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At Arizona State University we have built the first and only fully online Bachelor of Science degree in Physics, with a complete curriculum, including labs. The upper division Advanced Lab courses present a special challenge for online delivery. We address that using a set of custom-built simulator modules that replicate all the imperfections (noise, background, etc) inherent in real-world data. The set of experiments duplicates those of the in-person classes. In this paper, we present an overview of these labs and discuss the advantages and challenges of delivering them online. We assert that these labs provide a valid and rigorous component for the fully online degree. The entire set of labs is available as Open Source Supplemental Materials and is shared for others to use in part or in whole, with suitable attribution.

University research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it requires ongoing maintenance and is challenging to navigate. Drawing on formative interviews that revealed organizational memory challenges in labs, we designed CHOIR, an LLM-based chatbot that supports organizational memory through four key functions: document-grounded Q\&A, Q\&A sharing for follow-up discussion, knowledge extraction from conversations, and AI-assisted document updates. We deployed CHOIR in four research labs for one month (n=21), where the lab members asked 107 questions and lab directors updated documents 38 times in the organizational memory. Our findings reveal a privacy-awareness tension: questions were asked privately, limiting directors' visibility into documentation gaps. Students often avoided contribution due to challenges in generalizing personal experiences into universal documentation. We contribute design implications for privacy-preserving awareness and supporting context-specific knowledge documentation.

Although most teachers acknowledge the importance of taking investigative approached in students' science learning experiences, implementing them in high-school classes can be challenging for teachers. In this work, we analyzed data from multiple sources from a teaching Community of Practice (CoP) to investigate (a) barriers to using open-ended labs in physics classes, (b) shifts in teachers' beliefs about the use of open-ended labs in their classes during teachers' engagement in a physics teacher CoP in a partnership program, and (c) a case study of one teachers whose shifts in perceptions about open-ended labs led to her successful implementation of an open-ended lab in her class. The findings confirm the existence of well-known psychological and structural barriers that can prevent teachers from adopting investigative approaches in teaching physics labs. Moreover, we learned how the interaction of these barriers further complicates the adoption of open-ended approaches in physics classes. The study also revealed a significant gap between teachers' current practices and their desired method for conducting labs, particularly in terms of structured versus open-ended approaches. The

This paper presents the application of socio-cognitive mutation operators inspired by the TOPSIS method to the Low Autocorrelation Binary Sequence (LABS) problem. Traditional evolutionary algorithms, while effective, often suffer from premature convergence and poor exploration-exploitation balance. To address these challenges, we introduce socio-cognitive mutation mechanisms that integrate strategies of following the best solutions and avoiding the worst. By guiding search agents to imitate high-performing solutions and avoid poor ones, these operators enhance both solution diversity and convergence efficiency. Experimental results demonstrate that TOPSIS-inspired mutation outperforms the base algorithm in optimizing LABS sequences. The study highlights the potential of socio-cognitive learning principles in evolutionary computation and suggests directions for further refinement.

This review article provides an overview of research on the topic of gender equity in educational physics labs. As many institutions and instructors seek to evolve or transform physics lab learning, it is important that changes are made that improve equity for all students along multiple axes of identity, including gender. The studies highlighted in this review article describe the existence of complex gender-based differences, e.g., in opportunities to tinker with lab equipment, as well as differences in grades, conceptual understanding, and motivational outcomes across a broad range of lab curricula and contexts. The studies also illustrate and explore social interactions and structures that can impact students' experiences based on their gender identities. Although there has been less scholarship focused on proposals to reduce gender-based inequities in labs, this review article also provides an overview of some relevant proposals as well as associated research results. This overview of research on gender equity in physics labs helps to make clear that future scholarship on equity in physics labs should adopt gender frameworks that allow researchers to transcend binary gender id

