reflects the student experiences from one medium sized university in West Texas, thesefindings may not be representative of student experiences of a larger sample from other areas ofthe country. Further, as many of the participants were early in their academic careers, theirexperiences may not reflect those who are farther along in their STEM studies. Due to the cross-sectional nature of this study, retention rates of participants within STEM majors were notmeasured. One of the individuals who participated in the focus groups started college as a STEMmajor but changed their major to history. This student provided feedback about theirexperiences after changing majors saying: "I've definitely felt more supported in the historydepartment. Maybe it's
clear and logical algorithms is crucial, demanding proficiency incomputer programming languages commonly used in engineering, such as Python, Java,MATLAB, or others relevant to the discipline. Additionally, CT serves as a foundational skill fordata analysis and modeling across various engineering disciplines. Its widespread adoption inSTEM education institutions, as evidenced by the incorporation of Next Generation ScienceStandards (NGSS), reflects a positive trajectory in developing CT abilities and meeting thedemands for skilled technical workers [12]. The implementation of CT in engineering education necessitates a shift towards student-centered learning strategies to mirror the complexities of real-world problem-solving
spontaneous questions toexplore, deepen understanding, and clarify answers to earlier questions [15]. Interviews wereconducted by the third author during the latter half of the fall semester and were audio recordedbefore being transcribed by Otter.ai (Otter.ai Inc, 2023) and edited for clarity by the second author.Interview questions were derived from theory and prompted participants to reflect on theirexperiences with mastery-based learning, features of the program, individual and communityefficacy as educators, as well as their perceptions of the student’s failure mindset, attitudestoward assessment, performance/ competence, metacognition (thinking about learning process),agency (ownership of learning), and engineering identity (Table 1). The semi
disciplinary and everyday language in students’ responses. This can help us make thetool a more inclusive generative AI tool that understands the various language students may useto explain their thinking. In turn, instructors and researchers will be more aware of the diverselanguage and thought patterns students use to wrestle with challenging concepts in the discipline.AcknowledgmentsWe acknowledge the support from the National Science Foundation (NSF) through grant EEC2226553. Any opinions, findings, conclusions, or recommendations expressed are those of theauthors and do not necessarily reflect the views of the NSF.References[1] H. Auby, N. Shivagunde, A. Rumshisky, and M. Koretsky, “WIP: Using machine learning to automate coding of student
in Spring 2023Overall, compared to previous years [18],[19] the gender and racial diversity of the eligibleapplicants and ACCESS scholars decreased despite the wide range of outreach efforts, some ofwhich specifically targeted underrepresented groups of students. The decline in diversity,especially compared to Cohort 1, may partially be due to the fact that many current WestVirginia University students from underrepresented groups, who were eligible for the ACCESSscholarship, applied and were selected in the earlier years of the ACCESS project. In addition,decreased diversity may be reflecting the broader trends in college enrollment, broader genderand racial disparities in Computer Science and
al.’s researcher identity scales, which aim to measure the sameconstructs as in the current research, originally contained 26 total items, but were reduced 16total items following the factor analyses of these scales and those of the related identities(scientist and engineering). One unique advantage of Perkin et al.’s approach is that many of theitems provided a more detailed reflection on the specific context of doctoral education. Forexample, the dissertation advisor is proposed as a critical external source of recognition and thusthe following item was added: “My advisor(s) see me as a RESEARCHER.”2 Similarly, thecompetence scale in Perkins et al. work focuses more on specific competencies associated withresearch, such as delivering
their chosen study program, and highlight the importance of early courses for success in the later stages of a study program [13]. Our findings indicate that such a model improves the retention and persistence of students in the critical period of adaptation to college life.iii. A strategy to use a cognitive apprentice framework to combine coaching, peer-led team learning, and reflection/self-assessment to boost leadership skills among Hispanic LIATS [14]. The combination of these methodologies enabled the development of leadership competencies among students impacting their emotional intelligence and demonstrated, in later stages of the study, to influence the roles assumed by them when given the opportunity of
