseware Design. Paperpresented at the Annual Meeting of the American Educa­tional Researc.h Association (Chicago, IL, March 4, 1985).(ERIC Document Reproduction Service No. ED 266 766)

11. Sherwood, Robert D. & Hasselbring, Ted (1984). AComparison 0/Student AchiC\'ement Across Three Met1Jods 0/PresentQtiOIl 0/0 Computer Based Science SimulatiOll. (Tech­nical Report Series, Report No. 84.1.5). George PeabodyCollege for Teachers, Nashville, TN. (ERIC DocumentReproduction Service No. ED 262 750)

12 Thorndike, E.L & Woodworth, R.S. (1901). The lnfluenceof movemenl in one menial function upon the efficiency ofother functions. Psychological Review, 8,247-261.

13. Vockell, Edward L & Rivers, Robert H. (1984). Compu­tenud Science Simulations Stimulus to Generalized ProblemSolving Capabilities. Paper preseoted at the Anoual Conven­tion of the American Education Research Association (68th,New Orleans, LA, April 24, 1984). (ERIC Document Repro­duction Service No. ED 253 397).

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