in various places,enabling ubiquitous computing envisioned by Weiser a few decades ago [1], [2].National Research Council recommends including engineering and technology in K-12 scienceeducation for various reasons. The students are initially introduced to science through lecturesand reading assignments from their textbook, but they also need opportunities to see the practicalaspects of science in their daily lives and preferably through hands-on exercises [3]. The level ofexcitement and engagement of the students in science classes are many times as a result of theteachers’ technical abilities and willingness to provide guidance to students throughout theireducation. Inevitably, the teachers are expected to keep up with the ever-changing
system design experiments that the students could perform in their places using Matlab.We provided the academic access codes for Matlab to the students. We also demonstratedlaboratory experiments in pre-recorded videos prepared previously4.Reorganization of the course contentsAfter the revision of the course learning outcomes, we reviewed the topics and made changes tomatch the revised course learning outcomes. The changes were minimal. We organized the topicsinto six modules to match the summer schedule. The topics in these six modules are described asfollows: © American Society for Engineering Education, 2021 2021 ASEE Annual Conference Module 1: Mathematical modeling of
strategies that enhance students’cognitive and emotional engagement in their learning during online-only and face-to-faceinstruction.IntroductionUndergraduate students today are experiencing significant challenges as they are forced to adjustto online learning. The competitive, autonomous nature of contemporary engineering educationfurther challenges them to take responsibility for their learning to succeed. Learning to becomean engineer has always been rigorous, but the added stress of learning online has increased theneed for students to develop self-regulation skills that enable them to understand and managevarious facets of their learning such as motivation, organization, and time management [1], [2].Development of self-regulation skills
. The results will deepen our understanding of how these competencies evolve over timeamong students of different disciplines and suggest practical recommendations to improvecurrent teaching methodologies.Keywords: Effective Communication Skills, Inductive Teaching, Interpersonal Communication Introduction Over the past several years, professional communication skills have become one of thetop-ranked competencies that employers seek from new engineering college graduates [1] - [5].The urgent need to teach both technical skills and professional skills, including effectiveprofessional communication, to students is recognized by higher education professionals across avariety of the STEM disciplines [6
, use digital media toadvance their academic careers and have been exposed to this technology for all of their lives.This Generation Z cohort, students roughly between the ages of 17-22 have particular learningstyles and it is important as engineering educators to modify our teaching methods to best meettheir needs. Kalkhurst [1] writes that GenZ students are disrupting many ingrained practices ineducation and that colleges and universities are forced to adapt at a rapid pace or becomeirrelevant. GenZ students are accomplished self-learners, can process information at a fast paceand it is important to be brief and visual to capture and hold their attention [2].Seemiller and Grace [3] highlight an important characteristic of GenZ learners: a
of the implemented changes and the technologyused. Also, computer communication technology and the availability of sufficient internet bandwidthwere adequate. The students’ feedback shows the importance of having direct interaction with theinstructor affected by their experiences with the online portion of the semester. In conclusion, educationis strongly dependent on a trust-building process between the instructor and the learners. The studentscan follow-up and are involved positively in any modification of class format or methodology if theybelieve in their coach’s (instructor’s) competency.1. Introduction: COVID-19 pandemic hit unexpectedly during spring 2020. All life sectors were impacted significantly,including health, economics
to be shown at the ASEE conference as we analyze additional data. To aid thevirtual implementations, we created a number of engaging videos under two major categories:(1) demonstrations of each LCDLM showing live data collection activities and (2) short,animated, narrated videos focused on specific concepts related to learning objectives. In thispaper we present preliminary results from pre- and post- implementation conceptual assessmentsfor the hydraulic loss module and motivational surveys completed for virtual implementations ofLCDLMs and compare them with a subset of results collected during hands-on implementationsin previous years. Significant differences in conceptual understanding or motivation betweenhands-on and virtual
time in which our society required social distancing, studentsexpressed that their biggest struggle was that they could not interact with their peers.IntroductionWhile virtual instruction has been in practice for over a decade, its effectiveness continues to beinvestigated [1]. According to Banas et al. [2], distance learning can be traced back to 1892.Several studies conducted a meta-analysis to evaluate the effectiveness of virtual instruction withmixed results [3-6]. The primary benefits of virtual instruction include cost effectiveness [7] andexpanding access to post-secondary education on a global scale [8].Several studies have investigated laboratory courses in a virtual setting. One study from Corter etal. [9] found that remote and
