use of active learning, recognizing “that true learning resultsfrom doing things and reflecting on the outcomes, not from passively receiving information.” [1,p. 111] In design education specifically, Project-Based Learning (PBL) has become the de factostandard approach of active learning across engineering curricula [2]. Its success, however,depends on student motivation - without it, students may struggle to engage fully, potentiallylimiting the positive outcomes of the pedagogy.The key feature of active learning is that it is learner-centred and therefore places moreresponsibility on the student than teacher-centred methods. Past research has described severalbarriers to student engagement with innovative active learning approaches
% %Status Generation Not reported 12% - 3% - - - - % R1 University 65% 57% 71% 57% 65% 48% 52% %CarnegieClassification Non-R1 35% 43% 29% 43% 35% 52% 48% % Universities1 NHERI did not host an REU program in 2020 due to the COVID-19 pandemic.2 Reflects one student who participated twice and one student who left the program withoutcompleting it.MentorsFaculty mentors were selected every year by each site and were dependent on the projectsassigned to NHERI REU students. Faculty mentors’ mentoring experiences ranged fromunexperienced to highly experienced mentors. Faculty mentors were early career faculty, pre-tenured
engineering profession to provide guidance.” 30 The importance of sustainability is also reflected in Engineers Canada’s position statement on climate change, including a commitment to taking “a leadership role in assuring that codes, standards... promote a low-carbon, clean environment and a sustainable economy” 2 . • Engineers Australia: “Promote sustainability” is one of four key points in the Engineers Australia code of ethics. Similar to Engineers Canada, Engineers Australia has released a position statement on climate change 33 , a policy statement on sustainability 28 and a more practical set of guideline on sustainable development for its members 29 , which it positions, in part as a response to UN SDGs
like to give to the instructors are notsurprising. The instructors recognized that the students struggled at times but always pulledthrough those struggles. The message was also very clear that more information needs to beprovided about the different disciplines. Project scheduling also needs to be discussed in greaterdetail. These suggestions will be incorporated into the next offering of the course.Additional observations and reflections of the students and faculty can be found in a paperentitled “Intersection of Design and Society: Student and Faculty Reflection on anInterdisciplinary Course.” [25]Concluding Discussion and Final ThoughtsThe Intersection of Design and Society provides another option for structuring interdisciplinarygroups
their fellow travelers through teambuilding and group assignments,before the trip. During the trip, frequent checking of participants’ physical and mental status by theinstructors or among peers is important to reveal the seedling of any potential issues, and frequentreflections via journal taking or group discussions at various intervals helps the students make sense oftheir experiences and adjust their expectations. These reflections also stretch the students’ worldviews andsupports them to make the most of the trip. After the trip, an online meeting of the group, if any, andassignment collections are another opportunity for the group to reveal any remaining concerns in a timelymanner to get those concerns addressed.The rest of the paper is
reflect on their experiences as engineering studentsamidst the evolving COVID-19 crisis. Sensemaking is a research approach used to understandcomplex and ambiguous data such as narratives (Van der Merwe et al., 2019)Between June and July 2020, a pool of 500 micro-narratives was amassed from underrepresentedengineering students. Participants in this research were asked to respond to the followingprompt:“Imagine you are chatting with a friend or family member about the evolving COVID-19 crisis. Tell themabout something you have experienced recently as an engineering student.” The SenseMaker tool uses mixedmethods analysis to allow participants to use quantitative responses to reflect on their micro-narratives. The process of utilizing this data
Progress” paper will outline the steps wehave taken to utilize faculty input and established curriculum to develop an interdisciplinary programrequiring a small number of new courses yet still meeting both ABET requirements for mechatronicsand robotics and partner interest. Student reflections on the program and its first course offering are tobe gathered, along with reactions from faculty, to drive ongoing continuous improvement.2. INTRODUCTIONDigitally connected factories and robot-driven production processes have been highlighted as the futureof the manufacturing industry [1]. A growing national interest in accelerating industrial capacity andmodernizing education through capitalizing on advanced robotics systems supported by
