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515 17 Developing Understanding Through Model-Based inquiry James Stewart, Jennifer L. Cartier, and Cynthia M. Passmore A classroom of students need only look at each odher to see remarkable variation in height, hair color and texture, skin tone, and eye color, as well as in behaviors. Some differences, such as gender, are discrete: students are male or female. Others, such as hair color or height, vary continuously widhin a certain range. Some characteristics 10 fingers, 10 toes, and one head do not vary at all except in The rarest of cases. There are easily observed simi- lanties between children and Their parents or among siblings, yet There are many differences as well. How can we understand The patterns we observe? Students need only look through the classroom window to take These questions a next step. Birds have feadhers and wings characteristics on which They vary somewhat from each odher but on which they are com- pletely distinct from humans L)ogs, cats, and squirrels have four legs. Why do we have only two? As widh much of science, students can begin The study of genetics and evolution by questioning The familiar The questions mark a port of entry into more than a century of fascinating discovery that has changed our understanding of our similarities, our differences, and our dis- eases and how to cure Them. That inquiry has never been more vital Than it is today. It is likely that people observed and wondered about similarities of offspring and their parents, and about how species of animals are similar and distinct, long before The tools to record Those musings were available. But major progress in understanding These phenomena has come only rela- tively recently Through scientific inquiry. At The heart of That inquiry is The careful collection of data, the observation of patterns in The data, and The generation of causal models to construct and test explanations for those
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516 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM patterns. Our goal in teaching genetics and evolution is to introduce stu- dents to the conceptual models and the wealth of knowledge that have been generated by that scientific enterpr se Equally important, however, we want to build students' understanding of scientific modeling processes more gen- erally—how scientific knowledge is generated and justified. We want to foster students' abilities not only to understand, but also to use such under- standings to engage in inquiry, For nearly two decades, we have developed science curricula in which the student learning outcomes comprise both disciplinary knowledge and knowledge about the nature of science. Such learning outcomes are realized in classrooms where students learn by "doing science" in ways that are similar to the work scientists do in their intellectual communities. We have created classrooms in which students are engaged in discipline-specif~c in- quiry as they learn and employ the causal models and reasoning patterns of the discipline. The topics of genetics and evolution illustrate two different discipline-specific approaches to inquiry. While causal models are central in both disciplines, different reasoning patterns are involved in the use or con- struction of such models. The major difference is that the reconstruction of past events, a pnrnarv activity in the practice of evolutionary biology, is not common in the practice of genetics. The first section of this chapter focuses on genetics and the second on evolution. The third describes our approach to designing classroom environments, with reference to both units. Our approach to curriculum development emerged as a result of col- laborative work with high school teachers and their students (our collabora- tive group is known as MUSE, or Modeling for Understanding in Science Education).' As part of that collaboration, we have conducted research on student learning, problem solving, and reasoning. This research has led to refinements to the instruction, which in turn have led to improved student understanding, GENETICS An important step in course design is to clarify what we want students to know and be able to do.2 Our goal for the course in genetics is for students to come away with a meaningful understanding of the concepts introduced above—that they will become adept at identifying patterns in the variations and similarities in observable traits (phenotypes) found within family lines. We expect students will do this using realistic data that they generate themselves or, in some cases, that is provided. However, while simply being familiar with data patterns may allow students to predict the outcomes of future genetic crosses, it provides a very incomplete under- standing of genetics because it does not have explanatory power Explana- tory power comes from understanding that there is a physical basis for those
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D EVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 517 patterns in the transmission of genetic material (i.e., that there are genes, and those genes are "corned" on chromosomes from mother and father to offspring as a result of the highly specialized process of cell division known as meiosis) and as a result of fertilization. To achieve this understanding, students must learn to explain the pat- terns they see in their data using several models in a consistent fashion. Genetics models (or inheritance pattern models) explain how genes interact to produce variations in traits. These models include Mendel's sample domi- nance model, codominance, and multiple alleles. But to understand how the observed pairings of genes (the genotype) came about in the first place, students must also understand models of chromosome behavior, particularly the process of segregation and independent assortment during meiosis (the melotic model). We have one additional learning outcome for students—that they will couple their understanding of the transmission of the genetic material and their rudimentary understanding of how alleles interact to influence pheno- type with an understanding of the relationship of t)NA to genes and the role played by t)NA products (proteins) in the formation of an organism's phe- notype (biomolecular models) L)NA provides the key to understanding why there are different models of gene interaction and introduces students to the frontier of genetic inquiry today. These three models (genetic, melotic, and biomolecular) and the rela- tionships among them form the basic conceptual framework for understand- ing genetics. We have designed our instruction to support students in put- ting this complex framework in place. Attending to Students' Existing Knowledge While knowledge of the discipline of genetics has shaped our instruc- tional goals, students' knowledge—the preconceptions they bring to the classroom and the difficulties they encounter in understanding the new material—have played a major role in our instructional design as well. The genetics course is centered around a set of scientific models. How- ever, in our study of student learning we have found, as have others,3 that students have misunderstandings about the ongin, the function, and the very nature of causal models (see Box 12-1). They view models in a "naive realistic" manner rather than as conceptual structures that scientists use to explain data and ask questions about the natural world 4 Following our study of student thinking about models, we altered the instruction in the genetics unit to take into consideration students' prior knowledge about models and particular vocabulary for describing model attnbutes. Most important, we recognized the powerful prior ideas students had brought with them about models as representational entities and explic-