-mandatory, synchronous math tutoring was offered for an additional 4 hours eachevening Sunday through Thursday during the program duration. Short mandatory weekly “floormeetings” were held on Sunday evenings in conjunction with study hall to frame the upcomingweek’s schedule and activities; however, in practice these events were perceived as low-valuemeetings and poorly attended by program participants. Figure 1 shows the general weeklyprogram schedule. Figure 1: Example Weekly Program ScheduleAdditional Specifics of Program ExecutionThe following section pertains to some of the miscellanea around general execution of the virtualprogram not covered within the previous section on planning of larger program
Instruction” in conjunction with overlays of FredRogers’ and Benjamin Bloom’s contributions in the production and delivery of digital lessons.Gagne has created a standard for instruction that provides both a framework for building a solidlesson plan and a foundation for evolving numerous learning theories. Though Gagne is rarelyincluded in constructivist discussions, the melding of Gagne’s vision with constructivist ideologyin a quest to best support digital learners provides an enticing blueprint for the 21st centuryclassroom.Marcy Driscoll’s close association with Gagne provides a seemingly dichotomous message inPsychology of Learning for Instruction [1], yet Dr. Driscoll’s turmoil proved an effectiveincubator for creation of a new learning theory
Charlotte. He has taught undergraduate courses, and graduate courses in Purdue University Fort Wayne, and University of North Carolina at Charlotte. Dr. Alasti is a member of ASEE, a senior member of IEEE, and member of several IEEE societies such as communication society, and signal processing society. He has had several years of industry work experience in power system companies and field experience in power system communications.Work In-Progress: Turning A Legacy Robot to Collaborate To Fit in Industry 4.0 DemandsIntroductionRobotic machinery and programmable logic controllers (PLCs) have been the workhorse ofindustrial manufacturing for a long time [1]. The emergence of industry 4.0 has
Thermodynamics) and Spring 2020 (Dynamics only).T-tests showed that for all subgroups of students attending study sessions improved homeworkgrades. Additionally, a linear regression analysis was used to model the relationship betweenstudents’ exam improvement (between Exam 1 and Exam 2, and between Exam 2 and Exam 3)and their difference in study session attendance before each of the included exams. The analysisshowed that students who attended study sessions were positively affected overall, with each 20%increase in study session attendance (typically one study session) increasing grades by 2-3% (p=8.35E-4). One subset of students showed a negative correlation with attending study sessions:Hispanics who did not receive Pell-grants (p=0.972) but this
engineering students discuss based onboth previous literature and students’ responses to survey questions about models. In Fall 2019,the survey was administered to first-year engineering students to investigate their awareness oftypes of models and understanding of how to apply different types of models in solvingengineering problems. Students’ responses to three questions from the survey were analyzed inthis study: 1. What is a model in science, technology, engineering, and mathematics (STEM)fields?, 2. List different types of models that you can think of., and 3. Describe each differenttype of model you listed. Responses were categorized by model type and the framework wasupdated through an iterative coding process. After four rounds of analysis of
identified the main themesacross all interviews. These themes were then turned into a set of analytical codes, which thencreated a coding matrix that was used to analyze all interview transcriptions in NVivo.Through analyzing the interview transcriptions, nine stereotype themes and nine stereotype threatthemes were identified. During the data analysis process, stereotypes based on both race andgender were considered. The institution that the student attended as well as their year in schoolwere also taken into consideration.ResultsTable 1 shows the nine primary stereotype themes that were identified based on the collecteddata with their accompanying definitions. Table 1 – Stereotype Themes and Definitions
important that detailed information of acompany’s employees, vendors, and payment history are recorded in order for the business tooperate efficiently and comply with the law. The requirements for this system include: (1) theability to track employee information, such as salary, responsibilities, seniority and otherrelevant information, (2) the ability to track job applicants, including their contact information,resume, etc., (3) the ability to track vendors that the company purchases products or servicesfrom, as well as a history of any invoices that the company has received, and finally (4) theability to track payments that have been paid to both employees and vendors. With thecomplexity of the above system requirement, considering software
