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Previous work has shown that item response theory may be used to rank incorrect response options to multiple-choice items on commonly used assessments. This work has shown that, when the correct response to each item is specified, a nominal response model (NRM) may be used to rank the incorrect options. We seek to determine the robustness of these results by applying the NRM to all response choices, without specifying the correct response. We apply these analyses to multiple data sets (each with more than 9,000 response sets), including pre-instruction and post-instruction responses. We find that the rankings generated without specifying the correct response are consistent with the previously published rankings for one data set; however, we find noticeable differences between rankings generated from different data sets. We provide evidence that discrepancies may result from differences in response rates for less commonly chosen responses.

Unlike most human-engineered systems, many biological systems rely on emergent behaviors from low-level interactions, enabling greater diversity and superior adaptation to complex, dynamic environments. This study explores emergent decentralized rotation in the Loopy multicellular robot, composed of homogeneous, physically linked, 1-degree-of-freedom cells. Inspired by biological systems like sunflowers, Loopy uses simple local interactions-diffusion, reaction, and active transport of simulated chemicals, called morphogens-without centralized control or knowledge of its global morphology. Through these interactions, the robot self-organizes to achieve coordinated rotational motion and forms lobes-local protrusions created by clusters of motor cells. This study investigates how these interactions drive Loopy's rotation, the impact of its morphology, and its resilience to actuator failures. Our findings reveal two distinct behaviors: 1) inner valleys between lobes rotate faster than the outer peaks, contrasting with rigid body dynamics, and 2) cells rotate in the opposite direction of the overall morphology. The experiments show that while Loopy's morphology does not affect its angular velocity relative to its cells, larger lobes increase cellular rotation and decrease morphology rotation relative to the environment. Even with up to one-third of its actuators disabled and significant morphological changes, Loopy maintains its rotational abilities, highlighting the potential of decentralized, bio-inspired strategies for resilient and adaptable robotic systems., Comment: 7 pages, 9 figures

Traditional top-down robotic design often lacks the adaptability needed to handle real-world complexities, prompting the need for more flexible approaches. Therefore, this study introduces a novel cellular plasticity model tailored for bottom-up robotic design. The proposed model utilizes an activator-inhibitor reaction, a common foundation of Turing patterns, which are fundamental in morphogenesis -- the emergence of form from simple interactions. Turing patterns describe how diffusion and interactions between two chemical substances-an activator and an inhibitor-can lead to complex patterns and structures, such as the formation of limbs and feathers. Our study extends this concept by modeling cellular plasticity as an activator-inhibitor reaction augmented with environmental stimuli, encapsulating the core phenomena observed across various cell types: stem cells, neurons, and muscle cells. In addition to demonstrating self-regulation and self-containment, this approach ensures that a robot's form and function are direct emergent responses to its environment without a comprehensive environmental model. In the proposed model, a factory acts as the activator, producing a product that serves as the inhibitor, which is then influenced by environmental stimuli through consumption. These components are regulated by cellular plasticity phenomena as feedback loops. We calculate the equilibrium points of the model and the stability criterion. Simulations examine how varying parameters affect the system's transient behavior and the impact of competing functions on its functional capacity. Results show the model converges to a single stable equilibrium tuned to the environmental stimulation. Such dynamic behavior underscores the model's utility for generating predictable responses within robotics and biological systems, showcasing its potential for navigating the complexities of adaptive systems., Comment: 15 pages, 7 figures, Living Machines 2024

The explosion of artificial intelligence, possible end of Moore's law, dawn of quantum computing and continued exponential growth of data communications traffic have brought new urgency to the need for laser integration on the diversified Si platform. While diode lasers on III-V platforms have long powered internet data communications and other optoelectronic technologies, direct integration with Si remains problematic. A paradigm-shifting solution requires exploring new and unconventional materials and integration approaches. In this work, we show that a sub-10-nm ultra-thin Si$_{1-x}$Ge$_x$ buffer layer fabricated by an oxidative solid-phase epitaxy process can facilitate extraordinarily efficient strain relaxation. The Si$_{1-x}$Ge$_x$ layer is formed by ion implanting Ge into Si(111) and selectively oxidizing Si atoms in the resulting ion-damaged layer, precipitating a fully strain-relaxed Ge-rich layer between the Si substrate and surface oxide. The efficient strain relaxation results from the high oxidation temperature, producing a periodic network of dislocations at the substrate interface, coinciding with modulations of the Ge content in the Si$_{1-x}$Ge$_x$ layer and indicating the presence of defect-mediated diffusion of Si through the layer. The epitaxial growth of high-quality GaAs is demonstrated on this ultra-thin Si$_{1-x}$Ge$_x$ layer, demonstrating a promising new pathway for integrating III-V lasers directly on the Si platform., Comment: 16 pages, 5 figures

