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1. Learning Trajectories through Undergraduate Engineering Curricula and Experiences

   https://doi.org/10.18260/1-2--34905  

   Lande, Micah (June 2020, 2020 ASEE Virtual Annual Conference)

   null (Ed.)

   This NSF EAGER research paper investigates how undergraduate STEM and engineering students’ learning trajectories evolve over time, from 1st to senior year, along a novice to expert spectrum. We borrow the idea of “learning trajectories” from mathematics education that can paint the evolution of students’ knowledge and skills over time over a set of learning experiences. Curricula for undergraduate engineering programs can reflect an intended pathway of knowledge construction within a discipline. We intend our study of individual students within undergraduate STEM and engineering programs can highlight how this may happen in situ and how it may be similar or might differ from a given, prescribed programs of study among disciplines. We use a theoretical framework based in adaptive expertise and design thinking adaptive expertise to develop a design learning continuum further. Envisioned routes through disciplinary undergraduate curricula and student conceptions of their design process are explored through qualitative, semi-structured interviews with undergraduate 1st year and senior year students across STEM, engineering and non-STEM field such as computer science, mechanical engineering, general engineering, mathematics, science, English, and art. We also conduct similar interviews with faculty in these fields who are responsible and knowledgeable for undergraduate programs about their perceived benefits for the structure of their program’s curriculum. Additional information is collected from noticing the organizational and pedagogical structures of the relative undergraduate curriculum. Initial findings/outcomes suggest that traditions to knowledge construction both differ across disciplinary approaches and have similarities across non-obvious disciplinary relationships. Faculty have a firm understanding of how one class chains from one to another; students have less of a field of view for how mindful chunks of knowledge combine together. 

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2. Measurements Of Adaptive Expertise Among Low-Income STEM Students

   De Rosa, A.J.; Fisher, F.T.; Fontaine, M.; Lytle, A. (October 2022, 2022 ASEE Middle Atlantic Section Fall Conference)

   One of the goals of undergraduate education is to prepare students to adapt to a challenging career that requires continual learning and application of knowledge. Working professionals should have deep conceptual knowledge that they can apply in a range of contexts and possess the attitudes and skills of lifelong learners. The literature suggests the concept of Adaptive Expertise (AE), which can be defined as the ability to apply and extend knowledge and skills to new situations, describes some of these characteristics. Survey data concerning the level of AE displayed by various populations is extremely limited in most contexts, be it education or working professionals. As such, data concerning the level of adaptiveness displayed among various groups needs to be measured if activities designed to promote the development of AE are to be created and then tested in terms of their efficacy. This investigation provides this critical baseline data for future studies as we track the AE development of individual, first-year college students through their undergraduate program of study, with a focus on low-income students as a means to support retention. In this work, we assessed adaptive expertise among low-income STEM students using surveys and interviews. Low-income STEM students from various stages of their four year undergraduate program (n=208) completed an adaptive expertise survey in spring 2022. Following the survey, 24 of the low-income students (6 per year, 3 male, 3 female) were selected for targeted qualitative interviews to better understand the differences displayed by low and high AE students. Survey results from prior studies were used to draw comparisons between adaptiveness of low-income and non-low-income students. Results of the AE survey indicated no statistically-significant differences between low-income and non-low-income first-year students in terms of their level of adaptiveness. In addition, the level of AE displayed by low-income students increased through the program in a manner similar to that of non-low-income STEM students. Themes that emerged from the interviews included a general understanding of the importance and likelihood of learning new concepts continually while working in a professional role, and that students expressed growth in understanding the acceptance of reaching out for assistance from other students and faculty after exploring information on their own as they work through challenges in their academic assignments. Two dominant and divergent metacognitive processes were also observed: teaching/explaining concepts to others (highly adaptive) or primarily relying on exam/course grades for feedback on learning (low adaptiveness). Data gathered from interviews demonstrate the need for a greater emphasis on metacognitive practice to promote various aspects of AE. 

