While the scientific method doesn't exist, experimental methodology remains an important concept for secondary science education. It is, however, hindered by technical terminology that may take precedence in the classroom over conceptual understanding. I have fallen into this trap myself, but now I am less concerned about whether students can use the correct variable name at the right time, and more concerned about them thinking deeply about methodology.
It is important for me that students understand firstly that science is not an enterprise centred around experimental methodology. If we begin secondary education focused around variables and experimental design we might instill the idea that this is only what science is about.
Science is an endeavour to understand the world, and this does not necessarily mean the use of experiments. Most scientific work is descriptive, and with this data scientists infer to produce theories that explain. Experiments can be used to bolster the evidence base, but they may not be necessary for the acceptance of a theory. In historical biology fields at least, experiments are hard to perform, and often many instances of correlation can be triangulated to understand historical causation.
For secondary students, it is important to know that science begins with a question about the natural world, and the discovery of patterns plays a large role. This is how I frame it when I'm teaching variables, but this is only after lessons in which we discuss the scientific enterprise and the use of models.
Here's how I do it:
With my Y7 students (11 year-olds), I actually begin without mentioning experiments, or variables at all, but with a simulation, and a question.
This year we explored questions with the PHET diffusion simulation, and we discussed how we'd manipulate the simulation to ensure the results we obtained would reliably answer our question. Here, students are talking about variables and constructing their understanding without having to use any technical terminology.
Simulations are a great place to start because the variables are already reduced to a handful of the most important causal variables, and students, in my case, have already seen the simulation in lesson before.
This means that students can't get distracted by minor causal variables that students assume (and sometimes insist) to be important. It avoids cognitive overload as the variables are already chosen for them, and they are visually displayed. Students can focus on the logic of the methodology.
Next, we agree, with my guiding questions, that changing just one thing is the most reliable way to see if a pattern emerges. This year, with the Phet diffusion simulation, we looked at what might affect the time to reach equilibrium. The students then individually give it ago, recording their results. We finish that lesson discussing with each student if they had found a pattern and what it was.
In a later lesson, we return to their questions, and I begin analysing the question structure with the class. Now I begin making it explicit to students, and we see how the questions form this pattern:
By relating this back to their exploration of the simulation, we agree that X is something that changes, to see if Y changes in some related way. To see if Y changes, we'll have to measure it. Students are talking about logic and variables freely, but without the baggage of the technical vocabulary.
We then spend the rest of the lesson with practice on the question structure. I give them some unstructured questions, such as 'so and so wants to find out what happens to Y if X, for example. Students restructure these into the format of Does X affect Y?
We return to the questions again, but this time with a focus on what you would actually do to see if X affects Y, i.e. the specific conditions of the independent and dependent variables. So, for a given example, say, Does the mass of salt in water affect how quickly the water evaporates? students suggest what masses they would use (at this point I'm happy to accept 1 tea spoon, 2 tea spoons, etc), and how they would actually measure the evaporation. This is done for a variety of examples.
Finally, I announce that they students will carry out their own personal investigation over the next six months, carried out in the stages of: Planning, carrying it out, analysing. The investigation focuses entirely on methodology and avoids any superfluous theories, or predictions, et cetera. For examples, the introduction is purely the reasoning behind the variables they chose.
Students choose a question to investigate from a list that I share with them (a continual work in progress). All are simple experiments that can be carried out at home, and all are written in the format of Does X affect Y? All are titles that allow students to focus on methodology and analysis, i.e. simple ones. Plant experiments are avoided, for example, because there are too many distracting variables for students, and the experiments take too long (and may fail).
The next stage is to determine the conditions of the variables, and now I introduce the terminology. However, I expect the terminology to be associated with their conceptual understanding with time, there is no rush. As a class, we begin designing the variables for the individual investigations. I provide a template to help them, it begins with:
Over the course of six months, every now and then we return to the investigation for the next steps. All the written work is done in lesson time and the experiment is carried out at home. As time passes, the students will have more and more exposure to the terminology in relation to their own personal investigation. The concrete with the abstract.
The conceptual understanding becomes associated with technical vocabulary over time, and I avoid letting the latter becoming a barrier to understanding experimental methodology, which is really the goal.
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Christian Moore Anderson
@CMooreAnderson (on twitter)