This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 50% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug's software and design files., Comment: 7 pages, 7 figures

Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to the complex environments. Inspired by the process of morphogenesis in nature, a bottom-up design approach for robot morphology is proposed to treat a robot's body as an emergent response to underlying processes rather than a predefined shape. This paper presents Loopy, a "Swarm-of-One" polymorphic robot testbed that can be viewed simultaneously as a robotic swarm and a single robot. Loopy's shape is determined jointly by self-organization and morphological computing using physically linked homogeneous cells. Experimental results show that Loopy can form symmetric shapes consisting of lobes. Using the the same set of parameters, even small amounts of initial noise can change the number of lobes formed. However, once in a stable configuration, Loopy has an "inertia" to transfiguring in response to dynamic parameters. By making the connections among self-organization, morphological computing, and robot design, this paper lays the foundation for more adaptable robot designs in the future., Comment: 6 pages, 6 figures, IROS 2023

Breakthroughs extending nanostructure engineering beyond what is possible with current fabrication techniques will be crucial for enabling next-generation nanotechnologies. Nanoepitaxy of strain-engineered bent nanowire heterostructures presents a promising platform for realizing bottom-up and scalable fabrication of nanowire devices. The synthesis of these structures requires the selective asymmetric deposition of lattice-mismatched shells-a complex growth process which is not well understood. We present the nanoepitaxial growth of GaAs-InP core-shell bent nanowires and connecting nanowire pairs to form nano-arches. Compositional analysis of nanowire cross-sections reveals the critical role of adatom diffusion in the nanoepitaxial growth process, which leads to two distinct growth regimes: indium-diffusion limited growth and phosphorous-limited growth. The highly controllable phosphorous-limited growth mode is employed to synthesize connected nanowire pairs and quantify the role of flux shadowing on the shell growth process. These results provide important insight into three-dimensional nanoepitaxy and enable new possibilities for nanowire device fabrication.

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

In this report, we describe a large-area Laser Beam Induced Current (LBIC) microscope that has been adapted to perform Intensity Modulated Photocurrent Spectroscopy in an imaging mode combined with automated J-V electrical characterization. Henceforth we refer to the method as either Intensity Modulated Photocurrent Micro-spectroscopy or simply IMPS microscopy. Microscopy based IMPS aims to use the localised frequency response to correlate the optoelectrical response with a particular interface, bulk material, specific transport layer or transport parameter. The system developed here is specifically designed to track the decomposition of organic solar cells including hybrid-inorganic PSCs during long-term exposure to quantities such as light, oxygen, moisture, and heat

Multiple-choice multiple-response (MCMR) items (i.e., multiple-choice questions for which more than one response may be selected) can be a valuable tool for assessment. Like traditional multiple-choice single-response questions, they are easy to score, but MCMR items can provide more information about student thinking by probing multiple reasoning facets in a single problem context. In this paper, we discuss differences in performance on MCMR items that result from differences in administration method (paper vs. online). In particular, we find a tendency for ``clickiness'' in online administration: students choose more responses to MCMR items when taking the electronic version of the assessment. Based on these findings, we recommend that online administration be compared only to other online administrations and not to paper administrations when comparing student results on MCMR items. These results also suggest that MCMR items provide a unique opportunity to probe differences in online and on-paper administration of low-stakes assessments., Comment: arXiv admin note: text overlap with arXiv:2009.10865

Ishimoto, Davenport, and Wittmann have previously reported analyses of data from student responses to the Force and Motion Conceptual Evaluation (FMCE), in which they used item response curves (IRCs) to make claims about American and Japanese students' relative likelihood to choose certain incorrect responses to some questions. We have used an independent data set of over 6,500 American students' responses to the FMCE to generate IRCs to test their claims. Converting the IRCs to vectors, we used dot product analysis to compare each response item quantitatively. For most questions, our analyses are consistent with Ishimoto, Davenport, and Wittmann, with some results suggesting more minor differences between American and Japanese students than previously reported. We also highlight the pedagogical advantages of using IRCs to determine the differences in response patterns for different populations to better understand student thinking prior to instruction.

