the dynamics of learning in a computer simulation environment

Journal of Science Teacher Education, 7(I), 41-58 @1996 Kluwer Academic Publishers, Printed in the Netherlands

The Dynamics of Learning in a Computer Simulation Environment

Nancy Roberts and George Blakeslee

Lesley College, 29 Everett Street, Cambridge, Massachusetts 02138-2790, USA

William Barowy

BBN Laboratories, 70 Fawcett Street, Cambridge, Massachusetts 02138, USA

This pilot study represents an initial step to better understand both the potential for and limitations to leaming science when middle school students interact with expert computer simulations; that is, simulations designed by scientists and built by professional programmers. The eight middle school students in this study were a diverse group from an urban school. For the first part of the study, we collaborated with the middle school science teacher. For the second part, we divided the students, meeting with half of them in the grant offices and half in the school. This move solidified our hypothesis that the interaction among the teacher's pedagogical style, the student's subject matter interest and knowledge, and a computer simulation form a system that can either promote or hinder effective learning. The findings of this pilot study strongly suggest that computer simulations can be used effectively for learning and concept development when teachers:

1. use student learning needs, rather than student learning gains, to guide their choice of pedagogical style,

2. use direct instruction to stimulate student interest as well as build basic knowledge, and

3. altemate their pedagogical style between direct instruction and student exploration.

This subtle balance needs to be discussed and modelled by science educators.

This material is based upon work supported by the National Science Foundation (Grant No. RED-9153871). Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the National Science Foundation.

42 NANCY ROBERTS ET AL.

Procedure

All interactions among students, teacher, researchers, and computer simulations were videotaped and transcribed. The project proceeded in three major phases. The first phase consisted of two sets of clinical interviews: (a) the Nature of Models interview (Grosslight, Unger, Jay, & Smith, 1991) with individual students followed by (b) the Explorer: Waves computer simulation model 1 in which pairs of students explored, with minimal intervention, a highly abstract model of wave phenomena (see Figure 1). These interviews established a baseline of the students' understandings of the concept model and their ability to interact with a computer simulation.

Figure 1. The Explorer waves model.

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~ 6 r a p h e r ~ l ~ - I

e (=3 "-34

J Frequency

m I Number l of snaps

- I Frtctlon 20 a 40' 60' 00' 100'

[ LJneGreph ] [ Controls J

Ol

In the second phase, students were engaged in teaching experiments on equilibrium in aquatic populations, using the Explorer: Population Ecology model (see Figure 2). Unlike the first phase, the population ecology teaching experiment was conducted in a school with the students' science teacher.

Based upon findings from the first two phases, the third phase involved the impact of combining three leaming strategies: (a) experimentation and data collection, (b) the Explorer: Cardiovascular computer simulation model (see Figure 3), and (c) causal diagramming. During this phase, half of the students participated in a section run by the teacher in the middle

THE DYNAMICS OF LEARNING 43

school and half participated in a section run by the researchers in their office.

Figure 2. The Explorer population model. !RJ====.==.~= P o p u l a t i o n G r o w t h " [ ]

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Number Number Number of Fl~h of of algae

daphn|l

mmF~ 200

150

100

50-

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!1''- ! I1 l l l n

lOO'

T |n~

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Figure 3. The Explorer cardiovascular simulation.

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Heart Rat@

0 2O 40 Tlme6O

Heart Rate

Volume of Blood Pumped

cc per min


Background

There is much evidence pointing to a set of skills students must have in order to learn science while using computer simulations. Studies done at the University of London found that middle school students either have or can readily acquire these sells (Bliss et al., 1992, 1993). This study produced similar findings (Barowy, 1994). Skills important to modelling and simulation are similar to student-centered experimentation with real phenomena and include testing behavior by isolating variables, testing limits, examining interrelationships between parameters, and controlling parameters (Arons, 1979). Students must also have an appreciation of the issues of internal model validity such as collecting evidence to see if the model behaves consistently and logically under a wide variety of conditions, looking for patterns in model behavior, and looking for consistency among the variety of ways simulations display data (Saeed, 1995).

