and a guide to enhanceteamwork in course projects. Based on TPB, three interventions were developed: (1) a projectdescription document including real-world examples of problems that can be solved with skillsdeveloped through the course project; (2) an accountability plan for the instructional team toprovide social pressure to participate; and (3) a project management plan for the students to havea structure in the groups with well-defined roles. The interventions were adopted in two Fall2023 courses (n = 39). Findings revealed significant improvements in student engagement, taskcompletion, communication, role adoption, goal clarity, and conflict management post-intervention. These results confirm the efficacy of TPB-based interventions in
on acceptable traits of moral behavior or character as perceived by oneself and others, andis more generalized to all aspects of an individual’s life rather than as a specific way to solveethical problems [1], [2].Virtue ethics has been described as a form of ethical reasoning based on what a virtuous personor a person of good character would do when faced with a difficult decision [3], [4]. There isalso debate as to whether virtues can be taught, as well as whether they are exercised consistentlyor are dependent on circumstances or context [4], [5]. Therefore, the ambiguity surrounding thevirtue ethics framework could provide evidence for students’ difficulty in understanding andapplying it.By contrast, Deontology, or ethics by rules, is
will provide a comprehensive understanding of both time-tested fundamentals, such as internal combustion engines and vehicle dynamics, alongside thelatest advancements in electric powertrains, autonomous driving systems, and AI applications inthe automotive domain [1-3].Second, to develop a deep understanding of the pivotal role of AI in modern automotiveengineering: AI is rapidly transforming every aspect of the automotive industry, from design andmanufacturing to performance optimization and autonomous driving. This course will emphasizethe practical applications of AI in various automotive sub-systems and equip students with theability to leverage its power for innovative solutions [4-6].Lastly, to foster a project-based learning
Undergraduate Engineering Student PopulationIntroductionIt has been previously documented that severe weather events cause a wide range of directmental health concerns, including depression, PTSD and anxiety in individuals living in theaffected community [1]. However, as the urgency around broader climate change has increased,and countries race to meet the 2050 goal of net zero emissions to limit global warming [2], a newphenomenon known as “Climate Anxiety” has emerged [3]. Climate anxiety is a form of anxietyinduced by the existence of climate change and concerns about this change, rather than discreteweather events. Simply being aware of climate change and its negative impacts on our naturaland social systems can cause a severe anxiety response. The
structure previously determined through exploratory and confirmatory factor analysisrevealed five latent variables that align with a framework proposed by Fila et al. [1] for teachingengineering within a humanistic lens to help students develop a sense of belonging and theirengineering identity. Our SEM analysis showed that for all students, academic self-confidenceand self-efficacy and a broad understanding of engineering both have a significant positiveinfluence on their sense of belonging, which in turn has a significant influence on their attitudestoward persisting and succeeding in engineering. Appreciating the importance of non-technicalskills in engineering had no significant influence on most students’ sense of belonging with theexception
skills to unfamiliar contexts.Since the turn of the century, extensive educational research and industry training-orientedefforts have worked on developing mechanisms to assess this transfer. However, many existingassessment methods are proprietary or very tailored to specific training applications. In thisstudy, the authors adapt the Factors for the Evaluation of Transfer (FET) model [1] to evaluatethe effectiveness of transfer of learning in a pre-college engineering short course. This modelconsiders the transfer of learning through dimensions (trainee, training, and organization),achieved learning, and intent to transfer. The instructors implemented curricula emphasizingcivil engineering applications related to buildings, water systems
theundergraduate researchers (first author) on undergraduate electrical engineering students’perceived self-efficacy and Impostor Syndrome during their participation in RED programactivities.Self-efficacy refers to the “students' beliefs in their ability to achieve tasks,” [1] while ImpostorSyndrome is defined as a “psychological term that refers to a pattern of behavior wherein people(even those with adequate external evidence of success) doubt their abilities and have a persistentfear of being exposed as a fraud,” [2]. Impostor Syndrome is known to occur more frequently inscientific communities, along with marginalized communities and communities frequently facingmental health issues, such as anxiety and depression [3]. For this project, the goal is to