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518 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM BOX12-1 Student Conceptions of Models One early study of student learning in the genetics unit focused on identifying the criteria students used when assessing their models for inheritance phenomena.5 The study was predicated on a commitment to developing with students early in the course the idea of consistency as a basis for model assessment. Students read a mystery scenario involving a car accident and evaluated several explanations of the cause of the accident. Each explanation was problematic because it was either (1) inconsistent with some of the information the students had been given, (2) inconsistent with their prior knowledge about the world, or (3) unable to account for all of the information mentioned in the original scenario. Students discussed these explanations and their shortcomings, and the teacher provided the language for talking about model assessment criteria: she instructed them to seek explana- tory power, predictive power (which was discussed but not applied to the accident scenario), internal consistency (among elements within the model), and external consistency (between a model and one's prior knowledge or other models). Throughoutthe genetics unit, students were prompted to usethese criteria to evaluate their own inheritance models. Despite the explicit emphasis on consis- tency as a criterion for model assessment, however, we found that very few stu- dents actually judged their models this way. Instead, students valued explanatory adequacy, visual simplicity, and "understandability" more strongly. A closer look at the work of students in this study showed that most of them viewed models not as conceptual structures but as physical replicas, instructional tools, or visual rep- resentations. In fact, the common use of the term to describe small replicas—as in model airplanes—sometimes interferes with students' grasp of a causal model as a representation of a set of relationships. Similarly, when attempting to apply model assessment criteria to their explanations for data patterns in liquid poured from a box, several students treated "internal consistency" and "external consistency" literally: they evaluated the box's proposed internal components and the external phenomena (observations) separately Th is conf usion stemmed f rom students' prior understanding of concepts associated with the vocabulary we provided: clearly "internal" and "external" were already meaningful to the students, and their prior knowledge took precedence over the new meanings with which we attempted to imbue these terms. Given this misunderstanding of models, it was not surprising that our genetics students neither applied nor discussed the criterion of conceptual consistency within and among models.
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 519 itly addressed These ideas at The outset of The unit. In The genetics unit, teachers employ tasks early on that solicit students' ideas about scientific models and explicitly define The term "model" as it will be used in The science unit. Frequendy, teachers present sample models dhat purport to explain The phenomena at hand and ask students to evaluate These models. Teachers create models dhat have particular shortcomings in order to prompt discussion by students. Most commonly, students will describe The need for a model to explain all The data, predict new experimental outcomes, and be realistic (dheir term for conceptual consistency). Throughout the course, teach- ers return to These assessment criteria in each discussion about students' own inheritance models. A subsequent study has shown dhat These instructional modifications (along with other curricular changes in The genetics unit) help students un- derstand The conceptual nature of scientific models and learn how to evalu- ate Them for consistency with o her ideas 6 We now provide an example of an initial instructional activity—The black box—designed to focus students' attention on scientific modeling, As Chapter 1 suggests, children begin at a very young age to develop informal models of how things work in the world around them. Scientific modeling, however, is more demanding. Students must articulate Their model as a set of propositions and consider how those propositions can be con- fimmed or disconf rmed. Because tills more disciplined modeling is different Tom what students do in their daily lives, we begin The course with an activity that focuses only on The process of modeling. No new scientific content is introduced. The complexity of The task itself is controlled to focus students on the "modeling game" and introduce Them to scientific norms of argumentation concerning data, explanations, causal models, and Their rela- tionships. This initial activity prepares students for similar modeling pursuits in The context of sophisticated disciplinary content. Turing The first few days of The genetics course, The teacher presents The students widh a black box—eidher an actual box or a diagram and descnp- tion of a hypothetical box—and demonstrates or describes the phenomenon associated with it. For example, one box is a cardboard detergent container that dispenses a set amount of detergent each time it is tipped, while anodher is a large wooden box widh a funnel on top and an outlet tube at The bottom that dispenses water in varying amounts, shown in Figure 12-1. Once The students have had an opportunity to establish The data pattern associated widh The particular box in question, The teacher explains dhat The students' task is to determine what mechanisms might give r se to tills observable pattern L)unng This activity (which can take anywhere from 3 to 11 class penods, depending on The black box That is used and The extent to which students can collect Their own data), the students work in small teams. At the conclusion of The task, each team creates a poster representing its explana-
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520 HOW STUDENTS LEAFN: SC ENCE N THE C ASSFOOM 1- ~ If and so forth. Black Box A typical pattern of data would be: Water in (ml) Water Out (ml) 400 400 400 400 600 400 400 400 O 400 1000 400 O 400 400 FIGURE 12 1 One block box used in the MUSE science curriculum and ~picol doto patterns ossocio ad with he box tion for the box mechanism and presents it to the class. Classmates offer cnticism and seek clarification during these presentations As the dialogue below suggests, the exercise begins with students en- gaged in a central activity of scientists making observations. Teacher Making observations is important in science. I want you to observe this carton. Just call out what you notice and I will write it on the board. The students respond with a variety of observations: lan Delia Sarah Owen The box is white with blue lettering. The contents slosh around and it looks like liquid soap when we pou r it. Hey, it stopped coming out! Try to pour it again so we can see what happens. It always pours about the same amount then stops.
