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  1. Free, publicly-accessible full text available June 26, 2023
  2. This project aims to enhance students’ learning in foundational engineering courses through oral exams based on the research conducted at the University of California San Diego. The adaptive dialogic nature of oral exams provides instructors an opportunity to better understand students’ thought processes, thus holding promise for improving both assessments of conceptual mastery and students’ learning attitudes and strategies. However, the issues of oral exam reliability, validity, and scalability have not been fully addressed. As with any assessment format, careful design is needed to maximize the benefits of oral exams to student learning and minimize the potential concerns. Compared to traditional written exams, oral exams have a unique design space, which involves a large range of parameters, including the type of oral assessment questions, grading criteria, how oral exams are administered, how questions are communicated and presented to the students, how feedback were provided, and other logistical perspectives such as weight of oral exam in overall course grade, frequency of oral assessment, etc. In order to address the scalability for high enrollment classes, key elements of the project are the involvement of the entire instructional team (instructors and teaching assistants). Thus the project will create a new training program tomore »prepare faculty and teaching assistants to administer oral exams that include considerations of issues such as bias and students with disabilities. The purpose of this study is to create a framework to integrate oral exams in core undergraduate engineering courses, complementing existing assessment strategies by (1) creating a guideline to optimize the oral exam design parameters for the best students learning outcomes; and (2) Create a new training program to prepare faculty and teaching assistants to administer oral exams. The project will implement an iterative design strategy using an evidence-based approach of evaluation. The effectiveness of the oral exams will be evaluated by tracking student improvements on conceptual questions across consecutive oral exams in a single course, as well as across other courses. Since its start in January 2021, the project is well underway. In this poster, we will present a summary of the results from year 1: (1) exploration of the oral exam design parameters, and its impact in students’ engagement and perception of oral exams towards learning; (2) the effectiveness of the newly developed instructor and teaching assistants training programs (3) The development of the evaluation instruments to gauge the project success; (4) instructors and teaching assistants experience and perceptions.« less
    Free, publicly-accessible full text available June 26, 2023
  3. This work-in-progress paper presents an innovative practice of using oral exams to maintain academic integrity and promote student engagement in large-enrollment engineering courses during remote instruction. With the abrupt and widespread transition to distance learning and assessment brought on by the COVID-19 pandemic, there has been a registered upsurge in academic integrity violations globally. To address the challenge of compromised integrity, in the winter quarter of 2021 we have implemented oral exams across six mostly high-enrollment mechanical and electrical engineering undergraduate courses. We present our oral exam design parameters in each of the courses and discuss how oral exams relate to academic integrity, student engagement, stress, and implicit bias. We also address the challenge of scalability, as most of our oral exams were implemented in large classes, where academic integrity and student-instructor disconnection have generally gotten disproportionately worse during remote learning. Our survey results indicate that oral exams have positively contributed to academic integrity in our courses. Based on our preliminary study and experiences, we expect oral exams can be effectively leveraged to hinder cheating and foster academic honesty in students, even when in-person instruction and assessment resumes.
  4. The Improving Student Experiences to Increase Student Engagement (ISE-2) grant was awarded to Texas A&M University by the National Science Foundation, through EEC-Engineering Diversity Activities (Grant No. 1648016) with the goal of increasing student engagement and retention in the College of Engineering. The major component of the intervention was a faculty development program aimed to increase active learning, improve classroom climates, and decrease implicit bias and deficit thinking. Faculty teaching first- and second-year Engineering courses participated in the ISE-2 faculty development program, with the first cohort (n = 10) in Summer 2017 and the second cohort (n = 5) in Summer 2018. This paper describes the content of each of these components of the faculty development program and provides access to a Google drive (still in development at the time of the abstract) with resources for others to use. The faculty development program consisted of three workshops, a series of coffee hour conversations, and two deliverables from the participants (a teaching plan at the conclusion of the summer training and a final reflection a year following the training). Anchoring the program was a framework for teaching in a diverse classroom (Adams & Love, 2009). Workshop 1 (early May) consisted ofmore »an overview of the ISE-2 program. During the first workshop, faculty were introduced to social cognitive biases and the behaviors that result from these biases. During this workshop, the ISE-2 team shared findings from a climate study related to the classroom experiences of students at the College of Engineering. Workshop 2 (mid-May) focused on how undergraduate students learn, provided evidence for the effectiveness of active learning strategies, and exposed faculty participants to active learning strategies. Workshop 3 (early August) integrated the material from the first two workshops as faculty participants prepared to apply the material to their own