An awareness of the issues of extemal model validity is also important. For evidence of this, look for students to be able to compare the model with the target phenomenon by comparing data generated by the model to data generated in other ways and question the simplicity or complexity of the model structure in relation to observed behavior. Also, look for students to demonstrate general model and simulation behavior such as that expected of a person using a model as an aid to thinking through complex decisions. Such abilities might be displayed by having students suggest alternative explanations for the same phenomenon, differentiate between the model and the experience, and express a sense of how the model explains the target phenomenon. Understanding of causation was also part of the initial investigation. Since simulations were used that incorporate feedback, students were expected to demonstrate an understanding of this concept.

Classroom Dynamics Theory

During the course of this 6-month project, we gathered sufficient evidence to document that middle school students can learn science from simulations in a way similar to scientists (Barowy, 1994). These professional behaviors, however, were not evident in all the students all the time. The same student could go from being totally uninterested in a simulation, to interacting with a simulation as he or she might interact with a video game, to using a simulation for doing science. This total change in behavior became the subject of closer study and hypothesis generation.


Impact of Teacher Behavior on Skill Development

Following the baseline evaluation, the students' first learning challenge was to design a classroom aquarium. As a tool for determining the components of the aquarium and the ratio of one to another, the population simulation, Explorer: Population Ecology, was used (see Figure 2). The program has three variables: (a) fish, (b) daphma, and (c) algae.

For this teaching experiment, Polly, the classroom teacher, and Bob, the researcher, each worked with four students. After the first few lessons, differences in their pedagogic styles were evident. The first dynamic that became very clear was the role of direct instruction when self-regulated by the teacher. This behavior was assumed to be the dominant one in the teaching experiments. For example, when a student needs to acquire skills while using a simulation, he or she will prompt the teacher to directly teach these skills. Once the student acquires these skills, the need for continuing direct teaching passes, suggesting a self-regulating feedback system (see Figure 4). The more skills the student needs, the more direct teaching is appropriate. The more direct teaching given, the more skills gained by the student, causing a need for less direct teaching.

Figure 4. Self-regulating, direct-instruction behavior based on student needs.

Amount of . A

Direct Teaching

• G "ned d ; n t

Skills Needed by Student

Other researchers suggest similar phenomena. Rieber (1991) found that "[1]eaming from apparently well-designed animated visuals can be subverted when students' attention is not sharply focused on the relevant information" (p. 81). Vosniadon and Brewer (1987), reviewing theories of knowledge


restructuring, found that a child's "actual restructuring must grow out of the attempts of the Socratic teacher to guide the child to construct the new schema" (p. 61).

The following transcript illustrates this dynamic for students, Naomi and Barney, and teacher, Bob, using the population simulation. Bob does both direct teaching by giving how-to information as well as engaging the students in concept development by listening to the students' explanations.

Bob:

Bob: Naomi: Barney:

Here, rll show you how you can do that. You have to restart. Why don't you click on this tool here? (He points and shows them how to see the whole graph. (Naomi and Barney use the mouse as instructed.) So, how does that work? It's doing it. Because, like, we raised the habitat level, we raised the algae, and we put down the fish so that the fish have enough to eat and have enough space and everything. So, they can eat and grow and have kids and all that good kind of stuff.

This self-regulating dynamic, as shown in Figure 4, however, can be negated by a teacher who is able only to act in a direct teaching manner. In this case, a teacher evaluates his or her teaching as helpful and continues to teach, turning off the students' interest in interacting with the simulation. This second dynamic is reflected by the addition of a second feedback loop, as shown in Figure 5, and is similar to what Lemke (1990) identified as initiate, respond, and evaluate behavior. For example, the more direct teaching that is given, the more skills that are gained by the student. This causes the teacher to positively evaluate his or her teaching and so continue to teach. Because of the increase or continuation of direct teaching, the students becomes less interested in working with the simulation. This eliminates the effectiveness of the serf-regulating feedback loop as shown in Figure 4.