Digital system design lectures are mandatory in the electrical and electronics engineering curriculum. Besides HDL simulators and viewers, FPGA boards are necessary for the real implementation of HDL, which were previously costly for students. With the emergence of low-cost FPGA boards, the use of take-home labs is increasing. The COVID-19 pandemic has further accelerated this process. Traditional lab sessions have limitations, prompting the exploration of take-home lab kits to enhance learning flexibility and engagement. This study aims to evaluate the effectiveness of a low-cost take-home lab kit, consisting of a Tang Nano 9K FPGA board and a Saleae Logic Analyzer, in improving students' practical skills and sparking curiosity in digital system design. The research was conducted in the EEE 303 Digital Design lecture. Students used the Tang Nano 9K FPGA and Saleae Logic Analyzer for a term project involving PWM signal generation. Data was collected through a survey assessing the kit's impact on learning and engagement. Positive Acceptance: 75% of students agreed or strongly agreed that the take-home lab kit was beneficial. Preference for Lab Types: 60% of students preferred classi

Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI/ML models like NVIDIA BioNeMo - which facilitates molecular interaction prediction and biomolecular analysis - Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery.

We report on a study of the effects of laboratory activities that model fictitious laws of physics in a virtual reality environment on (1) students' epistemology about the role of experimental physics in class and in the world; (2) students' self-efficacy; and (3) the quality of student engagement with the lab activities. We create opportunities for students to practice physics as a means of creating and validating new knowledge by simulating real and fictitious physics in virtual reality (VR). This approach seeks to steer students away from a confirmation mindset in labs by eliminating any form of prior or outside models to confirm. We refer to the activities using this approach as Novel Observations in Mixed Reality (NOMR) labs. We examined NOMR's effects in 100-level and 200-level undergraduate courses. Using pre-post measurements we find that after NOMR labs, students in both populations were more expertlike in their epistemology about experimental physics and held stronger self-efficacy about their abilities to do the kinds of things experimental physicists do. Through the lens of the psychological theory of flow, we found that students engage as productively with NOMR labs as

Group work is commonly adopted in university science laboratories. However, student small-group discourse in university science labs is rarely investigated. We aim to bridge the gap in the literature by characterizing student discourse group roles in inquiry-based science labs. The instructional context for the study was a summer program hosted at a private research university in the eastern United States. The program was designed as a bridge program for matriculating students who were first generation and/or deaf or hard-of-hearing (DHH). Accommodations such as interpreters and technology were provided for DHH students. We analyzed 19 students' discourse moves in five lab activities from the video recordings, resulting in a total of 48 student-lab units. We developed codes to describe student discourse moves: asking a question, proposing an idea, participating in discussion, chatting off-task, and talking with instructor. Through a cluster analysis using the 48 student-lab units on quantified discourse moves, we identified four discourse styles, High on-task high social, High on-task low social, Low on-task low social, and Low on-task high social. The results show that individual

The onset of the COVID-19 pandemic forced many universities to move to virtual instruction during the spring 2020 semester. The transition to remote learning was abrupt and overwhelming for teachers of all subjects, all across the US. Nowhere was this more true than in science lab courses. The experience nevertheless provides an opportunity to investigate the optimal design of remote labs, with similar learning goals as in-person labs. In this study we explore the three most common approaches to remote labs: recorded experiments, applet-based experiments, and at-home projects. We use surveys and interviews to make two comparisons: remote labs vs. in-person labs; the different types of remote labs. Examining these two questions we find that remote labs perform as well as in-person labs and students learn the most from at home physics experiments while also enjoying those the most.

Instructional labs are being transformed to better reflect authentic scientific practice, often by removing aspects of pedagogical structure to support student agency and decision-making. We explored how these changes impact men's and women's participation in group work associated with labs through clustering methods on the quantified behavior of students. We compared the group roles students take on in two different types of instructional settings; (1) highly structured traditional labs, and (2) less structured inquiry-based labs. Students working in groups in the inquiry-based (less structured) labs assumed different roles within their groups, however men and women systematically took on different roles and men behaved differently when in single- versus mixed-gender groups. We found no such systematic differences in role division among male and female students in the traditional (highly-structured) labs. Students in the inquiry-based labs were not overtly assigned these roles, indicating that the inequitable division of roles was not a result of explicit assignment. Our results highlight the importance of structuring equitable group dynamics in educational settings, as a gendered

