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Head-fake learning (“HFL”) is a form of teaching material wherein the students do not realize the form or complexity of what they are learning. This occurs primarily when the mechanism through which the material is taught makes the students think they are learning something entirely different, i.e. how to succeed in Minute to Win It style mini-games instead of learning projectile motion. We discuss how we utilized HFL to present introductory physics and mathematical principles in a newly designed course at UNC entitled Game Show Theory, which uses game show structure and optimal play as a driving motivator. In addition, we will present examples of student work and testimonials regarding how they interacted with the course (and it’s HFL methodology) and will discuss small-scale game show demonstrations which teach physics concepts with minimal cost.

AAPT SM 20

Presenting Physics Concepts via Head-Fake Learning

head-fake

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teaching

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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. 

Of all subjects, quantum mechanics is arguably the one that can benefit the most from integration of computation into the curriculum. Unlike classical mechanics or electromagnetism, quantum mechanics has only few time-independent problems, countable with one hand, that are analytically solvable. There are virtually no time-dependent problems with analytic solutions. This poses a clear challenge to helping students in introductory quantum mechanics in understanding the concepts and developing intuition or insights. Even for the straightforward analysis of two states in a superposition, the bedrock of quantum mechanics, it is hardly possible to follow the time evolution without the use of computation. In this presentation we discuss the integration of computation in an otherwise traditional undergraduate quantum mechanics course to deal with the challenge. Activities (see http://www.faculty.umassd.edu/j.wang/) include simple visualization of superposition, quantum revival, determination of eigenstates, wave packet motion, and screened hydrogen atoms.

Insights from computation in quantum mechanics

I have created a new inventory of innovative online introductory physics problems based on the findings of state-of-the-art research in physics pedagogy and my deep awareness of students’ needs. This inventory currently contains more than 250 items selected from the OpenStax College Physics Textbook and my own collection of 15 years of exams in the College Physics 1 course. These problems are implemented on the Varafy Online platform, and are readily integrated into Canvas (or other Learning Management Systems). They can be easily tailored to each individual instructor’s teaching style. This inventory is licensed under the Creative Commons Attribution 4.0 International License and is available at no cost to instructors.

An OER Inventory of Innovative Online Physics Problems

In the Chinese high school physics curriculum, the Ampere's rule, the left-hand rule, and the right-hand rule are used to determine the direction of the magnetic field, the ampere and Lorentz force, and the direction of the electric current. However, without a deep understanding of the basis of these rules, which arises from the concept of cross-product, it is easy for students to confuse these rules. This leads to inefficiency and difficulty in problem-solving, which is also the difficulty faced by most teachers. The central idea of these three rules is the cross product; therefore, by guiding students to establish the concept of cross product can cultivate deep learning of electromagnetism knowledge. In this presentation, a conceptual framework of the hand rules will be instructed along with an assessment tool that evaluates students’ understanding of the mastery of the three rules at different phases of their learning.

Assessment of student learning of the hand rules in electromagnetism

We’re developing a web-based utility to give instructors next morning formative reports on last night’s assignments, including the time and difficulty on questions and videos/readings to guide today’s instruction. Together with additional metadata, this information can guide revising the course for rerunning next year. Data are presented using color codes for quickly assessing how well students are doing on individual resources and on the entire assignment. This quickly identifies resources to eliminate or move elsewhere. We use “edx2bigquery” to convert edX log data into Google BigQuery which generates the dynamic reports. It also provides an easy way of adding static metadata via a popup in a modified version of open edX or directly into the resource database. These uses illustrate the desirability of “closing the loop” in education, a powerful way to improve instruction and content. The audience can suggest what information they desire - or to mohamedr@mit.edu

Report and Rerun: Closing the Loops in Physics Education

An essential element of STEM education of the 21st century is the development of scientific thinking, reasoning, and decision-making abilities. In the current literature, there exist multiple schools of thought on various aspects of scientific thinking and reasoning; however, the research community has yet to reach a consensus on the definition, theoretical foundation, and assessment scales of scientific reasoning. This presentation will introduce a comprehensive modeling framework as well as a new assessment instrument on scientific reasoning, which are developed with an emphasis on the process of theory-evidence coordination (TEC). Future work and possible collaborations will also be discussed.

A Modeling Framework of Scientific Thinking and Reasoning

A key strategy in solving geometric optics problems is drawing ray diagrams. However, through interviews with middle school teachers in China, it appears that the ray diagram is not emphasized in instruction due to the fact that most problems homework and exams can be solved by memorizing the final results of special cases (such as the three special rays that go into a lens). As a result, students did not make the connection between the principles of geometric optics and the final results that they memorized. In this talk, a conceptual framework on geometric optics is introduced to map out students’ knowledge structures. Based on the conceptual framework, a multiple-choice test is designed to further probe students’ conceptual understanding. Assessment outcomes will be discussed to shed light on new instruction methods that emphasize the ray diagram method for achieving deep understanding of geometric optics.

Assessment of knowledge integration in learning geometric optics

Students’ positions within the social network of a physics classroom has been shown to correlate with students’ sense of belonging and performance. Students that are more well-connected in the classroom also tend to persist in physics. Current research in PER aims to understand how different types of active learning classrooms promote the development of students’ social networks. In this work, we examine how these networks develop in introductory physics labs. Physics labs offer ample space and freedom for students to interact with their peers and build a network. We use video of students working in labs to capture the interactions between students, examine how network topologies compare between labs designed with different goals, and evaluate how these topologies evolve over the course of a semester. I also discuss the viability and efficiency of using video to construct student networks with large amounts of data.

Connecting the dots: Student social networks in introductory physics labs

Weber State University is an open-enrollment commuter school where each year about 300 students take the first semester of calculus-based introductory physics, while about 10 students receive physics bachelor's degrees. We have successfully put quite a bit of computation into the curriculum for the latter group, but not for the former. In this talk I will describe some of the computational pieces in our curriculum for physics majors, including a sophomore-level scientific computing course, microcontroller programming in our electronics course, and integration of numerical approaches into some of our core theory courses. I will also offer our excuses for not making computation a significant part of the introductory course.

Computational Physics at Weber State University: Successes and Excuses

Graduate program’s reported Physics GRE requirements, recommended minimum scores, and previous cohort score averages influence students’ application decisions. In recent years, the test-optional language of No Required Minimum and GRE Optional requirements has muddied the waters for prospective applicants, particularly for women. In this qualitative study, 60 graduate students (27-F; 31-M; 2-DND) from 24 different graduate programs were asked how they decided where to (and not to) apply to graduate school. Male and female students were equally likely to apply to graduate programs that stated No Required Minimum or GRE Optional language on their admission requirements webpage. This talk will highlight how Physics GRE requirements influenced students’ decisions on where to apply to graduate school and how students interpreted GRE-optional phrases when submitting their application materials. Supported by NSF-1633275

“Optional” General and Physics GRE Requirements: The Impact on Prospective Graduate

The quantum double well is familiar to most of us as a model of molecular orbitals and of wiggling ammonia molecules. It's also a step toward understanding band structure in periodic potentials, an elementary system that exhibits tunneling, and, when only the lowest pair of states is accessible, a beautiful example of a qubit. As a qubit with wavefunctions, it's the perfect system for making the connection between continuous wave mechanics and discrete matrix methods. On top of all this, a double well in two dimensions provides a good first example of an entangled two-particle state. But how can students actually solve the double well energy eigenvalue equation without getting bogged down in transcendental equations or the WKB approximation? The solution is fast and easy if they have learned any of several numerical methods.

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Insights from computation in quantum mechanics

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