One desired outcome of introductory physics instruction is that students will develop facility with reasoning quantitatively about physical phenomena. Little research has been done regarding how students develop the algebraic concepts and skills involved in reasoning productively about physics quantities, which is different from either understanding of physics concepts or problem-solving abilities. We introduce the Physics Inventory of Quantitative Literacy (PIQL) as a tool for measuring quantitative literacy, a foundation of mathematical reasoning, in the context of introductory physics. We present the development of the PIQL and evidence of its validity for use in calculus-based introductory physics courses. Unlike concept inventories, the PIQL is a reasoning inventory, and can be used to assess reasoning over the span of students' instruction in introductory physics. Although mathematical reasoning associated with the PIQL is taught in prior mathematics courses, pre/post test scores reveal that this reasoning isn't readily used by most students in physics, nor does it develop as part of physics instruction--even in courses that use high-quality, research-based curricular materials. As has been the case with many inventories in physics education, we expect use of the PIQL to support the development of instructional strategies and materials--in this case, designed to meet the course objective that all students become quantitatively literate in introductory physics.

Covariational reasoning -- reasoning about how changes in one quantity relate to changes in another quantity -- has been examined extensively in mathematics education research. Little research has been done, however, on covariational reasoning in introductory physics contexts. We explore one aspect of covariational reasoning: ``goes like'' reasoning. ``Goes like'' reasoning refers to ways physicists relate two quantities through a simplified function. For example, physicists often say that ``the electric field goes like one over r squared.'' While this reasoning mode is used regularly by physicists and physics instructors, how students make sense of and use it remains unclear. We present evidence from reasoning inventory items which indicate that many students are sense making with tools from prior math instruction, that could be developed into expert ``goes like'' thinking with direct instruction. Recommendations for further work in characterizing student sense making as a foundation for future development of instruction are made., Comment: under review for Physics Education Research Conference Proceedings 2020

Electromagnetism (E&M) is often challenging for students enrolled in introductory college-level physics courses. Compared to mechanics, the mathematics of E&M is more sophisticated and the representations are more abstract. Furthermore, students may lack productive intuitions they had with force and motion. In this article, we explore the mathematization of electric charge. Specifically, we explore how difficulties with positive and negative signs can arise for learners who approach integers primarily as positions on a number line., Comment: Accepted by The Physics Teacher

We have developed the Physics Inventory of Quantitative Literacy (PIQL) as a tool to measure students' quantitative literacy in the context of introductory physics topics. We present the results from various quantitative analyses used to establish the validity of both the individual items and the PIQL as a whole. We show how examining the results from classical test theory analyses, factor analysis, and item response curves informed decisions regarding the inclusion, removal, or modification of items. We also discuss how the choice to include multiple-choice/multiple-response items has informed both our choices for analyses and the interpretations of their results. We are confident that the most recent version of the PIQL is a valid and reliable instrument for measuring students' physics quantitative literacy in calculus-based introductory physics courses at our primary research site. More data are needed to establish its validity for use at other institutions and in other courses., Comment: accepted for publication: 2020 Physics Education Research Conference

We are developing a new research based assessment (RBA) focused on quantitative reasoning -- rather than conceptual understanding -- in physics contexts. We rapidly moved administration of the RBA online in Spring 2020 due to the COVID-19 pandemic. We present our experiences with online, unproctored administration of an RBA in development to students enrolled in a large-enrollment, calculus-based, introductory physics course. We describe our attempts to adhere to best practices on a limited time frame, and present a preliminary analysis of the results, comparing results from the online administration to earlier results from in-person, proctored administration. We include discussion of online administration of multiple-choice/multiple-response (MCMR) items, which we use on the instrument as a way to probe multiple facets of student reasoning. Our initial comparison indicates little difference between online and paper administrations of the RBA, consistent with previous work by other researchers., Comment: PERC 2020

Relating two quantities to describe a physical system or process is at the heart of "doing physics" for novices and experts alike. In this paper, we explore the ways in which experts use covariational reasoning when solving introductory physics graphing problems. Here, graduate students are considered experts for the introductory level material, as they often take the role of instructor at large research universities. Drawing on work from Research in Undergraduate Mathematics Education (RUME), we replicated a study of mathematics experts' covariational reasoning done by Hobson and Moore with physics experts [N. L. F. Hobson and K. C. Moore, in RUME Conference Proceedings, pp. 664-672 (2017)]. We conducted think-aloud interviews with 10 physics graduate students using tasks minimally adapted from the mathematics study. Adaptations were made solely for the purpose of participant understanding of the question, and validated by preliminary interviews. Preliminary findings suggest physics experts approach covariational reasoning problems significantly differently than mathematics experts. In particular, two behaviors are identified in the reasoning of expert physicists that were not seen in the mathematics study. We introduce these two behaviors, which we call Using Compiled Relationships and Neighborhood Analysis, and articulate their differences from the behaviors articulated by Hobson and Moore. Finally, we share implications for instruction and questions for further research., Comment: 4 pages, 3 figures, under review for the Physics Education Research Conference Proceedings 2019