An example of the feedback suggested by the outside loop in Figure 5 is illustrated by the following transcript. Polly, the teacher, explains the graph and asks about two pieces of information: (a) the number offish and (b) the habitat. Polly asks the students to tell her about the model and to name the two different species used in the model. Bamey and Naomi answer, "fish and algae." Polly then gives instructions on how to nan the simulation. Instead of allowing the students to get a feeling for the population system represented by the simulation, Polly continues to direct the students' interaction with the model.


Figure 5. Impact of direct teaching model.

Amount of Skills Direct -..

~" Needed by Teaching Student

T

S$ Student Interest

Polly:

Bamey: Naomi: Polly:

Bamey:

Barney:

Bamey:

Naomi:

Bamey: Naomi:

Do not change anything. Write in your notebooks how much the habitat can support and what the population is. Habitat supports how many algae? 400. Wait a minute. Wait a minute. I think I moved something. Press it again. I want to see what happens. How much does your habitat support? Naomi, show him. (Naomi points to the habitat setting on the screen.) Habitat supports 410 and the fish habitat supports 150. (Polly tells them to recheck it. She then asks if the number of fish they started with is the same as the number that the habitat will support.) Yeah. (Polly again stresses that she does not want them to change anything. Barney tells Naomi not to touch anything. Naomi's behavior suggests she is ready to explore the simulation; however, Polly's continued student direction eventually tums off the students to the computer investigation. Leave it alone. Justleave it alone. Gees. Don't touch anything. Stop! What's your problem? (They sit for a few minutes and do nothing.) Don't do nothing. I'm not doing nothing.


"When the goal of scientific instruction is understood as 'mastering' scientific theories and concepts, the scientific process of making sense of phenomena can be stifled" (Newman, Morrison, & Torzs, 1993, p. 1). Pintrich, Marx, and Boyle (1993) found literature to show that neither extreme of classroom dynamics--too much structure by the teacher or too little--leads to knowledge acquisition. Sutton (1993) explained this same phenomenon in terms of the language used by the teacher and how it is interpreted by the student. If students think of language as a labelling system, then they also believe that knowledge is transmitted from the teacher to the student. If, however, students think of language as an interpretive system, then the role of students is to interpret and re-express ideas. In this study, when Polly is the teacher, she clearly creates the environment for transmission of knowledge, and the students' roles are to be good receivers. When Bob is the teacher, he continues to coax the students to interpret their experiences. In the case of Bob, the self-regulating loop (see Figure 4) dominates the teaching dynamics. In the case of Polly, the self-perpetuating, evaluative loop (see Figure 5) dominates the teaching dynamics.

Figure 6 suggests the dynamic learning balance among teachers and their students in a computer simulated environment. Student's Conceptual Understanding is added as a key variable for teacher sensitivity in support of the student leaming cycle. For example, as students build their simulation skills, they begin to increase their conceptual understanding of the simulation. As their conceptual understandings increase, they might require additional skills, suggesting the appropriateness of direct teaching again. On the other hand, as students' conceptual understandings increase, they tend to explore the model more, needing less direct teaching and more guided exploration.

This situation is similar to what Collins (1990) refers to as cognitive apprenticeship--the student needs to be both shown the way and then left to learn by doing. It is also the philosophy of the Unified Science and Mathematics for the Elementary School (USMES) materials (1977). These materials have a series of how-to cards designed to provide students with the skills they need when the students need the skills. Several other researchers, some working in a computer environment, have found this balance between direct teaching and exploration critical (c.f., Ahem, Peck, & Laycock, 1992; Beckett & Boohan, 1993; Lemke, 1993; Newman & Torzs, 1991; Rieber, 1991; Westbrook & Rogers, 1994).

