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  1. his work-in-progress paper expands on a collaboration between engineering education researchers and machine learning researchers to automate the analysis of written responses to conceptually challenging questions in statics and dynamics courses (Authors, 2022). Using the Concept Warehouse (Koretsky et al., 2014), written justifications of ConcepTests (CTs) were gathered from statics and dynamics courses in a diverse set of two- and four-year institutions. Written justifications for CTs have been used to support active learning pedagogies which makes them important to investigate how students put together their problem-solving narratives of understanding. However, despite the large benefit that analysis of student written responses may provide to instructors and researchers, manual review of responses is cumbersome, limits analysis, and can be prone to human bias. In efforts to improve the analysis of student written responses, machine learning has been used in various educational contexts to analyze short and long texts (Burstein et al., 2020; Burstein et al., 2021). Natural Language Processing (NLP) uses transformer-based machine learning models (Brown et al., 2020; Raffel et al., 2019) which can be used through fine-tuning or in-context learning methods. NLP can be used to train algorithms that can automate the coding of written responses. Only a few studies for educational applications have leveraged transformer-based machine learning models further prompting an investigation into its use in STEM education. However, work in NLP has been criticized for heightening the possibility to perpetuate and even amplify harmful stereotypes and implicit biases (Chang et al., 2019; Mayfield et al., 2019). In this study, we detail the aim to use NLP for linguistic justice. Using methods like text summary, topic modeling, and text classification, we identify key aspects of student narratives of understanding in written responses to mechanics and statics CTs. Through this process, we seek to use machine learning to identify different ways students talk about a problem and their understanding at any point in their narrative formation process. Thus, we hope to help reduce human bias in the classroom and through technology by giving instructors and researchers a diverse set of narratives that include insight into their students’ histories, identities, and understanding. These can then be used towards connecting technological knowledge to students’ everyday lives. 
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  4. Many engineering problems assigned in undergraduate classes are numerical and can be solved using equations and algorithms—for example, truss problems in statics are often solved using the method of joints or the method of sections. Concept questions, which can be administered in class using active learning pedagogies, aid in the development of conceptual understanding as opposed to the procedural skill often emphasized in numerical problems. We administered a concept question about a truss to 241 statics students at six diverse institutions and find no statistically significant differences in answer correctness or confidence between institutions. Across institutions, students report that they are not accustomed to such non-numerical concept questions, but they grapple in different ways with the experience. Some frame engineering as inherently numerical, and thus do not value the conceptual understanding assessed by the question, while others recognize that developing conceptual knowledge is useful and will translate to their future engineering work. 
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  6. Several consensus reports cite a critical need to dramatically increase the number and diversity of STEM graduates over the next decade. They conclude that a change to evidence-based instructional practices, such as concept-based active learning, is needed. Concept-based active learning involves the use of activity-based pedagogies whose primary objectives are to make students value deep conceptual understanding (instead of only factual knowledge) and then to facilitate their development of that understanding. Concept-based active learning has been shown to increase academic engagement and student achievement, to significantly improve student retention in academic programs, and to reduce the performance gap of underrepresented students. Fostering students' mastery of fundamental concepts is central to real world problem solving, including several elements of engineering practice. Unfortunately, simply proving that these instructional practices are more effective than traditional methods for promoting student learning, for increasing retention in academic programs, and for improving ability in professional practice is not enough to ensure widespread pedagogical change. In fact, the biggest challenge to improving STEM education is not the need to develop more effective instructional practices, but to find ways to get faculty to adopt the evidence-based pedagogies that already exist. In this project we seek to propagate the Concept Warehouse, a technological innovation designed to foster concept-based active learning, into Mechanical Engineering (ME) and to study student learning with this tool in five diverse institutional settings. The Concept Warehouse (CW) is a web-based instructional tool that we developed for Chemical Engineering (ChE) faculty. It houses over 3,500 ConcepTests, which are short questions that can rapidly be deployed to engage students in concept-oriented thinking and/or to assess students’ conceptual knowledge, along with more extensive concept-based active learning tools. The CW has grown rapidly during this project and now has over 1,600 faculty accounts and over 37,000 student users. New ConcepTests were created during the current reporting period; the current numbers of questions for Statics, Dynamics, and Mechanics of Materials are 342, 410, and 41, respectively. A detailed review process is in progress, and will continue through the no-cost extension year, to refine question clarity and to identify types of new questions to fill gaps in content coverage. There have been 497 new faculty accounts created after June 30, 2018, and 3,035 unique students have answered these mechanics questions in the CW. We continue to analyze instructor interviews, focusing on 11 cases, all of whom participated in the CW Community of Practice (CoP). For six participants, we were able to compare use of the CW both before and after participating in professional development activities (workshops and/or a community or practice). Interview results have been coded and are currently being analyzed. To examine student learning, we recruited faculty to participate in deploying four common questions in both statics and dynamics. In statics, each instructor agreed to deploy the same four questions (one each for Rigid Body Equilibrium, Trusses, Frames, and Friction) among their overall deployments of the CW. In addition to answering the question, students were also asked to provide a written explanation to explain their reasoning, to rate the confidence of their answers, and to rate the degree to which the questions were clear and promoted deep thinking. The analysis to date has resulted in a Work-In-Progress paper presented at ASEE 2022, reporting a cross-case comparison of two instructors and a Work-In-Progress paper to be presented at ASEE 2023 analyzing students’ metacognitive reflections of concept questions. 
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    Free, publicly-accessible full text available June 1, 2024