How do I design introductory physics labs to meet specific goals?
There are a lot of things going on in a lab: from handling equipment, analyzing data, coordinating data with relevant physics ideas, to communicating results and working in a team. Labs seem to be an instructional context prone to “scope creep” where we try to do it all! Understandably, students cannot master all of these goals in a single semester lab course.
As with any good instructional design, we need to be as explicit as possible with our learning goals and make sure that our activities are specifically designed to teach those goals. Don’t expect any goals will just come along for the ride. You likely won’t be able to tackle nearly as many goals in a single course as we typically assume are being taught. You may be thinking that means we need more labs to develop these important skills – I completely agree!
So how do you decide what goals to focus on and what kinds of activities are best suited to those goals?
What kinds of goals might I have for introductory physics labs?
The American Association of Physics Teachers published a set of recommendations for learning goals for undergraduate physics labs:
- constructing knowledge,
- designing experiments,
- developing technical and practical skills,
- analyzing and visualizing data, and
Your first task is to come up with a finite set of goals and objectives that you can manageably teach students in a given course.
This list notably excludes using labs to verify physics principles. Research has continued to argue that labs are ineffective for this purpose and the myriad active learning strategies articulated throughout PhysPort are demonstrably more effective. Labs have the unique opportunity to show students what it means to do experimental physics, with all the messiness, creativity, and uncertainty that goes along with it. The goals we describe below, therefore, focus on skills and practices relevant to doing experimental physics.
PhysPort hosts instructional materials for several introductory lab curricula that implement these ideas in different ways: the Thinking Critically in Physics Labs (developed by my team), the Scientific Community Labs, and the Investigative Science Learning Environment Labs. In these examples, an individual lab may target one kind of goal at a time or try to tackle a few at once. Finding the right balance will depend on your goals, students, and your instructional context.
How do I design lab activities to address my intended learning goals?
How do I help students learn how to construct knowledge and build models in labs?
In the context of labs, constructing knowledge means that students are learning how to build new knowledge through their experiment. This is different from using the experiment to demonstrate or verify particular physics ideas. For students to learn how to construct knowledge through experiments, we need to use experiments where students can actually construct new knowledge (at least, new to them). That is, they should not come in with a clear sense for the expected result of the experiment.
Constructing knowledge necessarily interacts with modeling, where students are coordinating data, experimental procedures, and theory. Modeling may be quantitative, such as finding relationships between variables through data, or conceptual, such as describing mechanistically what a measuring device is reporting. In the context of labs, students may construct new knowledge through building and testing models. Model building means exploring a new, unfamiliar phenomenon and coming up with a quantitative or qualitative model of the phenomenon. Model testing means validating, evaluating, or extending either a previously built model or a given model.
Consider an experiment about light emitted from a light bulb as a function of the distance between the bulb and the detector. A model building experiment may have students collect the data and identify, quantitatively, whether the relationship is linear, exponential, or power law. Students may also explore conceptually why the relationship might take the form it does. A model testing experiment may have students identify whether a power law relationship breaks down at small or large distances. This testing can also connect to conceptual reasoning by having students explain physically what that breakdown should look like.
In general, you can design labs to help students in constructing knowledge in three ways:
- Use experiments where the result may be surprising or contradict a generally accepted physics concept, such as testing the angle dependence of the period of a pendulum with improved precision each time.
- Use experiments where the experimental details mean any number of outcomes is possible, such as testing whether stretchy objects from home obey Hooke's Law.
- Have students generate their own questions.
Depending on the level or point in the semester, use lab activities with some range of ambiguity in the experimental outcome. Avoid experiments where the result is clearly articulated in a standard physics textbook. Even better if the instructor doesn’t necessarily know the expected result.
How do I help students learn how to design complex experiments?
Experimental design is a multifaceted skill with many subtasks, from identifying variables (to control, vary, and/or measure), choosing equipment, and deciding what and how much data to collect. Some important ideas:
- Students need to be doing the designing, not you. As an instructor, your role is to support students to make decisions about the experimental design.
