United States

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.&amp;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 &amp;nbsp;century.&amp;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.&amp;nbsp; In addition, air capture will support a circular carbon economy.&amp;nbsp; Solar energy, water and carbon dioxide taken from the air will be the ingredients for synthetic fuels for airplanes, ships and trucks.&amp;nbsp; Then there will be no more need for fossil fuels.

AAPT WM 21

Cleaning Up Carbon

energy transition

carbon management

climate change

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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.

technical paper

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.

The Troublesome Tides

Benjamin Franklin was a first-rate physicist. Yet, his experiments and demonstrations almost killed him several times, and once even shocked him completely unconscious. Valiantly carrying on, Franklin was the de facto founder of the science of electricity and even a publishing member of the Royal Society of London. In this talk, allow me to introduce you to Benjamin Franklin the Physicist as I outline his most important and dangerous discoveries, demonstrate them, and explain why the heck he thought that positive charge flowed in conductors.

Benjamin Franklin’s Most Dangerous Experiments

The evolution of NGSS standards and the influence this has had at the HS level has created an important inflection point nationally for&nbsp;new innovation and policy about how we can integrate engineering&nbsp;and design, not just in science, but across entire campuses.&nbsp;Four years&nbsp;ago, the&nbsp;creation of the&nbsp;MakerDen at Ramona Convent Secondary School (the first all-girls makerspace in Southern California)&nbsp;allowed for a&nbsp;transformational&nbsp;change in&nbsp;not only how&nbsp;students,&nbsp;but teachers across all disciplines, integrate engineering skills.&nbsp;The&nbsp;goal&nbsp;was to&nbsp;create&nbsp;policies and procedures&nbsp;that allowed for the&nbsp;building&nbsp;of a&nbsp;new community of collaboration between teachers&nbsp;in&nbsp;an&nbsp;innovative&nbsp;shared physical space. This is a story of collaboration, stake holder engagement, empowering&nbsp;community, and then giving up control to let it thrive. The creation of this fertile soil resulted in the MakerDen being featured at the 2019 International Symp. of Academic Makerspace at Yale.

Freedom, Innovation, and Makerspaces; creating new campus realities.

Most teachers of general physics and other physics courses have been dismayed by the rampant student use of and dependence upon online homework solutions.&nbsp; I have developed a “5-Star Rating,” done by students, to indicate&nbsp;use, or no use, of online solutions in the solving of physics homework problems.&nbsp; A 5-star problem can be achieved if you can work a problem without use of the book, notes, or other people.&nbsp; Or, you can work your way down to a 0-Star problem utterly copying without understanding.&nbsp; The emphasis is on honesty and integrity.&nbsp; If students use&nbsp;an online solution, they must reference it!&nbsp; If someone else helps, give them credit!&nbsp; One’s homework grade is not reduced by star-rating the problem.&nbsp; In fact, a small bit of extra credit is awarded should a student use 5-Star Rating. &nbsp;&nbsp;The 5-Star Rating system and possible correlations between 5-Star Rating and performance will be discussed.

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