In this work, conceptual meanings for reproduction that were developed early in students’
activity were essential for supporting more sophisticated modeling practices later in the year. In the excerpt described in Phase 4, students applied reproduction to make sense of a complex comparison with numerous potential outcome measures, participated in theoretically justifying its use as a measure, and challenged the operationalization of the measure. It is not a stretch to conjecture that this level of sophistication of practice would have been significantly less likely to occur without a shared conceptual basis from which to build. Knowledge might play a similar supportive role when students select questions to study or choose and argue which features of a complex system, and in what ways, to represent in a microcosm. While the integral role of knowledge in practice is already well-accepted, studies might do more to specify what conceptual understandings are called for to support desired forms of practice and how established understandings might be recruited for nascent practices.
The integration of these three model-concept relations across the year of activity begins to explain why students developed flexible notions of reproduction that were grounded in practice. The idea of reproduction resurfaced in multiple contexts, accomplishing related, but somewhat different work in each. Seeds emerged, and were amplified by representations, early in the year. It is likely that the extended, multi-faceted nature of students’ scientific enterprise meant that seeds and reproduction continued to be powerful for students throughout their
activity, with subsequent investigations nested within the overarching goal of explaining how the plants got into the backyard. The multiple uses for reproduction in relation to students’ activity seemed to promote its stabilization in a network of shared practice. Nersessian and Patton (2009)
argue that scientific activity is supported by interlocking models that include concepts, devices, and instruments. The participants in the study seemed to form a similar network, encompassing concepts, devices, instruments, and both mental and physical representations.
This conjecture is strengthened by comparing students’ ideas of reproduction and the other focal concept, the needs-condition relation. As explained in the methods section, interview analysis demonstrated that students used the needs-condition relation less flexibly than ideas of reproduction. This concept was used by students in the post-interview only in those questions that were most parallel to the model investigations that they had used in the classroom, and these model investigations were almost always cited when students talked about the relation of needs to conditions in the backyard. In contrast to our early use of representations that made
reproduction visible, we did little to represent the conditions in the backyard in relation to the needs of plants until later in the year. The concept of the relation of needs to conditions was represented only in the model system used to test the effect of conditions, introduced in March.
A major focus of redesign in the next iteration of this design study was to represent and problematize place (as a conceptual resource for conditions) much earlier in the year,
repositioning ideas of conditions in relation to practice in multiple ways throughout the year and exploring the effects of this alteration to the design.
There is much work yet to be done in understanding the complexities of this perspective on the development of content knowledge and determining how best to support teachers in orchestrating it. The models and representational activities reported here—sharing questions, reading books, testing seed dispersal, and investigating growth conditions—are typical of
elementary school science education. However, both the extended nature of the investigation and the complex interrelation of learning goals present significant challenges for educators. These
include understanding which representations are likely to provide fruitful entry points for student practice, developing trajectories for increased student participation in modeling decisions, and using recurrent forms of support that push on student practice without entirely constraining it.
These are serious challenges, but the results in students’ thinking and expanded access to sophisticated forms of practice suggest that they are challenges worth pursuing.
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CHAPTER IV
OPENING UP LIGHT BOXES AND BLACK BOXES:
INITIATING YOUNG STUDENTS INTO EXPERIMENTATION AS A MODELING ENTERPRISE
Introduction
Understanding a scientific idea involves knowing something about how it is made and the purposes for which it is useful. Accordingly, science education is increasingly organized around engaging students in scientific practices, positioning them as makers of knowledge. However, there is significant uncertainty both about how to initiate students into these forms of practice and how domain knowledge and participation in practice should be integrated in instruction (Corcoran, Mosher, & Rogat, 2009; Duncan & Hmelo-Silver, 2009; NRC, 2011). I address these challenges in the context of third grade students modeling plant diversity in a backyard
ecosystem. In the course of their investigation, students implemented an experiment to understand whether patterns of sun and shade cast by their school wall could account for the distribution of plant types that they observed. I explore how the experiment served as a productive site for the development of multiple scientific practices (e.g. experimentation, argumentation, and measurement), as well as important ecological ideas (e.g., plant needs and reproductive success).