the ideas related to career readiness, employability, and life careers [4].According to NACE, career readiness is “a foundation from which to demonstrate requisite corecompetencies that broadly prepare the college educated for success in the workplace and lifelong1 This project is supported by NSF Grant #2000847. Findings, opinions, or recommendationsexpressed are those of the author(s) and do not necessarily reflect the views of the NSF.career management” [4, Para. 1]. Gained through a variety of actions and activities, the eightcareer readiness competencies are: career & self-development; communication; critical thinking;equity & inclusion; leadership; professionalism; teamwork; and technology.These competencies provide a helpful
. Wereceived both positive and negative team stories from the participants. In addition, we found itwas not only the engineering classes, clubs, and teams that seemed to affect the sense ofbelonging, but also where the participants lived. Our preliminary results indicate that students’making experiences, especially in the context of project teams, influence how they feel asengineers. We will continue to explore these themes into the second year of our project.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.2204738. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National
REU Site wassuccessful in its goal of providing an inclusive and supportive learning environment forneurodiverse students, suggesting that further research and programming in this area would bebeneficial.AcknowledgementsThis research was a part of a project funded by the National Science Foundation (NSF), Divisionof Engineering Education and Centers under the Award Number 2051074. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation. The authors alsoacknowledge and thank the graduate and faculty mentors for the participants.References1. Sparks RL, Javorsky J, Philips L. College students classified with ADHD
, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.8. References[1] N. Baumer and J. Frueh, “What is Neurodiversity?,” Harvard Health, 2021. [Online]. Available: https://www.health.harvard.edu/blog/what-is-neurodiversity-202111232645. [Accessed: 15-Dec-2022].[2] S. Comberousse, “A begginer’s guide to neurodiversity,” Learning Disability Today, 2019. [Online]. Available: https://www.learningdabilitytoday.co.uk/abeginners-guide-o- diversity. [Accessed: 15-Dec-2022].[3] E. V. Cole and S. W. Cawthon, “Self-disclosure decisions of university students with learning disabilities,” J. Postsecond. Educ. Disabil., vol
growth in adaptiveness as students progress through their degree program.The first two results of this study [18] are somewhat consistent with those of the previous study [17]. Thediscrepancies stated above may be attributed to the smaller sample size in the second study and will beinvestigated further in subsequent work. It should also be noted that an interview protocol was developedand interviews conducted with low-income students as part of [18]. Preliminary analysis of theseinterviews revealed that different majors at Stevens provide different metacognitive opportunities forstudents within that particular program. Particular reference was made to programming and designactivities that inherently required self-reflection at various points in
can learnfrom that” [Student 23] and another, ”Really nice intro course to data science, made taking theBusiness Intelligence class alongside it more manageable.” [Student 9]. This indicates that thequality of the support for hands-on exercises impacts student learning and interest in DataScience.AcknowledgementThis material is based upon work supported by the National Science Foundation under AwardIUSE 2021287. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation. The authors thank Dr. Kimberly Fluet for her contribution in designing the surveyquestions and collecting/analyzing the survey data. The authors also
groupdiscussion to reflect on the visit. Before the visit, the group was largely unaware of the high-techSTEM careers that existed “behind the scenes” of the heavy manufacturing setting, andmentioned looking forward to sharing the experience with their students.Figure 3. Teachers concluded the summer by presenting their research outcomes, lesson plans,and discussing plans for implementing their research experiences into their classrooms during theacademic school year.Teachers concluded the 6-week summer research experience with a final presentation of theirresearch results, reviewing the lesson plans they had developed, and discussing follow-up plansfor the academic year (Figure 3).Future WorkAt time of writing, the second cohort of teachers are
all.AcknowledgementsThis material is based upon work supported by the National Science Foundation under GrantNumbers 1726306, 1725423, 1725659, 1726047, and 1725785. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation. We would also like toacknowledge the collaborating faculty and students on the project, Dr. Julie Linsey, Dr. TracyHammond, Matthew Runyon, Dr. Vimal Viswanathan, and Dr. Ben Caldwell, for their assistancewith data collection and the development of the software.References[1] E. Odekirk-Hash and J. L. Zachary, “Automated Feedback on Programs Means Students Need Less Help From Teachers,” in ACM SIGCSE
