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Project-based learning centers student projects in the curriculum so that students learn the required material to complete the project. This pedagogy benefits students&#39; creativity, yielding a diverse set of projects. It is challenging to grade and provide formative feedback on such projects in large-enrollment courses with over 300 students. Even when students create projects in teams of 5 or 6, we have 50 or more projects. We discuss our use of formative peer feedback for student projects in an introductory undergraduate college-level physics course. In our implementation, rather than students grading each others&#39; final projects, students give feedback on a project draft in an effort to improve the authenticity and usefulness of peer feedback. We discuss our analysis of this formative peer feedback, comparing the difference between experts&#39; and students&#39; peers&#39; feedback. We also discuss the extent to which students find the peer formative feedback process valuable.

AAPT SM 22

Formative peer grading in project-based learning: is it reliable?

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Project-based learning centers student projects in the curriculum so that students learn the required material to complete the project. This pedagogy benefits students' creativity, yielding a diverse set of projects. It is challenging to grade and provide formative feedback on such projects in large-enrollment courses with over 300 students. Even when students create projects in teams of 5 or 6, we have 50 or more projects. We discuss our use of formative peer feedback for student projects in an introductory undergraduate college-level physics course. In our implementation, rather than students grading each others' final projects, students give feedback on a project draft in an effort to improve the authenticity and usefulness of peer feedback. We discuss our analysis of this formative peer feedback, comparing the difference between experts' and students' peers' feedback. We also discuss the extent to which students find the peer formative feedback process valuable.

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Smartphone based labs are a cornerstone of our efforts to reform the introductory labs in both our calculus-based and algebra-based classes. Students use smartphones to collect data in the lab (Bluetooth paired with sensors) and at home (using the native sensors in the phone). In this talk, we will give a brief overview of the project, then emphasize the at-home labs. These labs have allowed us to substantially reduce crowding in the lab, emphasize data analysis, and give students an opportunity to be more exploratory in their approach to lab. We will highlight examples of our at-home labs and discuss lessons learned during the first year of the project.

Smartphones in the introductory mechanics lab

QuarkNet is a project funded by NSF, ATLAS, CMS and Fermilab with additional support from participating universities. My role in the project is to oversee the Data Activities Portfolio (DAP), a compendium of activities to help teachers incorporate 21st Century physics into their curriculum. This talk will explain the DAP structure that follows guidelines for Effective Teacher Professional Development as described by Linda Darling-Hammond, Maria E. Hyler, and Madelyn Gardner, with assistance from Danny Espinoza. We will provide data about the efficacy of our activities in the classroom.

Planning Effective Professional Development

With the transition to remote learning, many lab-based curricula had to be quickly amended. Here, I present a build-it-yourself experiment that can be used by students with a wide range of mathematical backgrounds to explore principles of mechanics and experimental design. This coupled harmonic oscillator experiment, initially developed for a sophomore level waves and optics lab at UT Austin, is built with readily available materials and doesn't suffer from parametric oscillations: a common difficulty in such experiments. In an introductory physics class, this experiment can be used as a platform to discuss transfer of energy and momentum in a system that challenges students' intuition. Freedom in the setup's construction leads to student-led investigations of experimental error mitigation. Students familiar with trigonometry can explore the small angle approximation. Finally, students familiar with linear algebra can study decomposition of waves into sinusoidal (eigenfunction) components, and an introduction to Fourier transforms.

Take-home Experiment: Student-led Exploration of Coupled Harmonic Oscillators

For the second year of the pandemic, we developed new labs for our life-science stream intro physics courses. Despite the return to on-campus learning, our students engaged in unscheduled, remote experiments. The goal of our labs was to connect life science topics (fluids, optics, spectroscopy) to an everyday context through hands-on experiments. We created ‘at-home’ lab kits for students, combining common items (rulers, string, battery) reinforced with standardized items for consistency (e.g. known masses) and specialized materials (capillaries, LEDs, diffraction grating). We employed smart phone apps for measurement, embedded Geogebra for data analysis, and complemented activities with the video- and simulation-elements from virtual labs of the previous year. Some of highlights of our experience over the year include: a citizen-science lab, insights into the workflow of students, and their preference regarding ‘scheduled & on-campus’ vs ‘asynchronous & remote’.

