The Empirical Attitude, Material Practice and Design Activities

Abstract

This article is an argument about something that is both important and severely underemphasized in most current science curricula. The empirical attitude, fundamental to science since Galileo, is a habit of mind that motivates an active search for feedback on our ideas from the material world. Although more simple views of science manifest the empirical attitude through relation of theories to data, we describe more recent philosophical scholarship that characterizes the relation of theories to data through phenomena (regularities in nature’s behavior that can be identified and characterized through data). This view highlights the centrality of material practice, in which scientists design data collection events to inform phenomena. Thus manifestation of the empirical attitude in science is characterized as a design endeavor that involves considerably sophisticated coordination among theories, phenomena, data, and data collection events. If we want students to learn how to participate in such work, curricula should break down these complex processes into more basic components at least at the outset. Our recommendation is to begin with design activities that can focus on the empirical attitude initially without the complex coordination with phenomena and data. We present an example of such an activity and share results that suggest design activities can target the empirical attitude and be built upon in curricula to gradually include coordination with phenomena and theories.

1 Introduction

Since Galileo, the success of scientific ideas can be attributed to the empirical attitude, a habit of mind to actively search for feedback on one’s ideas from the material world. Despite its centrality to science, the empirical attitude as a learning goal is severely underemphasized in most science curricula. This article outlines an argument about how to begin supporting even elementary or middle school students to learn the empirical attitude through design activities. We do not argue that design is the same as science; on the contrary in what follows we will distinguish design from science in fundamental ways. In doing so we draw on philosophical literature to argue that the empirical attitude is manifested in science through a special case of purposeful design—that is, design of data collection events.¹

To understand the role of design in science and as a manifestation of the empirical attitude, we will draw on philosophical literature about the structure of empirical support in science. In sum, we will point out how recent developments in philosophy of science have considerably shifted discussion from the relationship between theory and data to a more complex structure of empirical support that involves the notion of phenomena (Bogen and Woodward 1988) and data collection events comprised by specialized material arrangements (Hacking 1983; Matthews 2004). This enriched vision of the structure of empirical support in science will enable a richer view of the reasoning scientists engage in while seeking empirical support for their ideas. Specifically, this reasoning is on the design of data collection events that yield evidence to identify and characterize phenomena.

The empirical attitude is both a habit of mind and a reasoning ability. Whereas as a habit it is enough to say the empirical attitude underlies a disposition to check one’s posited ideas with the way nature actually behaves, considering it as a reasoning ability requires definition of what is reasoned about and how. Characterizing the empirical attitude through the design of data collection events allows us to appreciate the sheer complexity of this reasoning ability, particularly when we acknowledge how, as manifested in science, it involves coordination of potential data collection events with theories, phenomena, and the data that might result from those events. In science, the empirical attitude is manifested as a second order search for feedback—that is, scientists are sensitive to feedback from the material world on their way to designing the best data collection event, which once achieved is itself a way to seek feedback from the material world for their claims. Understanding this complexity in scientific reasoning helps us realize why it is so difficult for students to learn.

We argue that in order to exert sustained attention on the empirical attitude across students’ educational careers, it could be taught consistently as a habit of mind, albeit in different contexts, but should be taught as a reasoning ability to develop from simple to more complex forms. That is, because of the complexity of its manifestation in science, we should begin in elementary and middle school with less complex manifestations of it with an assumption that its complexity in science then could gradually be taught. Thus we posit that supporting the empirical attitude could begin through design activities, which represent a first order search for feedback—meaning, in pure design tasks, feedback from the material world is sought only toward optimizing the design, not in order to obtain feedback on ideas. We will share student performances on a sample design activity to illustrate how the empirical attitude is manifested in pure design activities and therefore could be used to support initial stages of development. With this foundation, further activities could engage students in design for purposes of science, rather than design for itself, by adding coordination with theories, data and phenomena.

Our argument will be organized as follows. First, we will articulate clearly what we mean by the empirical attitude, pointing out the ways in which the typical science curriculum focuses insufficiently on its development. Then we will present a revision of the way we think of the scientific work involved in relating the way nature behaves, as seen in data, to our ideas about nature (i.e., theories, models, and the like). The key point in this revision is the important role of what we call material practice plays in this empirical aspect of science. We will then highlight how material practice in science is a particular kind of design work, drawing on both experimental and observational examples. Finally, we elaborate on the possibility that pure design activities may be a reasonable way to begin supporting students’ development of empirical attitude as a habit of mind and reasoning ability. To support this assertion, we present results from student performances on a design task—which was to build as high a ‘building’ as possible out of 54 wooden blocks to withstand a simulated earthquake. Our analysis highlights features of these performances that distinguish more successful from less successful students, and suggests that the empirical attitude can account for these differences. We then close by articulating more broadly how this rethinking of the relationship between science and design may suggest future research and curricular revision.

2 The Empirical Attitude

The empirical attitude is comprised by an active, inquisitive motivation to explore the way nature behaves in ways that inform our ideas about it. This attitude entails first and foremost the acknowledgement that ideas need to be tested against nature. It thus entails fundamentally an openness to changing ideas, that is, to learning. Finally, the empirical attitude entails an active search for information from the natural world that will teach us something new.