compare such terms as reflected in the pre- and post-survey responses. Fromthose student responses, MAXQDA identified 10 thematic constructs (Renewable Technologies,Health, Natural Resources, Policies/Rights, Climate Change, Equality/Equity,Income/Socioeconomic Status, Recycling, Pollution, and Environment) which were used as thecoding system to evaluate differences in the pre-/post-responses.Figure 1. Code Relations Browser visualization of (top) Pre- and (bottom) Post-Survey open-ended EJ definition responses illustrating frequencies for each of the coded themes extractedfrom student responses (MAXQDA 2020).Figure 1 illustrates how students’ definitions changed as a result of participating in class lessonsincorporating EJ-themed StoryMaps
Engagement and Practices examines how mentors guideddoctoral students during their pre-program internships, advising and/or residencies andsupporting them after they returned to academia. The subtheme During the Pre-programInternship or Residency reflects mentors’ efforts to provide students with hands-on learningexperiences that bridged the gap between academia and industry. One mentor explained,"Because I think part of what the student wants is to see how to apply their research in a real-world environment, so we worked closely to ensure that." This quote underscores theintentionality of mentoring efforts to contextualize academic research within practical, industry-relevant settings. By working “closely,” mentors offered opportunities for
in their demographic reporting, reflecting evolving approaches to genderrepresentation in academic research. Regarding graduate programs, the studies encompassed avariety of disciplines. Engineering programs were prominently featured in [15, 16, 17, 18], whileother studies focused on social work [8], natural sciences [6], and multiple disciplines [11].Some studies [5, 7, 9] examined graduate student experiences across various programs without aspecific disciplinary focus.For stressors affecting international graduate students (Table 1), academic challenges emerge asa consistent theme, with most studies highlighting coursework pressure, research requirements,and scholarly expectations [8, 10, 12, 13, 14, 15, 17, 18]. For instance, study [15
, and the practical demands of their environment, butthese skills do not always transfer smoothly to formal educational settings. These studieshighlighted the concept of cognition in context in a way that children's cognitive skills areshaped by their practical experiences and environments. The math skills demonstrated bystreet vendors in Carraher et al.’s study and candy sellers in Saxe's study are highly effectivewithin their specific real-world contexts, but transferring these skills to formal educationremains a challenge, as argued by Resnick for a restructuring to better reflect the social andpractical nature of cognition.Engineering education is undergoing rapid transformation, with cognitive perspectives onlearners taking center stage in
institutional structures influence thedynamics of mentoring. For instance, one graduate student noted that institutional evaluationforms could serve as valuable discussion tools between mentors and mentees to reflect onprogress and areas for improvement. Similarly, a faculty advisor highlighted the importance ofhelping students find their voice in academic settings, even when mentors themselves must bemindful of navigating institutional power dynamics. By recognizing these systemic factors,engineering programs may benefit from developing mentoring frameworks that includestructured feedback points to address both academic and emotional needs [37]. To furthersupport students' progress and well-being, emotional intelligence considerations could beembedded
engineering programs, the participants typicallyheld very low or non-existent industry recognition beliefs. The participants did not mentionpracticing engineers as a recognition source when reflecting on who saw them as engineers;instead, they focused on recognition sources they had regular interactions with, such as family,peers, and faculty. The participants had not yet perceived practicing engineers as a recognitionsource due to their limited access to or interactions with this group which aligns with the lack ofassociated industry recognition beliefs.Middle Program: Emergence of industry recognition beliefsPracticing engineers as recognition sources and industry recognition typically emerged duringthe participants’ third to fifth semester as they
universities in the United States [8]. By evaluating theseinitiatives using pre- and post-surveys, and participant reflections, the study provides actionableinsights for designing equitable AI literacy resources [9], [10]. Studies like this have the potentialto influence engineering education policies, bridge access gaps, and equip students and facultywith the skills needed to navigate the digital-intelligence transition [2], [11]. Additionally, thisstudy contributes to the literature on the important role that professional development plays in thedevelopment of AI literacy skills in students. The following evaluation questions (EQ) were askedto assess the impact of the workshop: EQ1 (Quantitative Question) Do students' perceived AI ethic