, the purpose of this poster paper is to identify the obstacles that have shaped,at times tacitly, our MCC-UMKC engineering transfer partnership. As Black and Gregersen(2002) noted, the first step toward implementing organizational change is to be able to see a needfor change. When we initiated our KCURE program in 2020, we didn’t see a need for change.This study provided us time to pause and reflect on what we did not earlier see. In Figure 1, wedetail the MCC-UMKC engineering transfer pathway obstacles that indicate the need for change.Figure 1: MCC-UMKC Engineering Transfer Pathway Obstacles Finances MCC Transfer UMKC Uncertainty
-timeFinally, students were queried on their experience and reflections on working within a team toadvance a grand challenge and how the construction of the team affected their experience on theproject. Relevant responses along with percentages are summarized below: 1. Do you think you learned/understood more about the project by working within such a team vs. working alone? Yes, learned/understood more by working within a team (87.5%) No (0%) Maybe (12.5%) 2. How did the multi-disciplinary (4 engineering department) construction of your team affect the research project performance? Positively (87.5%) Negatively (0%) Neutral (12.5%) 3. How did the multi-level (sophomore to senior
their ongoing support of the projectand work in conducting the interviews that provided the data for this paper.This material is based upon work supported by the National Science Foundation under grantnumbers DUE #1834425, 1834417 and 2022412. Any opinions, findings, and conclusions orrecommendations expressed are those of the authors and do not necessarily reflect the views ofthe NSF.References[1] E. Davishahl, T. Haskell and L. Singleton, "Engaging STEM Learners with Hands-on Models to Build Representational Competence," in 127th ASEE Annual Conference and Exposition, Virtual Online, 2020.[2] L. Singleton, E. Davishahl and T. Haskell, "Getting Your Hands Dirty in Integral Calculus," in 127th ASEE Annual Conference and Exposition
fieldof SciTS, including the five domains of team science competencies [4]: 1) building genuinerelationships, 2) team communication, 3) managing team research, 4) collaborative problem-solving and creativity, and 5) leadership.Some of the key topics covered across the workshops included: a) expanding our ability toparticipate in a shared vision, b) understanding the importance of diversity and practicing usingtools for inclusive teamwork, c) enhancing our awareness of developing shared language, d)exploring and practicing collaborative writing, e) drafting team charters, and f) developingguidelines for decision making.We gathered several key takeaways from our workshop reflections: • Being mindful of the value of team members when they are
localarea during the pandemic. Past reflections on the designs from year 1 and year 2 noted the largesize of each final design. As the goal was to make a hand washing station that was portable, theteam was required to modify previous designs so they could fit in the towing trailer used by theTranSCEnD team. Figure 3: TranSCEnD Cohort 3Year 4For the year 4 bridge project, TranSCEnD students were presented with the problem ofdeveloping a way for members of a remote village in Panama to pump water from the middle ofthe river that serves the village. Members of the cohort modified the design of a current seniordesign team in our Civil and Environmental Engineering Department to build a floating dockoutfitted with a pump
Content Access, Virtual On line . 10.18260/1-2—3500310. Hartenstine, D., & Fizzano, P., & Brobst, J. A., & Litzler, E., & Barber DeGraaff, R. (2020, June), CS/M Scholars Program - an NSF S-STEM Project Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2— 3436011. Burckhard, S. R., & Kant, J. M., & Michna, G. J., & Abraham, R. P., & Reid, R. (2018, June), Reflections of S-STEM Faculty Mentors Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2—3092512. Reisel, J.R., & Cancado, L., &Walker, C.M., & Mitrayani, D. (2015, June), Defining a Successful Undergraduate
the goal of building teacher confidence. Finally, the SEP2 appears to be a powerful tool forunderstanding the experience and perceptions of participants in research experiences. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(EEC-1711543). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. References[1] J. C. Brown, J. R. Bokor, K. J. Crippen, and M. J. Koroly, “Translating Current Science into Materials for High School via a Scientist–Teacher Partnership,” J