Lab Kits for ‘At-Home’ Experiments in Physics for Life Sciences

During the Spring and Summer sessions of 2020 and 2021, our students used the circuit kits we sent them to create light sources for use in the optics portion of the course. Various single-colored LEDs and an RGB LED were used with the Fresnel-lens book mark and diffraction grating slides included in their kit to explore ray optics, image formation, color mixing, and interference effects. Christmas lights, Fresnel book marks, magnifying glasses, and reading glasses were could be used in the explorations by those without access to the kits. The use of multi-colored light sources increased the quality of student discussion of the concepts involved.

Using Circuit Kits and LEDs to Teach Optics at Home

Machine Learning (ML) is becoming ubiquitous in the realms of science, business, law enforcement, and even the arts. I have developed a three-week general education course on machine learning for non-scientists. Each day in the first half of the course, students engage with demonstrations and tutorials. They learn how to implement simple examples of text and image classification as well as generative algorithms and adversarial networks. In the second half of the course, the students carry out a project of their own design. I do not assume students have previous experience with programming, calculus, or ML algorithms. They learn by experimenting with existing code to achieve goals of increasing complexity. I will report on the successes and challenges of this course and share what I have learned about how students can effectively grapple with the opportunities and dangers that ML represents.

Teaching Machine Learning to Non-Scientists

In an earlier study we showed that small amounts of extra credit offered for early progress on online homework assignments can reduce cramming behavior in introductory physics students. This work expands on the prior study by implementing a planning prompt intervention. In the prompt we asked students to what degree they intended to earn extra credit offered for early work on the module sequence, and what their plan was to realize their intentions. The survey was due several days before the first extra credit deadline. We found that students who completed the prompt earned on average 0.6 more extra credit points and completed the modules an average of 1.1 days earlier compared to a previous semester. We detect the impact of the survey by creating a multilinear model based on data from students exposed to the intervention as well as students in a previous semester.

Planning Prompt Surveys to Encourage Early Completion of Homework Assignments

Heileman et al. developed a methodology for quantifying the complexity of program curricula. Using that framework, they showed that Engineering programs that were deemed higher quality had curricula and degree plans that were simpler than engineering programs that were ranked to be lower quality. They also propose that curricula that is simpler is more agreeable to students, claiming that a simpler curriculum would be beneficial to student retention. The purpose of this presentation is to determine whether or not this trend holds for physics programs in the U.S. Physics curricula from different universities were analyzed using the methods developed by Heileman et al. to see if there is a correlation between program complexity and program quality.

Curricular Analytics in Physics

Research-validated clicker questions comprise an easy-to-implement instructional tool that serves to scaffold and formatively assess student learning. We present findings from the implementation, in consecutive years, of a research-validated Clicker Question Sequence (CQS) on student understanding of the uncertainty principle as it applies to two-state quantum systems. This study was conducted in an advanced undergraduate quantum mechanics course, in both the virtual learning environment of 2020 and the in-person classroom setting of 2021. Students were assessed after receiving traditional lecture-based instruction, and their performance was compared with that on a similar assessment given after engaging with the CQS. We examine and remark on the constant and differing trends observed in the two modes of instruction.

Clicker Question Sequence on Uncertainty Principle: Virtual and In-Person Implementation

The field of quantum computing is presently gaining tremendous attention from governments, researchers, engineers, academics and investors. There is an ongoing push for the development of quantum computers, advances in information technology, and the development of a quantum workforce. This needs to be accompanied by the development of quantum computing courses and curricula and the development and adoption of evidence-based materials and pedagogies to support the education of the next generation of quantum information scientists At Texas Tech University (TTU), we have introduced a course in Quantum Computing and have begun to research student understanding of topics in this field. Our goal is to develop evidence-based materials for the course. We did a series of interviews to identify students’ strengths and difficulties in these topics. We report on the results of these interivews and our initial work on the development of supplementary materials for the course.

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Smartphones in the introductory mechanics lab

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Network analysis of the CSEM with Modified Module Analysis

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