Classes IntroductionSense of belonging, here defined as students’ perceived social support, and feelings ofconnectedness, mattering, acceptance, and respect in socio-academic communities, is widelyconsidered an important antecedent to students’ socio-academic success in college [1].We use the term socio-academic to draw attention to the ways that students’ experiences outsideof the classroom can shape their academic lives in college, as well as to draw attention to theways that complex social interactions shape students’ academic experiences and outcomes [2].Indeed, decades of research has documented the ways that college students’ sense of belongingshape important socio-academic outcomes, such as major
structure a better capstonedesign program for senior students and will impact the engineering education field.Literature Review and Background ResearchArthritis and similar conditions affect millions of people all over the world. In fact, 20% of theentire world population deals with arthritis in some capacity [1]. Many of these people, despitetheir disabilities, are still required to work physically demanding jobs. The hardship such peopleface cannot be imagined.Exoskeletons are a form of wearable robotics that enhance human physical abilities. They aredesigned to augment strength, provide stability and support, and aid in tasks that would bechallenging without them. These arms provide numerous benefits across industries such asmanufacturing
graduate students and learningto develop professional skills. As the MAE was also conducted with students throughout all ofCECAS (n=1174), we are able to compare the results of SPECTRA students with data fromacross the entire college. Preliminary results show some statistically significant differencesbetween SPECTRA students and all of CECAS in subcategories within student sense ofbelonging and future-oriented motivation. The qualitative data from interviews was used tofurther explore these findings.SPECTRA Background The Student Pathways in Engineering and Computing for Transfers (SPECTRA) programis a NSF funded (Award#1834081) project which aims to accomplish three goals: (1) to provide scholarship opportunities to low-income
has been increasing over the past decade,yet women still only occupied 35% of the STEM jobs in 2021 [1]. Regarding degree attainment,the National Center for Science and Engineering Statistics expressed that women are particularlyunderrepresented within most STEM programs [1]. Interestingly, there was a steady increase inthe number of women earning a bachelor’s in engineering—more than a 100% increase between2011 and 2020. However, despite this increase, women were only representing a fraction of all ofthose who earned a bachelor’s (24%), master’s (27%), and doctoral (25%) degree in engineeringin 2020 [1]. A master’s or doctoral degree is important to attain when considering careeropportunities and advancement [1]. However, as Beck et al
also included sensors, actuators, resistors, LEDs, a breadboard, andjumper wires to connect components together [2]. Once supply chain issues were resolved amore elaborate IoT learning toolkit was developed based on an IoT learning platform, theKeysight U3810A [7]. This learning platform includes an integrated basic processor board, theBeagleBone Green, along with a variety of sensors and components mounted onto a larger circuitboard. The U3810A IoT learning platform is pictured in Figure 1. In addition to the U3810Aand its integrated basic processor board, the advanced learning toolkit includes jumper wires tomake connections among its components. A breadboard is also included to enable students toincorporate additional sensors, actuators
language and rhetorical strategies could produce a deterrent effect.Specifically, I use rhetorical theory and the concept of analogical imagination to illuminate thenature and power of implied messages and suggest conversation as a promising rhetorical modefor engaging a broader range of participants in the discourse on diversity. © American Society for Engineering Education, 2024 1 2024 ASEE Annual ConferenceThe discourse on diversity is organized around values that are cherished in the LiberalEducation/Engineering & Society Division of ASEE (LEES) and in the broader community ofpeople engaged in humanistic education for engineers. I want to emphasize that the
Universal Computing, Construction, and Engineering Education at Florida International University. ©American Society for Engineering Education, 2024Expanding the Broadening Participation in Engineering Focus to Include Data on Nontraditional StudentsIntroduction As the need for more technically skilled workers in the U.S. engineering workforceincreases, working adults are returning to college for degree attainment to advance their careers.Returning to college part-time has become more feasible for working adults with the increasingpopularity of online courses [19],[10],[4],[14], [1], [2]. However, the higher education systemwas not designed for working adults with many obligations that can
might be reflected in the underrepresentation of students with disabilities in thescholar community. Between 11% and 15% of U.S. college students identify themselves asstudents with disabilities [7] [8] and about only 4% of these students with disabilities haveenrolled in engineering majors [8]. As of 2015, while the 33% of the U.S. population held atleast a bachelor’s degree, only 14% of the population with disabilities had reached this level ofhigher education [9]. Furthermore, just 1% of students with disabilities have received a PhDdegree in 2017 [10]. These statistics provide a glance of the disadvantaged position that studentswith disabilities hold, as compared to the general population in the U.S. Given the historicallyexclusionary
