established AI curriculum, enhanced with specializedadaptations for neurodivergent learners. Students engaged with machine learning principlesthrough hands-on exercises in Python, working with frameworks including TensorFlow andKeras. The curriculum emphasized responsible AI design, particularly addressing machinelearning bias, a critical consideration given emerging research on algorithmic fairness. Projectwork spanned affective computing, computer vision, and natural language processing usingindustry-standard tools, including GitHub and Jupyter Notebooks.Pedagogical ApproachThe program's pedagogical design reflected the current understanding of neurodivergent learningpreferences. Technical content delivery incorporated frequent active learning
patternsAnalysis of instructional approaches showed both commonalities and distinctions between DFand CF. Both groups actively engaged in skill development, with very few skills reported as “notaddressed” by either group. Individual and team-based coaching emerged as a frequently usedapproach across both faculty types, suggesting a shared commitment to hands-on studentdevelopment.The data revealed different emphases in teaching approaches between the two faculty groups. AsFigure 1 shows, CF consistently reported providing specific assignment feedback across nearlyall skills, whereas DF showed more varied application of this instruction approach. Thisdistinction may reflect domain-dependent pedagogical traditions or comfort levels of individualswith
engineering doctoral students’ usage of ‘voice’ mechanismto express discontent with several groups including friends, family members, faculty, anduniversity administrators. The main findings that resulted from this study show students’ decisionto exit or consider existing their program were impacted due to a lack of support, response, and insome cases an active suppression of voice from faculty or graduate department. This studyhighlights that if institutions seek to learn about the underlying causes of graduate engineeringattrition, they need to show a willingness to reflect on the importance of graduate students’feedback and implement self-corrective actions.Introduction and Related Literature Graduate schools and graduate administrators
. Engagement Score E 1 The total engagement score received for the lab.The weights sum up to 1.00, ensuring a balanced and comprehensive scoring system formeasuring student engagement. This distribution reflects the relative importance of eachindicator in contributing to the overall engagement score. While time spent in lab activities (T)was given the highest weight (0.3) due to its direct relationship with active engagement, otherindicators such as pre- and post-lab quiz scores and time spent on instructions were weightedbased on their observed contribution to overall engagement.The engagement score (E) is calculated as a weighted sum of the normalized values of theseindicators, Eq. (6): 𝐸 = 0.1𝐿 + 0.1𝐼 + 0.3𝑇
incentivizing intellectual curiosity, allowing studentsto engage deeply with the material without sacrificing the practical importance of their academicrecords.Alternative grading also gives more meaning to earned grades over traditional grading models byensuring they directly reflect a student’s demonstrated understanding of key concepts. Instead ofrelying on partial credit or averaging scores across graded events, this approach requires studentsto meet clearly defined learning objectives before receiving credit. Furthermore, assessments areclearly and transparently mapped to learning objectives. As a result, grades become a more accu-rate representation of what students understand at the end of a course.In this work, we describe the implementation of
diversity of TNBGNCexperiences but also undermines their legitimacy and humanity. Research on TNBGNCindividuals has frequently reflected these societal biases, employing overly reductivemethodologies that fail to capture the complexities of their lived realities [4]. This underscoresthe need for a paradigm shift in research approaches- particularly within engineering educationand STEM fields more broadly- to ensure that the knowledge we produce uplifts and empowersthe TNBGNC community. Drawing on the interdisciplinary insights of trans studies, researcherscan adopt theoretical frameworks and methodologies that challenge the cisheteronormativeassumptions that dominate our field while prioritizing research outcomes which foster TNBGNCbelonging and
Education, 2025 Culturally Relevant Engineering Piñata Project for Elementary-Aged STEAM Programs (PK-12) (Work In Progress)AbstractThis paper presents an innovative, culturally-relevant STEAM education approach using apiñata-inspired engineering project for elementary-aged children. Implemented in California andMassachusetts, the project aims to broaden participation in STEAM fields, particularly amongtraditionally marginalized communities. By reimagining a historical artifact through STEAMprinciples, students learn spatial visualization skills, engineering design, and 3D shapeconstruction while personalizing their learning experience to reflect contemporary culturalidentities. The curriculum, piloted in various settings