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 521 After several minutes of listening to the students, the teacher stops them and invites them to take a closer look at the carton, prompting them to identify patterns associated with their observations. Their reflection on these patterns leads the students to propose manipulations of the container, which in turn produce more observations. The teacher now inter upts them to guide their attention, saying: Teacher Okay, you've made some wonderful observa- tions, ones that you are going to be using in just a few minutes. But, there is more to science than making observations. Scientists also develop ideas of what is not visible in orderto explain that which is. These ideas are called models. She goes on to challenge them: Teacher imagine an invisible "world" inside the container that, if it existed in the way that you imagine, could be used to explain your observations. I want you to make drawings of your imagined world and maybe some groups will have time to develop a three-dimensional representation too. And, one last thing, I want each group to develop at least one test of you r model. Ask you rself, "If the wo rid inside the carton is as I imagine it and I do X to the carton, what result would I expects" Over the next two class periods, the students work in animated groups to develop models that can be used to explain their observations. They describe, draw, and create three-dimensional representations of what they think is in the carton. They argue. They negotiate. They revise. Then they share drawings of their models with one another Sarah Scott Delia Hey Scott, you have a different idea than ours. How does that flap works The flap is what stops the detergent from gushing out all at once when you tip it. Yeah, I get that, but does you r design allow the same amount of detergent to come out every times Because we tried a flap, too, but we couldn't figure out how to get the amount to be the same.
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522 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM The students also propose tests of their models: Sarah Delia Well, Scott is saying that the flap is like a trapdoor and it closes to keep the detergent in. But I think that if there is a trapdoor-like thing in there, then we should be able to hear it close if we listen with a stethoscope, rights Hey, Mrs. S., can we get a stethoscopes A visitor to the classroom would notice that Mrs s. listens attentively to the descriptions that each group gives of its model and the observations the model is designed to explain. She pays special attention to the group's inter- actior s with other groups and is skillful in how she converses with the students during their presentat ons. Through her comments she demonstrates how to question the models of others and how to present a scientific argu- ment. To one group she says, "I think I follow your model, but I am not sure how it explains why you get 90 milliliters of liquid each tme you tip the box." To another she comments, "You say that you have used something similar to a toilet bowl valve. But I don't understand how your valve allows soap to flow in both directions." And to a third group she asks, "t)o you think that lan's model explains the data? What question would you ask his group at this points" By the end of the multiday activity, the students are explicit about how their prior knowledge and experiences influence their observations and their models. They also ask others to explain how a pro- posed model is consistent with the data and challenge them when a compo- nent of a model, designed to explain patterns in observations, does not appear to work as described. This act vity creates many opportunit es to introduce and reinforce foun- dational ideas about the nature of scientific inquiry and how one judges scientific models and related explanations. As the class shares early ideas, the teacher leads discussion about the criteria they are using to decide whether and how to modify these initial explanati ons. Together, the class establishes that causal models must be able to explain the data at hand, accurately predict the results of future expenments, and be consistent with prior knowl- edge (or be "realistic") (see the example in Box 12-2). Through discussion and a short reading about scientific inquiry and model assessment, the teacher helps students connect their own work on the black boxes with that of scientists attempting to understand how the natural world works. This frame- work for thinking about scientific inquiry and determining the validity of knowledge claims is revisited repeatedly throughout the genetics unit. Other modeling problems might serve just as well as the one we intro- duce here. What is key is for the problem to be complex enough so that students have experiences that allow them to understand the rigors of scien-
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DEVEEDP NG UNDERSTAND NG THROUGH MDDEE BASED INDU FY 523 BOX12-2 Assessing Knowledge Claims in Genetics While working to revise Menders simple dominance model to account for an in- heritance pattern in which there are five variations (rather than two), many stu- dents propose models in which each individual in the population has three alleles at the locus in question. However, such a model fails to hold up when evaluated according to the criteria established during the black box activity because it is in- consistent with the students' prior knowledge about meiosis and equal segrega- tion of parental information during gamete formation: Teacher David Michelle David Michelle Chee David Chee I'm confused. I'm just curious. I'm a newcomerto this research lab and I see you using two alleles in some areas and three in other areas. We got rid of the three allele model. Cross that out. It didn't work. We didn't know how two parents who each had three alleles could make kids with three alleles. When we tried to do the Punnett square and look at what was happening in meiosis, it didn't make sense. Right. We thought maybe one parent would give the kid two alleles and the other parent would just give one. But we didn't like that. We had to stick with only two alleles, so we just made it three different kinds of alleles in the population. But now every person has only two alleles inside their cells. Right? Teacher in otherwords, you didn't like this first, three allele, model because it is inconsistent with meFosis7 tific modeling. In particular, the activity is designed to give students an op- portunity to do the following: · Use prior- knowledge to pose problems and generate data. When sci- ence teaching emphasizes results rather than the process of scientific in- quiry, students can easily think about science as truths to be memorized, rather than as understandings that grow out of a creative process of observ- ing, imagining, and reasoning by making connections with what one already knows. This latter view is critical not only because it offers a view of science that is more engaging and inviting, but also because it allows students to
