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Much existing physics education research (PER) on student ideas about momentum focuses on the difficulties that students face when learning this topic. These difficulties are framed as obstacles for students to overcome in order to develop correct understandings of physics. Our research takes a resources-oriented approach to analyzing student responses to momentum questions, viewing student ideas as valuable and potentially productive for learning, over and above their correctness. Here, we highlight four conceptual resources that provide insight into students’ ideas about momentum, which are the conservation resource, direction resource, collisions resource and properties resource These resources are context-dependent and could be elicited and built on by instructors to support students in developing more complex and sophisticated understandings of physics.

AAPT SM 21

Student Resources for Understanding Momentum

momentum

conceptual resources

pedagogical content knowledge

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poster

A model train is automatically controlled for and physics experiments conducted by analyzing its motion. By measuring the motion of this model train using an mobile ultrasonic sensor unit (in the web site [1]) connected to a smartphone, live-time plots of distance, velocity, and acceleration can be obtained. This material will assist students in redefining their interpretation of physical experiences. Furthermore, programming the motion of a model train can be applied not only to physics education, but also to elementary and electronic work training[2]. [1] Moblie Physics Education Lab (Web site): https://sites.google.com/view/diracma888/ [2] A. Adachi: "New Physics Experiments using Model Train and Smartphone Apps: (Volume 1 Constant-Velocity Motion)" (e-books, 2021) https://www.amazon.com/dp/B08YD15NZX

Experiment of Model Train using Ultrasonic Sensor Connected to Smartphone

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

Faculty and graduate students develop as education researchers through different avenues. One possible path is through their participation in the Professional development for Emerging Education Researchers (PEER) program, which is designed to develop participants' discipline-based education research through hands-on activities during a series of workshops. We collected data before and during participation in one of the editions of this program conducted entirely online with primarily physics and math faculty. This data allows us to examine in what ways engagement in the workshops affects participants' research ideas and their self-efficacy. In particular, we explore the ways in which participants' perception of their growth in the field evolves as they engage in this professional development program. This work will enable us to analyze the impact of this virtual professional development experience as well as gain a deeper understanding of different facets of emerging education researchers’ professional development.

Growth of Emerging Education Researchers in Virtual Professional Development Program

This study advances the work of previous studies that have applied machine learning algorithms to identify students who are at risk of failure in introductory calculus-based physics. Those prior studies found it very difficult to predict the outcome of students who received a D, and F, or W (withdrawal from the course). This study applies ensemble machine learning—using two or more machine learning algorithms in tandem—to identify students at-risk of failing introductory calculus-based physics. These algorithms are applied using institutional variables such as college GPA and in-class variables such as homework grades. Non-cognitive variables gathered through in-class surveys, such as self-efficacy, are also used to improve identification accuracy.

Using Ensemble Machine Learning to Identify At-Risk Physics Students

MILES (Mentoring Innovative Learning Experiences for Students) at Santa Rosa Junior College (SRJC) is a National Science Foundation (NSF) S-STEM funded program designed to improve the academic outcomes of high-achieving, low-income STEM students. The program provides students not only with financial aid, but also a cohort experience with like-minded peers, an SRJC faculty mentor, and workshops and a one-unit class to build career skills. As originally conceived, students would enroll in cohorts of 20 for up to three years before transferring to a four-year degree granting institution. Now in the third year of the five-year grant, we will discuss the outcomes and challenges of this program so far. This work was supported by NSF DUE-1742635 and by Santa Rosa Junior College.

STEM MILES: Mentoring Innovative Learning Experiences for Students

This poster analyzes two examples of whiteboard meetings from a college calculus-based introductory physics course taught using University Modeling Instruction. In this pedagogy, students work in small groups to create a solution to the same problem on 2′ × 3′ whiteboards. They then sit in a large circle with their whiteboards held facing in and conduct a student-led whole-class discussion (“board” meeting) to reach a consensus. One example is given of a conversation where students overcame sharp disagreements to eventually reach whole-class consensus and another example is given where they did not. We examine how social positioning contributed to students either successfully examining and resolving different ideas or failing to do so. Our analysis suggests that the way students position themselves in discussions may open or close the collaborative space to productive sense-making.

Social-positioning correlates with consensus building in two contentious large-group meetings

In his well-known paper Hake states that “a consistent analysis over diverse student populations with widely varying initial knowledge states […] can be obtained by taking the normalized average gain <g> as a rough measure of the effectiveness of the course in promoting conceptual understanding.” The practice of measuring course learning gains is common, despite the fact that variation in <g> on the FCI has also been tied to population effects, such as SAT scores, along which diverse populations are spread [Coletta, 2007]. In our data set of thousands of introductory mechanics students over more than a decade, we see a strong correlation between FMCE gain and math preparation level. We present the lessons we have learned about using normalized gain to study physics instruction, and we suggest how PER researchers might move beyond it to better compare learning in courses that reflect the diversity of the student population.

Looking Beyond the Normalized Gain

Computer simulations have been used to support student learning in physics, not only to boost conceptual understanding but to make labs more widely accessible to different populations. However, their impact on student learning has shown mixed results. To better understand this impact, HTML5-based computer simulations for topics in mechanics were investigated in a large, algebra-based, studio physics course for life science students at a private, research-intensive institution. Over a period of three years, we compared learning outcomes of students using traditional hands-on equipment only, a simulation only, or a hybrid combination of both for activities on energy, momentum, simple harmonic motion, and rotational dynamics. We will present our findings of this study through an equity lens, both from the experience of online learning due to the global pandemic and from consideration of the limited access to lab equipment for many students.

Use of physics simulations in supporting equity and student learning

Calls to reform to science teaching have elicited numerous responses over the past decade. One avenue of reform, 3 Dimensional Learning (3DL), focuses on what students learn, instead of how students learn. 3DL achieves this by organizing science learning around the same 3 dimensions that experts organize their knowledge: Scientific Practices, Disciplinary Core Ideas, and Crosscutting Concepts. To aid transformations toward 3DL, the 3DL4US group has created protocols to help characterize when 3DL may occur on assessments and in instruction. In this talk, we present initial results that extend this effort to student engagement with the Scientific Practice of Developing and Using Models. We will look at results from student problem solving interviews to see how the practice manifested in their work. These results will help inform us about the effectiveness of instructional efforts to engage students with the Practice of Using Models as we attempt to create 3DL environments.

Student Engagement in the Practice of Developing and Using Models

Many students who enter college wanting to major in STEM, particularly females and members from underrepresented groups, end up switching to non-STEM majors. At the U.S. Air Force Academy, specifically, previous studies have uncovered that, despite a strong academic preparation, of the cadets who reported their intention to complete a STEM degree as incoming freshmen, 36.4% graduated in a non-STEM discipline, with quantitative classes (Calculus-based Physics, Calculus I, Chemistry I) pinpointed by the data analysis as gateways. In this follow-up study (Year 3), data from the Registrar’s Office and a cadet survey were analyzed to identify factors associated with STEM attrition, a starting point for the conceptualization of strategies to address it at the U.S. Air Force Academy and in Puerto Rican universities.

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