How is the empirical attitude manifested in science? Given what we know from recent work in philosophy of science, it would be a profound oversimplification to characterize the empirical attitude as merely relating theory to data or even theory to experiment. Rather, Bogen and Woodward (1988) articulated a view in which data inform phenomena, and phenomena inform our ideas about nature. This not always, but often manifests itself as an interest in intervening, building, and ‘tinkering’ with nature (Hacking 1983), and designing data collection events (Matthews 2004), what we will elaborate on below as ‘material practice’ in science (c.f., Pickering 1995). In this way, we characterize the way the empirical attitude is manifested not only in the structure of empirical support, but also in scientific reasoning, as a coordination among theory, phenomena, data, and the data collection events that would yield them. It is probably the case that this reasoning becomes simplified in some more focused material work to coordination among data collection events, data, and phenomena. We will elaborate more on this point in the next section.

The empirical attitude is a central aspect of science, thus it is worthwhile to rethink the extent to which it is taught in science curricula. Even a quick look at typical science curricula would reveal how very little of the empirical attitude is taught. Consider first that most content is taught without sufficient reference to data. As we know, textbooks are better at explaining scientific ideas with use of examples and the like than they are at presenting actual data that relate to them (Duschl et al. 2007). Second, consider that when data are provided, they are quite different from the data scientists work with. Rather than being ‘messy’, plagued with measurement error and ambiguity, data that appear in curricular materials are ‘cleaned up’, essentially simplified, in order for the ideas’ elucidation through them to be clear.

Most importantly, science curricula rarely, if ever, pose students with the task to design ways to collect data in order to inform their ideas. Consider that laboratory experiences, rather than being a window into actual scientific work, are more accurately characterized as guided construction to demonstrate a foregone conclusion (Singer et al. 2006). This is not to say that such demonstrations are not useful for some learning goals—coming to understand the ideas at play, for example. But they do not support development of the empirical attitude. Osmosis and diffusion, for example, are demonstrated through laboratory activities in which solutions with different concentrations influence the movement of solute and solvent across a permeable barrier, typically a special kind of plastic bag. It is never the case that the notions of osmosis and diffusion are being tested—the results of a lab, even if they come out as incongruent, are never considered a basis for rejecting those ideas. We accept osmosis and diffusion as correct, and assume some technical error was made in carrying out the data collection and analysis if results do not confirm these.

It is precisely the task of discerning whether some sort of technical error is reflected in data or not that is often at the center of scientific work. If the way science is taught frames this problem not only as inconsequential but even as dismissively trivial, then science is being distorted fundamentally. Moreover, as we elaborate below, this kind of work is a key reflection of the empirical attitude, and as such, should be taught in the curriculum. We do not believe that recasting laboratory activities would be desirable or sufficient for teaching this aspect of science. It may not be desirable because as demonstrations, these traditional labs may successfully support understanding of key ideas, like osmosis and diffusion. It may not be sufficient because the empirical attitude permeates science, and therefore may require a much more extensive rethinking of how the curriculum could support it.

We believe that design activities may play a role in supporting the empirical attitude, not because they are sufficient, but because they allow both relatively easy access to the empirical attitude and, at least theoretically, may also set up students for next steps in its development. That is, design activities that aim to produce an artifact that functions in a particular way may be a reasonable precursor to design activities that aim to produce data that can inform ideas about nature. We posit this more than anything else to emphasize the need for rethinking the curriculum if we accept the importance of the empirical attitude as a learning goal and the centrality of design in science’s material practices. Before turning to these arguments, in the next section we elaborate on material practices in science and the way to consider design as a central aspect of it.

3 Material Practice in Science as Design

In the relatively recent past, when science educators and developmental psychologists have addressed scientific reasoning, it has been conceived in terms of coordination between ideas (theories, models, mechanisms, etc.). and data (e.g., Klahr 2000; Kuhn 1991; Kuhn 2001; Passmore and Stewart 2002). Recent work in philosophy of science enriches this picture in two key ways. First, rather than data being related to ideas, Bogen and Woodward (1988) articulated a view in which data inform phenomena, and phenomena inform our ideas about nature. Bogen and Woodward’s notion of phenomena is thus an important intermediary between ideas and data in scientific reasoning and in practice. Second, the work involved in relating data to phenomena involves careful framing and measuring of nature (e.g., Latour 1999; Pickering 1995) through design of data collection events (Hacking 1983; Matthews 2004).² Under this view, data often are not the result of unproblematic observation, with instruments or not, but rather result from an iterative process of designing events, trying out measurement techniques, and refining these in order to identify and characterize a phenomenon as clearly as possible. Bogen and Woodward’s (1988) notion of phenomenon allows us to distinguish the conceptual aspects of scientific practice from the material aspects (See Fig. 1).

Fig. 1

Science as conceptual and material practices

Others have highlighted the way in which theoretical frameworks lead to the collection and characterization of different kinds of data (e.g., Duhem 1908/1969; Hanson 1958). The argument here goes beyond this. Scientists cannot make data to be whatever they want them to be, in the sense that would lead to relativism. But they do focus their efforts on figuring out how to zero in on data reflective of the behavior of nature, on precisely the aspect that is salient for a theoretical claim. This ‘zeroing in’ on a particular aspect of how nature behaves requires framing, that is, collecting information on the right aspect of nature’s behavior and it also requires measurement, which, beyond quantification of this aspect, also involves removal of error. Both framing and measurement are iterative processes. This work we refer to as ‘material practice’, work that is fundamentally a special kind of design activity.