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524 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM grasp that what we understand today can be changed, sometimes radically, by tomorrow's new observations, insights, and tools. By carrying out a mod- eling activity they see as separate from the academic content they are study- ing in the unit, students are more likely to engage in understanding how models are generated rather than in learning about a particular model. · Search for patterns in data Often the point of departure between science and everyday observation and reasoning is the collection of data and close attention to its patterns. To appreciate this, students must take part in a modeling activity that produces data showing an interesting pattern in need of explanation. . Develop causal models to act for patterns.7 The data produced by the activity need to be difficult enough so that the students see the mod- eling activity as posing a challenge. If an obvious model is apparent, the desired discourse regarding model testing and consideration of the features of alternative models will not be realized. · Use patterns in data and models to make predictions. A model that is adequate to explain a pattern in data provides relatively little power if it cannot also be used for predictive purposes. The activity is used to call students' attention to predictive power as a critical feature of a model. . Mace ideas public, and revise initial models in light of anomalous data and in response to critiques of others. Much of the schoolwork in which students engage ends with a completed assignment that is graded by a teacher Progress in science is supported by a culture in which even the best work is scrutinized by others, in which one's observations are complemented by those of others, and in which one's reasoning is continually antiqued. For some students, making ideas public and open to critique is highly uncom- fortable. A low-stakes activity like this introductory modeling exercise can create a relatively comfortable setting for familiarizing students with the cul- ture of science and its expectations. A teacher might both acknowledge the discomfort of public exposure and the benefits of the discussion and the revised thinking that results in progress in the modeling effort. Students have ample opportunity to see that scientific ideas, even those that are at the root of our most profound advances, are initially critiqued harshly and often rejected for a period before they are embraced. Learning Genetics Content Having provided this initial exposure to a modeling exercise, we turn to instruction focused specifically on genetics. While the core set of causal models, assumptions, and argument structures generated the content and learning outcomes for our genetics unit, our study of student understanding and reasoning influenced both the design and the sequencing of instruc- tional activities. For example, many high school students do not understand
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 525 the interrelationships among genetic, melotic, and biomolecular models, re- lationships that are key to a deep understanding of inheritance phenom- ena.9 To deal with this problem, we identified learning outcomes that ad- dress the conceptual connections among these families of models, and the models are introduced in a sequence that emphasizes their relatedness. Ini- tially, for example, we introduced genetic models, beginning with Mendel's model of simple dominance, first. This is typical of many genetics courses. In our early studies (as well as in similar studies on problem solving in genetics9), students often did not examine their inheritance models to see whether they were consistent with meiosis. In fact, students proposed mod- els whereby offspring received unequal amounts of genetic information from their two parents or had fewer alleles at a particular locus than did their parents.'° Because of their struggles and the fact that meiosis is central to any model of inheritance, we placed this model first in the revised curricular sequence. Students now begin their exploration of Mendelian inheritance with a firm understanding of a basic melotic model and continue to refer to this model as they examine increasingly complex inheritance patterns. A solid integration of the models does not come easily, however in early versions of the course, it became apparent that students were solving problems, even sophisticated ones, without adequately drawing on an inte- grated understanding of melotic and genetic models." In response, we de- signed a set of data analysis activities and related homework that required students to integrate across models (cytology, genetics, and molecular biol- ogy) when conducting their genetic investigations and when presenting model-based explanations to account for patterns in their data. By providing tasks that require students to attend to knowledge across domains and by structuring classrooms so that students must make their thinking about such integration public, we have seen improvements in their understanding of genetics.' We then focus on inheritance models, beginning with Mendel's model of simple dominance. Mendel, a nineteenth-century monk, grew generation after generation of pea plants in an attempt to understand how traits were passed from parent plants to their offspring. As Chapter 9 indicates, Mendel's work represented a major breakthrough in understanding inheritance, achieved in large part by selecting a subject for study—peas that had dis- continuous trait variations. The peas were yellow or green, smooth or wrinkled Peas can be self-fertilized, allowing Mendel to observe that some offspring from a single genetic source have the same phenotype as the parent plants and some have a different phenotype. Mendel's work con- fimmed that individuals can car y alleles that are recessive—not expressed in the phenotype. By performing many such crosses, Mendel was able to de- duce that the distribution of alleles follows the laws of probability when the pairing of alleles is random. These insights are fundamental to all the work