universities [1], it’s important to examine all aspects and impacts ofthese programs on all students served. Over several years, it became apparent that the mentoringprogram had quite a positive effect on the mentors themselves as well as the protégés. Intriguedby higher graduation rates of former peer mentors, the researchers sought to discover andexamine the academic and social benefits peer mentors found by participating in this program. Arandom number generator was used to select twenty people from a list of all mentors who servedat least two years in the program (n=101) since 2010. Many of these mentors had graduated andworked in various engineering positions, while others were current students. Phone interviews ofeight current and former mentors
, harnessing the value of developingintervention programs that are deeply integrated in a scale that accommodates diverse student participants,and developing programs that have interdisciplinary scopes with room for inclusivity. It is also ofimportance to note that there are culture gaps in the learning pedagogy of today’s students such that it is ofsignificance to connect the education of the students to the local community and for K-12 education systemto transition to project-based learning.1. IntroductionThe premise of convening a workshop to highlight the strategies to improve student engagement byenhancing the curriculum of engineering education draws on Linus Pauling’s suggestion, that, “To have agood idea you must first have lots of ideas.” [1
laboratory sessions [1]-[3].A pandemic is defined as an epidemic of infectious disease that has spread across manycountries. COVID-19 was declared by World Health Organization (WHO) as a pandemic onMarch 11th. 2020 [4]. The recent COVID-19 pandemic has globally affected all activities whichrequire social interaction. University experience is one of these affected areas and educatorsworld-wide are devising innovative ways to minimize the impact of the pandemic on studentlearning [5]. The most popular approach is to move instruction online. Online instruction is goodfor teaching theoretical knowledge [6]. However, laboratories require hands-on execution ofexperiments. Simulations can replace the hands-on experiments to a limited extend. Moreover
an engineer throughout their undergraduate experience. Thisprocess happens formally through the curriculum and informally through the behaviors andattitudes brought on through interactions with faculty members, peers, and various educationalsettings, e.g., courses and extracurricular activities. It also relates to both the technical andprofessional competencies that engineering students are expected to develop [1]. Driven byaccreditation [2], The Engineer of 2020 report [3], and industry expectation [4], engineeringprograms in the United States over the past 20 years have increasingly recognized the importanceof ethical and societal responsibility [5]. The need to enculturate ethical awareness andresponsibility in engineering education
Society for Engineering Education, 2021Undergraduate STEM Students’ Comprehension of Function Series and RelatedCalculus Concepts 1 Emre Tokgöz, 1Berrak S. Tekalp, 1Elif. N. Tekalp, and 2Hasan A. Tekalp 1 Emre.Tokgoz@qu.edu, 1Elif.Tekalp@qu.edu, 1Berrak.Tekalp@qu.edu, 2Hasan.Tekalp@qu.edu 1 Industrial Engineering, School of Engineering, Quinnipiac University, Hamden, CT, 06518 2 Mechanical Engineering, School of Engineering, Quinnipiac University, Hamden, CT, 06518Action-Process-Object-Schema (APOS) is a constructivist methodology that relies on learners’ ability to constructand reconstruct certain mental structures and
process.Background and motivationEngineering curricula are typically structured with courses in mathematics, scientific theory, andapplied mathematical and physical analysis methods. Despite a decades-long push for designcourses and activities, studies show that engineering programs focus too heavily on teachingscience and analysis rather than holistic design [1]. This conflicts with the needs of modernsociety, which requires products that take into consideration factors unrelated to technical skills,such as user needs and sustainability [2]. In other words, technical design does not take place in avacuum; market and environmental factors play a critical role in design success. In fact, “designin context” that considers consumer needs and market
urban, commuter, public research university; an urban, private,teaching-focused university; and a rural, public, teaching-focused university.The survey questions have three parts: 1) student perspectives in writing in engineeringdisciplines; 2) how students use prior writing knowledge when writing lab reports in engineeringlab courses; and 3) how engineering lab course writing instructions impact students’ engineeringlab report writing. Findings suggest that the three transfer groups present statistical distinctionson the readiness of writing engineering lab reports (concurrent group as the highest and absentgroup as the lowest). The three groups also show different perspectives on how their freshmenwriting courses contributed to their
c Society for Engineering Education, 2021WIP: Understanding Context: Propagation and Effectiveness of the Concept Warehouse in Mechanical Engineering at Five Diverse Institutions and Beyond – Results from Year 2It has been well-established that active learning strategies increase student retention, improveengagement and student achievement, and reduce the performance gap of underrepresentedstudents [1], [2]. Concept-based learning is a particular form of active learning which “is the useof activity-based pedagogies whose primary objectives are to make students value deepconceptual understanding (instead of only factual knowledge) and then to facilitate theirdevelopment of that understanding” [3], and its
