United States

Often the skills required to use the Unix/Linux command line are perceived as old-fashioned, difficult to learn, and unnecessary. However, the Linux command line is an extraordinarily flexible environment for programming, data reduction and analysis, simulation, and visualization, and is thus in wide use throughout the physics research community and beyond. These skills are surprisingly accessible to students. In this talk, I will argue that the command-line environment is a pedagogically beneficial environment for students to learn computational physics, with advantages both within the classroom and beyond it, and demonstrate some of the tools and methods the computational physics students at Syracuse use for simulation, visualization, animation, and data analysis.

AAPT WM21

PL03 PLENARY: MCKENSIE MACK, MCKENSIE MACK GROUP (MMG)

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AAPT SM21

keynote

We encounter variables with little variation often in PER due to the demographics of physics and the questions we ask. For example, in course completion studies, most students will earn high enough grades to pass the course. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the fraction of positive outcomes, feature imbalances, and odds ratios. We find the algorithms treat features with the same odds ratios differently based on their imbalance and the outcome imbalance. While none of the algorithms solved the problem, some reduced the scale of the problem. Our results suggest that PER studies may contain false negatives when determining which variables are related to an outcome. We propose recommendations for researchers and then illustrate these by predicting which applicants will be admitted to a graduate physics program.

1539 - Addressing Rare Outcomes in PER Quantitative Studies

Course grades are concrete outcomes with real-world consequences for students. To what extent are grades, as they are currently awarded, consistent and fair? &nbsp;Higher Educational Institutions are sitting on vast amounts of data that can help us to gain more insight into these questions and perhaps lead us to more desired outcomes. I will discuss three data analytics projects we conducted. First, I will examine how student-level factors such ACT score and demographic group effect grades and exam vs. non-exam grade components. This will reveal demographic disparities. &nbsp;Next, by considering measures of prior preparation, such as ACT scores and prior course grade, I will apply the concept of differential prediction to examine the extent to which grading is consistent between sections, instructors, and semesters, and further examining how consistency between these groups extends to the demographic factors of gender and race. Areas of concern are found for all factors. &nbsp;

1630 - Consistency and Fairness in Introductory Physics Course Grades

Quant PER is a growing subdiscipline that shows promise in helping our students succeed. My research group has sprouted&nbsp;from this opportunity. I'll talk about building a functioning learning analytics lab, testing waters with exploratory statistics, moving into correlation data mining, and then proceeding onto predictive analytics. We've used maching learning to predict students overall class grade percentage to within about 6% average error, before they even start the class. Come see all the fun things the BoxSand Research Group at Oregon State University is doing.

2010 - The Growth of a Quant PER Group

Machine learning algorithms represent an exciting new class of quantitative methods to understand physics classes and students. Recent work has applied these algorithms to understand physics major retention to degree and the risk factors influencing success in introductory physics. This talk will explore the application of these algorithms to identify students most at risk of failure in introductory calculus-based physics. These predictions are complicated by the very unbalanced nature of the samples; most students do not fail physics. Machine learning algorithms&nbsp;will be explored in depth including&nbsp;decision trees and random forests. These will be applied to understand student risk both using institutional variables such as college GPA and within class variables such as homework grades with the goal of making accurate predictions early in the class. Limitations and data requirements for these algorithms as well as their accuracy for underrepresented populations will also be discussed.

1506 - Using Machine Learning to Identify At-Risk Students in Physics Classes

Hands-on experience is of utmost importance to physics laboratory experiences. Like most other schools and colleges, classes at Central Washington University had to adjust quickly to online-learning setups this past quarter. This new teaching style unfortunately pushed aside in-person interactive laboratory sessions that could have been used to supplement lecture-style videos that students watched as a part of their introductory medical physics seminar. Utilizing pre-measured data, videos, and photographs from a live clinical linear accelerator, the team at Northwest Medical Physics Center was able to create an online laboratory experience for real-time student engagement. In this presentation, the method of implementation of the lab will be discussed, along with goals for future expansion of the virtual medical physics lab program.

Virtual Medical Physics Undergraduate Laboratory Experience with Linear Accelerator Data

This educational research is about teaching electromagnetic interaction or rock formation. We use&nbsp;&nbsp;&nbsp;papers showing the elementary electron-electron-photon&nbsp;Feynman diagrams or&nbsp; sentimentation -metamorphism-melting geo-diagrams. The individual papers could be rotated, inverted, and combined to provide diagrams for any electromagnetic interaction process or rock formation. Combining orientations of single paper ,we make combinations of three or more papers. &nbsp;Time is running vertically upward in every diagram. Given the opportunity to manipulate the papers, students became involved and rapidly developed understanding of&nbsp;electromagnetic interaction or rock formation. The students did strikingly better on the post-tests than the pre-tests.

1522 - Teaching Physics with Hands-on Diagrams

2020 has seen a release of multiple exciting new results in gravitational wave science! In this session, we will review these latest discoveries that have opened new windows into understanding the universe. Then we will introduce teachers to activities that can be used in the classroom, both remote and in-person, to introduce secondary science students to some of the major concepts related to understanding and detecting gravitational waves, including the new results! Detectors such as LIGO and Virgo will be examined, as well as the use of Multimessenger Astronomy.

1543 - Gravitational Wave Science for the Secondary Classroom: Discoveries and Activities

The Frayer Model is a reading strategy presented as a graphic organizer.&nbsp; Researchers have shown that the approach promotes vocabulary development for specified scientific terms among high school students.&nbsp; This empirical research study sought to explore a way to develop scientific vocabulary among English Language Learners and Students with Disabilities in a synchronous learning environment between in-person and virtual learners by implementing the Frayer Model in a digital format using Google Slides. Google Slides settings allow each student to interact independently with the content while simultaneously sharing with the group.&nbsp; A convenience sample consisted of eleven high school students, seven in-person learners, and four virtual learners.&nbsp; Daily bellwork composed of one question provided post data to assess retention and understanding.&nbsp; Observational data captured in reflective logs shows increase active learning, and students form personal connections between the scientific terms and their personal experiences.

1512 - Frayer Model: A Digital Format for Synchronous Learning

Sharing strategies for engaging labs and group worthy experiences in an online setting

PL03 PLENARY: MCKENSIE MACK, MCKENSIE MACK GROUP (MMG)

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