Executive Summary

A student’s first experience with physics is often challenging. We have all had the experience of watching students struggle, only to have it all “click” about halfway through the semester. Suddenly, they just get it. You can see it in their eyes. It’s a very satisfying experience for both the teacher and the student, but what makes it happen – and more importantly, how can we make it happen sooner?

In 2020 (just before the pandemic brought most in-person research to a halt), a set of detailed, IRB-approved experiments was carried out using the microtask framework that underlies this curriculum. Two groups used the new framework, while a control group used the traditional curriculum. One of the experimental groups participated in project-based learning in addition to the microtasks. Three separate studies were conducted: a measurement of knowledge gained as a result of each format (using normalized gain scores with concept inventories published by Bardar et al., 2009, and Bailey, 2009), a measurement of retention of that material (using a new statistical quantity derived and published by the author earlier that year), and a measure of each of the five components of motivation (using an instrument created by Glynn et al., 2011). The studies found significantly greater increases in knowledge gained by the two experimental groups. Furthermore, both experimental groups achieved the same knowledge retention scores on the material they learned. Both groups using social learning-based microtasks showed an increase in intrinsic motivation (motivation to learn science for its own sake), self-efficacy (a student’s personal estimation of their ability to succeed in science), and self-determination (the student’s estimation that their work is responsible for their understanding). Only the project-based learning group, however, experienced a gain in career motivation (the motivation to pursue a career in science). The PBL format enabled students to see themselves as actual scientists, doing science, and succeeding in doing so, encouraging them to consider STEM as a vocation.

A review of the literature makes it clear that both social learning and project-based learning are essential to promote student success in science. Social learning theory has its origins in Vygotsky’s (1978) work on social constructivism. Social constructivism posits that learners build frameworks of knowledge through observation and interaction with others in the learning environment. Bandura (1971) independently provided a thorough explanation of how social learning takes place. Neurological studies (Kay and Kibble, 2016) have found that in order for learning to occur, symbolic coding of the information must take place; there are four distinct but interrelated processes which work together to enact this encoding. Each of these processes is significantly enhanced by social learning, so it is unsurprising that social learning leads to greater long-term retention of material. Student success is not just successful completion of a course, but successful transfer of material to long-term memory – which, arguably, is the purpose of higher education.

Microtasks are short, focused, in-class work that allows students to develop their knowledge of physics through interaction with their peers, optimally in a group of four students. While in the traditional physics lecture an instructor might derive an important relationship on the board, this often has little meaning to beginning physics students until they have had an opportunity to interact with the material with their group. In this book, very few equations are presented to the students explicitly. Instead, the microtasks walk the students through the concepts (mathematical and physical) which enable them to derive the desired relationships themselves – a large ask for students who may never have taken a calculus-based physics course. On their own, few students would be able to achieve this goal. Social learning, however, empowers all the students to learn the material and achieve success together. Thanks to Vygotsky, Bandura, and many others, there is a broad base of educational research that not only shows that the model works, we even know why it works.

Scenarios are in-class physics and engineering situations in which the students are tasked to design a solution (mathematical or physical) to the problem at hand. Scenarios are always solved using the same five-step procedure. Scenarios focus on learning to solve any situation they might encounter in the field. The five-step method has a pedagogical base as well as a practical one. Students begin by writing a short one-sentence statement of the scenario – what exactly are they being asked to do? Many students simply dive into the equations without actually reading the problem. Step 1 has the students draw a diagram, but not just any diagram. The diagram should contain all of the information in the problem – ideally, once the diagram is made, the students won’t refer back to the scenario’s text (of course, this comes with practice!).

Step 2 has the students propose a design – what approach will they take to solve the problem? At this level, they often simply don’t know; knowing the most efficient path comes with experience that the students don’t yet have. They are told, however, that none of these steps must be completed sequentially – in fact, they will bounce back and forth between the steps continuously as they solve the scenario. From a pedagogical standpoint, having the students come back and fill out Step 2 helps to transfer what they did to long-term memory, a critical step in mastery of the material.