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556 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM and the extent to which students' knowledge is integrated). We have seen, time and again, teachers becoming aware of students' common struggles and beginning to "hear" dheir own students differently. Thus, an important feature of instructional activities dhat give students opportunities to make dheir thinking and knowledge public and Therefore visible to teachers is dhat they make assessment and instruction seamless. This becomes possible when students articulate The process of arriving at a solution and not simply the solution itself Because students struggle widh conceptual problems in The genetics unit, for example, we incorporate a number of assessments that require them to describe The relationships between models or ideas that they have learned (see Box 12-7). Whenever possible, we design formal assessments as well as written classroom tasks dhat redect The structure of students' work in The classroom. Our students spend a great deal of dheir class time working in groups, pouring over data, and talking with one another about dheir ideas. Thus, assessments also require dhem to look at data, propose explanations, and describe the thinking that led to particular conclusions. In The evolution course, students are required during instruction to use The natural selection model to develop t)arwinian explanations that account for mich data sets. To Then ask them about data or The components of natural selection in a multiple-choice format dhat would require them to draw on only bits and pieces of knowledge for any one question appears incomplete at best. Instead, we provide dhem with novel data and ask dhem to describe dheir reasoning about those data using the natural selection model—a task analogous to what they have been doing in class. An instance of tills type of assessment on The final exam asks students to write a t)arwinian explanation for The color of polar bear fur using information about ancestral populations. In tills way, during assessment we draw on students' ideas and skills as They were developed in class rather than asking students to simply recall bits of information in contrived testing situations. While assessments provide teachers with information about student un- derstanding, students also benefit from assessments that give them opportu- nities to see how their understanding has changed duling a unit of study. One method we have used is to require each student to critique her or his own early work based on what she or he knows at The conclusion of a course. Not only does This approach give teachers insights into students' knowledge, but it also allows students to glimpse how much their knowl- edge and their ability to critique arguments have changed. Students' consid- eration of dheir own ideas has been incorporated into The assessment tasks in bodh units. On several occasions and in different ways, students examine their own ideas and explicitly discuss how Those ideas have changed. For example, one of the questions on The final exam in evolution requires stu- dents to read and critique a t)arwinian explanation they created on The f 1 st
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DEVEEOF NG UNDERSTAND NG THROUGH MDDEE BASED INDU FY 557 BOX12-7 Sample Exam Question: Consistency Between Models This exam question is one of several tasks designed to produce evidence of stu- dents' understandings about the need for models to be consistent with one an- other and with the data they purport to explain. Below is a concept map that represents the relationships among specific models, models in general, and data. Use the map to respond to the tasks below. a. Remember that a line in a concept map represents a relationship between two terms (concepts, ideas, etc.) in the map. Write a few sentences that describe the numbered relationships between the terms given. Be as specific as you can: use the appropriate vocabu- lary of genetics to make your point as clearly as possible. b. Draw a line (not necessarily a straight one) to separate the world of ideas from that of observations on this map. Please label both sides. Justify your placement of that line. - ~ data ( scientdic~~~ ~C~\ \2 _ ~ a ~ ~ ( meiosis ~ \ ~ atomic ~ 3 I/ \ model j / / . / ~ 4 I pedigree Mendel `` day of class (see Box 12-8). We have found this to be one of the most powerful moments for many students, as they recognize how much their own ideas have changed. Many students are critical of the need-based lan- guage that was present in their original explanation, or they find that they described evolutionary change as having happened at the individual rather than the population level.
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558 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM BOX12-8 Examples of Students' Critiques of Their Own Darwinian Explanations On the first day of class, students were asked to explain how the carapace of Galapagos tortoises may have changed from the dome shape to the saddleback shape. As part of the f inal exam for the class, students were asked to critique the explanation they had given on the first day. Below are the original explanation and critique offered by one student. Original Answer The saddleback carapace came into being due to the need of migrating tortoises to adjust to a new environment. On Albermarle Island the domed shaped carapaces served wed for shedding rain and eating ground vegetation. However, when the tortoises began to migrate to a smaller, drier island with less ground vegetation, they had to adapt in order to survive. The majority of the food was now higher up and the domed shell served as a hindrance. Over time, the saddleback carapace developed to allow the neck to extend further, thereby allowing the tortoises to reach the fleshy green parts of the prickly pear cactus. This evolutionary process created a new species of giant tortoise that could live successfully in a new environment. Critique on Final Exam In my original answer, I used an almost exclusive Lamarokian definition of evolution. In my introductory statement I stated that the saddleback carapace came into being due to the need of the tortoise to fit its environment. I needed to acknowledge the existence of variation within the tortoise population of the shape of the shelf My original explanation makes the evolutionary process sound like a physical change taking place during the life of the tortoise and then being passed on to the of Spring. I now know that variations that are advantageous give animals a better chance of survival (survival of the fittest!) and allow them a better chance of passing on their advantageous trait to their of Spring. In my original explanation I also touched on ideas of use and disuse to explain how the saddleback carapace came to be, this is a Lamarkian model of evolution which is incorrect. I did explain how the saddleback carapace was an advantage because it allowed the tortoise to eat higher vegetation. Since I didn't understand evolution through the generations, I wasn't able to describe how the species changed over time. Overall, I would say I had a basic but flawed understanding of evolution but I lacked the tools to explain evolution from a scientific and Darwinian perspective, until now.