The following transcript suggests the importance of this balance as students develop science concepts. The students are using the computer simulation to determine the appropriate ratios offish, daphnia, and algae for their classroom aquarium. Dick, a student, wants to experiment with the number of fish, daphnia, and algae that the habitat will support.


Figure 6. The need for teacher sensitivity.

Amount of Skills Direct ~"-..,, Needed by

aoling ~1~ ~ Student~ X \ ~ Teacher ~ ~ \Effectiveness ~

Evaluation ~ , ~ Ev~uation

_ . '~, Student Skills ~aJnea Student ~ by Student Exploratmn Interest

Student's j Conceptual Understanding

Bob:

Dick:

Bob:

Dick:

Dick:

Does it make sense to change the feeding rates in trying to simulate what this is going to be like? (He points to the classroom aquarium.) Yeah, except you can only put about 10 fish in there and a certain amount of algae. Excellent, so that's right. The initial numbers we'll have to keep reasonable. Should you be able to change feeding rate? Does that seem reasonable? Yeah, before you change feeding rate, you need to see how much they eat. (Dick works the simulation.) I think if you had 5 fish, it should be, like, 20 algae. The numbers I had levelled off at 19 fish and 56 daphnia which is about 3 times as many daphnia as there are fish, and there was about 187 algae. That's about 3 times as many algae than fish. So, this seems to be multiplied by 3 until you get to the top one. (When Bob notices Barney and Dick are ignoring the variable habitat support, Bob asks Barney if he thinks they should change it.)


Bob:

Barney:

Dick:

Does it make sense to change habitat support for any of these guys if you want to use the program to think about this thing? Yeah, 'cause look, if the algae has so much space and the daphnia aren't eating a lot of algae and the fish aren't eating a lot of daphnia, that's just going to grow and grow and grow. It will grow faster than it should and so everything is just going to, like, get swallowed up inside the algae. (Bob, satisfied that Barney has the concept of population balance allows Dick and Bamey to go back to the simulation. Dick asks skill-based questions when he needs information.) You also have to play with life span. Does the program do that?

Environment for Learning and Problem Solving

In addition to the teacher's choice of instructional style, the student's level of interest is an important determinant of his or her ability to learn science from a computer simulation. Natalie is illustrious of how one student can react very differently, depending on which feedback loop is dominant. In the first transcription, Natalie is completely turned off to the simulation because she has no knowledge of, or interest in, waves. This information was collected on Natalie and the other seven middle school students during an interview prior to instruction. Those students who could relate what they saw on the screen to a previous experience had some interest in working with the simulation (Lemke, 1993; Wheafley, 1991). Those students who could not, such as Natalie, acted completely uninterested. This phenomenon can be expressed dynamically as a self-reinforcing feedback loop (see Figure 7). For example, the more knowledge a student has of a

Figure 7. A self-reinforcing feedback loop.

Amount of KnDe°g Wlre e e dogf e Interest


topic, the more interest he or she will have in a simulated environment of that topic. An interest in a topic and, therefore, attention to it, builds the knowledge base for the student. Conversely, if the student has little or no knowledge in a particular area, he or she would likely have less interest in the topic and tend not to build a base of knowledge.

The potential problem seen here also relates to the dynamics shown in Figure 4. If Natalie was encouraged to explore the wave simulation by direct teaching, she might have gained enough simulation skills and knowledge to pique her interest. Since there was no direct teaching in this situation, Natalie's attention was not captured and she did not explore on her own, as the following transcript suggests.

Natalie: Bob:

Natalie:

Natalie:

Natalie:

Bob: Natalie:

I don't really know [what's happening]. It looks like big waves. Try frequency. (Natalie has changed several variables without resetting previous ones.) It's hard to tell which one [wave particles] is going where. It [changing frequency 50] made them [wave particles] separate more and go, like, at the same time and all at once. No idea why line graph comes out as it does. (Bob shows her how to use variable slider bar to change a value.) Let's move them [variables] as far as they will go [up]. (Bob asks why she is setting everything to their upper limits. I want to see what happens. It doesn't move much. (She proceeds to move the slider bar higher.) Let's do something else [with the model.] The end of it [wave animation] is going up. Does that matter? I don't know.

