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1. Using computational thinking for data practices in high school science.

   Peters-Burton, E. E.; Rich, P.; Cleary, T.; Burton, S.; Kitsantas, A.; Egan, G.; Ellsworth, J. (February 2020, The science teacher)

   When conducting a science investigation in biology, chemistry, physics or earth science, students often need to obtain, organize, clean, and analyze the data in order to draw conclusions about a particular phenomenon. It can be difficult to develop lesson plans that provide detailed or explicit instructions about what students need to think about and do to develop a firm conceptual understanding, particularly regarding data analysis. This article demonstrates how computational thinking principles and data practices can be merged to develop more effective science investigation lesson plans. The data practices of creating, collecting, manipulating, visualizing, and analyzing data are merged with the computational thinking practices of decomposition, pattern recognition, abstraction, algorithmic thinking, and automation to create questions for teachers and students that help them think through the underlying processes that happen with data during high school science investigations. The questions can either be used to elaborate lesson plans or embedded into lesson plans for students to consider how they are using computational thinking during their data practices in science. 

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2. Biology exams rarely use visual models to engage higher-order cognitive skills

   https://doi.org/10.1371/journal.pone.0317077  

   Uminski, Crystal; Cammarota, Christian; Couch, Brian A; Wright, L Kate; Newman, Dina L (July 2025, PLOS One)

   Wessner, David R (Ed.)

   Visual models are a necessary part of molecular biology education because submicroscopic compounds and processes cannot be directly observed. Accurately interpreting the biological information conveyed by the shapes and symbols in these visual models requires engaging visual literacy skills. For students to develop expertise in molecular biology visual literacy, they need to have structured experiences using and creating visual models, but there is little evidence to gauge how often undergraduate biology students are provided such opportunities. To investigate students’ visual literacy experiences, we surveyed 66 instructors who taught lower division undergraduate biology courses with a focus on molecular biology concepts. We collected self-reported data about the frequency with which the instructors teach with visual models and we analyzed course exams to determine how instructors incorporated visual models into their assessments. We found that most instructors reported teaching with models in their courses, yet only 16% of exam items in the sample contained a visual model. There was not a statistically significant relationship between instructors’ self-reported frequency of teaching with models and extent to which their exams contained models, signaling a potential mismatch between teaching and assessment practices. Although exam items containing models have the potential to elicit higher-order cognitive skills through model-based reasoning, we found that when instructors included visual models in their exams the majority of the items only targeted the lower-order cognitive skills of Bloom’s Taxonomy. Together, our findings highlight that despite the importance of visual models in molecular biology, students may not often have opportunities to demonstrate their understanding of these models on assessments. 

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3. Work in Progress: Teaching Engineering Students to Self-Transform: Parallelisms between Product Innovation and Student Career Path Planning

   Vargas_Hernandez, Noe; Ortega, Javier; Lozano, Karen; Fuentes, Arturo; Marquez, Eleazar (June 2024, ASEE Conference)

   Freshman engineering students can have a hard time transitioning to college. The freshman year is critical to the students’ academic success; in this year they learn basic skills and establish essential networks with other students, faculty, and resources. How can we help these freshman engineering students in this transition? We propose that freshman students can learn from the engineering design innovation process and apply it by analogy to the design of their academic pathways. There are multiple similarities between product innovation (i.e., technology) and the continuous academic challenges faced by the student. Engineers as designers and innovators have a vast and rich repository of techniques, tools, and approaches to develop new technologies, and a parallelism can be drawn between the design and innovation of a technology (e.g., redesign of a kitchen appliance), and the “design” of the students’ academic career pathways. During the Spring 2023 semester pilot, students in Intro to Mechanical Engineering (Course A) worked in teams in a 6-week product innovation project to redesign a simple kitchen appliance. Students learned basic concepts of the design process (e.g., creative exploration of solutions, decision making, multi objective evaluation, etc.). These same students concurrently took Course B (Learning Frameworks) where they worked on a 6-week project to define their career pathways. Both projects, product innovation and career pathways, followed the Challenge Based Instruction (CBI) approach. Periodically, participant students were shown how to use the lessons from product innovation by analogy and reflection in their career pathways project. The objective is for students to learn about the engineering design process and to apply it to their academic challenges by analogy. This prepares students with meta skills to help solve future problems in their academic path, and at each iteration, the students transform themselves, hence the use of the term self-transformation (also referred as “self-innovation”). Data collected from pre and post surveys will be presented to measure self-efficacy in engineering design, grit, motivation to learn, and STEM identity. Participant interviews provide a qualitative insight into the intervention. This project is funded by NSF award 2225247. 