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU FY 559 Community-Centered As Chapter 1 suggests, The knowledge-centered, learner-centered, and assessment-centered classrooms come together in The context of a class- room community. The culture of successful scientific communities includes bodh collaboration and questioning among colleagues. It involves norms for making and justifying claims. At The source of The productivity of such a community is an understanding of central causal models, The ability to use such models to conduct inquiry, and The ability to engage in the assessment of causal models and related explanations. We have found that These out- comes can be realized in classrooms where students are full participants in a scientific community.34 Interestingly, one unexpected outcome of structur- ing classrooms so that students are expected to participate in The intellectual work of science has been increased involvement and achievement by stu- dents not previously identified as successful in science. In addition to establishing expectations for class participation and a shared framework for knowledge assessment, MUSE curricula promote metacognitive redection on The part of students by incorporating tasks that require discourse (formal and informal) at all stages of student work. While working in groups and presenting results to the class as a whole, students are required to share Their ideas even when those ideas may not be fully formed. Moreover, recall dhat The context for idea sharing is one in which discipline-specific criteria for assessment of ideas have been established. Thus, discourse is anchored in norms of argumentation that reflect scientific practice to The extent possible. Learning with Understanding While the four features of classroom environments can be described individually, in practice They must interact if students are to deeply engage in learning for understanding. High school students have had more Than 9 years of practice at playing The "game of school." Most have become quite adept at memorizing and reiterating information, seeking answers to ques- tions or problems, and moving quickly from one topic to anothe -. Typically during The game of school, students win when they present The correct answer The process by which one determines The answer is irrelevant or, at best, undervalued. The students described here are quite typical in this re- gard: they enter our genetics and evolution classes anticipating dhat they will be called upon to provide answers and are prepared to do so. In fact, seek- ing an end product is so ingrained dhat even when we design tasks Blat involve multiple iterations of modeling and testing ideas, such as within the genetics course, students frequendy reduce The work to seeking algorithms that have predictive power instead of engaging in The much more difficult
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560 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM task of evaluating models on The basis of their conceptual consistency within a family of related ideas s After studying how people solved problems in a variety of situations, Klayman and Ha36 noted The frequent use of what they call a "positive test strategy." That is, solvers would propose a model (or solution) and test it by attempting to apply it to the situation most likely to fit The model in The first place. If the idea had explanatory or predictive power, The solver remained satisfied widh it; if not, The solver would quickly test another idea. The positive test strategy was frequendy applied by students in early versions of our genetics course.37 This medhod of problem solving does not map well to scientific practice in most cases, however: it is The absence of disproving evidence, and not the presence of confirming evidence that is more com- monly persuasive to scientists. Moreover, testing a model in limited situa- tions in which one expects a data-model match would be considered "con- f~rmation bias" within scientific communities. Nevertheless, Klayman and Ha point out that this positive test strategy is often quite useful in real-life situations. Given our students' facility with the game of school and the general tendency to apply less scientific model-testing strategies when problem solv- ing, we were forced to create tasks That not only afford The opportunity for rejection, but actually require students to drink more deeply about The ways in which they have come to understand science concepts, as well as what is involved in scientific argumentation. We want students to realize that The models and explanations They propose are likely to be challenged and Slat The conflicts surrounding such challenges are The lifeblood of science. Thus, we explicitly discuss with our students The expectations for their participa- tion in The course. Teachers state that the students' task is not simply to produce an "answer" (a model in genetics or a t)arwinian explanation in evolutionary biology), but also to be able to defend and critique ideas ac- cording to The norms of a particular scientific discipline. In o her words, we ask The students to abandon The game of school and begin to play The game of science. Examination of ideas requires more than simply providing space for reflection to occur; it also involves working widh students to develop sys- tematic ways of critiquing their own ideas and those of odhers. This is why we begin each course with an activity whose focus is The introduction of discipline-specific ways of generating and critiquing knowledge claims. These activities do not require That students will come to understand any particular scientific concepts upon Their completion Rather, they will have learned about The process of constructing and evaluating arguments in genetics or evolutionary biology. Specific criteria for weighing scientific explanations are revisited throughout each course as students engage in extended inquir- ies within These biological disciplines.