The next transcript is from the first teaching experiment. Natalie showed some interest in the population dynamics of an aquarium, but her interest was motivated by her desire to accomplish the goals of the teacher, rather than to learn more about balancing an aquarium. Initially, Natalie reacted negatively to persistent direct teaching. The following transcript shows Polly's inability to let the students, Natalie and Cathy, use the simulation as a tool to better understand population dynamics.

Cathy:

Polly:

We decreased the number of fish so the fish would have more food to eat. So, you decreased the algae. Did you get that number?. You've


Natalie: Polly: Cathy:

Natalie: Polly:

Natalie: Polly:

got almost opposite what you want it to be doing. How do you think you can get it to go this way? To really lower the algae and the fish. Okay. So, you want us to make it look like that? (She moves the fish count all the way down.) Why did we decrease the algae? I don't know. How do you think it is going to look? If you decrease the algae, what do you think the fish will do? Are you ready to run that one? No, I still don't have a reason for decreasing algae. Well, it's the food for the fish, so why would you want to decrease it?

Finally, Natalie emerges as a class leader when several elements of the environment are present (c.f., Alexander, Kulikowich, & Schulze, 1994; Brown, Collins, & Duguid, 1988; Driver & Bell, 1986; Gabel, 1994; Pintfich et al., 1993; Rieber, 1991; Tobias, 1994;), and the class is studying the heart. The elements in this case are:

1. an interest in and some initial conceptual understanding of the subject,

2. a teacher who understands how to balance the dynamics of direct teaching with student exploration, and

3. an understanding, skill in, and desire to explore the subject using a computer simulation.

After studying the heart in class and repeating some of the class experiments with the students in the study, Natalie developed the hypothesis about how the heart works, and she expressed it in a causal-loop diagram (see Figure 8). Natalie's intuitive sense of dynamics contrasted to what the researchers at King's College, University of London, found while working with the same age students. These researchers worked with their students to build causal models so that "eventually, notions of dynamics may supplant the static models we tend to keep in our minds and to use in our daily lives" (Riley, 1990, p. 262). Natalie's intuitive understanding of the heart system's dynamics is due to her interest and knowledge of the subject.

The following transcript shows the level of discussion that occurred as Natalie emerged as group leader. Helen is one of the researchers.


Figure 8. Natalie's theory of how the heart works.

how much exercise

how tired how fast

your heart beats

howfast you'm

Natalie:

Helen: Natalie: Helen: Natalie: Bamey: Natalie: Bamey:

Helen: Bamey:

Natalie:

I don't know how much exercise you would have to do, how long, and how hard, but I did not put them all in together. I just put how much exercise, which was pretty much a lot, and then how much the heartbeat, which was pretty fast. The more exercise you did the faster your heartbeat and that makes you breathe faster because you're trying to get it down. But you didn't collect data on that. Right. But if you breathe faster, you're more tired. You could ask Bamey if he got tired. Did you get tired, Bamey? Not a lot. But you got tired some? Yeah, but what I noticed was that even when I'm resting, I breathe fast. When I'm running, I breathe fast, but when I sit down and rest, I breathe slow because I take deep breaths. So, what you're saying is this diagram makes sense to you? Yeah, because when you're running, you breathe fast and then you stop and relax and you take deep breaths so you breathe slower. Thank you.

The next hypothesis proposed by the students revolved around the rate of change of the heartbeat when someone goes from running to walking to rest. Natalie's hypothesis was that it takes the heart the same amount of time


to go from running to resting as from running to walking. One of the students pointed out that the hypothesis did not make sense because the heart rate has to pass through the walking rate on its way to the resting rate, so it has to take longer to go from running to resting. Natalie held to her hypothesis until she saw the cardiovascular simulation. She then understood she was mistaken.