teaching. Prior to each workshop, the faculty participants were provided with pre-workshop readings to familiarize them with some of the content matter. Coffee hour conversations—informal discussions between the participating faculty and the ISE-2 team centered around a teaching topic selected by participants—were conducted on a near-weekly basis between the second and third workshops. Handouts and worksheets were provided at each coffee hour and served to guide the coffee hour discussions. After the last workshop but before the Fall semester, faculty participants created a teaching plan to incorporate what they learned in the ISE-2 program into their own teaching. At the end of the academic year, the faculty participants are tasked with completing a final reflection on how ISE-2 has affected their teaching in the previous academic year. In this paper, we will report the content of each of the three workshops and explain how these workshops are related to the overarching goals of the ISE-2 program. Then, we will discuss how each of the coffee hour conversation topics complement the material covered in the workshops. Lastly, we will explore the role of the teaching plans and final reflections in changing instructional practices for faculty.« less
  5. “Improving Student Experiences to Increase Student Engagement” (ISE-2) was funded by the National Science Foundation, through EEC-Engineering Diversity Activities, at Texas A&M University. The grant activity focuses on a faculty development program for faculty who teach first- and second-year engineering courses. As part of the evaluation plan, classroom observations were conducted by the ISE-2 team to assess the classroom climate and teaching practices of ISE-2 faculty participants and non-participant faculty peers. Since Spring 2017, the team has conducted 78 classroom observations. The ISE-2 evaluation team had expert classroom observers train novice observers. The observer training sessions became the basis of this DIY Classroom Observation Toolkit, which is available for people who are interested in conducting systematic classroom observations but have limited experience with qualitative coding and observational research. The goal of the Toolkit is for these individuals to teach themselves using the Toolkit components: a) an annotated bibliography introducing articles that are helpful to understanding and conducting classroom observations, b) training videos teaching viewers to conduct classroom observations using a protocol, and c) a series of sample classroom videos and validation keys for each of the sample videos. This paper serves as a user manual for the Toolkit, which canmore »be accessed at« less
  6. “Improving Student Experiences to Increase Student Engagement” (ISE-2) was awarded to Texas A&M University by the National Science Foundation, through EEC-Engineering Diversity Activities. ISE-2 is a faculty development program focused on reducing implicit bias and increasing active learning, with the goals of (a) increasing student engagement, success, and retention, and (b) ultimately seeing greater increases for underrepresented minority (URM), women, and first-generation students. Ten faculty teaching first- and second-year Engineering courses participated in the first cohort of ISE-2 in Summer 2017, which consisted of three workshops and six informal “coffee conversations”. At the conclusion of the workshops, each faculty was tasked with completing a teaching plan for the Fall 2017 semester, to incorporate the strategies and knowledge from ISE-2 into the courses they plan to teach. Focus groups with the ISE-2 faculty were conducted in Fall 2017 to obtain feedback about the faculty development program. Classroom observations were conducted using environmental scans and the Classroom Observation Protocol for Undergraduate STEM (COPUS)1 to assess the classroom climate of faculty in the experimental (ISE-2) and control groups. Student surveys were also administered to students who were taught by ISE-2 faculty and control group faculty to assess student engagement and classroom climate. Whilemore »the project is still ongoing, feedback from faculty regarding ISE-2 have been positive.« less
  7. Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
    Free, publicly-accessible full text available December 1, 2023
  8. Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
    Free, publicly-accessible full text available December 1, 2023
  9. A bstract Measurements of the production cross-sections of the Standard Model (SM) Higgs boson ( H ) decaying into a pair of τ -leptons are presented. The measurements use data collected with the ATLAS detector from pp collisions produced at the Large Hadron Collider at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV, corresponding to an integrated luminosity of 139 fb − 1 . Leptonic ( τ → ℓν ℓ ν τ ) and hadronic ( τ → hadrons ν τ ) decays of the τ -lepton are considered. All measurements account for the branching ratio of H → ττ and are performed with a requirement |y H | < 2 . 5, where y H is the true Higgs boson rapidity. The cross-section of the pp → H → ττ process is measured to be 2 . 94 ± $$ 0.21{\left(\mathrm{stat}\right)}_{-0.32}^{+0.37} $$ 0.21 stat − 0.32 + 0.37 (syst) pb, in agreement with the SM prediction of 3 . 17 ± 0 . 09 pb. Inclusive cross-sections are determined separately for the four dominant production modes: 2 . 65 ± $$ 0.41{\left(\mathrm{stat}\right)}_{-0.67}^{+0.91} $$ 0.41 stat − 0.67 + 0.91 (syst) pb for gluon-gluon fusion, 0 .more »197 ± $$ 0.028{\left(\mathrm{stat}\right)}_{-0.026}^{+0.032} $$ 0.028 stat − 0.026 + 0.032 (syst) pb for vector-boson fusion, 0 . 115 ± $$ 0.058{\left(\mathrm{stat}\right)}_{-0.040}^{+0.042} $$ 0.058 stat − 0.040 + 0.042 (syst) pb for vector-boson associated production, and 0 . 033 ± $$ 0.031{\left(\mathrm{stat}\right)}_{-0.017}^{+0.022} $$ 0.031 stat − 0.017 + 0.022 (syst) pb for top-quark pair associated production. Measurements in exclusive regions of the phase space, using the simplified template cross-section framework, are also performed. All results are in agreement with the SM predictions.« less
    Free, publicly-accessible full text available August 1, 2023