universityafter more than 20 years in industry or other nonacademic positions. These faculty benefited from a moretargeted set of discussions focused on learning with understanding. Important here was attention to whatstudents bring to the learning environment (prior knowledge), organization of facts and ideas around aconceptual framework to facilitate its use in various contexts (connections within and across courses), andhelping students reflect on what they do or do not understand (metacognitive strategies) [6].Faculty and student data were collected over the five years of the project. Three sources of faculty datainclude interviews (subset each year beginning Spring 2016), reports/presentations (subset each yearbeginning Fall 2016) and teaching
. Helen L. Chen, Stanford University Helen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering at Stanford University. She has been involved in several major engineering education initia- tives including the NSF-funded Center for the Advancement of Engineering Education, National Center for Engineering Pathways to Innovation (Epicenter), as well as the Consortium to Promote Reflection in Engineering Education. Helen holds an undergraduate degree in communication from UCLA and a PhD in communication with a minor in psychology from Stanford University. Her current research and scholarship focus on engineering and entrepreneurship education; the pedagogy of portfolios
materialsdevelopment activities that seek to support the success of all students. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(DUE-1625378). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of NSF. References[1] E. Cech, B. Rubineau, S. Silbey, and C. Seron, “Professional role confidence and gendered persistence in engineering,” Am. Sociol. Rev., vol. 76, no. 5, pp. 641–666, Oct. 2011, doi: 10.1177/0003122411420815.[2] K. A. Robinson, T. Perez, J. H. Carmel, and L. Linnenbrink-Garcia, “Science identity
opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] M. K. Orr, M. W. Ohland, R. A. Long, C. E. Brawner, S. M. Lord, and R. A. Layton, “Engineering matriculation paths: Outcomes of Direct Matriculation, First-Year Engineering, and Post-General Education Models,” Proc. Front. Educ. Conf. FIE Proc. - Front. Educ. Conf. FIE, 2012.[2] K. Reid, D. Reeping, and E. Spingola, “A Taxonomy for Introduction to Engineering Courses *,” Int. J. Eng. Educ., vol. 34, no. 1, pp. 2–19, 2018.[3] H. Matusovich, R. Streveler, and R. Miller, “Why Do Students Choose Engineering? A
. Researchers have used a rangeof approaches to categorize students’ questions, varying in complexity depending on the contextin which student questions were being solicited (e.g., [2], [3]). Marbach-Ad and Solokove [4]used a large sample of questions generated by biology students to develop a six-level, "semi-hierarchical” taxonomy based on question sophistication. Encouragingly, their work also showsthat students are able to pose more high-quality questions after being instructed in the taxonomyfor classifying the quality of their questions [5]. This approach has also been adapted forclassifying questions asked by physics students as part of a written reflection on their learning[6].Along with explanatory question taxonomies, question-asking can be
. Whilecorrelation coefficients between items were all positive, there were only four eigenvalues greaterthan 1.0 on both ECTD beta A and B versions. This indicates there were four independent factorsmeasured by the instruments. Most items were loaded onto one factor and only one or two itemsloaded onto each of the other three factors. As the factor analysis results from the ECTD beta Aand B versions were not the desired model that can reflect the five computational thinkingfactors, there was a need for another round of revisions.Instead of designing two compatible versions A and B, the 30 items from the beta versions of theECTD were revisited for reanalyzes of content and face validity. The research team selected fourbest items to be indicators of each of
for funding this work underGrant # 1834465. Any opinions, findings, or conclusions found in this work are those of theauthors and do not necessarily reflect the views of the sponsors.References[1] O. Ashour and C. Tucker, “Leveraging Virtual Reality to Connect Learning and Integrate Course Knowledge in the Industrial Engineering Curriculum,” 2018. [Online]. Available: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1834465.[2] J. E. Rudin, “Using virtual reality in education,” in STC, Education, Training and Research, 1995, pp. 55–58.[3] Accreditation Board for Engineering and Technology (ABET), “Criteria for Accrediting Engineering Programs 2018-2019,” 2017.[4] J. E. Froyd and M. W. Ohland, “Integrated Engineering
, recruit the new cohort of ACCESS scholarshiprecipients, and continue to connect students with peers, mentors, and industry and governmentprofessionals, providing them opportunities to network, learn from, and interact with potentialemployers for internships or full-time positions.The material is based upon work supported by the National Science Foundation under Grant No.1930282. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.8.0 References[1] “Heatmap,” cyberseek.org. [online]. Available: https://www.cyberseek.org/heatmap.html. [Accessed March 3, 2021].[2] Bureau of Labor Statistics, U.S. Department of