multidisciplinary use. We hope that the analysis and reflections on our initial offeringshas improved our understanding of these challenges, and how we may address them whendesigning future data science teaching modules. These are the first steps in a design-basedapproach to developing data science modules that may be offered across multiple courses.1. Introduction As technology advances, familiarity and expertise in data-driven analysis is becoming anecessity for jobs across many disciplines. Data science is an emerging field that encompasses alarge array of topics including data collection, data preprocessing, data quality, data visualization,and data analysis using statistical and machine learning methods. A recent National Academy ofSciences
Engineering Education, 2021 Understanding eLearning Acceptance of Generation Z Students: An Extension of the Technology Acceptance Model (TAM)Abstract:The COVID-19 pandemic disrupted instructional practices at educational institutions.Countermeasures included transitioning the majority of classes from primarily in-class learningto primarily eLearning. This shift has been met with varied levels of resistance and acceptance,while one study showed that 85% of higher education students prefer in-class learning [1].Models developed years ago don’t account for the dynamic nature of the education world and thestudents within it. As a result, there is demand for an understanding of the unique set of needspresented by Generation Z, the
parentalcareer were the most influential factors of persistence. Using this information, combined with thetheoretical underpinnings of these constructs, may provide areas in which to focus andspecifically target in order to improve persistence rates in engineering education.Introduction Compared to other degree programs, persistence rates of undergraduate engineeringprograms are low. Engineering programs have up to 50% of students who persist and graduatewith an engineering degree whereas persistence rates of other majors such as education (81%),business (80%), and humanities (64%) are greater [1], [2], [3]. Programs develop curricula,establish course structures, provide resources, and implement support intended to improvestudent persistence [4
understand this current cohort of students, followed by the second prong of a needsassessment survey distributed to all undergraduate women in our college of engineering. Theresults of these first two approaches were reported upon separately [1]. The survey left us withseveral unanswered questions that required further examination to better understand the declinein current student engagement with our program. Specifically, we needed to understand how ourhistorically events-driven organization could offer academic support (the top concern acrossevery year and engineering major) without duplicating existing services; and also why, whenasked to rank their top concerns as women studying engineering, the environment for womenwithin the engineering
bytheir fathers and teachers, however, at the end of high school, female students were more likelyto be encouraged by their fathers and siblings.This study helps disentangle the influence social agents have on female high schoolers’ interestin engineering careers. Furthermore, a deeper understanding of how factors influence the chancesof female students’ engineering career interest during high school and first semester of collegewill help the engineering education research community develop more effective strategies inimproving female and minority student participation.IntroductionBroadening participation in engineering has been a critical topic for more than a decade [1].Moreover, engineering continues to be a male-dominated field; in 2017, the
education. Instructors must consider elements of course design, such asmodality, pace, means of communication, and student feedback [1].The University of Pittsburgh adopted a HyFlex teaching model called Flex@Pitt The Flex@Pittmodel was designed to maximize the in-person educational experience, while still allowinginstructors and students to engage in the course instruction method for which they feel the mostcomfortable. The classes at the University of Pittsburgh include a mix of synchronous andasynchronous. This flexibility allows for classes to meet in-person, when safety protocols andsocial distancing protocols are met, while the class is also available to students who wish toattend fully online via livestream videoconferencing. Under the Flex
(2017) notes that these traditional students “receive the vast majority of attention and resourcesfrom colleges and universities” (p. 1). There is some irony here since “the majority of students inundergraduate programs can be classified as nontraditional, suggesting that the traditional student…is nowactually the exception rather than the norm” (Chen, 2017, p. 1). For this reason, nontraditional students aswe describe them here are now often referred to as post-traditional learners, a term acknowledging thatnontraditional student populations are now often the “norm.” Moreover, Kim et al. (2010) suggest that usingpredefined labels to define nontraditional students may be less useful than allowing this population to self-identify based on