United States

This study reports the predictive power of different parameters towards FMCE (Force and Motion Conceptual Evaluation) post-test scores controlling for FMCE pretest scores. The sample consists of students (N=1060) from a large eastern land-grant university enrolled in Physics 1 (calculus-based mechanics class). These students are primarily pursuing engineering degrees. Multiple linear regression was used with FMCE post-test score as the dependent variable and a broad collection of independent variables including high school GPA, ACT/SAT score, college math entry level, AP classes taken, transfer credits, college credits, self-efficacy, sense of belonging, and personality. The degree to which these variables are still important if FMCE pretest scores are controlled for is examined.

AAPT SM 21

What factors predict FMCE post-test scores controlling for pretest scores?

high school preparation

conceptual understanding

assessment

Join AAPT and Physics Educators worldwide in developing and strengthening your professional connections by attending the 2021 AAPT Virtual Summer Meeting. AAPT meetings are a great way to reflect on your teaching, recommit to best practices and re-energize your career with new research, products, and techniques.

Listen to internationally known plenary speakers, attend sessions, workshops, and special activities. Attending the virtual summer meeting is an excellent opportunity to share your expertise and learn from and network with leaders in the field.

Please log in with your AAPT account or register for the event if you haven't already.

If you are registered please note that you need to be logged in with your AAPT email / credentials in order to get access.

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

Dual Process Theories of Reasoning (DPTOR) state that cognition has two major processes: a fast, automatic process (heuristic) and a slow, reflective process (analytic). A common heuristic among introductory physics students is that acceleration and velocity are the same thing. This heuristic has proved remarkably resilient to instruction in the author’s classes. In a Conceptual Physics class, multiple choice questions on free fall were combined with a set of knowledge statements in order to trigger the analytic process. Students were asked to choose if each knowledge statement was helpful, unhelpful, or wrong in the context of the problem. Data suggest that the inclusion of the knowledge statements served as a productive intervention for engaging the analytic process. Student performance improved compared to the population that were not served the knowledge statements. An analysis of question time stamps was also performed to examine what proportion of students changed answers after viewing the knowledge statements.

Knowledge statements as a productive intervention to prompt reflective thinking

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

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

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.

Student Resources for Understanding Momentum

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.

STEM Attrition at a Military Academy: Cadets’ Experiences and Recommendations

Descriptions of the Nature of Science are in contrast with the widespread notion that “physics” is objective, unaffected by human influence. To better understand students’ thinking about the nature of physics, we taught a lesson from the Underrepresentation Curriculum and collected written responses before and after in-class discussion about the nature of physics. In this manuscript, we share student responses about the presence of subjectivity in physics and relate them to commonly accepted ideas about the Nature of Science. Prior to discussing subjectivity, students tend to describe physics without reference to human influence. Afterwards, many students identify that while physical properties may be objective, human interpretation, biases, and values influence physics as well. Some students also consider our understanding of physics as incomplete. These discussions are a first step towards increasing awareness of structural and individual subjectivity. We believe this will ultimately support a more equitable, robust scientific community.

What factors predict FMCE post-test scores controlling for pretest scores?

Next from AAPT SM 21

Experiment of Model Train using Ultrasonic Sensor Connected to Smartphone

Similar lecture

What does the Force and Motion Conceptual Evaluation pretest measure?