Buffalo, The State University of New York Andrew Olewnik is an Assistant Professor in the Department of Engineering Education at the University at Buffalo. His research includes undergraduate engineering education with focus on engineering design, problem-based learning, co-curricular involvement and its impact on professional formation, and the role of reflection practices in supporting engineering undergraduates as they transition from student to professional. ©American Society for Engineering Education, 2025 Troubleshooting in Engineering Education: A Systematic Literature ReviewAbstract This full-length theory paper reports on the results of a systematic review of the literature relatedto
thatare being used in both personal and educational settings.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.2333393. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation. The authors thank project advisory board members Drs. Katey Shirey and JulieMartin for feedback on the research design and analysis. The authors also thank the teachers whoparticipated in the survey and cognitive interviews.References[1] G. Biesta, M. Priestley, and S. Robinson, “The Role of Beliefs in Teacher Agency,” Teach. Teach., vol. 21, no. 6, pp. 624–640, Aug. 2015
Paper ID #49295BOARD #106: Investigating Factors Influencing Performance in an IntroductoryProgramming CourseAmanda Nicole Smith, University of Florida Amanda is an undergraduate student pursuing a Bachelor of Science in Computer Science at the University of Florida, with an expected graduation in Spring 2025. Her research interests focus on computer science education, particularly how educators can use machine learning models to provide real time intervention strategies to optimize individual student outcomes. This paper is a reflection of her commitment to improving educational strategies and fostering an inclusive
process through cutting-edge technologies like industrial PCs, HumanMachine Interface Controllers, Various sensing and tracking devices and vision cameras.This paper emphasizes the growing significance of project-based learning, noting its alignment with newtechnological trends such as Industry 4.0 and smart manufacturing and assembly. The integration of smart andsustainable manufacturing in capstone topics mirrors this shift, contributing to the development of leadershipskills, creativity, and innovation among students. With over 65% of capstone projects focused on manufacturing,energy, and sustainability, students engage with open-ended projects that reflect real-world uncertainties andrequire them to determine optimal solutions. Through this
reasoning behind why participants rate the two badging systemsthe way they do in this study. While the questionnaire responses show a slight preference for theDBCS, individual feedback reflects many positive aspects of the Industry-Standard, including asingle-screen view of badge criteria and evidence for review, an “intuitive and user-friendlydesign," a well-organized layout, and the wallet feature. However, one feature that stands out inthe DBCS were consumer-focused dashboards. The closest idea participants see in the Industry-Standard system is an “Organization Admin dashboard." While seeing badge issuance data isimportant, a more critical feature for consumers and decision-makers may be the ability to viewbadge holder data and make informed
productivity,has also been the focus of discussion. The H-index is often discussed both for its ability toindicate productivity and serve as a point of comparison between an institution’s departmentsor individual researchers [4], [5], [6]. While its importance in assessing research units isrecognized, there is broad agreement that the metric could be refined to better reflect thecomplexities of research impact. Alongside the analysis of scholarly metadata, significant attention has also been givento institutional collaboration. Collaboration among researchers, universities, industries, andinstitutions can influence productivity, with its effectiveness shaped by factors like partnershiptype, proximity, and academic discipline [7], [8]. For example, a
their evidence-based practices. Theanalysis is ongoing and will be presented in a future paper to highlight how they are used toupdate our change framework and activities.AcknowledgementsThis material is based upon work supported by the National Science Foundation under AwardDUE- 2021532. 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] Chan Hilton, A.B. (2024). Board 429: Work in Progress: Capacity-Building for Change Through Faculty Communities Exploring Data and Sharing Their Stories. ASEE 2024 Annual Conference and Exhibition, NSF Grantees Poster Session, Portland, OR, June 2024
[10, 13],particularly in manufacturing programs where iterative experimentation, physical manipulation ofequipment, and real-time data collection are essential [14–18].Among the emerging solutions for remote labs, simulation-based platforms have garneredattention for their wide accessibility and relatively low setup costs. These virtual environmentsenable students to practice and visualize engineering concepts without geographic or schedulingconstraints. However, although simulations can effectively reinforce theoretical knowledge, theyoften lack the physical realism and unpredictability of authentic lab work [19–23]. Updating orexpanding simulation environments to reflect changing industrial practices can also be expensiveand time-consuming
