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The meaning and representation of basis is fundamental to many quantum mechanics topics, including probability, measurement, and time evolution. Furthermore, students are often required to change the basis of a state in order to gain other information about a system. Data collected from “spins-first” courses across three institutions has illuminated a variety of challenges for students, related to procedures, interpretation, and notation. To help improve student understanding, we designed an activity that makes an analogy between spin basis vectors and more familiar two-dimensional Cartesian unit vectors. In this activity, students draw vectors for a state and discover that changing the basis is analogous to using a different set of coordinate axes to represent the same state. We provide evidence that this activity supported students in their understanding of basis representation and in the procedure of changing basis.

AAPT SM 20

From Cartesian to Hilbert space: Improving understanding of quantum bases

dirac notation

basis

projection

linear algebra

quantum mechanics

upper-division

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. 

technical paper

There have been many recent calls to overhaul the introductory lab course. This is because it doesn’t meet a commonly stated goal of reinforcing lecture content (Holmes et al., Physics Today, 2018), nor is it focused on teaching experimental skills like designing experiments and analyzing data (Kozminski et al., AAPT, 2014). In addition, it doesn’t teach computational modeling—where students use computational tools to conceive, construct, or test models—even though the laboratory is where this modeling would naturally occur (Behringer et al., AAPT, 2017). One method to overhaul the course that is gaining traction is to have students engage in open-ended projects. I will discuss implementation of this method and how it can be used to teach computational modeling to introductory students without any computational skills. This discussion will include computational practicums, emphasizing modeling in the lab notebook, requiring students to vary two variables in their models, and building in iteration.

Adding Computation to the Introductory Physics Lab

To assist physics departments and faculty as they improve undergraduate education, the American Physical Society, in collaboration with the American Association of Physics Teachers, is developing a comprehensive guide for program improvement and evaluation, drawn from research findings and community knowledge. Scheduled for release in late 2020, the guide will provide concise advice on nearly all aspects of undergraduate education including suggestions on how to effectively evaluate impact. The EP3 guide will also offer departments guidance on how to produce detailed evidence of how they plan, evaluate, and improve undergraduate learning to help meet university accreditation requirements or prepare for or conduct external site reviews. This presentation will give a broad introduction to the guide and solicit feedback from the audience on how best to make these materials available to the community.

A Guide to Department Improvement: Effective Practices for Physics Programs (EP3)

Many instructors are working to transform their traditional labs to better develop scientific thinking, reasoning, and decision-making abilities. Traditional labs, defined as being highly structured (students follow a predetermined protocol) and confirmatory (that protocol is supposed to demonstrate a predetermined physics principle), have long been criticized for not engaging students with authentic experimentation. As defined, students have very little opportunity or incentive to make decisions or think critically about the experiments or results. In response, we often default to reducing structure and affording more agency so students can practice making experimentation decisions. Research is suggesting that this response may be overly simplistic. In this talk, I’ll discuss the nuances of structure and agency in teaching scientific decision-making and argue that the role of confirmation may actually be the more critical issue than structure.

The role of structure & confirmation in developing scientific thinking, reasoning, & decision-making abilities

PiA - Physic in Advent https://www.physics-in-advent.org is an Advent calendar of a special kind: a physics Advent calendar. From 1st to 24th December, small physics experiments using household material are presented every day as youtube videos by Mr. Santa or Ms. Santa. Participants do the experiments and answer a question on the PiA website. On the following day, there will be a solution video and possibly a point. After 24th December, all participants receive individual certificates. Among the best participants, prizes will be raffled off in the categories individual, school class or school. „PiA - Physik im Advent" is aimed at children and pupils aged between 11 and 18 years. It is intended to awaken the joy of experimenting and offer education, entertainment, and fun at the same time. In 2019, a new record was set with 43,500 registered participants, 49% of whom were girls, and 1.7 million visitors overall.

