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One of two complementary panels dedicated to raising awareness about improving the accessibility of undergraduate physics laboratory courses. In this panel, undergraduate and graduate physics students talk about their previous and current experiences in physics labs, and they make suggestions for improving accessibility of labs.] Physics labs are an exciting opportunity to teach and learn about physics by collaboratively planning, building, and carrying out experiments and interacting with advanced phenomena, apparatus, and software. Students, staff, and faculty with disabilities deserve full access to these learning environments, and it is everyone’s responsibility to recognize and mitigate the specific barriers to participation posed by space, technology, and pedagogical constraints that are unique to labs. To that end (and with support from an AAPT Special Projects Grant), the AAPT Committee on Laboratories assembled a task force whose charge is to issue an open call for increased investment in accessible physics labs.

AAPT WM 21

Making Physics Labs More Accessible: Perspectives of Current Physics Students

disability

laboratories

undergraduate

physics

accessibility

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Steve Oldham is CEO of Carbon Engineering Ltd., a Canadian-based clean energy company. Carbon Engineering&nbsp;is focused on the deployment of Direct Air Capture technology that captures carbon dioxide out of the atmosphere at large scale, so it can be stored permanently underground or turned into clean transportation fuels. As more and more government and corporate leaders around the world are committing to net zero emissions, attention now turns to the cost, feasibility and societal impact of different solutions. Steve will discuss how Direct Air Capture technology is a feasible, available and affordable solution for removing carbon dioxide from the air at large scale;&nbsp;the opportunities it brings for the economy and how it provides the key missing ingredient in net zero plans.

Direct Air Capture and Achieving Net Zero Emissions

Electricity production is one of the top contributors to global carbon dioxide emissions. In 2019 almost two thirds of the world’s electricity was produced by fossil fuels; over half of that was produced by coal alone. As the rate of climate warming increases, it is essential that renewable and low-carbon generation be expanded, both to add new generation capacity and replace fossil fuel plants. For over 50 years the world’s nuclear power plants have produced reliable electricity, and nuclear is the best option for replacing base-load fossil fuel sources like coal, oil, and gas with a tiny fraction of the carbon emissions. NuScale Power, LLC, an Oregon-based startup, has developed a small modular reactor that combines that nuclear operating experience with an existing supply chain to provide evolutionary advances in efficiency and safety.

The Climate Change Equation Must Contain A Nuclear Energy Term

The world’s energy infrastructure creates a huge, unmanaged CO2 waste stream. Fossil carbon dumping is putting us onto an overshoot trajectory relative to the 1.5°C target set in Paris.&nbsp; Carbon must be drawn down at a staggering scale. Picking up carbon litter implies collecting and storing more carbon than was emitted during the 20 th &nbsp;century.&nbsp; Storage technologies are unpopular. However, Iceland shows how to form mineral carbonates from CO2 and basalt deep underground. Collecting carbon poses new challenges. Biomass capture is unlikely to scale. Direct air capture is scalable, but untested and too expensive. Once affordable, it will likely play an important role in climate stabilization.&nbsp; In addition, air capture will support a circular carbon economy.&nbsp; Solar energy, water and carbon dioxide taken from the air will be the ingredients for synthetic fuels for airplanes, ships and trucks.&nbsp; Then there will be no more need for fossil fuels.

Cleaning Up Carbon

Network analytic techniques such as Modified Module Analysis (MMA) and Modified Module Analysis – Partial (MMA-P) are powerful new tools which can identify coherent patterns of student responses to physics conceptual instruments. MMA and MMAP are used to analyze large datasets by forming networks of responses then using community detection algorithms to identify sets of responses that are consistently selected together. These techniques were applied to 4000 student responses to the Force Concept Inventory (FCI) and the Force and Motion Conceptual Evaluation (FMCE) both prior to and post instruction. These studies identified a coherent set of non-Newtonian misconceptions in both the FCI and the FMCE. They also revealed communities of isomorphic items, issues with item-blocking, and items not functioning as intended within the instruments.