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4. Board 120: Development of an Engineering Identity and Career Aspirations Survey for Use with Elementary Students

   https://doi.org/10.18260/1-2--32210  

   Paul, Kelli; Maltese, Adam; Miel, Karen; Portsmore, Merredith; Sung, Euisuk (June 2019, ASEE Conferences)

   nterest in science, technology, engineering, and mathematics (STEM) begins as early as elementary and middle school. As youth enter adolescence, they begin to shape their personal identities and start making decisions about who they are and could be in the future. Students form their career aspirations and interests related to STEM in elementary school, long before they choose STEM coursework in high school or college. Much of the literature examines either science or STEM identity and career aspirations without separating out individual sub-disciplines. Therefore, the purpose of this paper is to describe the development of a survey instrument to specifically measure engineering identity and career aspirations in adolescents and preadolescents. When possible, we utilized existing measures of STEM identity and career aspirations, adapting them when necessary to the elementary school level and to fit the engineering context. The instrument was developed within the context of a multi-year, NSF-funded research project examining the dynamics between undergraduate outreach providers and elementary students to understand the impact of the program on students’ engineering identity and career aspirations. Three phases of survey development were conducted that involved 492 elementary students from diverse communities in the United States. Three sets of items were developed and/or adapted throughout the four phases. The first set of items assessed Engineering Identity. Recent research suggests that identity consists of three components: recognition, interest, and performance/competence. Items assessing each of these constructs were included in the survey. The second and third sets of items reflected Career Interests and Aspirations. Because elementary and middle school students often have a limited or nascent awareness of what engineers do or misconceptions about what a job in science or engineering entails, it is problematic to measure their engineering identity or career aspirations by directly asking them whether they want to be a scientist/engineer or by using a checklist of broad career categories. Therefore, similar to other researchers, the second set of items assessed the types of activities that students are interested in doing as part of a future career, including both non-STEM and STEM (general and engineering-specific) activities. These items were created by the research team or adapted from activity lists used in existing research. The third set of items drew from career counseling measures relying on Holland’s Career Codes. We adapted the format of these instruments by asking students to choose the activity they liked the most from a list of six activities that reflected each of the codes rather than responding to their interest about each activity. Preliminary findings for each set of items will be discussed. Results from the survey contribute to our understanding of engineering identities and career aspirations in preadolescent and adolescent youth. However, our instrument has the potential for broader application in non-engineering STEM environments (e.g., computer science) with minor wording changes to reflect the relevant science subject area. More research is needed in determining its usefulness in this capacity. 

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5. Using multimodal learning analytics to validate digital traces of self-regulated learning in a laboratory study and predict performance in undergraduate courses.

   https://doi.org/10.1037/edu0000890  

   Bernacki, Matthew L; Yu, Linyu; Kuhlmann, Shelbi L; Plumley, Robert D; Greene, Jeffrey A; Duke, Rebekah F; Freed, Rebekah; Hollander-Blackmon, Christina; Hogan, Kelly A (February 2025, Journal of Educational Psychology)

   Undergraduates enrolled in large, active learning courses must self-regulate their learning (self-regulated learning [SRL]) by appraising tasks, making plans, setting goals, and enacting and monitoring strategies. SRL researchers have relied on self-report and learner-mediated methods during academic tasks studied in laboratories and now collect digital event data when learners engage with technology-based tools in classrooms. Inferring SRL processes from digital events and testing their validity is challenging. We aligned digital and verbal SRL event data to validate digital events as traces of SRL and used them to predict achievement in lab and course settings. In Study 1, we sampled a learning task from a biology course into a laboratory setting. Enrolled students (N = 48) completed the lesson using digital resources (e.g., online textbook, course site) while thinking aloud weeks before it was taught in class. Analyses confirmed that 10 digital events reliably co-occurred ≥70% of the time with verbalized task definition and strategy use macroprocesses. Some digital events co-occurred with multiple verbalized SRL macroprocesses. Variance in occurrence of validated digital events was limited in lab sessions, and they explained statistically nonsignificant variance in learners’ performance on lesson quizzes. In Study 2, lesson-specific digital event data from learners (N = 307) enrolled in the course (but not in Study 1) predicted performance on lesson-specific exam items, final exams, and course grades. Validated digital events also predicted final exam and course grades in the next semester (N = 432). Digital events can be validated to reflect SRL processes and scaled to explain achievement in naturalistic undergraduate education settings. Educational Impact and Implications Statement Instructors often have difficulty identifying and helping struggling students in courses with hundreds of students. Digital trace data can be used to efficiently and effectively identify struggling students in these large courses, but such data are often difficult to interpret with confidence. In our study, we found that using verbal trace data to augment and validate our inferences about the meaning of digital trace data resulted in a powerful set of predictors of students’ achievement. These validated digital trace data can be used to not only identify students in need of support in large classes, but also to understand how to target interventions to the aspects of learning that are causing students the most difficulty. 

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