undergraduate engineering students toco-teach robotics lessons to fifth graders. Using a multiple-embedded case study approach, weexamine how the interactions and teaching roles within these partnerships influenced PSTs’teaching self-efficacy. Drawing on reflections, lesson recordings, surveys, and interviews, wepresent the cases of three PSTs—Lisa, Madison, and Kayla—who experienced varying levels ofpartner support and student engagement. Lisa and Madison were both compelled to lead roboticsinstruction due to perceived lack of support from their engineering partners, yet they experiencedcontrasting outcomes: Lisa struggled with disengaged students and malfunctioning robots, whichdiminished her self-efficacy, while Madison's success with highly engaged
faculty’s observations and reflections about theirredesigned course. We aimed to evaluate a) what course interventions were made, b) theperceived impact of these interventions, and c) whether the interventions proved sustainable. Thelist of courses included in this study, along with their enrollment in the Fall semester in year 5 ofthe project, is presented in Table 4. Table 4. Redesigned courses included in the sample Course/Enrollment (Fall Semester – Year 5) Applied Mechanics I 173 Probability and Statistics in Civil Engineering 65 Mechanics of Materials 116 Construction Management I
. While computational fields such as computer science and electrical engineering havelong embraced data-driven approaches, interdisciplinary domains like civil and environmentalengineering (CEE) are increasingly integrating data science into their education and practice. Inaddition, while the programming skills used in computational fields often lend themselves well todata science practice, there is more often a gap in skills for practitioners in other interdisciplinarydomains. For instance, the traditional CEE curriculum could benefit from a greater emphasis onpopular open source programming languages such as Python. This shift reflects a growing needfor future CEE practitioners to have the skill sets and tools to analyze and understand
’ responses reflected a reinforcing cycle, where purpose-driven actions influenced identity development, shaping how difficulties were perceived andaddressed. Emerging findings further highlight the importance of supportive, interdisciplinaryresearch environments in fostering graduate students’ identities and motivations.IntroductionIntegrating Artificial Intelligence (AI) into engineering has revolutionized how engineeringproblems are tackled and solved across disciplines [1], [2], [3], [4], [5], [6]. In a GraduateResearch Group (GRG) at a private Northeastern university in collaboration with a publicSoutheastern university, engineering graduate students work with AI, defined as machinelearning models and computer-guided tools to optimize
initiatives: (1) creating user-friendly learning modules that are straightforward toimplement and accessible to students; and (2) integrating these modules into courses whileassessing their impact on student learning outcomes and overall effectiveness.2. Methodology2.1 Experiential Learning Model and Bloom’s TaxonomyTo achieve the project objective, a multistate and learner-centered approach was designed andimplemented, utilizing the experiential learning model (ELM) which emphasizes activeengagement and personal experience in the learning process. This model allows students to learnthrough direct experience and reflection, which has been successfully implemented in highereducation and engineering courses to promote critical thinking skills and
, andmechanism for facilitating more inclusive language ideologies from elementary teachers,particularly within the context of engineering lessons in their classrooms. Our approach works toprovide teachers with sustained time to reflect on what they believe about language, theirteaching of linguistically and racially minoritized students, and their interactions withmultilingual students around engineering content.In sum, in this project we seek to understand: How do elementary teachers of multilingualclassrooms shift their positions with regard to: language ideologies, understanding and/orapplication of translanguaging and understanding and implementation of engineering?MethodsThe overall project, funded by the National Science Foundation, follows a
counterparts[4].Compared to Western students (primarily from the USA), Chinese students are more likely toperceive knowledge as certain and the ability to learn as innate[5]. Such differences inepistemic beliefs may shape Chinese students’ unique perceptions of and responses to modernteaching methods, such as active learning and reflective thinking, in Western classrooms.These beliefs could influence how students engage with learner-centered approaches,potentially affecting their adaptability and overall learning outcomes.Moreover, commonly used tools for assessing epistemic beliefs, such as the widely usedEpistemological1 Beliefs Assessment for Physical Science (EBAPS)[6], may not adequatelycapture the unique characteristics of students with
leadership practices to bring back and foster acollaborative culture within the Innovation Wing.In September 2024, fourteen leaders representing seven SIGs took part in the pilot program. Theyengaged in ice-breaking activities to dismantle silos, brainstorming sessions to strategize how theirSIGs could enhance the HKU Innovation Wing, goal-setting discussions to define outcomes for theirinvolvement in an overseas makerspace symposium, presentations to share their insights, andknowledge-sharing sessions to disseminate experiences and conclusions to other makerspacemembers.Surveys and analysis of written reflections from the team leaders indicate that the overseas team-building program effectively dismantled silos, enhanced collaboration, and promoted