Most, if not all, physics undergraduate degree programs include instructional lab experiences. Physics lab instructors, both faculty and staff, are instrumental to student learning in instructional physics labs. However, the faculty-staff dichotomy belies the complex, varied, and multifaceted landscape of positions that lab instructors hold in the fabrics of physics departments. Here we present the results of a mixed methods study of the people who teach instructional labs and their professional contexts. Recruiting physics lab instructors across the US, we collected 84 survey responses and conducted 12 in-depth interviews about their job characteristics, professional identities, resources, and experiences. Our investigation reveals that lab instructors vary in terms of their official titles, job descriptions, formal duties, personal agency, and access to resources. We also identified common themes around the value of instructional labs, mismatched job descriptions, and a broad set of necessary skills and expertise. Our results suggest that instructors often occupy overlapping roles that fall in between more canonical jobs in physics departments. By understanding the professional c

Instructional labs are widely seen as a unique, albeit expensive, way to teach scientific content. We measured the effectiveness of introductory lab courses at achieving this educational goal across nine different lab courses at three very different institutions. These institutions and courses encompassed a broad range of student populations and instructional styles. The nine courses studied had two key things in common: the labs aimed to reinforce the content presented in lectures, and the labs were optional. By comparing the performance of students who did and did not take the labs (with careful normalization for selection effects), we found universally and precisely no added value to learning from taking the labs as measured by course exam performance. This work should motivate institutions and departments to reexamine the goals and conduct of their lab courses, given their resource-intensive nature. We show why these results make sense when looking at the comparative mental processes of students involved in research and instructional labs, and offer alternative goals and instructional approaches that would make lab courses more educationally valuable.

PubMed is a freely accessible system for searching the biomedical literature, with approximately 2.5 million users worldwide on an average workday. We have recently developed PubMed Labs (www.pubmed.gov/labs), an experimental platform for users to test new features/tools and provide feedback, which enables us to make more informed decisions about potential changes to improve the search quality and overall usability of PubMed. In doing so, we hope to better meet our user needs in an era of information overload. Another novel aspect of PubMed Labs lies in its mobile-first and responsive layout, which offers better support for accessing PubMed on the increasingly popular use of mobile and small-screen devices. Currently, PubMed Labs only includes a core subset of PubMed functionalities, e.g. search, facets. We encourage users to test PubMed Labs and share their experience with us, based on which we expect to continuously improve PubMed Labs with more advanced features and better user experience.

This report presents a practical approach to teaching quantum computing to Electrical Engineering & Computer Science (EECS) students through dedicated hands-on programming labs. The labs cover a diverse range of topics, encompassing fundamental elements, such as entanglement, quantum gates and circuits, as well as advanced algorithms including Quantum Key Distribution, Deutsch and Deutsch-Jozsa Algorithms, Simon's algorithm, and Grover's algorithm. As educators, we aim to share our teaching insights and resources with fellow instructors in the field. The full lab handouts and program templates are provided for interested instructors. Furthermore, the report elucidates the rationale behind the design of each experiment, enabling a deeper understanding of quantum computing.

The Living Labs for Academic Search (LiLAS) lab aims to strengthen the concept of user-centric living labs for academic search. The methodological gap between real-world and lab-based evaluation should be bridged by allowing lab participants to evaluate their retrieval approaches in two real-world academic search systems from life sciences and social sciences. This overview paper outlines the two academic search systems LIVIVO and GESIS Search, and their corresponding tasks within LiLAS, which are ad-hoc retrieval and dataset recommendation. The lab is based on a new evaluation infrastructure named STELLA that allows participants to submit results corresponding to their experimental systems in the form of pre-computed runs and Docker containers that can be integrated into production systems and generate experimental results in real-time. Both submission types are interleaved with the results provided by the productive systems allowing for a seamless presentation and evaluation. The evaluation of results and a meta-analysis of the different tasks and submission types complement this overview.