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3. Towards an Adaptable Curriculum-Driven Block-based Learning Environment

   https://doi.org/10.1145/3545947.3576357  

   Gardner-McCune, Christina; Jimenez, Yerika; Magda, David; Kulkarni, Abhishek; Chu, Sharon (March 2022, SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education)

   Over the past year, our AI4GA team of university faculty and middle school teachers have co-designed a middle school AI curriculum. In this poster we share how we used co-design both as a tool for collaboratively developing engaging AI activities and as a mechanism for mutual professional development. We explain our co-design process, give examples of curriculum materials provided to teachers, and showcase several teacher-created activities. We believe this approach to curriculum development centers the lived experiences of teachers and leverages the knowledge and expertise of university researchers to create high quality and engaging AI learning experiences for K-12 students. 

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4. Facilitating Student's Learning Transfer in a Database Programming Class

   https://doi.org/10.1145/3641554.3701828  

   Zhou, Yuzhe; Magana, Alejandra J; Li, Tianyi (February 2025, ACM)

   Transferring programming skills learned in the classroom to diverse real-world scenarios is both essential and challenging in computing education. This experience report describes an approach to facilitate learning transfer by fostering adaptive expertise. Students were engaged in co-creating contextualized worked-out examples, including step-by-step solutions. Through three homework assignments in a Spring 2023 database programming course, we observed substantial improvements, where students generated detailed and accurate solutions and enriched their problem-solving contexts from simple phrases to detailed stories, drawn from 17 real-life scenarios. Our results also suggest that the peer assessment process cultivated a supportive learning environment and fostered adaptive expertise. We discuss the lessons learned and draw pedagogical implications for integrating student-generated contextualized materials in other programming courses. 

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5. Student Learning Trajectories from Making and Engineering Activities

   Lande, Micah (January 2018, Review & directory - American Society for Engineering Education)

   The research objective of this NSF-funded study is to explore and understand how open-ended, hands-on making work and activities can reflect student learning trajectories and learning gains in the product-based learning, undergraduate engineering classroom. The aim is to expand understanding of what making learning in the context of engineering design education might be and to illustrate educational pathways within the engineering education curriculum. Making is rooted in constructionism – learning by doing and constructing knowledge through that doing. Aspects of making work and activities that are unique to making that could appear in the engineering classroom or curriculum include: sharing, practical ingenuity, personal investment, playful invention, risk taking, community building and self-directed learning. The main research questions of this work is: How do engineering students learn and apply making? What are the attributes of making in the engineering classroom? Empirical evidence of what making in the engineering classroom looks like, and how it changes over time, and how students conceptualize making through making, designerly, and engineering ways of knowing-doing-acting will come from revisiting and additional qualitative analysis of student project data collected during a product-based learning course engineering design course. To best address the research question, this proposed study proposes multiple qualitative methods to collect and analyze data on engineering students learning making. We aim to triangulate what students think they are learning, what they are being taught, and what students are demonstrating. This work is exploratory in nature. In our approach to understanding making outside of formal engineering education, at events like Maker Faires in the Maker Community, it does seem evident that there is a lot of overlap between a making mindset and a designerly way of knowing or engineering way of knowing. In the sphere of formal engineering education however, making is regularly viewed as lesser than engineering, engineering design without the engineering science or analysis. Making is not yet valued as part of formal engineering education efforts. If making is something that can be connected to beneficial student learning and is additive to the required technical content and provides a means for students to figure out what area of problems they want to tackle in the studies and beyond, it would make for a student-centered making revolution. This study advances the knowledge of the learning pathways of making by capturing empirical evidence of such learning trajectories. This study will advance the currently limited knowledge of learning in the making community and making in the engineering classroom. Initial findings generated during this study describe the learning trajectories of engineers learning making. By examining the engineering student making learning experience through the lens of cognitive science and illustrating empirical making learning trajectories, this work may impact the quality of engineering design teaching. By sharing learning trajectories across multiple communities, we seek to change the conversation by illuminating pathways for a wider array of student makers to become the makers and engineers of the future. 

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