- Of course, students may not make good decisions at first, so you should provide feedback, guidance, and a chance to try again. This means that students should have ample time for each experiment, and you may need to spread individual experiments out over multiple lab sessions.
- Getting students to design experiments doesn’t have to mean that they design entire, complex experiments from scratch. Break down each of the experimental design objectives you care about and introduce a subset of them at a time so they start with simple experiments and move towards more complex ones.
As an example, in one of our first labs, we focus first on getting students to think about how much data they need to collect to test whether the period of a pendulum depends on the amplitude. We decide what data students will collect (e.g., which variable to test, which values of the variables to measure) and what equipment to use, so they can focus on the intended goal. We introduce them to methods of estimating uncertainty from repeated measurements and, by iteratively finding ways to reduce their uncertainty, consider the balance of collecting lots of data with being efficient with their time and resources and being able to measure an effect, should it exist. They have two weeks to work on this experiment, so they have lots of time to implement initial designs, analyze the data, design improvements, and evaluate the outcomes of those improvements. In subsequent labs, we focus on new experimental design decisions, with some repetition and feedback (think of drills or exercises in sports and music training).
Don’t try to get students to design the whole experiment at once – introduce new aspects of experimental design over time. Then, the lab activities should allow students to try a design, reflect on whether the design was a good one, get feedback about the design choice, and then try an improved design.
How do I help students learn how to analyze and visualize data?
Engaging students in analyzing and visualizing data is another multifaceted skill with many layers of depth one may focus on in any individual lab course. We can think of two main umbrellas for these skills: understanding data and its associated uncertainty and understanding and carrying out the wide range of data analysis and visualization procedures. There are a few important notes for helping students develop an understanding of data and measurement uncertainty:
- Students often struggle to distinguish uncertainty from mistakes made by the measurer (“errors”), so exclusively and explicitly use the term “uncertainty” instead of “error.”
- Treat uncertainty as a physical phenomenon, not just a calculational or procedural one.
- Take time to discuss how different kinds of measurements have different kinds of uncertainties, such as uncertainty in variability of repeated measurements versus uncertainty in digital rounding.
- Discuss what a particular data analysis tool does and how it does it so that students can eventually decide what tool to use when.
The specific data analysis and visualization tools can be taught with your favorite active learning strategies, whatever they may be. For example, you could give students a worked example, where they are given sample data and stepped through the particular analysis procedure you’re introducing. Or you could have a pre-reading followed by a mini-lecture with peer instruction clicker questions that introduces the analysis procedure. Or you can use an invention activity where students are given carefully crafted contrasting cases (e.g., four different fit lines), compare and contrast the cases, and then to invent a general procedure to solve the task at hand (e.g., to quantify which line is a better fit to do the data). Analyzing and visualizing data can also be a great place to introduce computation, if that is a relevant goal for your course or curriculum.
Include activities that teach both the underlying nature of measurement and uncertainty, the conceptual aspects of a particular analysis procedure, and the technical procedures. Ultimately, we want students to learn to decide what analysis procedure to use in a given context and how to carry it out.
How do I help students develop technical and practical skills?
Labs include a range of technical and practical skills from setting up equipment, operating measuring devices, coordinating with data collection software, to building new apparatus. As with data analysis and visualization skills, you can use any of your favorite active learning strategies to develop students’ technical and practical skills. At the risk of becoming a broken record, the key here is to figure out what aspects of hands-on skills are important for your course and your students and isolate those as much as possible in the learning activities. Some key ideas to think about:
- Just because students are working with a piece of equipment does not mean they are going to learn how to use it or what it does. You can have students use equipment without developing related technical skills or you can design exercises to explicitly build their technical proficiency. Go back to your learning goals and decide whether learning how to use the equipment and/or learning how it works is one of your learning goals or whether the equipment is a means to an end.
- Given how rapidly technology advances and how diverse a set of careers students will explore, teaching students how to use a particular piece of equipment may be less important than teaching them the problem solving and troubleshooting skills associated with working with a new, unfamiliar piece of equipment.
