Daniel Z. Meyer, Illinois Institute of Technology

Leanne M. Avery, State University of New York College at Oneonta


The Problem of Nature of Science

While there is near universal agreement within the science education community that a strong understanding of the nature of science (NOS) – both as a student goal and a teacher attribute - is critical, we still struggle with how to achieve these aims.  Barriers range from pragmatic logistics to fundamental curricular tensions.  In this paper, we share a classroom activity designed to aid in the learning of key NOS principles that we have found constructive in overcoming such barriers. 

Principles of the Nature of Science

Researchers in science education have settled on several tenets that depict the nature of science or NOS (Lederman 1992; Abd-El-Khalick, Bell et al. 1998; Akerson, Abd-El-Khalick et al. 2000; Abd-El-Khalick 2002). These general aspects of NOS are that scientific knowledge is tentative (subject to change), empirically-based (based on and/or derived from observations of the natural world), subjective (theory-laden), necessarily involves human inference, imagination, and creativity (involves the invention of explanations), and is socially and culturally embedded.  Many of the national reform efforts have called for infusion of understandings of the nature of science in science education (American Association for the Advancement of Science 1993; National Research Council 1996)Science educators have tried both implicitly and explicitly to incorporate NOS into their courses (Akerson, Abd-El-Khalick et al. 2000; Abd-El-Khalick 2002) and this has had mixed results. This still challenges science educators today. That is, to find ways to teach about NOS using explicit, implicit, and inquiry approaches that will foster teachers’ abilities to teach it in their own classrooms.

Approaches and Problems in Developing NOS

Recent research has focused on distinguishing between historical, implicit and explicit approaches in teaching NOS (Khishfe and Abd-El-Khalick 2002).  Historical approaches, utilizing case studies as a means of exposing students to the nature of science, has had a mixed record (Solomon, Duveen et al. 1992).  Implicit approaches engage students in inquiry based activity (e.g., engaging the learners in asking scientifically oriented questions, collecting and using evidence to address these questions, formulating explanations and evaluating them in light of alternatives, and communicating and justifying their proposed explanations (National Research Council 1996)) with the intention that by doing science, students will gain an understanding of the nature of science.  Some have criticized this approach for taking understanding of NOS as an affective attribute rather than cognitive outcome, and therefore argued for explicit discussion of NOS principles.  Approaches including explicit instruction have shown to be more effective (Khishfe and Abd-El-Khalick 2002).

In the logistical realm, time and materials are often barriers.  At the systemic level, misperceptions about inquiry, pressures of accountability for content coverage, and beliefs centering around the appropriateness of teaching science as it’s practiced in school science (Cunningham 1995; Avery 2003) also impede the implementation of inquiry-based science in classrooms.  At the curricular level, inquiry-based activities that are both complex enough to demonstrate NOS principles and accessible are difficult to design and implement.

The difficulty in designing activities to demonstrate NOS principles can be summarized by considering two balancing acts.[1]  The problem space – particularly the data participants will utilize – can range from simple to complex.  If the problem space is too simple, the activity becomes a confirmation lab; if it is too complex, is becomes undoable in the classroom.  Likewise, the particular assignment or charge given to participants can vary from very specified to completely open-ended.  Too specified an assignment will be a traditional cookbook lab; too open-ended an assignment will leave students not knowing where to go. We have found utility in an activity that addresses the issues surrounding implementing inquiry as well as those related to curricula design.

The Flow Lab

We first present the barebones Flow Lab activity, and then discuss how it might be implemented. 

The Basic Activity

The Flow Lab activity originated as an activity for modeling the dynamics of a water tower (Carlsen and Trautmann 2004).  The basic activity uses the following simple apparatus:  The bottom of an empty plastic bottle is cut off, and a small hole is drilled in the cap.  The bottle is held upside down on a ring stand.  A known amount of water is poured into the bottle while the hole is held shut.  The hole is then released for ten seconds, and the amount of water that flows out is measured.  Students are then instructed to experiment with different starting volumes in order to gain an understanding of the relationship between starting volume and collected volume.   They are told that they will be challenged to predict the outflow volume for a new starting volume. 

What appears to be a straight-forward (and somewhat traditional) lab activity then generates experiences around key NOS activities.  We will discuss these by dividing the experience into three phases: data collection, data analysis and sharing, and explicit reflection.

Data Collection

Participants are immediately faced with a situation not experienced in traditional “cookbook” lab exercises: while they have been given the basic protocol for measurement, they must establish the specific implementation to acquire the necessary data.  More specifically, they must decide the number, range and distribution of test points.  There is no external authority directing this choice.  However, neither are the participants working in a vacuum, where anything is equally valid.  They have the pressure of an explicit challenge: effectively predict the outflow for a starting value they have not tried.  This will motivate their argumentation and resolution on what test range to use.

Besides the data range, participants flesh out other details of the procedures.  Routines will be established such as who does what tasks, how timing is coordinated, etc.  These are not necessarily conscious decisions, but rather represent the development of tacit skills around this research agenda.