Mathematical reasoning flexibility across physics contexts is a desirable learning outcome of introductory physics, where the math world and physical world meet. Physics Quantitative Literacy (PQL) is a set of interconnected skills and habits of mind that support quantitative reasoning about the physical world. The Physics Inventory of Quantitative Literacy (PIQL), which we are currently refining and validating, assesses students proportional reasoning, covariational reasoning, and reasoning with signed quantities in physics contexts. In this paper, we apply a Conceptual Blending Theory analysis of two exemplar PIQL items to demonstrate how we are using this theory to help develop an instrument that represents the kind of blended reasoning that characterizes expertise in physics. A Conceptual Blending Theory analysis allows for assessment of hierarchical partially correct reasoning patterns, and thereby holds potential to map the emergence of mathematical reasoning flexibility throughout the introductory physics sequence.

The Physics Inventory of Quantitative Literacy (PIQL), a reasoning inventory under development, aims to assess students' physics quantitative literacy at the introductory level. The PIQL's design presents the challenge of isolating types of mathematical reasoning that are independent of each other in physics questions. In its current form, the PIQL spans three principle reasoning subdomains previously identified in mathematics and physics education research: ratios and proportions, covariation, and signed (negative) quantities. An important psychometric objective is to test the orthogonality of these three reasoning subdomains. We present results from exploratory factor analysis, confirmatory factor analysis, and module analysis that inform interpretations of the underlying structure of the PIQL from a student viewpoint, emphasizing ways in which these results agree and disagree with expert categorization. In addition to informing the development of existing and new PIQL assessment items, these results are also providing exciting insights into students' quantitative reasoning at the introductory level., Comment: submitted to 2019 PERC Proceedings

Research-based assessment instruments (RBAIs) are ubiquitous throughout both physics instruction and physics education research. The vast majority of analyses involving student responses to RBAI questions have focused on whether or not a student selects correct answers and using correctness to measure growth. This approach often undervalues the rich information that may be obtained by examining students' particular choices of incorrect answers. In the present study, we aim to reveal some of this valuable information by quantitatively determining the relative correctness of various incorrect responses. To accomplish this, we propose an assumption that allow us to define relative correctness: students who have a high understanding of Newtonian physics are likely to answer more questions correctly and also more likely to choose better incorrect responses, than students who have a low understanding. Analyses using item response theory align with this assumption, and Bock's nominal response model allows us to uniquely rank each incorrect response. We present results from over 7,000 students' responses to the Force and Motion Conceptual Evaluation.

Mathematical reasoning skills are a desired outcome of many introductory physics courses, particularly calculus-based physics courses. Positive and negative quantities are ubiquitous in physics, and the sign carries important and varied meanings. Novices can struggle to understand the many roles signed numbers play in physics contexts, and recent evidence shows that unresolved struggle can carry over to subsequent physics courses. The mathematics education research literature documents the cognitive challenge of conceptualizing negative numbers as mathematical objects--both for experts, historically, and for novices as they learn. We contribute to the small but growing body of research in physics contexts that examines student reasoning about signed quantities and reasoning about the use and interpretation of signs in mathematical models. In this paper we present a framework for categorizing various meanings and interpretations of the negative sign in physics contexts, inspired by established work in algebra contexts from the mathematics education research community. Such a framework can support innovation that can catalyze deeper mathematical conceptualizations of signed quantities in the introductory courses and beyond.

In an effort to improve the quality of citizen engagement in workplace, politics, and other domains in which quantitative reasoning plays an important role, Quantitative Literacy (QL) has become the focus of considerable research and development efforts in mathematics education. QL is characterized by sophisticated reasoning with elementary mathematics. In this project, we extend the notions of QL to include the physics domain and call it Physics Quantitative Literacy (PQL). We report on early stage development from a collaboration that focuses on reasoning inventory design and data analysis methodology for measuring the development of PQL across the introductory physics sequence. We have piloted a prototype assessment designed to measure students' PQL in introductory physics: Physics Inventory of Quantitative Literacy (PIQL). This prototype PIQL focuses on two components of PQL: proportional reasoning, and reasoning with negative quantities. We present preliminary results from approximately 1,000 undergraduate and 20 graduate students.