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 561 SUMMARY For students to develop understanding in any scientific discipline, teachers and curriculum developers must attend to a set of complex and interrelated components, including the nature of practice in particular scientific disci- plines, students' prior knowledge, and the establishment of a collaborative environment that engages students in reflective scientific practice. These design components allow educators to create curricula and instructional materials that help students learn about science both as and by inquiry. The students in the biology classrooms described in this chapter have developed sophisticated understandings of some of the most central ex- planatory frameworks in genetics and evolutionary biology. In addition, they have, unlike many high school students, shown great maturity in their abili- ties to reason about realistic biological data and phenomena using these models. Moreover, they have accomplished this in classrooms that are struc- tured along the lines of scientific communities. This has all been made pos- sible by a concerted collaboration involving high school teachers and their students, university science educators, and university biologists. That MUSE combined this collaboration with a research program on student learning and reasoning was essential. With the knowledge thus gained, we believe it is possible to help others realize the expectations for improving science education that are set forth in reform documents such as the National Sci- ence Education Standards 39 In particular, there has been a call for curricu- lar refomms that allow students to be "engaged in inquiry" that involves "combin[ing] processes and scientific knowledge as they use scientific rea- soning and critical thinking to develop their understanding of science. 9 Recommendations for improved teaching of science are solidly rooted in a commitment to teaching both through and about inquiry. Furthermore, the National Science Education Standards do not simply suggest that science teachers incorporate inquiry in classrooms; rather, they demand that teach- ers embrace inquiry in order to: · Plan an inquiry-based science program for their students. . Focus and support inquiries while interacting with students. · Create a setting for student work that is flexible and supportive of science inquiry. . Model and emphasize the skills, attitudes, and values of scientific inquiry. It is just these opportunities that have been described in this chapter
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562 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM NOTES 1. We encou age readers to visit our website (www.wcerwisc.edu/ncusla/muse/). The site includes discussions of student knowledge and reasoning, intended learning outcomes, irst uctional activities, inst uctional notes, assessments, examples of student work, teachers' ref ections, and connections to the Na- tSonal Science Education Standards and Benclhmarksfor Science Literacy 2. Wiggirs and McTighe. 1998, Chapter 1. 3. Gross ight et al., 1991. 4. Gross ight et al., 1991; Harrison and T¢agust, 1998. 5. Cartier, 2000a. 6. Cartier, 2000b. 7. We consider a causal model to be an idea or set of ideas that can be used to explain pa ticular natu al phenomena. Mode s are complex constructions that consist of conceptual objects (e.g., alleles, populations) and processes (e.g., selection, independent assortment) in which the objects participate or interact. 8. Cartier, 2000a; Kindfreld, 1994; Wynne et al., 2001. 9. Kindfreld, 1994. 10. Cartier, 2000a. 11. Cartier, 2000a; Wy me et a ., 2001. 12. Cartier, 2000b. 13. Darden, 1991. 14. Mel osis is the process by which sperm and egg cells are formed. During mefo sis, ch omosomal replication is followed by two rounds of cell division. Thus, one cell undergoing meiosis produces four new cel 5, each of which contains ha f the number of ch omosomes of the onginal parent cell. 15. Kitcher, 1984, 1993. 16. Kitcher, 1984, p. 356. 17. Kitcher, 1984, p. 356. 18. Mendel, 1959. 19. Discontinuous traits are those for which two or more distinct categories of phenotypes (or vanants) am identified. For example, Mendel studied the trait of height in pea plants. He noted that the pea plants we¢ either short (18 in.) or tall (84 in.). In cont ast, height is not a discontinuous t art in humans: hu- man height is best characterized as continuously vanable, or nondisc¢te, be- cause humans are not simply either 18 or 84 in. tall. Thus, the phenotype categones for height in humans are not clear-cut. 20. Galley and Jungck, 2002. 21. Achondroplasia is indented in a codominant fashion. Individuals with two disease alleles (2,2) am severely dwarfed and seldom survive. Individua s who am heterozygous (1,2) am achondroplastic dwarfs, having disproportionately short arm and leg bones relative to their torsos. Thus while these two pheno types differ from nommal stature, they am distinct from one another 22. In the past, our students have developed the following explanations for pa tein action m traits inherited m a codominant fashion:
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DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 563 · One allele (designated 1) codes for an active protein. The other a ele codes for an inactive protein. Thus, individuals with genotype (1,1) have the greatest amount (or dose) of active protein and the associated phenotype at the organismal level. Individuals who are (2,2) have little or no measurable protein activity, and this is Of e ted in the phenotype. Hetetozygous individu- a s (1,2) have an intermediate level of protein activity and a phenotype that is a so intermediate. For example, in the case of achondroplasia, (1,1) individu- a s would have two alleles for a g owth receptor and a phenotype of normal stature; (2,2) individuals would have few or no functional receptors and suffer from severe growth retardation. and heterozygotes (1,2) would have ha f as much g owth receptor activity as the (1,1) individua s and consequent y be short-statured achondroplastic dwarves without the additional health prob ferns of the (2,2) individua 5. This example of codominance is admitted y sim- plified, as students do not study the systemic effects of achond oplasia. How- ever, this model is applied widely in genetics and sometimes referred to as the "dosage" model. . Both alleles code for active proteins, giving use to observable pheno types at the macroscopic level. Hetetozygotes display the phenotypes associ- ated with both alleles. For example, in human blood types, individua s car y- ing alleles for protein A and protein B have both of these proteins on their blood cel 5. The phenotype is not blended or dosage dependent as in the achondroplasia example above. Instead, both proteins are detected intact in hetetozygous individuals. 23. Cartier, 2000a, 2000b. 24. White and F¢deriksen, 1998, p. 25. 2S Calnel 2000a' 2000b 26. Mayr, 1982, P. 481. 27. Kitcher, 1993, pp. 20 21. 28 Richards'1992' p 23 29. O'Hana, 1988. 30. Mayr, 1997, p. 64. 31. Bishop and Anderson, 1990; Demastes et al., 1992, 1995, 1996. 32. Bishop and Anderson, 1990. 33. Carder, 2000a, 2000b; Passmo¢ and Stewart, 2002. 34. Carder, 2000b; Passmore and Stewart, 2000. 35. Carder, 2000a. 36. Klayman and Ha, 1987. 37 Carher, 2000a 38 National Research Council, 1996. 39 National Research Council, 1996, p. 105. REFERENCES Bishop, B.A., and Anderson, C.W. (1990). Student conceptions of natural selection and its role in evolution. Journal of Resear
564 HOW STUDENTS LEARN: SC ENCE N THE C ASSFOOM Calley, J., and JunF;ck, JR. (2002). Genetics construction kit. (The BioQUEST Library IV, version l.lB3) [Computer software]. New York: Academic Pess. Cartier, J.L. (2000a). Assessment of explanatory models in genetics Insights into stu- dents, conceptions of scientific models Research report 98-1 for the National Center for Impmving Student Leaming and Achievement in Mathematics and S cience. Ava Cable: http: //www. wcer.wis c.e du/ncisla/publi cati ons/ma in. html#eports/RR98-l.pdf [accessed February 3, 2003]. Cartier, J.L. (2000b). Using a modeling approach to explore scientific epistemology u ith kigk school biology students. Research report 99-1 for the National Center for Improving Student Leaming and Achievement in Mathematics and Science. Avai able: http ://www.wcer.wisc.edu/ncisla/publications/reports/RR99 1.pdf [accessed February 3, 2003]. Darden, L. (1991). Theory change in science Strategies from Mendelian genetics New York: Oxford University Pess. Demastes, S., Good, R., and Peebles, P. (1995). Students' conceptual ecologies and the process of conceptual change in evolution. Science Education, 79 6), 637- 666 Demastes, S., Good, R., and Peebles, P. (1996). Patterns of conceptual change in evolution. Journal of Research in Science Teaching, 33 4), 407-431. Demastes, S.S., Tmwbndge. J E., and Cummins, C.L. (1992). Resource paper on evo lotion education research. In R.G. Good, J.E. Trowbridge. 5 5. Demastes, J.H. Wande see, M. S . Hafner, and C. L. Cummins (Eds .) , Proceedings of the l 992 Eve - lutSon Education Conference, Louisiana State University, Baton Rouge, LA. Gmsslight, L., Unger, C., Jay, E., and Smith, C.L. (1991). Understanding mode s and their use in science: Conceptions of midd e and high school students and ex- perts. Journal of Research in Science TeackSng, 28, 799 822. Harrison, A.G., and Teagust, D.F (1998). Modeling in science lessons: Are there better ways to learn with mode s? School Science and Mathevwtics, 98 8), 420 429. Kindfreld, A.C.H. (1994). Understandir.g; a basic biological process: Expert and nov- ice models of met as is Science Education, 78 3), 255-283. Kitcher, P. (1984). 1953 and all that: A tale of two sciences. The Phik~sophical Review, 93, 335-373. Kitcher, P. (1993). The advancement of science Science u ithorrt legend, objectivity u ithout illusions. New York: Oxford University Press. K ayman, J., and Ha, Y. (1987). Confirmation, discon immation, and information in hypothesis testing. Psychological Review, 94, 211-228. Mayr, E. (1982). Tlhegrowtlh of biological thought Diversity, evolution, and Snkerit- ance Cambridge, MA: Be knap Pess of Harvard University Pess. Mayr, E. (1997). This is biO.70gl~ Tl~escSenceoftl~elSvingworld. Cambridge, MA: Be knap Press of Harvard University Pess. Mendel, G. (1959; Original pub Cation date 1865). Expenments on plant hybndiza- tion. In J. Peters (Ed.), CSassScpapers SngenetScs. Upper Sadd e River, NJ: Prentice Hall.
DEVEEOF NG UNDERSTAND NG THFDUGH MDDEE BASED INDU BY 565 National Research Council. (199 7:). National science education standards. National Committee on Science Education Standards and Assessment, Center for Science, Mathematics, and Engineering Education. Washington, DC: National Academy Pass. O'Hata, HJ. (1988). Homage to C lo, or, toward a histoncal phi osophy for evolution- arybiolo,gy. Systematic Zoology, 37, 142-155. Passmore, C.M., and Stewart, J. (2002). A modeling approach to teaching evolution- ary biology- in high schoo s. Journal of Research in Science Teaching, 39, 185- 204. Richa ds, RJ. (1992). The st uctu¢ of narrative explanation in history and biology-. In M.H. Nitecki and D.V Nitecki (Eds.), /]Sstory and evolution (pp. 19-53). Albany, NY: State University of New York P¢ss White, B.Y, and Fredenksen, J R. (1998). Inqui y, modeling, and metacogmtion: Mak- ing science accessible to all students. Cognition and Instruction 16 3 118 Wiggins, G., and McTighe, J. (1998). Understanding ~ design. Upper Sadd e River, NJ: Me dll/Prentice-Ha 1. Wy me, C., Stewart,J., and Passmo¢, C. (2001). Hi,ghschoolstudents'useofmefosis when solving genetics problems. The InternatSonalJournal of Science Educa- tSon, 23 5), 501-515.
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