Steps 3 and 4 are the meat of the technique. Students are not permitted to begin mathematically solving the scenario until they have the same number of equations in Step 4 as they have unknowns in Step 3. They will often begin by writing down all of the “knowns” in Step 3 and the single “unknown” that the scenario is asking for. The goal is to put as few equations in Step 4 as possible. They find an equation (usually from their “flip books”, spiral memo books that they have been creating as the semester progresses), and see if the conditions for using that equation are met. If no new symbols are introduced in the equation, they’re done! More often, however, the equation introduces a new unknown symbol. That symbol is added to the list in Step 3, and the students begin to search for an equation or relationship they can use to find it. The process continues until the condition of equal equations and unknowns is met. At this level, the students simply cannot holistically see their way to a final solution as a more experienced physicist can. Using this technique, the students focus on the relationships between quantities and the scenario before they try to solve the full problem.

Once the condition of equal equations and unknowns is met, the students simply combine those equations in Step 5 to get their answer. Once they complete Steps 3 and 4, the physics is over – all that’s left is “just math”. While the students may complain about the math in physics, it’s usually getting to the math that is the problem, not the math itself. The problem may in fact require rather complicated mathematical manipulations (integrations to find the moment of inertia, for example), but much of the stress is lifted. If their equations and symbols are valid, they know the solution is there waiting for them.

Practices are just that – opportunities to practice applying the techniques the students are learning. Working twenty small and quick homework problems – most of which require students to see the “trick” to them – isn’t actually all that useful. Instead, practices present realistic situations that allow the students to not not only become familiar with the scenario-solving technique but also give them confidence that they can solve any situation they may face. Social learning is critical to this process as well; research shows that student learning is dramatically improved by working with their peers. While these practices can and should be graded for accuracy, the fact that there are generally only 2-3 problems per section keeps them from being overwhelming. The students begin to see how these techniques can be directly applied to exam problems as well!

Labs in this curriculum depart from the “cookbook” labs that are typical of many physics lab courses. While there is value in learning how a particular concept or relationship was discovered, that value is often much less in the real world than the ability to design and implement a solution to a particular problem. For this reason, each lab is the front side of a single page – nothing more. The lab’s objectives – the “design challenge” – are clearly spelled out, but the students are not given explicit instructions on how to achieve those objectives. This freedom empowers student creativity, which in turn gives them a deeper connection to the material they are learning in lecture. Robotics was chosen as the “hook” for this curriculum, but in practice nearly any topic which serves as a motivation for the students works equally well. The robotics kits for this lab were purchased for less than $250 each (with four students sharing each kit); the financial outlay was not insignificant, but was far less than many other lab systems, while being far more flexible and motivating.

Often students want to go directly to assembling their robot, writing the full code, and getting their data – and then they can’t figure out why their design doesn’t work. In the real world, each subsystem must be designed, tested, and verified individually before a “systems integration test” brings all the subsystems together. The “design tasks” section of the lab sheet gives a set of “deliverables” that students must produce that show they have conducted the required subsystem testing. Even when the students are told repeatedly that “you’ll get your bot working more quickly if you test subsystems”, they will resist, even when they have spent hours trying to figure out why their bot doesn’t work. The design tasks force them to learn good development habits. It is suggested that each lab be given two sessions to complete whenever possible: the “deliverable” for the first session is a completed design (even if it is not yet working) while the second session is reserved for the test, evaluate, and revise process and gathering the required data.

Lab reports take the form of “design documents”, which mimic the format of documents they will use in the real world. Each design document follows the same format; once learned, the students begin to spend more time analyzing their design instead of just checking the box. Some instructors want individual reports (“group responsibility, but individual accountability”), while others will want a single document from each group. If the latter course is chosen, it’s important for a single student to be responsible for one of the four main sections personally (the executive summary should always be written as a group in this case). This maintains that measure of individual accountability. In general, it’s suggested that half the students’ lab grade comes from the design document and the other half from the performance of the design itself.