activities.A key objective of this adaptation is to prepare students for a future where AI-generated solutionsmay surpass even the best human abilities. However, a skill that remains irreplaceable is theability to critically assess the correctness of solutions—whether human or AI-generated. Thispaper presents findings in the form of student reflections on this modern adaptation ofcomparative analysis.1 IntroductionAeroelasticity is a field in aerospace engineering combining aerodynamics and structuralmechanics to understand the interaction between aerodynamic forces and structural responses. Atthe University of Colorado Boulder, a sophomore-level Aerospace Sciences Lab introducesstudents to these concepts through an experiential learning framework
which they apply models to draw inferentialconclusions about real-world data. An interdisciplinary team of instructors has enriched thecourse’s existing case studies with STS frameworks to provide students the necessary scaffoldingto engage in substantive critical work on final projects.This paper reflects on the broader goal of building a sociotechnically integrated undergraduatedata science curriculum including a dedicated STS class on “human contexts and ethics” and apedagogical training class. Through these case studies and reflections, the paper sharesinstitutional and interdisciplinary lessons learned from co-designing multiple courses withinstructors across disciplines
socio-economic challenges, including rapid urbanization, poverty and limitedinfrastructure development (Ganda, 2019). Countries in these regions are classified as“developing” based on criteria such as lower GDP per capita, less developed technologicalinfrastructure and ongoing challenges in areas such as healthcare, education and povertyalleviation.2. Understanding the Modular Construction ConceptModular construction has deep historical roots and a rich evolution that reflects humanity’spursuit of efficiency in building practices. The origins of modular construction trace back to the17th century, when prefabrication techniques began to emerge. One notable example is the “kithouses” exported from England to colonial America in the 1620s
and participated in a semi-structured interview to reflect on theirexperiences in the SEES cohort. Specifically, we aimed to evaluate two elements of the SEESProgram: ● How well supported did the SEES cohort feel by the program’s structure? ● What challenges did the SEES cohort encounter when developing their sociotechnical modules?In November, after the completion of the interview and submission of their final module, thegraduate students received a stipend for their participation.Data Collection and MethodsTo evaluate the program we collected two types of data. First, we created an end-of-programsurvey using a Google form. The survey assessed cohorts’ satisfaction with both the program andthe module development process, the level
fields of ComputerScience and Information Technology, reflecting the technical foundation of chatbot development.Key sources of publications include Lecture Notes in Computer Science (36 publications), ACMInternational Conference Proceeding Series (28 publications), Communications in Computer andInformation Science (23 publications), and Computers and Education: Artificial Intelligence (13publications). Engineering-related research represents 7% of the total publications (91 studies),with a subset of five focusing specifically on CEM. Notably, the ASEE Annual Conference andExposition has contributed three publications explicitly mentioning AI chatbots, furtherhighlighting their relevance in engineering education and research. Although still a
streamline undergraduate STEM education.Vidya Reddy Madana, Purdue University Vidya Madana is an undergraduate student in the Department of Computer Science at Purdue University, concentrating on machine intelligence and software engineering. She is expected to graduate with a Bachelor of Science degree in May 2027. Vidya’s research interests include artificial intelligence, machine learning, and data visualization. In addition to her academic pursuits, she has experience in STEM education, robotics, and journalism, reflecting her broad interests and diverse skill set. ©American Society for Engineering Education, 2025 Gender Differences in Global Identity Development: Implications for
providing an avenuefor open dialogue, mentors enable their mentees to develop self-awareness, confidence, and a senseof purpose [11]. These skills are important in ensuring success not only in academics orprofessional settings but also in general life satisfaction and mental health [17]. Mentorship allowsfor the opportunity to develop important life skills like communication, critical thinking, and timemanagement. These forms of success are developed through seeking and receiving guidance,reflecting on feedback, and applying learned principles in real situations [18]. By supportingstudents' pursuits, mentorship helps individuals handle challenges independently and equips themwith skills necessary for success in whichever situations they meet [19