Architecture [15] • Prompt/Response (PR): Simple input-output model • Multi-Prompt/Automated Response (AR): Generated response gives additional prompts • Human in the LLM Loop (HiLL): Human guides task based on LLM responses • Retrieval Augmented Generation (RAG) systems [16, 17] with memory and retrieval capabilities 2. LLM Reasoning of Thought • Nothing of Thought (NoT): Baseline without reflection • Self-improved of Thought (SoT): Reflects and improves on the given prompt [18, 19] • Chain of Thought (CoT): Linear generations with reasoning [20] • Tree of Thought (ToT): Branching generation paths for alternatives [21] • Graph of Thought (GoT): Network of
conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] Austin Cory Bart, Dennis G. Kafura, Clifford A. Shaffer, and Eli Tilevich. Reconciling the promise and pragmatics of enhancing computing pedagogy with data science. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, Baltimore, MD, USA, February 21-24, 2018, pages 1029–1034, 2018.[2] Jeffrey S. Saltz, Neil I. Dewar, and Robert Heckman. Key concepts for a data science ethics curriculum. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, Baltimore, MD, USA, February 21-24, 2018, pages
. Real-time assessments,such as quizzes or activities during lectures, were perceived as less engaging by some students, likelybecause of the pressure to respond immediately and the lack of time for reflection (unless specificallybuilt into the assessment). Overall, scaffolded projects emerged as the most consistently favored format,while multimodal and real-time assessments showed potential but may require further refinement tomeet diverse student preferences.Students perceive that redesigned assessments significantly improve their critical thinking skills, witha mean rating of 4.27 and a standard deviation of 0.90. Additionally, they believe these assessmentsenhance their ability to apply course concepts to real-world situations, as reflected
orientations toward cultural differences based on the Intercultural Development Continuum (e.g., denial, minimization, acceptance), your score, and understanding how it leads to different thoughts about reducing our carbon footprint, recycling, response to weather changes, etc. 3 Form a more complex view of culture through metaphors, hidden rules, and cultural worldview frameworks; explore Country Navigator’s WorldPrism Profile and reflect on how this impacts your collaborating with people from different countries on energy. 4 Explore the way you deal with differences by developing your awareness of your own unexamined assumptions and better understand how to navigate cross
exams. Becauseproblems had to be solved in a group, there was better attendance. So, class participationimproved. Since the in-class problems were based on the current lecture, students paid moreattention to the lecture and asked more questions to clarify doubts. Solutions to the problems hadto be submitted by the end of the day, so it ensured students were better prepared for the nextlecture. This did not change in the Summer 24 semester when the course was offered online withthe intervention. D. Impact on Instructor Performance: self-reflectionBased on self-reflection, we feel that this model improved the instructor’s performance. Theinstructor planned for aligning the in-class problems, lectures and homework for the next weekand realigned
develop their skills through various levels. The major includes leadership in variousmethods from interpersonal, and self-reflection, to large teams to provide a multitude ofleadership opportunities in numerous arenas where the personal, interpersonal, team, andorganization (PITO) model is the framework for leadership. The model begins with personalleadership, builds interpersonal leadership, followed by team leadership, and culminates withorganizational leadership. Personal leadership focuses on mastery of primary duties, personalawareness, followership, and leading by example. Interpersonal leadership focuses on the abilityto coach others, effective communication, and develop planning skills. Team leadership ischaracterized by the ability to
, Alfa has served as a lecturer in Indonesia. Alfa is mainly interested in investigating the implementation of reflective activities in large classrooms and assessing how reflective activities affect student learning and academic performance. ©American Society for Engineering Education, 2025Integrating STEM Disciplines to Transform Indonesia’s Educational Landscape: An Evaluation of the ‘Merdeka Belajar’ Curriculum ImplementationExecutive SummaryThis paper evaluates Indonesia’s ‘Merdeka Belajar Kampus Merdeka’ (MBKM) curriculum,launched in 2019, which aims to transform the nation’s education system to meet 21st-century demands and prepare students for the Fourth Industrial Revolution
University at Raleigh Leah Granger is a postdoctoral researcher for Engineering Education and a course instructor for the Chemical and Biomolecular Engineering Department at North Carolina State University. ©American Society for Engineering Education, 2025 Hidden Trends in Data on Women in STEMIntroductionThe use of data to monitor progress in the recruitment and retention of underrepresentedpopulations in STEM encourages careful consideration of the manner in which data are groupedin the analysis. Trends present in the overall population of study – for example, college studentsenrolled in a STEM program – may not be an accurate reflection of trends in specificsubpopulations. Numerically