career path trajectories. The often-obscured implications of career paths on professionaloutcomes, and in particular the ways in which race and gender can be associated with career pathstreaming, serve as the rationale for our current study. Our guiding research question is simple:How do race and gender intersectionally influence the career path trajectory, and by extensionthe sense of professional belonging and identity, of engineering graduates in Canada?Ample research has documented workplace marginalization, exclusion, discrimination, andmisogyny experienced by women in engineering [1], [2], [3]. Robust theoretical work, groundedin empirical findings, has demonstrated the way societal gender norms are entrenched in the wayengineering, a
comprehensive coverage ofpervasive computing cybersecurity allows students to learn state-of-the-art research findings, gainhands-on experiences with recent software, and engage with cutting-edge cybersecurity technol-ogy. Finally, we share the lessons we learned from our study, make ReScuE lab materials availableto the public, and aim to benefit the broader audience of cybersecurity education.1 IntroductionAs a growing computing paradigm, pervasive computing allows devices to interconnect and un-derstand their surroundings with minimal human intervention. With the empowerment of high-performance cloud infrastructure and low-cost network connectivity, pervasive computing canperform collaborative jobs by collecting and analyzing data and communicating
engineering faculty members’ values as it relates tograduate education. By exploring faculty readiness we will uncover barriers that must beconsidered before addressing equity work in a local context. 1. Introduction There is a growing awareness of the inequities that are embedded within graduateeducation in engineering. For instance, it is well documented that women are less likely to earnengineering graduate degrees than men, along with being slightly less likely to receive federalsupport to fund their education [1]. In 2022, at the doctoral level, 26.2% of engineering doctoralstudents were women, despite making up 50.4% of the United States population [2], [3].Additionally, Black and Hispanic Americans made up 3.9% and 7.5% of
applications in engineering education research.3.1 Cluster analysis in engineering education researchBelow, we give a brief overview of cluster analysis methods and applications within engineeringeducation research. In-depth reviews about cluster analysis techniques can be found in [1], [27],[28]. Within engineering education research, studies applying cluster analysis are rather limited.[1] only identified five articles that have applied cluster analysis in the Journal of EngineeringEducation by 2017. We have only found three empirical papers using cluster analysis withinASEE Engineering Research and Methods (ERM) division [6]-[8]. Although applications arelimited, engineering education researchers have used this exploratory approach in various
have shifted along with advances intechnology used in both engineering practice and education. A brief but comprehensive historyof civil engineering education including the 18th and 19th centuries is given by Aparicio andRuiz-Teran [1]. Civil engineering education in the U.S., starting around the late 18th century,followed two European traditions of British and French origins. The former placed emphasis onpractical application of scientific principles, while the latter put more emphasis on soundtheoretical understanding as a basis of engineering practice. However, many civil engineers werestill trained through apprenticeships and so they received a great deal of practical training.With the technological and economic advancements of the mid and
route for the class as some students has no initial project ideas. A hybrid teamformation strategy was suggested for first-year student project team: the MD approach is firstapplied in the class, then followed by the BD approach.IntroductionTeamwork is a common practice for engineering professionals in the form of project teams. Thegroup of individuals known as the "project team" is in charge of carrying out the activities andcompleting the deliverables specified in the project plan and schedule as instructed by the projectmanager, at the degree of effort or involvement specified for them [1]. The outcome of a specificproject is dependent on the collective individual contributions of every team member. Teamsutilizing individual knowledge and
naturedifferently.IntroductionThe Engineer 2020 report has identified the ability to function on multidisciplinary teams as an essentialskill for engineering students [1]. In essence, it has become necessary to support student learningthrough student-centered pedagogies that enable students to transcend cross-disciplinary boundaries todevelop the competencies required to solve complex engineering challenges [1], [2]. Biologicallyinspired design (BID) as a pedagogical approach has emerged in higher education as a unique disciplinethat can support multidisciplinary collaboration, help students develop some of these competencies, andapproach design and problem-solving with a wider lens [1]. BID is a method of using principles fromnature to solve engineering design challenges
matching the instructionalapproach of the intervention. Research on implementation factors also adds to the fields inunderstanding how and why teachers in various settings and with various backgrounds makeadaptations as they implement curricula [1]. In their discussion of the importance of flexibilityand fit of interventions, Harn, Parisi, and Stoolmiller [12] argue that "one of the best ways tomatch contextual and intervention characteristics to optimize implementation with fidelity overtime may be to adapt evidence-based practices to better match school-level context." Although research explicitly examining factors influencing the implementation ofengineering curricula is scarce, studies on the enactment of engineering curricula