3.1 Centrality of Material Practice in Science

In what follows, we argue that material practice is both a central part of science and should be taught in science curricula. We further argue that material practice is fundamentally a special kind of design work, and we suggest that student engagement in design activities may represent a way of providing initial access to the empirical attitude. Here we provide a few examples from the history of science to support the notion that material practice is central to scientific work.

Perhaps the centrality of material practice is most obvious in domains that rely on expensive equipment in order to extend the senses. Probably because of this, Pickering (1995) articulated his notion of material practice with the case of the bubble chamber in subatomic physics. The bubble chamber is an instrument that allows the collection of data about these particles, which because of their size do not lend themselves to easy observation (or, in fact, direct observation at all). The bubble chamber exploits facts about nature—in particular, that when accelerated subatomic particles pass through a pressurized liquid, they create tiny bubbles of gas. These bubbles are used to infer the path of these particles, through high-speed photography that captures the states of the bubbles at key points in time most informative to the scientists.

Other examples are less obvious. Drake (1978) noted that Galileo invested significant effort building inclined planes with properties desirable for informing his theorizing about free fall. These properties, as Galileo learned, involved a groove on the plane, smoothness of the groove, a weighty ball, and cords that were movable up and down the ramp. The smoothness of the groove and the weight of the ball (not to mention its shape) minimized the friction and maximized the way in which the data collected from the inclined plane would actually be caused by the phenomenon of acceleration caused by gravity. With this apparatus, Galileo was able to make good on his guiding assumption that, regardless of the plane’s inclination, ‘nature is written in the language of mathematics’. Meaning in this case, the way in which objects are accelerated by gravity can be described by the distances traveled over equal time intervals as the odd numbers from 1 (Galilei 1638/1974).

Although it might be tempting to identify material practice as a trivial aspect of science, it is far from it. The norm that ideas be ‘explicitly connected’ to nature (Rouse 1996) is probably at least partly responsible for the stunning successes of science as we know it today. Consider, for example, Galileo’s position that painstaking work collecting data and identifying the phenomenon of constant acceleration on inclined planes could be informative to our understanding of nature. Notice that whereas Galileo was concerned with theory in terms of his inclined planes being motivated by an argument about Copernican theory, his work was designing data collection events on those planes to characterize this phenomenon of constant acceleration. Although it may seem obvious in hindsight, this was not at all accepted by all of Galileo’s peers to be a promising way to build a science. In contrast, Descartes focused more on logical inference, a position that highlighted rigorous theorizing as the path to reliable knowledge. This is not to say that theorizing, or as we characterize it, conceptual practice is not an important part of science, as the work of Newton and Einstein attests. We merely wish to highlight material practice as a part of science at least as important, if less glamorous.

Quite often, it is precisely material practice that is the work through which scientists sort out controversies. Collins and Pinch (1993) described several high profile cases in which careful measurements were (and are still) at the center of scientific debates about phenomena. For example, the currently unresolved question of whether gravity waves exist is being pursued through construction of larger and better instruments that may (or may not) detect these posited waves (predicted by movements of great masses through space). Laser Interferometer Gravitational Wave Observatory (LIGO) sites employ very long perpendicular laser beams (about 2.5 miles to a side) that, according to theory, should become offset slightly when a gravity wave passes through them. There are two sites, one in Louisiana and the other in Washington. Given the distance between them, a wave may pass through one up to ten milliseconds before the other. The recorded difference can be used to identify the cosmic origin of the wave through a triangulating analysis.

We share these details not to be tedious, but to illustrate the centrality of material practice to science. Scientists probe and learn about nature by designing particular kinds of material arrangements that can produce data that inform particular phenomena, (e.g., the passing of a gravity wave). These phenomena are then scrutinized in terms of their causal influences (e.g., the time difference that long laser beams become ever so slightly offset in Louisiana and Washington) to infer features of cosmic events far off in space (e.g., supernovas).

3.2 Material Practice as Design

To illustrate our claim that material practice can be considered a special kind of design task, we briefly elaborate how framing and measurement are part of the bubble chamber and LIGO work. Both framing and measurement involve design, not only because in these two cases sophisticated artifacts were constructed. On a more fundamental level, scientists, when framing or devising ways to ‘see’ a phenomenon they have already posited, need to anchor their reasoning in material artifacts and what they know about their constraints and affordances. Moreover, and perhaps more convincingly, we argue that when iteratively working to identify phenomena in data by removing other sources of error from those data, they similarly need to reason about constraints and affordances of the material world. When doing this work, scientists, like engineers and other designers, do not always know everything they need to know about how the material will behave during their prized events. Therefore, a key aspect of their attitude needs to be not only openness but also an active search for feedback that tells them whether the event they believed would reveal a phenomenon actually does.

