An official website of the United States government
The large introductory physics lab course at the University of Colorado Boulder, which serves primarily engineering and physical science majors, was recently completely redesigned to align with new explicit learning goals. One of the learning goals of the new course was to have students enjoy working on physics experiments and to see value in experimental physics as a discipline. Additionally, we wanted to make the student workload consistent with a one credit course. To help achieve these goals, we created custom interactive videos that were viewed by the students before the lab to help them prepare for the lab activities. We present design principles for creating these videos, as well as data regarding student engagement and perceptions of this part of the course. Physics Education Research Conference 2019 Part of the PER Conference series Provo, UT: July 24-25, 2019
more »
« less
Methodological development of a new coding scheme for an established assessment on measurement uncertainty in laboratory courses
Methodological development of a new coding scheme for an established assessment on measurement uncertainty in laboratory courses written by Benjamin Pollard, Robert Hobbs, Dimitri R. Dounas-Frazer, and H. J. Lewandowski Student understanding around measurement uncertainty is an important learning outcome in physics lab courses across the US, including at the University of Coloroado Boulder (CU), where it is among the major learning outcomes for the large introductory stand-alone physics lab course. One research tool for studying student understanding around measurement uncertainty, which we use in this course, is the Physics Measurement Questionnaire (PMQ), an open-response assessment for measuring student understanding of measurement uncertainty. Interpreting and analyzing PMQ data involves coding students' written explanations to open-response questions. However, the preexisting scoring scheme for the PMQ does not fully capture the breadth and depth of reasoning contained in our students' responses. Therefore, we created a new coding scheme for the PMQ based on responses from our students. Here, we document our process to develop a new coding scheme for the PMQ, and describe the resulting codes. We also present examples of what can be learned from applying the new coding scheme at our institution. Physics Education Research Conference 2019 Part of the PER Conference series Provo, UT: July 24-25, 2019
more »
« less
- Award ID(s):
- 1734006
- PAR ID:
- 10137837
- Date Published:
- Journal Name:
- PERC Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null; null; null (Ed.)Measurement uncertainty and experimental error are important concepts taught in undergraduate physics laboratories. Although student ideas about error and uncertainty in introductory classical mechanics lab experiments have been studied extensively, there is relatively limited research on student thinking about experimental measurement uncertainty in quantum mechanics. In this work, we used semi-structured interviews to study advanced physics students’ interpretations of fictitious data distributions from two common undergraduate laboratory experiments in quantum mechanics and one in classical mechanics. To analyze these interpretations, we developed a coding scheme that classifies student responses based on what factors they believe create un- certainty and differentiates between different types of uncertainty (e.g. imprecision, inaccuracy). We found that participants in our study expressed a variety of ideas about measurement uncertainty that varied with the context (classical/quantum) and the type of uncertainty.more » « less
-
Preliminary model for student ownership of projects written by Dimitri R. Dounas-Frazer, Laura Ríos, and H. J. Lewandowski In many upper-division lab courses, instructors implement multiweek student-led projects. During such projects, students may design and carry out experiments, collect and analyze data, document and report their findings, and collaborate closely with peers and mentors. To better understand cognitive, social, and affective aspects of projects, we conducted an exploratory investigation of student ownership of projects. Ownership is a complex construct that refers to, e.g., students' willingness and ability to make strategic decisions about their project. Using data collected through surveys and interviews with students and instructors at five institutions, we developed a preliminary model for student ownership of projects. Our model describes ownership as a relationship between student and project. This relationship is characterized by student interactions with the project during three phases: choice of topic, execution of experiment, and synthesis of results. Herein, we explicate our model and demonstrate that it maps well onto students' and instructors' conceptions of ownership and ideas presented in prior literature. Physics Education Research Conference 2019 Part of the PER Conference series Provo, UT: July 24-25, 2019more » « less
-
Concepts and practices surrounding measurement uncertainty are vital knowledge for physicists and are often emphasized in undergraduate physics laboratory courses. We have previously developed a research-based assessment instrument—the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE)—to examine student proficiency with measurement uncertainty along a variety of axes, including sources of uncertainty, handling of uncertainty, and distributions and repeated measurements. We present here initial results from the assessment representing over 1500 students from 20 institutions. We analyze students’ performance pre- and postinstruction in lab courses and examine how instruction impacts students with different majors and gender. We find that students typically excel in certain areas, such as reporting the mean of a distribution as their result, while they struggle in other areas, such as comparing measurements with uncertainty and correctly propagating errors using formulas. Additionally, we find that the importance that an instructor places in certain areas of measurement uncertainty is uncorrelated with student performance in those areas. Published by the American Physical Society2024more » « less
-
his work-in-progress paper expands on a collaboration between engineering education researchers and machine learning researchers to automate the analysis of written responses to conceptually challenging questions in statics and dynamics courses (Authors, 2022). Using the Concept Warehouse (Koretsky et al., 2014), written justifications of ConcepTests (CTs) were gathered from statics and dynamics courses in a diverse set of two- and four-year institutions. Written justifications for CTs have been used to support active learning pedagogies which makes them important to investigate how students put together their problem-solving narratives of understanding. However, despite the large benefit that analysis of student written responses may provide to instructors and researchers, manual review of responses is cumbersome, limits analysis, and can be prone to human bias. In efforts to improve the analysis of student written responses, machine learning has been used in various educational contexts to analyze short and long texts (Burstein et al., 2020; Burstein et al., 2021). Natural Language Processing (NLP) uses transformer-based machine learning models (Brown et al., 2020; Raffel et al., 2019) which can be used through fine-tuning or in-context learning methods. NLP can be used to train algorithms that can automate the coding of written responses. Only a few studies for educational applications have leveraged transformer-based machine learning models further prompting an investigation into its use in STEM education. However, work in NLP has been criticized for heightening the possibility to perpetuate and even amplify harmful stereotypes and implicit biases (Chang et al., 2019; Mayfield et al., 2019). In this study, we detail the aim to use NLP for linguistic justice. Using methods like text summary, topic modeling, and text classification, we identify key aspects of student narratives of understanding in written responses to mechanics and statics CTs. Through this process, we seek to use machine learning to identify different ways students talk about a problem and their understanding at any point in their narrative formation process. Thus, we hope to help reduce human bias in the classroom and through technology by giving instructors and researchers a diverse set of narratives that include insight into their students’ histories, identities, and understanding. These can then be used towards connecting technological knowledge to students’ everyday lives.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1119/perc.2019.pr.Pollard