To date, research has tended to focus on identifying important practices and developing supports for initiating students into those forms of practice. One method for providing support is to explicitly teach a practice’s structure, for instance, by directing students to write an argument in the form of claim, evidence, and reasoning (McNeill, Lizotte, Krajcik, & Marx, 2006), or to use a control-of-variables strategy to plan an experiment (Chen & Klahr, 1999). Another is to
reduce the complexity of the setting; students might be asked to argue about social questions before they address scientific questions (Kuhn, 2010) or to interpret small data sets without irrelevant variables before tackling complex data (Berland & McNeill, 2010). However, these approaches have drawbacks. First, students can adopt the structures of taught practices without understanding their purposes (Berland & Reiser, 2009; Jimenez-Aleixandre, Rodriguez, &
Duschl, 2000). For example, Kuhn and Pease (2008) found that students sometimes control variables without realizing that the purpose for doing so is to find something out. Second, practices such as investigation, explanation, and argumentation are often investigated
independently, an approach that provides little guidance about how to exploit their relationships with each other. Moreover, highlighting and simplifying practices can lead to their decoupling from core disciplinary ideas, so that students learn about a domain idea, such as plant needs, on one day, then a practice on another, or use either ideas or practices as a superficial context for the other, as has characterized the enactment of inquiry science curricula (Davis, Petish, & Smithey, 2006; Enfield, Smith, & Grueber, 2008; Furtak & Alonzo, 2010). Increasingly, the field is turning its attention to understanding the long-term development of practices in contexts where scientific knowledge is being fostered and the practices have genuine utility for students (Cavagnetto, 2010; NRC, 2011; Ryu & Sandoval, 2012).
To contribute to this agenda, I sought to design a learning environment that would highlight uses, rather than forms, of practices and ideas; establish a context in which these functions would be sensible to students; and situate the co-development of practice and
knowledge. These goals were informed by treatments of knowledge and practice that emphasize their co-origination in meaningful activity (Lave & Wenger, 1991; Vygotsky, 1978). Ways of talking, acting, and using ideas have stable expressions in established disciplines, but their forms
and functions are interactionally accomplished in communities as members attempt to align their behavior and accomplish goals (Hutchins, 1995; Saxe & Esmonde, 2005; Wenger, 1998).
Similarly, for students to develop a “grasp of practice” (Ford, 2008), they must experience those practices as powerful for knowing something and see reasons for refining them.
With these goals in mind, I engaged students in using an experiment to understand the diversity of plant life in the backyard setting and allowed them to grapple with the challenges entailed in using an experiment to develop explanations. Both the topic—plants’ need for light—
and the form of investigation—an experiment—are typical foci for elementary school students.
However, the approach taken here differed from typical treatments, which use experiments either to demonstrate facts about plants (e.g., their need for light or stages of growth) or to teach the structure of controlled variation. Instead, I treated the experiment as embedded in a modeling enterprise. I sought to engage students in constructing and critiquing the experiment as a model of the backyard that allowed them to “know” something about it. In the following section, I describe how I frame science as a modeling enterprise and explore the implications for supporting and representing students’ practice. Next, I contextualize this approach within the backyard setting.
Science as a Modeling Enterprise
Constructing, testing, and revising models are at the heart of the scientific endeavor (Giere, 1990; Grandy & Duschl, 2007; Nersessian, 2008). In the National Research Council’s (2011) Framework for K-12 Science Education, modeling is identified as one of eight scientific practices in which students should participate. Indeed, several science educators argue that it is not one practice among others, but the central scientific enterprise (Lehrer & Schauble, 2006;
Passmore & Stewart, 2002; Windschitl, Thompson, & Braaten, 2008). From this perspective, what unites scientists’ work across disparate domains is the development of representations that simplify aspects of the world, facilitating description, prediction, and explanation (Giere, 1999).
Latour (1987, 1990, 1999) points out that modeling involves a series of transformations at each stage of which phenomena are reduced: some aspects are selected, and others fall away.
In turn, reduction promotes amplification, in that the phenomena of interest are made more visible, manipulable, transportable, and subject to calculation and standardization. For example, understanding the activity of an endorphin might entail choosing the guinea pig as a model, isolating its gut, injecting hormones to study interactions, making contractions visible by hooking them up to a stylus, then publishing the inscription made by the stylus and using it to support statements about endorphin behavior (as described in Latour, 1987). Each of these transformations, from mammal to guinea pig, guinea pig to guinea pig gut, gut to gut
contractions, contractions to marks on a paper, and marks on a paper to an account of hormone function, is an act of modeling with epistemic payoff, in that some aspect of the phenomenon is made more visible. “Knowing” about endorphin activity entails making (or accepting) the representational chain stretching from the statement about endorphins through the inscription to the piece of gut in a laboratory, taking each object to stand in for the previous one in the chain.
As such, engaging in scientific activity entails building a system of carefully stacked “epistemic levels” (Kelly & Takao, 2002).
These transformations are conjectural and become stabilized in relation to each other in what Pickering (1995) calls the “mangle of practice.” Scientists have to make guesses about how to make aspects of the world visible, then variabilize, manipulate, and relate them even before they fully understand them (Gooding, 1990; Shapin & Schaffer, 1985). When something goes