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What are the conditions for students to experience real joy in physics learning? Work from authors like Valerie Kaur &amp; Brené Brown suggests that one of the conditions is the opportunity for students to make the continuous choice of being in the emotional discomfort of learning. In this research presentation we will explore frameworks for connecting discomfort to joy in physics learning . To ground our thinking, we will use video data from one group of students in an Introductory Physics for Life Science majors course, who are collaborating on a computational activity to understand physics concepts. We present the analysis of one student’s learning experience, working on a computational activity of containing particles in a box and modeling diffusion, using a lens of joy and discomfort.

AAPT SM 21

Can learning physics be both joyous and uncomfortable?

uncomfortableness

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What are the conditions for students to experience real joy in physics learning? Work from authors like Valerie Kaur & Brené Brown suggests that one of the conditions is the opportunity for students to make the continuous choice of being in the emotional discomfort of learning. In this research presentation we will explore frameworks for connecting discomfort to joy in physics learning . To ground our thinking, we will use video data from one group of students in an Introductory Physics for Life Science majors course, who are collaborating on a computational activity to understand physics concepts. We present the analysis of one student’s learning experience, working on a computational activity of containing particles in a box and modeling diffusion, using a lens of joy and discomfort.

technical paper

A few years ago, the IUPUI Physics Department decided to introduce computational methods in its undergraduate curriculum to enhance student learning outcomes and engagement. The overall goal is for our graduates to consider computations as a complement to traditional analytic methods, and to develop the judgment and intuition to use a judicious mix of computational and analytic methods to investigate a problem. Since the project is in its infancy, the onset of the pandemic required some adjustments to the implementation plans. In this talk, I will describe our efforts to get buy-in from every faculty member in the department, training faculty members who were not facile with computations, implementing computations in every course, and developing an assessment instrument. I will also discuss some on-going and future work that is supported by an NSF-IUSE grant to advance the goals of the project and how the pandemic provided some unexpected silver linings.

Normalizing Computations across the Physics Curriculum

Computational modeling can provide a hands-on interactive component in a course that is otherwise entirely Zoom-based. In the introductory, calculus-based course, students can build and explore principle-based models of both hypothetical and real-world systems. They can explore the effect of varying parameters, and can construct models to help determine whether videos of physical phenomena are real or faked. However, the challenges of teaching basic computational ideas to students with no previous computational experience are also magnified in this environment. Creating pre-lab activities to teach basic concepts; ensuring that computational activities are adequately scaffolded, and convincing students to interact with each other in breakout rooms have proven central to success.

Integrating computation in a remote introductory course

There is a national push to better prepare our physics students in computation. How did these efforts translate to the rapid adaptation to virtual learning in response to the pandemic? In this talk, I will outline 1) computational tools that enabled remote learning of computation in my classes and 2) modifications to how I traditionally integrate computation to address the accommodation and empathy necessary as the students navigate the uncertainty of the pandemic. I will discuss using Jupyter and JupyterHub for various courses, Gradescope for real-time code feedback, and CodeCollab for collaborative coding.

Computational tools and humane instincts: computation in a pandemic

This study investigates what college-level and high-school-level variables significantly predict FMCE (Force and Motion Conceptual Evaluation) pretest scores and whether differences in these variables explain gender differences observed in FMCE pretest scores. The sample consists of students attending a large eastern land-grant university in the US who are enrolled in the introductory calculus-based mechanics class (N=1060). Variables examined include college performance, high school preparation, and non-academic variables such as sense of belonging, self-efficacy, and personality. Correlation analysis and linear regression were used to understand relations between these variables. 



What does the Force and Motion Conceptual Evaluation pretest measure?

Practice exams with immediate feedback and video solutions are among the highest rated learning resources by students taking introductory physics at the University of Illinois. These ratings align with a research showing the positive impact of formative assessment and the testing effect. Despite this alignment, data on our student use of practice exams suggests a majority of students are missing the most important benefits. In particular, the large fraction of students choosing to complete their first practice exam less than 24 hours before the exam, suggests they are primarily being used for cramming. During the Spring 2021 semester we introduced a “mock” proctored exam one week prior to the actual exam. We look at how performance on the mock exam impacted student predictions, preparation and performance on the actual midterm exam. Our results show that despite the valuable formative assessment available from the mock exam, students were resilient in their dependence on cramming. 


Practice exams impact on student exam preparation and performance

We present a qualitative analysis of twenty-six interviews about computation in introductory mechanics courses. These interviews are part of a longer term project to develop an assessment protocol for computation in introductory physics. Interviewees were drawn from either: introductory physics, research on computation within physics, or an industrial physics career that involves computation. The interviews were coded into twenty-seven different nodes and sub-codes within each node to concentrate and differentiate between node details. We find that an assessment should not be language specific, even though Python is a favorite. The computational level at which interviewees expect students to have when they exit the course varies immensely. Some interviewees have a goal of getting students thinking about or feeling better about computational practices without performing computational practices themselves. Other interviewees have a goal of students solving many problems computationally, the capability to use multiple programming environments, and writing programs from scratch.

Computational Assessment in Introductory Physics: Codes From Qualitative Interview Analysis

As part of a project to develop a written instrument for assessing learning assistants (LAs) pedagogical content knowledge (PCK) in the context of questioning (PCK-Q), we are experimenting with alternate question formats. Previously developed and validated free-response questions serve as the basis for a new question format. This new format consists of Likert-style answer choices that require the LAs to assess a scenario where a student has a misunderstanding, evaluate the situation, and score potential LA responses. The instrument will examine an LAs ability to identify appropriate responses that provide evidence of PCK-Q in the classroom. 


Using Ranking Question to Assess Students Assistants' PCK-Q

We report on the development of a written assessment designed to analyze pedagogical content knowledge in the context of questioning (PCK-Q). We discuss the process of analyzing and coding classroom videos and writing and testing a written assessment. The classroom observations have first been coded using a coding scheme that analyzes levels of questioning in an inquiry-based classroom. A written instrument was then developed based on the classroom videos, administered to student teaching assistants (TAs and LAs) and compared with the video results to validate the written instrument. We report on the coding scheme and the validation of results as part of this ongoing project.

Development of an Instrument for Analysis of Student Assistants’ PCK-Q

The Force Concept Inventory (FCI) and the Brief Electricity and Magnetism Assessment (BEMA) were used in our sophomore-level courses on classical mechanics and electricity and magnetism, respectively, in 2020-21. Both courses used pedagogical methods to encourage interactive, engaged learning. The assessment results and students’ course grades are compared. The result shows that the assessment tools help to diagnose weak topics in student’s command of concepts. The effect of the pandemic of COVID-19 on students’ learning is discussed.

Using research-based assessment tools in intermediate physics courses

Postponing work impacts virtually everyone at some point, especially students. We found academic procrastination is easily observed among students by studying the student’s online submission times. We characterized physics student assignment completion times (ie. procrastintation) behaviors for online homework assignments into 4 completion time classes that clearly distinguish between week-to-week patterns, mean course grades, assignment completion rates, and proportion of women. Consistent with many studies, we find that procrastinating students tend to have lower course grades. Closer examination of exam and non-exam grade components reveals that exam and non-exam components are separately predicted by the two “orthogonal” measures, ACT score and completion time, and we propose that these are measuring so-called cognitive and non-cognitive factors, respectively. Identification of procrastinating students (using completion time) could be important in measuring non-cognitive factors that lead to early identification of at-risk students in physics courses.

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