Helen:

Natalie: Dick:

P e t e r :

Natalie:

So, which do you thinkis right--your predictions from last week or the computer simulation? Which do you think is more accurate? The simulation. The simulation is better because you could do the heart beat of three or four different people, and with our experiments, you c a n . . . .

But, also, it seems right because it seems that if it's [simulated heart rate] going down further it should take longer. It [simulated heart rate] does seem right.

Since the students concluded that the simulation is, indeed, helpful in understanding the heart, they recommended that the measurement of time should be speeded up so they can more easily do other experiments (Hestenes, 1992; Mayer, 1989).

Bob:

Peter:

Natalie:

Bob: Peter:

Okay. So, Peter, you said from running to walking went slowly and from running to resting went slowly. Is that all that happened? I don't know. This seems to drop slow. Maybe it's just the way the graph is set up, but it seems to drop slowly. I still say you should make the time go a lot faster. Yes. It's easier when you have a tenth of a second there. It goes faster for you. Is that a reason not to trust the results? No, no, it's not that. It just takes a long time for it to happen.

The students were anxious to try their experiments on simulated people who had different levels of physical fitness.

Natalie:

Bob:

I'm gonna try a different one instead of normal. Couch potato, I want to try the couchpotato. The couch potato, God, he's slow, 132. I'm gonna try the one with hard arteries. Okay. Natalie, which kind of person?


Natalie: Bob:

Hard arteries. Hard arteries. Peter, you're doing the athlete?

The students seemed to really understand the use of computer simulations in science experimenting. They realized the wide range of people they could test with the simulation. They also recognized the use of mathematical models to vary the rate of the passage of time. Experiments that might take, in this case, several class periods, could be done in just one. The students also discussed the validity of the simulation model, deciding that it was an acceptable model for studying the heart based on the data they collected experimenting with their own bodies. Although not as sophisticated as a group of adult scientists, the conversations certainly had scientific authenticity and, undoubtedly, will grow in sophistication as the students gain more experience, maturity, and knowledge.

Implications for Science Teacher Education

For the past decade, science teacher education has been focusing on hands-on science teaching. The current science standards (National Research Council, 1995), although shifting emphasis to such things as the importance of knowledge depth and students constructing knowledge as scientists do, teachers often interpret this as meaning more hands-on classroom activities. Polly was chosen to participate in this study because she had been recommended as someone who embraced hands-on science, had attended several workshops on the subject, and was one of the best science teachers in the school system. This study shows, that at least in the area of computer simulation, hands-on experience is only one of several important variables in science leaming.

Model building and simulations are important tools for scientists today. Theliterature documents that middle school students can easily use computer simulations to leam science. Helping teachers use simulations successfully is critical. The following strategies can aid in effectively integrating science simulations into classrooms:

1. Teacher education courses must include science simulations as an important science leaming tool.

2. Science education faculty must be sensitive to the delicate balance between direct teaching and student exploration.

3. Science educators, by involving their students in computer simulations, must develop ways to model this dynamic balance in their preservice and inservice courses.


Conclusions

The results from this study developed into a dynamic hypothesis relating teacher pedagogical style to student learning in an expert computer simulation setting. Several critical variables for productive learning were found (see Figure 6), the most important being the teacher's pedagogical style (direct teaching) and student interest in a subject. The teacher must be sensitive to both the need to empower students' exploration by providing them with the skills they need to explore the simulation through direct teaching as well as allowing and encouraging students to do their own exploring. This balance is not easy to find, especially for beginning teachers. Giving enough knowledge and skills without boring the student requires an understanding of the dynamic at work.

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Footnote

1Explorer series mailing address is Logal Software, Inc., 125 Cambridge Park Drive, Cambridge, Massachusetts 02140.

the dynamics of learning in a computer simulation environment

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