At some point, participants will need to grapple with the occurrence of potential anomalies.  Results will occur that vary from the participants come to expect.  They will then have to decide how to respond: should it be rejected as an anomaly?  Should the trial be repeated?  As discussed below, there is no external arbiter.

For this portion of the activity, the instructor’s role is as a facilitator, and a fairly passive one.  For many questions participants pose, the instructor can refer back to the basic challenge, and impress on them the need for the group to make choices in light of that challenge.  For example, when participants as how many data points are necessary, the instructor can say that that is up to them, so long as they meet the challenge of being able to predict the outflow.

Data Analysis and Sharing

Analysis of the resulting data can proceed in two phases: within the group for the purpose of addressing the challenge and sharing between groups.  Appendix I shows examples of participant data.  As we discuss below, the variety seen in the data sets is crucial to the NOS experience.

As with the section, participants will often behave in the mode of doing a traditional lab – they will often ask if their results are correct.  The instructor can turn participants’ focus first to the general question of conclusions about the relationship between starting volume and outflow, and then to the specific challenge of predicting an outflow.  Since these two questions are in essence the same question – making a prediction requires a general conclusion about the relationship – the instructor can move back and forth between the two to respond to different participant reactions.  For example, some participants may be reluctant to make a general conclusion, or simply be unused to being asked such an open question.  The instructor can therefore shift to asking the group how they would go about making a prediction.  This might prompt participants to identify a best-fit curve as the tool they would use, which essentially reifies their general conclusion. 

Alternatively, the instructor can also ask specific questions about trends, outliers, etc. in order to bring them to the participants’ attention.  For example, with Data Set 5, participants could be queried about the data point at a starting volume of 500ml.  (Note that the two circled data points did not exist during this initial analysis.)  Is this point an outlier?  Is the phenomenon following a smooth curve?  The ambiguous nature of the data means that the resulting discussions around these questions are not trivial, but represent genuine scientific argumentation.

The choice of what specific starting value to give to each group to make a prediction for can be used in a very calculated way to prompt certain deliberations on the part of participants.  For example, again with Data Set 5, giving a challenge of predicting the out flow with a starting volume of 525ml was used as a way of forcing a conclusion about where the 500ml data point was an outlier and if the phenomenon was following a smooth curve.  Questioning the scope of conclusions is another common issue that can be raised at this point.  In Data Set 2, the participants were given a challenge point outside the general range of their data.  In addition, this forced consideration of whether or not the curve was asymptotic or continue to rise.

Beyond analysis within one data set, comparing data between groups can raise additional issues, and lead to additional debates.  Having groups use different bottle types and different hole sizes helps insure this variation.  While some data sets might appear quite linear, others will display more of a logarithmic curve.  Others still will exhibit some plateau phenomena.

Sharing data sets in the order they are shown in the Appendix is an example of how the instructor can foster critical debate.  An issue that arose in some groups but not other was whether there was a plateau phenomena.  Therefore, when Data Sets 1 and 2 were shared, those groups considered any slight dips as anomalies, and this position was supported by the class as a whole.  However, when Data Set 3 was presented, that group considered the dip to be real, and argued as much.  As additional data sets were shared, this position grew in acceptance.

As arguments for various conclusions are made, participants will also make restrictions on their claims in order to gain more acceptance.  In particular, arguments will be made that data sets such as 1 and 3 are not in contradiction, but rather represent two different areas of the phenomenon.  Besides the plateau question, the long-range nature of the phenomenon (asymptotic, linear, logarithmic) is another common point of dispute.  Participants will often make reference to external theories, such as arguing that there is a limit to how much water molecules can be compressed.

Participants will also make references to the differences in bottles.  They will suggestion, or the instructor can probe for, possible follow-up experimentation to clarify some of the ambiguity caused by the differences in bottles and holes, and otherwise explore more the phenomenon.  This variation in experimental setup might seem like a poor experimental design – and to some extent it is.  However, by having the problems emerge in this manner the importance of holding variables constant becomes apparent rather than just be an isolated mandate by the teacher authority.

Explicit Reflection

After deliberation over the flow data itself, discussion can turn to more explicit, reflective consideration of what happened.  This can also include references to various NOS related readings.  Thus this activity provides the opportunity to make explicit their implicit experience of NOS concepts and personal their academic study of NOS concepts.

The most significant concept generated by the Flow Lab activity is the occurrence of interpretive flexibility.  The data that is generated, both within a single group and between groups, is ambiguous enough that multiple conclusions are possible (and arguable).  The need to adopt a position is driven by the challenge to make a prediction.  This phenomena mirrors the research in gravitational waves reported by Collins (1975; 1985) and neutrino detection reported by Pinch (1981; 1985).