Scientists imagined a phenomenon by which accelerated subatomic particles would turn pressurized liquid into a gas and then constructed a bubble chamber to bring this about. After its construction, scientists needed to refine aspects of the events that played out in that chamber (separation and movement of subatomic particles) in order to ‘see’ these events better. Toward this end, they had to refine both the event itself and the method of data collection in ways that minimized influences on the data that were unrelated to the phenomenon (i.e., what we might call errors of various sorts). For example, a lot of work went into getting the pressure right in the chamber so bubbles would appear at a large enough scale to be photographed well. Similarly, a lot of work went into getting the images captured to clearly depict the paths of those particles (work that involved special cameras, lighting, and the like).

Consider LIGO. Because the passing of a gravity wave is inferred from a slight movement of a laser beam, an elaborate and expensive apparatus of movement dampers was installed as a key feature of LIGO. If a shake is caused not by a gravity wave, but by traffic, wind, or something else, then the data do not inform the astronomer’s understanding of supernovas. Given the arrangement of material that constitutes the apparatus, a similar shake detected by both LIGO sites would be convincing that the data reflected a gravity wave. Getting this to be so is far from trivial.

Both framing and measurement in material practice reflect how design is a central part of science. This does not mean that design and science are the same. In fact, this design aspect of science is different from other design domains (e.g., engineering), and can be thought of as a special case of design. Whereas our typical vision of design is an endeavor to create artifacts that function in practically useful ways, the aim of design in science aims for artifacts that function to yield data about phenomena. This difference in function makes the material practice of science a special case of design—rather than aiming for a practical use, it is aiming at a conceptual use.

Even so, the key parts of the process of design are remarkably similar both in and out of science, and these parts are centrally reflective of the empirical attitude. An arrangement of material is imagined, and salient features of this arrangement are identified. It is posited that these features will yield the ‘behavior’ that is sought for, a playing out of mechanisms in the physical world that will be informative or practically useful. Both in and out of science, positing the material arrangements is just the first step because unforeseen aspects of the material’s interactions invariably become apparent once the building begins. Both in and out of science, the material arrangements must be revised in order to improve on the artifact’s ‘behavior’. In science, this involves adjusting things and setting up the most detailed ways of collecting feedback. Out of science, this involves the same. Whereas out of science, the feedback is sought to zero in on a practical function of the artifact, in science feedback is sought to zero in on a phenomenon and collect data that reflects it and nothing else.

Through this work, the empirical attitude motivates a sensitivity to and a willingness to learn from feedback. In both cases the successful harnessing of material’s behavior implies a lot of learning. It is a process in both cases of instantiating one’s ideas in material arrangements, paying respectful attention to feedback, and learning from it. It is not immediate but iterative and demands both openness and patience. It is not immediate in the sense that the right answer or correct idea is not readily available. The key is not to know everything you need to before beginning, but to learn. A design improves in terms of its efficient and effective practical function through this process of learning, and the learning occurs through an active search for feedback from the material world. A scientific idea improves in terms of its alignment with nature through this process of learning, and the learning similarly occurs through an active search for feedback from the material world.

It is in this sense that we believe there is a key overlap involving design in and out of science, an overlap in terms of the empirical attitude. It is for this reason that we believe design activities are potentially an ideal way for introducing students to the empirical attitude and then supporting its development with next steps. Rather than focus exclusively on the conceptual aspects of science practice, as the typical science curricula do now, we envision curricula that focus on both.

We believe design activities are a good way to begin supporting the empirical attitude because students generally find these activities accessible, and because the empirical attitude is directly related to success on them. Design activities are accessible because they have a coherent meaning for students (i.e., they understand what the task is about), so this is not like asking students to train on a skill doing an activity wherein the meaning is not readily apparent. A practical function is something even young students can discern (and designs can begin with relatively simple materials—for example, wooden blocks, as we show below). A practical function also can serve to guide students to iteratively revise their designs in response to feedback.

We also believe design activities can provide a foundation for students in material practice, even though they represent a simplified part of it. Simplification of material practice is likely necessary in the beginning, because material practice is dizzyingly complex. Consider material practice in light of the examples above. Scientists not only need to engage in the conceptual practice of positing phenomena that might inform theoretical ideas, but they also need to constrain these posited phenomena to the possible designed material arrangements that might frame and measure them. Thus, the process is not unidirectional, but bidirectional. The more a scientist is able to devise creative material arrangements (constructed or otherwise, in a broader sense) that afford observing things that had until then not been observed, the more possible ways there are to inform posited phenomena. Similarly, the more phenomena that a scientist can think of that might inform a theory, the greater the variety of possibilities for designing material arrangements that could inform them. Through this, the scientist needs to be reasoning not only about the conceptual problems, but also about the material problems and how they could be potentially overcome by designs.³

Given the complexity of this kind of reasoning, we believe it would be extremely difficult to engage students in the complete process authentically and all at once. A more reasonable strategy may be to engage students in design, in which they first learn that they need to be creative, open to changing their ideas, actively seeking informative feedback, and able to revise accordingly. They should learn from design activities that the point is not to get the entire thing right at the outset, but to iteratively test their designs, actively seeking feedback that can inform what they have done and how it can be improved.

This, of course, is a manifestation of the empirical attitude. Because the empirical attitude itself should be a key learning goal for science education, it makes sense to support it consistently at multiple grade levels. Once students become facile at design by employment of this empirical attitude, they may be in a better position to be introduced to the way in which artifact designs in science serve conceptual purposes, rather than practical ones. That is, once they are good at design and are sensitive to the empirical attitude in design activities, students could be asked to engage the special case of design in science—to create artifacts iteratively that afford the collection of good data for addressing conceptual issues.