In addition to the lab write ups, the lab workbook also provides several “guides” that teach students tasks ranging from using the programming environment to implementing numerical integration. The lab workbook also provides “spec sheets” for each piece of equipment the students will use. While dramatically simplified, these spec sheets follow the same format of spec sheets the students will encounter in the real world. A common refrain in the lab when something isn’t working is “Did you read the spec sheet?” Often the solution to their trouble was right there. FInally, a series of videos – one for each lab – is available which will walk the instructor through the important tips to remember.

The curriculum does not present topics in their “traditional” order. Instead, it follows Richard Mazur’s (2021) approach which begins with energy and conservation laws. Not only are these concepts much more physically intuitive to students, it also allows them to learn fundamental concepts in one dimension. Once they have mastered the concepts, expanding to forces, vectors, and multiple dimensions turns out to be a fairly small task for them, so these concepts are postponed until much later in the curriculum. While this curriculum uses robots to illustrate the physical principles (simply to reinforce the lab), there’s nothing inherently unique about them – any theme can be used. The editable Google Docs files are available upon request – feel free to make the curriculum your own.

The curriculum is designed to be flexible enough to work with any teaching style. If an instructor prefers the traditional “talk and chalk” method of instruction, the lecture workbook serves as an excellent organizer for the students’ note taking. Microtasks are ideally done solely by students in groups (with the instructor traveling around the room providing coaching as required), but when time is short – and it will be! – the microtasks can be done as a whole-class experience, or can be explicitly led by the instructor as required. The lab and practice workbooks are designed to reinforce each other, but they are completely interchangeable with any other lab or homework system the instructor prefers to use. The workbooks are laid out so that you can delete entire topics if you desire – just regenerate the table of contents and you will be good to go. Nothing is lost by mixing and matching; again, feel free to make the curriculum your own.

The entire curriculum – all three books – are presented as Open Educational Resources (OER) and are freely available to anyone who wishes to use them. The instructor versions (and editable Google Docs versions of all the workbooks) are available by simply emailing the author at keith.watt@gccaz.edu – we ask that you don’t put these versions out on the net, for obvious reasons. The curriculum is distributed under the Creative Commons “BY-NC-SA” license. Simply put, this means you are free to use and modify the curriculum however you wish, provided that you give attribution to the original author, all use is non-commercial (you may charge for your actual copy costs, but not for profit), and any derivative work (changes and edits you made) are distributed under the same license.

We hope this curriculum is as exciting for your students as it has been for ours. If we can provide any advice or answer any questions, please don’t hesitate to get in contact.

Best of luck,

Dr. Keith “Kilo” Watt
Professor of Engineering Physics
Physics Academic Program Director
Glendale College, Arizona

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References

Bailey, J. M. (2009). Development of a concept inventory to assess students’ understanding and reasoning difficulties about the properties and formation of stars. Astronomy Education Review, 6(2), 133–139. https://doi.org/10.3847/aer2007028

Bandura, A. (1971). Social learning theory. Morristown
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Bardar, E. M., Prather, E. E., Brecher, K., & Slater, T. F. (2009). Development and validation of the Light and Spectroscopy Concept Inventory. Astronomy Education Review, 5(2), 103–113. https://doi.org/10.3847/aer2006020

Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176. https://doi.org/10.1002/tea.20442

Kay, D., & Kibble, J. (2016). Learning theories 101: Application to everyday teaching and scholarship. Advances in Physiology Education, 40(1), 17–25. https://doi.org/10.1152/advan.00132.2015

Vygotsky, L. S. (1978). Mind in society (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Cambridge, MA: Harvard University Press.

Watt K. (2020). Guided simulation: A new model of game-based pedagogy for non-STEM students in the community college (Publication No. 28155962) [Doctoral dissertation, Gwynedd Mercy University]. ProQuest Dissertations and Theses Global.

Final Product

Engineering Physics – Student

Engineering Physics – Instructor – contact Dr. Keith “Kilo” Watt for access.

Contact Information

Dr. Keith “Kilo” Watt: keith.watt@gccaz.edu