Interpretive flexibility is tightly connected with the concept of experimenter’s regress (Collins 1985): when a new phenomenon is being studied, no external arbiter exist to distinguish whether unexpected results are due to a mistake in the theory the prediction is based on or a flaw in experimental technique.  This is precisely the position participants find themselves in (and never do in confirmation labs).  A surprising data point may be anomalous, or may be revealing the trend they are trying to find.

The interpretive flexibility in turn prompts significant argumentation and negotiation among the participants around different scientific conclusions.  This reflective discussion can be used to explore all the nuances of that process: how restrictions are added or removed from claims to make them more acceptable (Latour and Woolgar 1986); how outside references are utilized (Toulmin 1964; Latour 1987); how presentation can effect audience response (Collins 1975; Tufte 1997).  This can all be viewed as part of the broad process of social construction of knowledge, and provides a way out of the experimenter’s regress.  In particular, a contrast can be shown between groups comparative lack of confidence when only looking at their own data, and how that confidence changes as they interact with other groups.  This reflects the process of closure described by Collins (1981) and numerous other sociologists (see for example Latour and Woolgar 1986; Bijker 1987; Cowan 1987; Kline and Pinch 1996).

While taken for granted during the activity itself, the concept of a black box – a conceptual or physical tool that is utilized without any challenge to its validity – can be explicitly discussed afterwards.  Participants both make significant use of pre-existing black boxes (e.g. graduated cylinders, the concept of a line of best fit) and black box new concepts during the course of the activity (e.g. routines for running trials, “plateauing” as a reference to the data flattening out for a range).  Furthermore, it can be pointed out how in the face of disputes, some areas of the experimental process were more likely to be questioned than others.  This is comparable to Pinch’s (1985) description of how competing researchers would look for weak points in the inference chain to attack neutrino data results.

Finally, the participants work can be seen as occurring within a preexisting framework or paradigm.  They have a degree of expectation about the results that at the very least includes it being a continuous function that generally increases and never decreases as the input is increased, and in some cases is restricted to a linear relationship.  Adherence to a paradigm can be seen in participants’ conclusions and arguments, just as described by Kuhn (1970).

Instructor Role

While we present this activity as one inherently rich in NOS principles, we also want to emphasize the importance of the instructor in maximizing the activity's benefit.  It takes an instructor who, besides having a strong understanding of NOS themselves, has a keen sense of how and when to interact with students in order to positively effect their experience.  This is an instance where the traditional student centered vs. teacher centered dichotomy is insufficient, and even counter productive.  We agree whole heartedly with the spirit of student centered learning in so far as students must be engaged in substantive, active work, and teachers must avoid being sources of simple, unchallengeable information.  But this does not mean that students should be placed on auto-pilot with a hands-off policy on the part of teachers.  In addition, given that intervening too overtly and dominantly is the overwhelming tendency for teachers, perhaps we should consider waiting and holding back as an active move on the teacher’s part.


Spectrum of Implementation

Our development and reporting of this approach is based on utilizing the activity in a variety of settings over multiple iterations.  It has served as the introduction to the nature of science in elementary and secondary methods and curriculum design classes.  It has been incorporated into science content classes.  We have used it as a workshop activity for inservice science teachers and NSF GK12 graduate Fellows.

The basic activity is adjustable to participants’ varying needs.  For example, the context of modeling water towers is included with elementary teachers to provide purposefulness for the activity, while secondary science teachers tend to be more comfortable with a more abstract experiment.  In general, the accessibility of the activity is a key element for overcoming elementary teachers’ aversion to science whereas the ambiguity of the results is a key for breaking secondary science teachers preconceptions about lab activities.


We see two major advantages to this approach.  First, the activity provides a mechanism to essentially couple the historical, implicit and explicit approaches.  Concepts that can be explicitly discussed and illustrated through case studies are also made concrete through the students’ experience.  Second, the activity effectively achieves a balance point on the axes described above.  The data generated are sufficiently complex and ambiguous to generate experiences of NOS principles.  At the same time, the activity is relatively cheap, quick, and accessible to a wide range of students.  The charge to students is both concrete and significant.

We see a number of additional appeals.  The activity:

The essential element in the activity is in the phenomenon itself, and the data it tends to generate.  As can be seen in the example data sets, the Flow Lab will generate data that is sufficiently ambiguous that interesting discussion can occur.  Providing students with an experience in which they participate in the process of making raw, ambiguous data into scientific conclusions requires access to interesting (i.e. messy) data.  The Flow Lab’s benefit is in providing predictably unpredictable results.



The following are examples of participants’ data from the flow lab activity.  The different groups used a variety of bottle types and hole sizes.  In most diagrams, participants have also labeled their predicted outflow volume and actually outflow volume for the starting volume the instructor provided.


Data Set 1


Data Set 2



Data Set 3



Data Set 4



Data Set 5



Data Set 6



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            Vol 56, no 7, January 1996, DAI Order Number DA9538839

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[1] This can bee seen as a summary of the somewhat more general problem of designing inquiry based education.