Network Analysis of the FCI and the FMCE

This research applied Modified Module Analysis (MMA) to over 5000 student responses to the Conceptual Survey of Electricity and Magnetism (CSEM). The CSEM was given at two major US land-grant universities in the introductory Electricity and Magnetism courses. MMA is a powerful tool for analyzing large datasets that uses the correlation matrix to form a network, community detection algorithms, and bootstrapping. Many studies have investigated student misconceptions in Newtonian physics by analyzing instruments such as the Force Concept Inventory or the Force and Motion Conceptual Evaluation. These studies showed that students hold non-Newtonian views on classical mechanics even post-instruction. However, much less research has investigated misconceptions in E&amp;amp;M. This study identifies communities of correct and incorrect answers to the CSEM to explore the structure of student misconceptions.

Network analysis of the CSEM with Modified Module Analysis

Modified Module Analysis using partial correlations (MMA-P) has been a productive method for studying concept inventories. MMA-P finds patterns in student responses, which can represent common alternative conceptions, test-taking strategies, or structures arising from the construction of the instrument. MMA-P has shown that because of these structures, grading each item independently underestimates students’ understanding of the material and that concept inventories written with highly attractive distractors may not detect the full spectrum of students’ reasoning. MMA-P can be extended by using multilevel network analysis. With multilevel techniques, populations of students can be disaggregated in the MMA-P analysis. For example, a multilevel network could be constructed with data from different institutions forming different levels in the network. Within each level, MMA-P finds modules of popular responses within an institution. Common responses form edges that link institutional networks together, opening a new dimension for MMA-P to find patterns that cross institutional boundaries. &nbsp;

Multilevel Network Analysis: A New Dimension for Analyzing Concept Inventories

This talk presents a network analysis of group exam data from a two-class introductory physics sequence. In each class, on each of four exams, students took the exam individually and then reworked it with classmates. Students recorded their collaborators before submitting the exam, and these reports are used to build directed networks. The networks are partitioned using positional analysis, which uses similarities in linking behavior to detect blocks of actors in a network. By calculating block structure for each exam and mapping over time, it is possible to see a stabilizing social structure over the two semesters. Some students find a coherent group immediately, while others take longer, but almost all settle into a regular set of collaborators by the second semester. We will discuss this evolving block structure, what it suggests about the social positions available to students in the classes, and how network position connects to exam grades.

Social Positions in Group Exam Networks

We have developed a multiple-choice test for measuring students’ quantitative literacy in the context of physics (Physics Inventory of Quantitative Literacy, PIQL). Items were created based on the framework of conceptual blending in which students’ mathematical reasoning and conceptual physics understanding cannot necessarily be separated. As such, we include several multiple-choice/multiple-response (MCMR) items to which students may select as many answer choices as they think are correct. Common factor analysis techniques for identifying groups of items that share common traits are inappropriate because these methods require MCMR items to be scored dichotomously, which destroys the rich information available in students’ response pattern. As an alternative, we use Modified Module Analysis (MMA) to identify communities based on networks of correlations between answer choices. We present the results from using MMA to analyze networks of correct responses to PIQL items, networks of incorrect responses, and networks of all responses combined.

Examining Response Patterns to Multiple-Response Items Using Module Analysis

We have all experienced it; watching a movie (cartoon or not) where something happens that is a physical impossibility making all the physicists&nbsp;in the room cringe.&nbsp; Someone walks off a cliff and doesn’t fall until the look down (looking at you Wyle E. Coyote) or the path of a launched object looks nothing like a parabola…it can be infuriating.&nbsp; But, instead of raging against the incorrect, why not use it to teach how the world really works? Students are surprisingly good at picking out the incorrect physics when watching cartoons, but they also can fall into traps believing that real life emulates the cartoon world.&nbsp; During this talk, I will detail my experiences using cartoons to teach physics to high school students and pre-service elementary teachers.

Cartoon Physics in the Classroom

Before being solved by Newton and Laplace, explanation of the cause of the tides was a long-standing problem. In this talk, we will look at two failed attempts to explain tides: Galileo’s “alternately accelerated and retarded motion” published in Discourse on Two World Systems in 1632 and George-Louis Leclerc’s hilariously implausible description of tides due to blocking of ocean currents in The System of Natural History, Vol.1 in 1741, over 50 years after Newton.

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Making Physics Labs More Accessible: Perspectives of Current Physics Students

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