In the remainder of this article, we turn to empirical evidence that supports the centrality of the empirical attitude to design activities generally. As stated earlier, we believe that design activities may play a role in supporting the empirical attitude, but they are likely not sufficient. Thus, the described activity provides an example of the type of design project that could introduce students to the empirical attitude, serving as a precursor to design activities that focus on producing data that can inform a phenomenon. That is, in the design activity described the aim was not to collect data to inform a phenomenon, but rather merely to achieve a practical effect. The task was to construct as tall a structure as possible out of 54 wooden blocks that can withstand a simulated earthquake. We will first characterize general aspects of student performances on this task, and then we will focus on three cases that illustrate how the empirical attitude is reflected both in successful performance (when students iteratively learn and revise the design) and accounts for less successful performances, when it is evidently absent.

4 The Empirical Attitude in Design Performances

The design performances that we share were documented as part of a larger study that examined student learning in an 8-week high school design-based learning curriculum unit. This study was conducted at a large urban public high school in a city in the northeastern United States.

4.1 Participants

Although we will focus only on three cases of student performances in this design task, we briefly share some overall data regarding the performances of the 19 students who completed it. This we provide general information about the task and the overall ways students reacted to it (the common issues encountered and the nature of the challenge that the task presented). This overall group of 19 participants included 10th and 11th grade high school students enrolled in a general chemistry course.

4.2 Design Task

Students were given 54 wooden blocks with which to build as high a structure as possible to withstand a 20-s simulated earthquake. Students were allowed to build and test as many structures as they wanted, and were given approximately 30 min to complete the task. The task was chosen because it met two important criteria. First, the task had the three elements that Cross (1994) describes as being common to all design problems: (1) a specified goal, (2) constraints within which the goal must be achieved, and (3) criteria for recognition of a successful solution. Second, while many students possessed some prior knowledge about building structures, physics and earthquakes, the task did not require students to have any particularly specialized prior knowledge about the domain.

4.3 Data Collection and Analyses

Students were videotaped while completing the earthquake task. They were invited to explain each design, then after feedback (the structure falling or standing during the simulated earthquake), to account for the design’s performance and to attempt a taller structure. Video data were analyzed in multiple passes (Miles and Huberman 1994). First, the videos were segmented at naturally occurring breaks in the design activities (i.e., when students began a new design or when students began testing their design, etc.). Start and stop times for each segment were recorded, as well as relevant information related to the design (e.g., height of structure, principles evident in the design, success of design, etc.).

4.4 Overall Themes in Student Performances

Success with the earthquake task was contingent upon the extent to which students explored the set of possible designs. Two main features of the material context became salient for many of the students, and these features were key to their understanding of the challenge they faced. The first was the relative strength of shaking produced by the simulated earthquake, and the second was recognizing that the number of blocks with which they had to build their structure was limited. In the majority of cases, the number of blocks became relevant for students only after they recognized that the shake of the earthquake was not extremely strong.

Against this backdrop of information regarding the overall student performances, we now present three cases. Of the three cases we describe, one illustrates a very successful performance, while the other two cases represent two different ways in which successful performances were precluded. By sharing these unsuccessful performances in contrast to the more successful one, our purpose is to highlight ways in which student approaches to design tasks can exhibit reasoning patterns that stand at odds with the empirical attitude. As such, these cases illustrate what students need to be taught, even as they begin work on the scientific attitude while working on design tasks

4.5 Successful Case: ‘The Successful Learner’

This student designed and tested seven structures in all, having success with three of the seven designs (as defined by the building standing throughout the earthquake). His designs were complex and included many elements important for designing stable structures such as a strong center of gravity and symmetry. Table 1 provides an overview of this student’s design performance.

Table 1 Overview of ‘The Successful Learner’s’ performance

4.5.1 Designs 1 and 2

From the first design, it was clear that this student exhibited some of the key characteristics of the empirical attitude. While building his first structure (see Fig. 2) he asked the researcher if he had to use all the blocks, and when told no, he decided to stop his design at five levels high and test it. When asked to discuss his design he stated:

Fig. 2

The Successful Learner’s first design

Student: ‘It’s like, I don’t know what I did, I just tried to make it like so when it starts shaking, like, there’s like a little space right there so that if this slides, you know what I mean?’

From this quote we can see that the student was already positing a possible weakness in his design strategy. That is, the shake may cause blocks to slide horizontally and the building to subsequently collapse. His design anticipated this by placing blocks on subsequent levels at angles to each other, so that there was extra ‘space’ on each level for the blocks to slide out, without causing the building to collapse. The empirical attitude is evident in that he seemed to recognize this as a guess, regarding what the shaking would cause and what would be sufficient in his design to address it. Because he recognized this as a guess, he requested a ‘test’ in order to see if the behavior of this material arrangement would indeed be what he anticipated.

The test of his structure was successful, therefore he decided to add the rest of the remaining blocks to the structure in the same pattern. This design reached a height of ten levels. During the testing of the second design, he commented:

• Student (S): It ain’t even moving.