students with a sense of purpose and agency. This alignmentwith real-world issues has been shown to enhance long-term engagement in STEM careers,particularly for underrepresented groups who may not see themselves reflected in traditionalSTEM narratives [8], [9].This study investigates the dual outcomes of engagement and self-efficacy within the context ofthe “United We End Racism” STEM Fair. Specifically, it addresses the following researchquestions: 1. How do themed STEM activities, such as Ducks and Diversity, foster engagement and self-efficacy among underrepresented K-12 students? 2. What impacts do these experiences have on students’ interest in STEM careers and their self-confidence in applying engineering concepts?To explore
Province, China, into decision-making for regional development. In their seminal work, Wang and Burris (1997) describe thephotovoice method and how it was developed. They state photovoice has three primary goals,“(1) to enable people to record and reflect their community's strengths and concerns, (2) topromote critical dialogue and knowledge about important issues through large and small groupdiscussion of photographs, and (3) to reach policymakers” (p. 370). The method has since beenadopted across several disciplines, including engineering education, to allow participants tocapture their experiences and communicate ways to change their circumstances. Photovoice is acritical methodology that centers the voice of the participants, allowing them to
. Instructors can activate students’ funds ofknowledge, helping them understand that their prior everyday experiences are a valuableresource in their formal learning [8]. While this can be challenging in higher education settings,where it is not typically possible for instructors to visit students’ home communities tounderstand their cultural and everyday experiences and then design curricula that connect tothose experiences, there are ways to identify such funds of knowledge [9]. For instance, facultymay survey their students [6] or ask students to write reflections that connect their funds ofknowledge to course activities [10].Querencia, a specific form of place-based learning, refers to attachment to a place that signalsreciprocity with place [11
-based learning contexts.Dr. Andrew Olewnik, University at 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 An emerging assessment framework for problem-based learning environments based on Jonassen’s design theory of
Mission for a Holistic Education: Pilot ImplementationAbstractThe evolution of engineering education over the past few decades reflects the growingcomplexity of the challenges engineers encounter in today’s world. Where once technicalproficiency was the primary emphasis of engineering education, there is now a growingrecognition of the distinct but complementary role that professional formation plays in shapingwell-rounded engineers [1] [2] [3]. A holistic approach to engineering education will help usshape future engineers who possess the foundational knowledge and applied skills in theirdiscipline, as well as across disciplinary boundaries, along with global and cultural awareness,social responsibility, ethical leadership, and sustainability
, treating them asperipheral to the core responsibilities of engineers [1], [2].The foundations of engineering ethics can be traced back to early professional codes developedto address the responsibilities of engineers in ensuring public safety and reliability. For example,the Canons of Ethics by the American Society of Civil Engineers emphasized technicalcompetence, safety, and accountability [5]. Over time, engineering ethics evolved to includebroader societal concerns, such as environmental stewardship during the environmentalmovements of the 1960s and 1970s. Frameworks like sustainable design and corporate socialresponsibility emerged, reflecting a growing recognition of the interconnectedness betweenengineering practices and societal impacts [4
applied to non-competitive fields of use. Feedbackfrom key stakeholders including industry, business, educational, and commercial mentors,technology providers, and student participants, will be used to assess the effectiveness of thisapproach. Reflections and insights gathered from these stakeholders will inform potential futureiterations of the program with additional student teams. The preliminary findings from this studywill also guide the development of future full-scale studies and curriculum improvements, with afocus on assessing the generalizability of the approach.Additionally, this study contributes to workforce development by equipping students with theinterdisciplinary skills and problem-solving capabilities that align with the
and achieve higher goals [15]. Resilience,closely related to self-efficacy, reflects the ability to cope with stress and rebound from adversity.It encompasses personal resources such as optimism, coping strategies, and social support [16,17]. Together, self-efficacy and resilience form a dynamic interplay that helps individualsnavigate academic and life challenges [18-20].This study builds on these theoretical foundations to investigate how achievement goals shapemotivational profiles and their impact on self-efficacy and resilience among undergraduateengineering students. By employing cluster analysis, we identify distinct motivational profilesthat reveal nuanced patterns in how students balance mastery-oriented growth and performance