PiA - Physics in Advent, a hands-on physics advent calendar

Machine learning algorithms represent an exciting new class of quantitative methods to understand physics classes and students. Recent work has applied these algorithms to classify students as those likely to receive an A or B or students were likely to receive an C. D, F in a physics class. The metrics used become unreliable when the outcome variable is substantially unbalanced. This talk further explores the classification and extends those methods to predicting whether a student will receive a D or F. This study also investigated the previous finding that demographic variables such as gender, underrepresented minority status, and first-generation status had low variable importance for predicting class outcomes; down sampling revealed that this result was not due to the underrepresentation of these students in the class studied.

Extending Machine Learning to Predict Unbalanced Physics Course Outcome

This work examines peer-quizzing with metacognition in calculus based introductory physics. Students take individual quizzes and then redo part of the quiz with a partner. Because the students to choose which problems to redo, they must reflect on their confidence level on different concepts (questions). The results show there is a clear benefit to introducing weekly quizzes. While peer quizzes do not result in a clear improvement in the final concept assessment score, there is improvement in the normalized gain. Further, there is a clear signal that students who score above average on their quiz redos will also show above average assessment gains. These results show that while the intervention does not improve the overall grade, it helps individual students at all grade levels improve their understanding in relationship to their peers. It also suggests that interventions for students with low redo scores will have significant impact.

Utilizing Peer-Instruction and Metacognition on Quizzes to Improve Concept Learning

Science education studies framed around funds of knowledge, mental models and student resource framework appear to have three general characteristics: they are learner centered, tend to deviate from the deficit student model of teaching that present learners as empty vessels, but rather present learners as individual beings with different sets of skills and needs. However, there is not a consensus on the ways, as far as we know, in which these three concepts relate with each other. We conduct a systematic inquiry of the literature available for how funds of knowledge, mental models, and resource framework are respectively used in science education, and present a scheme relating these three frameworks that can be utilized when evaluating students’ ideas about physical concepts such as energy. Considering how those frameworks connect with one another might provide additional avenues to further explore how students' conceptual understanding of the physical world are formed.

Characterizing Conceptual Understanding in Introductory Physics using Funds of Knowledge, Mental Models and Resource Framework

You and/or your department are interested in introducing computational modeling into your introductory physics courses, but want some concrete ideas of how to accomplish this. You want it to be student friendly; i.e., not requiring much prior student programming experience. You want it to be instructor friendly; i.e., not requiring huge time investment for the faculty to implement. You want it to be affordable; i.e., not requiring a huge investment by the department, faculty or student. This presentation will illustrate a way to accomplish introducing computational modeling into introductory physics courses.

Computational Physics in Introductory Physics

As a first step in a larger study of student difficulties in upper-division thermodynamics and statistical mechanics, we present the results of think-aloud interviews with graduate students on a set of entropy related questions. The four interview questions were developed to probe student understanding of entropy as a pressure towards equilibrium, as a quantity maximized in equilibrium, as a connection between microstates and macrostates, and as a macroscopic state-function. Exploring graduate students' understanding entropy and their ability to solve problems and reason with entropic arguments will provide insights into how physicists develop a mature understanding of entropy as a physical quantity. Specifically, we hope to see if new conceptual difficulties emerge as students progress to graduate school, and whether difficulties seen in undergraduate courses persist, evolve, or cease to present issues in the graduate setting.

Investigating how graduate students connect microstates and macrostates with entropy

Computers and algorithms have fundamentally reshaped our world in the last few decades. Data is the new oil. NGSS has codified computational thinking as a science practice. Yet programming has yet to penetrate compulsory K-12 schooling in a meaningful way. A number of high school Modeling Physics teachers have been teaching their students to program for two decades. Although teaching programming basics cut into the finite number of minutes they would otherwise have devoted to physics concepts, they valued the trade-off—equipping students with a tool for visualizing abstractions (e.g., EM fields), and for more tightly connecting mathematical, graphical and diagrammatic representations of conceptual models. Five years ago AMTA and AAPT joined forces to adapt HS physics teachers’ pioneering work in computing for use in 9th grade Physics First classrooms. This is a report on where we are presently and what we have learned along the way.

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From Cartesian to Hilbert space: Improving understanding of quantum bases

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Spanning the space of student ideas on change-of-basis in quantum mechanics

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