• Researcher (R): Why do you think it’s standing?

• S: Maybe because the structure is not shaking hard enough.

• R: What do you think about the structure? What about your design makes it stand?

• S: Like I said before, the little extra room. Plus it’s like everything is pretty much bunched together and its got a lot of weight so that’s probably (inaudible).

• R: So you think you can build something higher than 10?

• S: Yeah. I wasn’t sure how hard it would shake.

From this exchange it is evident that the student was closely attending to the behavior of his structure in relation to the shake of the table. Moreover, it seems he was gathering information not only on the utility of his design strategy, but also on the strength of the shake, a crucial aspect of feedback that was important for his next step.

At this point, he had used all 54 available blocks and therefore was faced the challenge of creating a taller structure with the same number of blocks. Because the design strategy he chose initially required six blocks per level, the number of levels his structure could reach was limited. Given that he noticed that the shake was relatively weak, and had moved his structure relatively little, he chose to adopt a new design strategy.

4.5.2 Design 3

For his third design, this student employed a 2 × 2 configuration, in which each level had two parallel blocks, subsequent levels perpendicular to each other (see Fig. 3). He built a structure that was 18 levels high, once again using all the blocks. This exchange followed the prompt that he explain:

Fig. 3

The Successful Learner’s third design

• Student (S): This design is just a risk. Like, I don’t know, I was just trying to get it high.

• Researcher (R): Do you think it’s going to stand?

• S: {Shakes his head no}

• R: You don’t think so? How come?

• S: Cause it’s just…it’s only got 2 things under here (referring to base) and it fell when I was putting it up, maybe I hit it, I don’t know. It might.

• R: Ready to try it?

• S: Yeah.

The test of the design was unsuccessful, and the student stated:

• Student (S): I don’t know, there was probably too much weight on one side. It was probably too much weight on this side [indicating the back side where the blocks fell].

• Researcher (R): You think so?

• S: Because it fell that way.

• R: So now what do you think you are going to try?

4.5.3 Design 4

For his next design, the student once again chose to create a structure that was entirely different from his previous design. Before building a structure on the earthquake machine, he spent a few moments modeling designs off the table, commenting: ‘I don’t know what to do. It’s like, I got the styles, but it would be like too many blocks for each level’. From this quote we can see that the student was struggling with ways to overcome the second constraint of the task: the limited blocks available. Ultimately, he built a structure that utilized a pattern of 3-block and 2-block layers, that reached 25 levels high. He described his design as follows:

• Student: I tried to put the 2 together to make it high, and then the 3 in between to make more weight on it, make it sturdy I guess.

• Researcher: Do you think this one is going to stand?

• Student: I don’t know. I mean it might but it needs to be like, even, see? But it might, it might stand. [student straightens blocks].

4.5.4 Designs 5, 6 and 7

After a successful test of his 4th structure, the student decided to keep the basis of the structure and make minor modifications to add a few levels to it. At this point the student asked the researcher the height of the tallest successful structure, and determined that he would need to get above 27 levels to beat the current record for tallest structure. He made modifications, adding levels in several different ways over the next three design trials. For his 5th design trial he decreased the top to one block per level, making them perpendicular to each successive level. This design was unsuccessful, with the top blocks falling towards the back, just before the 20-s trial was complete. For his 6th design, the student’s attention was notable toward the direction of the fall, and so to remedy this flaw (i.e., the way his structure interacted with the particular way the shaker simulated an earthquake—with more motion front–back than side–side), he adjusted the orientation of the top three levels (i.e., those that fell). This design was also unsuccessful, falling towards the left. For his 7th and final design, he adjusted the top levels again, making the center more massive, using two blocks per level instead of three. This design was successful at 26 levels high.

4.5.5 Discussion of The Successful Learner’s Performance

This student achieved a high level of success in the earthquake task. He also demonstrated some of the key aspects of an empirical attitude. It was clear from this performance that he acknowledged some ideas needed to be tested, and he appeared willing and open to changing his ideas, in particular when he noticed that his original strategy would not result in a very tall structure. Moreover, he paid detailed attention to feedback, and learned from that feedback, using it to make modifications to his next designs. He paid attention to increasingly more detailed aspects of his design’s performance, in particular toward the end, when he succeeded in reorienting and thickening the top three levels of his design (over two tries) to remain solidly in place during the shake. In short, he was willing to learn from the behavior of his material arrangement and knew how to do this flexibly, both in the initial stages of his performance and in the later stages. This learning is tightly intertwined with his skill in pushing his design toward the limits that the shake and number of blocks would allow. In this way, this manifestation of the empirical attitude is a combination of intellectual and material skill, as the ideas (and an awareness of what one needs to learn) become translated into material arrangements, and the material arrangements, when allowed to play out, in turn inform those ideas.

We describe two other cases to draw illustrative contrasts with The Successful Learner. We are not claiming that these cases are somehow representative of the entire group of students, but that they are representative of the kinds of things students do that impede their performance. In ‘The Architect’s’ case, the student’s beliefs about what constitutes a strong building were dogmatic and unchanging. As a result, he did not alter the central principles in his designs. In ‘The Bulldozer’s’ case, the student was willing to change beliefs about good designs, but failed to sufficiently seek feedback from the buildings’ performances. Our argument here is that these impediments reflect a lack of the empirical attitude, and indeed, these students probably thought their way of approaching the task was fine. In this way, the aspects of performance that we single out are a subset (although we believe, an important one) of the kinds of heuristics that compete with the empirical attitude.