Students will not necessarily learn these skills just by exposure to equipment. Design activities that foreground the practical skills you want them to learn and make sure they are getting repeated practice and feedback on their implementation.
How do I help students learn how to communicate about their experiments?
A range of lab skills exist related to communication, including maintaining a lab notebook, writing journal-style articles, presenting work orally or through a poster presentation, and teamwork and collaboration. I intentionally exclude lab reports from this list, as many researchers and instructors have flagged the inauthenticity of a typical lab report and have moved instead towards lab notebooks or journal articles when developing students’ written communication skills.
When thinking about written communication skills, the key instructional question is “Are your students learning how to do it or just being asked to do it?” Think about how a research advisor would teach a student how to write a paper: the student might form some kind of draft, receive feedback, and then have the opportunity to revise and resubmit – maybe multiple times. For any communication task (writing lab notes, reports, papers, giving presentations), there are a number of ways to structure this.
- Break down the task into sub-pieces and have students only focus on one piece at a time, say the results section or a key data figure one week.
- Provide feedback on the submission and give them an opportunity to revise-and-resubmit.
- Then, have students submit that piece plus a new piece, say the results and the methods, again with feedback and revision.
- Next, have students add the discussion section to the mix, and so on.
Alternatively, you can design active learning lessons or homework assignments related to each subsection such that students learn key concepts and ideas before applying them to their own experiments. You can also have students, rather than instructors, provide the feedback through peer review, simultaneously broadening the feedback each student receives and giving all students (as reviewers) a wide range of examples of materials. Also, check out this expert recommendation about including writing goals in physics courses.
Similar ideas apply to developing teamwork and collaboration skills: Just because students are working in a group does not mean they're learning teamwork skills. Research is very mixed about the best ways to structure groups and for students to distribute tasks or roles among group members. While much more research is needed, some recommended strategies include:
- Make teamwork an explicit goal of your course that includes grade weights,
- Have students develop team contracts for how they will collaborate,
- Get students to reflect on their teamwork,
- Ask students to constructively evaluate their own and their teammates’ contributions to the team.
Just because you are asking students to communicate in a particular way does not mean they are learning how to do that communication.
Below are a few articles intended for physics teachers that summarize research related to the ideas above and a few research articles from our physics education research group that motivate some of the ideas above:
- N. G. Holmes and E. M. Smith, Operationalizing the AAPT Learning Goals for the Lab, 57 (5) 296-299 (2019).
- N. Holmes, B. Keep, and C. Wieman, Developing scientific decision making by structuring and supporting student agency, Phys. Rev. Phys. Educ. Res. 16 (1), 010109 (2020).
- N. Holmes, J. Olsen, J. L. Thomas, and C. E. Wieman, Value added or misattributed? A multi-institution study on the educational benefit of labs for reinforcing physics content, Phys. Rev. Phys. Educ. Res. 13 (1) 010129 (2017).
- N. G. Holmes and C. E. Wieman, Introductory physics labs: We can do better, 71 (1) 38-45 (2018).
- N. Holmes, C. Wieman, and D. Bonn, Teaching critical thinking, Proc. Natl. Acad. Sci. 112 (36), 11199 (2015).
- Z. Kalender, E. Stump, K. Hubenig, and N. Holmes, Restructuring physics labs to cultivate sense of student agency, Phys. Rev. Phys. Educ. Res. 17 (2), 020128 (2021).
- E. M. Smith and N. G. Holmes, Best practice for instructional labs, Nat. Phys. 17 (6) 662-663 (2021).
- E. Smith, M. Stein, and N. Holmes, How expectations of confirmation influence students’ experimentation decisions in introductory labs, Phys. Rev. Phys. Educ. Res. 16 (1), 010113 (2020).
- E. M. Smith, M. M. Stein, C. Walsh, and N. Holmes, Direct Measurement of the Impact of Teaching Experimentation in Physics Labs, Phys. Rev. X 10 (1) 011029 (2020).