Graduate StudentsAbstractThe first year of graduate school can produce great angst in students undertaking a fundamentalidentity shift from student to researcher [1]. In interdisciplinary programs, acquiring confidencewith an additional disciplinary framework and threshold concepts brings additional challenges[2]. Solutions often focus on mentoring [3], but students entering highly interdisciplinarygraduate programs may need additional support that helps them integrate the unique challengesfaced by students changing or integrating multiple disciplinary backgrounds and identities. Wepropose that formalizing career path exploration, with an emphasis on surfacing students’ angstabout their options and career paths through a professional development
and diversity,equity, and inclusion (DEI). The authors described how these subcategories would need to becategorized properly in future revisions, but the idea is they heavily dictated a student’sconfidence and sense of belonging.Summarizing this listing, we concluded with a motivational category list of interventionsubcategories as follows: task-value interventions (e.g., utility-value, communal value), framinginterventions (e.g., self-efficacy, belonging), personal value interventions (e.g., valueaffirmations), mitigating stereotype threat, and changing attributions, as shown in Table 1.Donker et al (2014) conducted a meta-analysis on teaching strategies that help studentmetacognition and self-regulation to find which specific tactics
Emerging Technologies through Co-design Workshop (RTP)AbstractArtificial Intelligence(AI) and Machine Learning (ML) touch every aspect of modern life andwill continue to influence us more than ever in the future. Schools and teachers should beprepared to let the children explore ML to help them understand how the world around themfunctions. It has been shown that children as young as three years old can not only interact withML technologies but also produce ML data sets and models[1].In this paper, we explore factors influencing the growth of teacher confidence in implementingemerging ML technologies within engineering educational settings. Five teachers from St. Louis,USA, engaged in a co-design workshop to explore an emerging ML toolkit and to
such as lead time andthe number of attempts. In addition, student perception was evaluated through the use of voluntary,anonymous mid-term and final course surveys. Qualitative faculty observations are included aswell. Student response to the unlimited attempts on homework assignments was overwhelminglypositive. Initial data show that students achieve higher final scores on homework assignmentswhen they attempt 1) the assignment earlier and 2) accomplish more attempts with a moderatecorrelation for both. It appears that 5% bonus points for early completion has a slight increase inmotivation, for roughly half of students.Background & Prior WorkThis work intends to build on previous work on effective course design [1] [2] [3]. The
mindset. To achieve thisobjective during the first offering, this course utilized active learning techniques, personalreflection, and the development of an individualized career-impact roadmap by each student. Inorder to work in conjunction with programming available from existing career centers andacademic advising, this interdisciplinary course placed an emphasis on personal reflection andthe roles of innovation and technology commercialization in creating societal impact. This paperdescribes the logistics of developing and implementing this 1-credit hour course and providesdetails of the assignments used to assess student learning. This course can serve as an example toother institutions who seek to more fully empower their students to
)and its intersection with Internet of Things (IoT) hardware technologies, a vital focus must beplaced on fostering the growth and development of its specialized technical workforce in theElectrical and Computer Engineering (ECE) and other related fields [1]. This strategic focus iscrucial given the escalating demand for proficiency in critical domains like embedded systemspaired with machine learning, sensor-driven big data analytics, edge computing, andcybersecurity [2]. The combination of AI and IoT, known as AIoT, embodies the convergence ofadvanced technologies that rely on seamless collaboration between AI algorithms and IoTinfrastructure. This integration drives innovation and efficiency across various industries,highlighting the urgent
-generation category. Weanalyzed survey responses assessing sense of belonging, self-efficacy, and institutionalsupport. The survey explores three dimensions: 1) general belonging, 2) belonging ineducational interactions, and 3) self-efficacy, each with eight items. The survey coversvarious aspects of the institution's student services, including psychological support,academic planning, tutoring, health and well-being services, sports, and supplementary areaslike leadership, diversity, gender, and participatory meetings. It totals 29 items. Respondentsexpressed their views using a 5-point Likert scale, from "strongly agree" to "stronglydisagree." Our findings reveal that all surveyed students exhibit a strong sense of belonging(both in general and