4.6 Less Successful Case 1: ‘The Architect’

This student designed and tested three structures in all, having success with two of the three designs. His designs were complex and included many elements important for designing stable structures such as a wide base and strong center of gravity. Table 2 provides an overview of this student’s design performance.

Table 2 Overview of ‘The Architect’s’ performance

4.6.1 Design 1

From this student’s first design, it appeared that his ideas about earthquake proof structures included features such as a wide base, vertical blocks for support beams like those that appear in real buildings, and a pyramid-shaped structure. Using all the available blocks, he built a structure that was seven levels high and used ten blocks for the base (see Fig. 4). The first test revealed that his initial design was indeed very stable.

Fig. 4

The Architect’s first design

After observing the effects of the earthquake on his structure, the student was asked if he thought he could build a taller structure. He replied with: ‘Yeah, I think I could. I’ll give it a try’. Since he had used all the available blocks in the first design, the student had two options for creating a taller structure: (1) modify the initial, already stable design, or (2) create a new design from scratch. Unlike the ‘Successful Learner’, this student chose to modify his initial design slightly, apparently not recognizing how little the shake actually moved the blocks.

4.6.2 Design 2

For his second design, he chose to remove blocks from the middle of the structure, keeping the base and sides intact. He then proceeded to add more vertical support blocks in the middle. After making some other minor modifications to the design, the student had a structure that was nine levels high (see Fig. 5). When the student was asked to explain this design, he responded:

Fig. 5

The Architect’s second design

• Researcher (R): Can you explain it to me?

• Student (S): Well it seems that the higher up, the more unstable the structure is because it’s more stable at its base where it’s grounded. And the energy transfers, and becomes stronger at the top, and that’s where it affects it the most.

• R: So can you tell me why you built it this particular way?

• S: Well, I used the blocks straight up as support basically. Every place has support beams, you can see the library even does, and ah, I figured that the ones coming out could possibly work as horizontal support because it needs both vertical and horizontal. That way one half of it doesn’t collapse and the other half is still there. But…that’s pretty much what I think could be done.

• R: So what do you think? Do you think it will hold up?

• S: I don’t know, it looks a bit uneven [reaches out to straighten structure], but we’ll give it a go.

As predicted by the student, his structure was not stable enough to withstand the earthquake, with the top, right-side blocks falling off. Nevertheless, we note that this student had some rather developed ideas about what matters for structural integrity. He believed that the vertical columns that appear in many buildings are crucial for maintaining the integrity during an earthquake (regardless of whether they are firmly secured in place). This idea did not get revised in his second structure, nor in his third.

4.6.3 Design 3

For his third design, the student once again chose to modify slightly, keeping the base and modifying the top portion of his structure (see Fig. 6). In describing his modifications, and the anticipated effects, the student stated:

Fig. 6

The Architect’s third design

• Student (S): I think it will hold.

• Researcher (R): What did you do differently?

• S: I put more support in the center. I made it more like a pyramid, as opposed to before where it was kind of just large base and then got smaller immediately, it’s more of a gradual incline.

• R: So that will help…?

• S: With the stability.

The modifications made to the structure by the student did appear to increase the stability, resulting in a successful trial of his design, again at the same nine levels. Once again the researcher prompted the student to build something taller. Rather than attempt a new design, which would require modifying his strongly held ideas about features of structures that withstand earthquakes, he declined. He replied, ‘I think it’s pretty much the maximum height that you can get with it’.

4.6.4 Discussion of The Architect’s Performance

The Architect’s’ performance is a good illustration of a student who shows limited facility with the empirical attitude. Overall, it seems that The Architect had clear ideas about how buildings should be constructed, and never diverted from them, even though they did not work (i.e., vertical orientation of blocks was highly unstable, only exasperated by horizontal placement of blocks on them).

Whereas he made minor adjustments to each design, he used these same features in each, only modifying how they were arranged. There was no evidence in his performance that he ever questioned the value or utility of the any of the features for this particular design task. He did not try various designs that included some features while omitting other features to determine what impact, if any, each feature might have on the stability of the structure. In addition, after his third design, he stated that he believed a taller (and stable) structure could not be built. Thus, one might suggest that this student’s poor performance on the earthquake task can be attributed to a lack of openness to changing his ideas, little evidence that he consciously looked for feedback on these ideas, and seemingly a limited interest in intervening in targeted ways to generate such feedback.

4.7 Less Successful Case 2: ‘The Bulldozer’

This student designed and tested three structures, only one of which was successful. He did, however, build many more structures, which he did not decide to test, but rather tore down to begin from scratch, often changing his design strategy completely. His designs were extremely intricate and complex, often utilizing vertical and horizontal blocks as support beams, like The Architect. Table 3 provides an overview of this student’s design performance.

Table 3 Overview of ‘The Bulldozer’s’ performance

4.7.1 Design 1, 2, and 3

It was apparent from the outset that this student had many ideas for ways to build an earthquake proof structure. En route to building his first design to test, this student built and demolished two other designs before subjecting them to the simulated earthquake (see Figs. 7, 8). Unlike other students who may have removed a few blocks at a time to make modifications to their structures as they were building, this student actively removed all the blocks from the platform each time, and began new designs before testing what he had built.

