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1. A Qualitative Approach to Understanding Computing Self-Efficacy Development Among Girls of Color

   Garcia, Patricia; Perez, Melissa; Culler, Tori (July 2024, Proceedings of the 2023 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology)

   Research on increasing diversity in computing requires focused investigations of how girls of color develop their self-efficacy beliefs. Measures of self-efficacy are commonly collected using validated survey instruments that require selfreporting. In this paper, we present an observational protocol based on Bandura’s four sources of efficacy beliefs that can be used in conjunction with existing surveys to capture qualitative data on how computing self-efficacy beliefs develop. We present the observational protocol as a complementary instrument that can be used alone or in conjunction with validated surveys to capture learners’ observable behaviors as they learn new computing knowledge and skills. 

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2. The Development and Validation of a Survey Measuring Opportunity to Learn Spatial Reasoning Skills at Home

   https://doi.org/10.7275/pare.1885  

   Wellberg, Sarah; Sparks, Anthony; Ketterlin-Geller, Leanne (October 2023, Practical assessment research evaluation)

   The early development of spatial reasoning skills has been linked to future success in mathematics (Wai, Lubinski, & Benbow, 2009), but research to date has mainly focused on the development of these skills within classroom settings rather than at home. The home environment is often the first place students are exposed to, and develop, early mathematics skills, including spatial reasoning (Blevins-Knabe, 2016; Hart, Ganley, & Purpura, 2016). The purpose of the current study is to develop a survey instrument to better understand Kindergarten through Grade 2 students’ opportunities to learn spatial reasoning skills at home. Using an argument-based approach to validation (Kane, 2013), we collected multiple sources of validity evidence, including expert review of item wording and content and pilot data from 201 parent respondents. This manuscript outlines the interpretation/use argument that guides our validation study and presents evidence collected to evaluate the scoring inferences for using the survey to measure students’ opportunities to learn spatial reasoning skills at home. 

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3. A Tool for Gaining Insight into Students’ Self-Directed Learning Skills

   Opanuga, T.; Diefes-Dux, H. A.; Panther, G.; Perry, L. (June 2024, American Society for Engineering Education)

   New engineering educators need to be equipped with instruments that can provide easy and meaningful insight into students’ self-directed learning (SDL) status so they can better foster students’ success. Students who are self-directed learners can independently initiate and take full responsibility for learning, effectively utilize available resources in the pursuit of their goals, develop awareness of their learning, and demonstrate the appropriate attitude essential for individual and collaborative learning. Despite these benefits, developing SDL skills in engineering students is often overlooked. To address this, educators have a facilitating role to play in the development of engineering students’ SDL skills, however, this role can be challenging for them due to the (a) high cost of using SDL instruments, especially in a large classroom and (b) uncertainty about the validity of SDL instruments. Moreover, these challenges may be more pronounced for new engineering educators. This study addresses these challenges by reporting the validity evidence for an SDL assessment instrument called the Self-Rating Scale of Self-Directed Learning (SRSSDL). The SRSSDL instrument has been widely utilized in medical education, but in this study, it was modified for the engineering education context. The utility of this 8-constructs, 46-item scale was demonstrated in engineering education with 111 undergraduate students across all academic levels, and the validity test was conducted in line with the contemporary validity framework. The result of the validity test of the SRSSDL revealed inconsistencies or instability of its constructs in the engineering education context. 

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4. Developing a flexible learning activity on biodiversity and spatial scale concepts using open‐access vegetation datasets from the National Ecological Observatory Network

   https://doi.org/10.1002/ece3.7385  

   Styers, Diane M.; Schafer, Jennifer L.; Kolozsvary, Mary Beth; Brubaker, Kristen M.; Scanga, Sara E.; Anderson, Laurel J.; Mitchell, Jessica J.; Barnett, David (March 2021, Ecology and Evolution)

   Abstract Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists. 

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5. Data proficiency in MAE education: Insights from student perspectives and experiences

   https://doi.org/10.1177/03064190241290897  

   Aikins, Godwyll; Berdanier, Catherine_G_P; Nguyen, Kim-Doang (November 2024, International Journal of Mechanical Engineering Education)

   This paper explores how mechanical and aerospace engineering (MAE) students understand and improve their data proficiency throughout their engineering curriculum. Data is essential for engineering students to be proficient in handling, as it is involved in every aspect of engineering. With the growing ubiquity of data and data analysis in all engineering fields, engineering students need to learn and master data skills to be competitive in the current and future job market. However, there is a lack of research on how non-computer science or software engineering majors perceive data proficiency and how they seek opportunities to develop data skills, especially as it relates to specific subdomains. In this paper, we investigate how students perceive data proficiency and how they develop using interview data from N = 27 MAE students at a research institution in the southeastern United States. Using the How People Learn framework, we analyzed the data through thematic analysis methods with a postpositivist approach, considering the bounded context of this study. The results show that MAE students value data proficiency as a crucial skill for their future careers and recognize its importance in making evidence-based engineering decisions. The study also reveals that, even though data proficiency is often a “hidden competency,” MAE students intuitively find various ways to enhance their data skills. These findings may help engineering educators to tailor their instruction to their students’ needs, address misconceptions about data and data proficiency, and prepare a data-literate future engineering workforce. 

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