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1. A New Survey of Student Attitudes Toward Statistics: The S-SOMAS

   https://doi.org/10.52041/iase.icots11.T14D1  

   Whitaker, Douglas; Unfried, Alana; Batakci, Leyla; Bolon, Wendine; Bond, Marjorie; Kerby-Helm, April; Posner, Michael (October 2022, Bridging the Gap: Empowering & Educating Today’s Learners in Statistics. Proceedings of the 11th International Conference on Teaching Statistics (ICOTS11 2022), Rosario, Argentina.)

   Peters, S. A.; Zapata-Cardona, L.; Bonafini, F.; Fan, A. (Ed.)

   The Student Survey of Motivational Attitudes toward Statistics is a new instrument designed to measure affective outcomes in statistics education. This instrument is grounded in the established Expectancy-Value Theory of motivation and is being developed using a rigorous process. This paper provides an overview of the four pilot studies that have been conducted during the survey development process. Additionally, a description of the methods used for analyzing the data and the way the results are used to holistically make decisions about revisions to the survey is included. Brief confirmatory factor analysis results are included from two pilot studies to demonstrate that substantial progress has been made on the development. Once finalized (Spring 2023), the survey will be made freely available. 

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2. WIP: Expert Feedback and Student Think-Alouds for the Learning through Making Instrument

   Marcos, L; Nagel, R; Linsey, J; Alemán, M; Douglas, K; Holloway, E (November 2025, IEEE Frontiers in Education)

   Abstract—This WIP research paper presents validity evidence for a survey instrument designed to assess student learning in makerspaces. We report findings from expert reviews of item content and student interpretations of survey questions. The instrument was developed using a theory-driven approach to define constructs, followed by the development of questions aligned with those constructs. We solicited written feedback from 30 experts in instrument development and/or makerspaces, who rated the alignment of items with our constructs. Based on this input, we revised our items for clarity and consistency. We then conducted 25 cognitive interviews with a diverse group of students who use makerspaces, asking them to explain their understanding of each item and the reasoning behind their responses. Our recruitment ensured diversity in terms of race, gender, ethnicity, and academic background, extending beyond engineering majors. From our initial 45 items, we removed 6, modified 36, and added 1 based on expert feedback. During cognitive interviews, we began with 40 items, deleted one, and revised 23, resulting in 39 items for the pilot survey. Key findings included the value of examples in clarifying broad terms and improved student engagement with a revised rating scale—shifting from a 7-point Likert agreement scale to a self-description format encouraged fuller use of the scale. Our study contributes to the growing body of research on makerspaces by offering insights into how students describe their learning experiences and by providing initial validation evidence for a tool to assess those experiences, ultimately strengthening the credibility of the instrument. 

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3. Leveraging Mixed Reality and the Three Apprenticeships Model to Facilitate & Assess Authentic Learning Experiences for Civil Engineering Students

   https://doi.org/10.1109/FIE44824.2020.9274289  

   Perry, Logan; London, Jeremi; Wu, Wei; Ayer, Steven; Smith, Kieren; Patil, Karan (October 2020, 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   This work-in-progress paper presents highlights from a multi-year study aiming to develop and assess the impact of a mixed reality experience that sufficiently replicates the learning civil engineering students experience during a physical design and construction task. Human Centered Design principles and tenets of the Carnegie Foundation's Three Apprenticeships Model (i.e., learning related to "Head", "Hand", and "Heart") inform the project design, development, and assessments. The development of heart-focused assessments is one focus during the second year in this three-year project. This paper includes a brief overview of the project progress, in general, along with preliminary findings regarding the instrument development. It summarizes the results of a pilot study, including an item analysis of the survey responses. These findings offer preliminary evidence for the content validity and substantive validity of the instrument. Next steps and implications for the engineering education community are also discussed. 

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4. Leveraging Mixed Reality and the Three Apprenticeships Model to Facilitate & Assess Authentic Learning Experiences for Civil Engineering Students

   Logan Perry, Jeremi London (January 2020, The 2020 Frontiers in Education Conference)

   This work-in-progress paper presents highlights from a multi-year study aiming to develop and assess the impact of a mixed reality experience that sufficiently replicates the learning civil engineering students experience during a physical design and construction task. Human Centered Design principles and tenets of the Carnegie Foundation’s Three Apprenticeships Model (i.e., learning related to “Head”, “Hand”, and “Heart”) inform the project design, development, and assessments. The development of heart-focused assessments is one focus during the second year in this three-year project. This paper includes a brief overview of the project progress, in general, along with preliminary findings regarding the instrument development. It summarizes the results of a pilot study, including an item analysis of the survey responses. These findings offer preliminary evidence for the content validity and substantive validity of the instrument. Next steps and implications for the engineering education community are also discussed. 

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5. Board 236: Design for Sustainability: How Mental Models of Social-Ecological Systems Shape Engineering Design Decisions

   Katz, A; Paretti, M; Shealy, T; Bilow, F (June 2024, American Society for Engineering Education)

   Understanding how engineers connect technical work to broader social-ecological systems is critical because their designs transform societies and environments. As part of a national study to explore how civil and chemical engineers navigate design decisions, we are developing a survey instrument to assess mental models of social-ecological-technical systems (SETS). Mental models (Johnson-Laird, 2001; Rouse & Morris, 1986), are internal representations that individuals use to describe, explain, and predict the form, function, state, and purpose of a system. In this case, the system is the connection between technical design and broader social-ecological systems. The project is informed by three frameworks: 1) planned behavior, 2) mental models, and 3) social-ecological-technical systems (SETS). The project integrates the theory of planned behavior with mental models to build fundamental knowledge of engineers’ mental models of SETSs, changes in their mental models over time, and relationships between mental models and design decisions. This paper presents the instrument development process centered on eliciting mental models of SETS. SETS (McPhearson et al., 2022) is a generalized framework that positions social, technical, and ecological elements of a system as vertices of a triangle, with interactions in all directions. The instrument will include both closed-ended and open-ended items, allowing us to leverage advances in natural language processing to scale qualitative data analysis and combine an inferential framework often associated with quantitative studies with the richer information flow associated with qualitative studies. Previous work using SETS has identified individual components within each vertex salient to the specific context (Bixler et al., 2019). In this paper, we report on the phases of instrument development that support this contextualization: 1) Initial interview protocol development followed by semi-structured interviews with six engineering students outside the target majors to test how well the protocol elicits information about students mental models of SETS, 2) revisions to the interview protocol followed by semi-structured interviews with senior-level students in chemical and civil engineering students (12 per discipline), 3) deductive and inductive analysis of those interviews, using SETS as our deductive coding scheme followed by inductive coding to refine and contextualize the analysis and support survey development. We conclude with the initial survey instrument, which will undergo pilot testing in the summer of 2024. The results both support instrument development and offer an exploratory analysis of civil and chemical engineering students’ mental models of SETS. 

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