Fig. 7

The Bulldozer’s first demolished design

Fig. 8

The Bulldozer’s second demolished design

After numerous revisions, he finally settled on a design (see Fig. 9) that he was ready to test. After a failed test, he initially began his next design by modifying the design he had just tested. However, he almost immediately changed his mind and once again designed and demolished two more structures before committing to a design for his second test. This structure was 14 levels high, and was successful when tested. Since he did not use all his blocks for this structure, he first began by adding on to his existing successful structure, but once again chose to demolish the structure and proceed with a completely new design. His final design was 17 levels high, and unsuccessful.

Fig. 9

The Bulldozer’s first tested design

The Bulldozer demolished and began anew throughout his performance. While this behavior may suggest openness to changing ideas, it was not a change informed by feedback. Whereas having openness to new ideas is key, it is also important that one recognizes the need to test ideas against nature so that they may gather evidence to help them refine those ideas. While this particular student showed great interest in intervening, building and tinkering with the materials, he was unable to make progress on his designs, or understanding of the constraints of the task, because he did not do enough testing of his ideas. Thus, this student illustrates the importance of testing ones ideas against nature, to the empirical attitude.

4.8 Conclusion and Future Research

These three cases of student performances illustrate how the empirical attitude is key to success in a design task. The two less successful cases illustrate very different reasons that performances can be less successful, both of them ways in which the empirical attitude is not apparent. In the case of The Architect, ideas were dogmatically held and not subjected to question. In the case of The Bulldozer, ideas were not dogmatically held, but their change was not informed by feedback from the behavior of nature. In both cases, the students lacked some aspect of the empirical attitude that precluded their iterative learning and refinement that was apparent in The Successful Learner.

Because the empirical attitude is key to success in this and, we believe design tasks generally, design tasks may represent a useful context for instruction to support initial development of the empirical attitude. This is valuable in itself, but such an instructional strategy would represent only the beginning of such support. For, although the empirical attitude is evidently relevant to design tasks, this does not address its employment in science. Whereas in design tasks, the empirical attitude facilitates learning and successful harnessing of material arrangements for practical functionality, in science the empirical attitude is key for testing, refining, and indeed learning, theoretical assertions (and phenomena) about nature. In this way, although the empirical attitude may be taught in a sustained way as a habit of mind, it would change and develop into more sophisticated forms as a reasoning strategy, first through trial and error feedback on design tasks and later through designing data collection events for conceptual issues. And considering that in science, data collection events are designed (through feedback) with an aim to collect feedback about abstract ideas, it seems a reasonable strategy to focus first on the first order search for feedback in pure design generally first.

We envision that students could learn to engage in designing ways to collect data to inform theoretical assertions and phenomena as a reasonable next step—once students become facile with the empirical attitude for design tasks, they may be in a better position to be introduced to the way in which artifact designs in science serve conceptual purposes, rather than practical ones. Once students understand the ways in which to be sensitive to feedback when designing things (as illustrated by the successful performance here), they could tackle the more complex task of noting feedback while designing things that themselves are data collection events for seeking feedback for ideas. We could imagine a progression in which students ultimately identify and characterize particular phenomena through their design and collection of data. Students should be challenged to posit phenomena that are implications of theoretical principles as well.

Our purpose in suggesting this longer-term curricular vision here is less to prove it would work than to argue that it is important that we consider ways to rethink science curricula so the empirical attitude is taught. Currently, students virtually never design and refine ways to collect data that would inform posited phenomena. Equally stark is the lack of attention to the empirical attitude more generally, in which as a habit of mind, ideas are tested and refined in response to an active search for feedback from the material world.

We sketch this longer-term curricular vision also to note the kinds of things we need to know in order to move toward instilling curricula with this key aspect of science. First, we need to know more about what students know and can do in terms of employing the empirical attitude. Do they view the iterative use of feedback from nature as key to scientific reasoning? Do they understand that scientists focus on material practice to leverage their arguments’ theoretical ideas and their implications in terms of phenomena? How good are students at positing what phenomena might exist and could inform theoretical ideas? How good are students at managing the bidirectional reasoning between posited phenomena and the possible material arrangements that might inform them?

In addition to this baseline information about student thinking, we also need a series of classroom studies to inform how, under particular forms of instructional support, students can develop the empirical attitude. The results of this program of research would provide empirical evidence for a ‘learning progression’ in this domain (Smith et al. 2006). How might the scientific layer be added instructionally once students are relatively successful at design activities? What forms of instructional support are necessary and sufficient for learning these ideas and abilities?

It is not by chance that modern science began with Galileo, who, despite the disagreement of Descartes and other contemporaries, argued that careful collection of data was the best way to develop sound theoretical ideas. Give the centrality of the empirical attitude and its role in distinguishing modern from medieval science, it should not remain neglected in science teaching. Beyond that, it represents a principled and productive humility that may be integral for learning, not only in science, but more broadly in life as well. Students should not leave school without knowing that their ideas are not only are changeable, but that they could be improved upon, and purposeful interaction with the world can help make these improvements happen.