cognitive levels of complexity: knowledge, comprehension, application, analysis,synthesis, and evaluation4. Engineering students beginning their core engineering curriculumstruggle to move between “knowledge” and “comprehension”. Entwistle5 discusses a lesscomplex model that incorporates three levels of learning and can easily be applied to Universitycurriculum. Level 1 “surface learners” have mastered the memorization technique and use theequations without deep thought or evaluation. Level 3 learners adopt an in-depth approach,striving to comprehend the concepts and the application of the new material. Level 2 “strategiclearners” fall between these two levels, commonly utilizing the surface approach, but they usetheir Level 3 skills only when
design and teaches in a Masters of Arts program designed for second career STEM professionals. He also teaches a variety of courses to as- sist classroom teachers with curriculum development, analyzing their instruction and conducting action research. Dr. Marlette was awarded his Ph.D. from Kansas State University in 2002. During his pro- fessional career he has taught both middle and high school science, worked in professional development schools, and provided teacher professional development at all grade levels (K-12). He regularly collabo- rates with STEM faculty on various projects and grants to improve K-12 STEM learning. He currently is serving as a faculty fellow in the SIUE Center for STEM Research, Education
responses from the mentees, manywere positive, but three of the eleven were negative or had negative undertones.One of the most positive responses to the reflective questions was from Becky. She stated: “I do feel loyal to my mentor since he has put in so much of his own time and effort into teaching me. I know he has done his best to support my personal growth and loyalty is only a fraction of what I could possibly pay him for what he's done for me. If I had to choose between a new mentor that was a master on a difficult subject and Josh, I would want to work with Josh even if he didn't know anything about the project. He's a fantastic learner and brings me along every time that he does.”Then in stark contrast
toclass.Engineering students must be able to understand context and project-specific design details whenworking in the industry to ensure the successful implementation of their engineering solutions[15]. These design details provide vital information about the specific requirements andconstraints of a project, enabling engineers to fully comprehend the scope and objectives of theirwork. By understanding the project-specific design details, engineers can effectively analyze theproblem, identify potential challenges, and develop optimized solutions. It helps in minimizingerrors, enhancing the efficiency of the design process, and ensuring the final product meets thedesired specifications. Hence, engineering students who master project-specific design
highlighted how underclassmen have less power when working with graduate students whonot only have more experience than them but also have more ownership of the project, “[As] an undergrad, working in the lab, the PhD and the master student are going to have way more power, I guess, than the undergrad student, and they rightfully should, because it's their project. And they have a lot more experience working in a lab environment.” (S3)Several participants discussed how students who are considered smart or competent can be seenor treated differently.S4 mentioned how she thought that S2 would have more confidence since she was smart enoughto come to university before graduating high school, “I always expect the really
families from traditionally underrepresented populations in engineering are able to develop engineering interest, skills, knowledge, and ways of thinking as a result of engaging in authentic engineering activities within a wide range of learning contexts.Catherine Wagner, University of Notre Dame Catherine Wagner is a research staff member at the Center for STEM Education at the University of Notre Dame. She earned her Master of Education degree from Notre Dame in 2019 while teaching middle school science. She has collaborated with faculty in the Center for STEM on engineering research for several years, most recently leading an undergraduate research lab on early childhood engineering research. In the Center, she also
as a software engineer at Sina for one year after I graduated as a master from China Agriculture University in 2009. He received the Best Paper Award from IEEE Edge in 2019.Jin Lu, University of Georgia Jin Lu received his Ph.D. degree in computer science and engineering from the University of Connecticut, USA in 2019. He worked as an assistant professor at the University of Michigan - Dearborn from 2019 to 2023. He is currently an assistant professor at the School of Computing at the University of Georgia. My major research interests include machine learning, data mining, and optimization. I am particularly interested in transparent machine learning models, distributed learning algorithms, optimization and so
Paper ID #41561Insights and Lessons Learned from Engineering OER AuthorsDr. Jacob Preston Moore, Pennsylvania State University, Mont Alto Jacob Moore is an Associate Professor of Engineering at Penn State Mont Alto. He has a PhD in Engineering Education from Virginia Tech and a Bachelors and Masters in Mechanical Engineering. His research interests include open educational resources, concept mapping, and student assessment techniquesDr. Daniel W Baker PhD P.E., Colorado State University Daniel Baker, Ph.D. PE is a Teaching Associate Professor and is the primary instructor for the on-campus and online sections of CIVE 260
presented research about out-of-school learning, science and nature education, and about collaborations to promote natural resources management. In addition, Rebecca is a Wisconsin Master Naturalist, and enjoys hiking, reading, connecting with others, and learning languages.Dr. Ryan Robert Hansen, Kansas State University Dr. Hansen is an associate professor in the Tim Taylor Department of Chemical Engineering at Kansas State University.Nathan P. Hendricks, Kansas State UniversityGaea A. HockDr. Stacy L. Hutchinson, Kansas State UniversityPrathap Parameswaran, Kansas State University Prathap Parameswaran is currently an Associate Professor and the Fornelli Engineering professorship holder at the Civil Engineering department
forest models; entropy;computer adaptive testing; artificial intelligenceIntroduction Effective and impactful education is reliant on accurate and equitable assessment oflearning and proficiency. Large-scale and local assessments are used for determining admissioninto programs, for course placement, for determining which students have mastered courselearning outcomes, for reinforcing learning and providing feedback, for informing pedagogy andinterventions, and for developing self-regulated learning skills [1], [2], [3], [4]. Cognitive fatigue (CF) is a well-documented phenomenon characterized by diminishedperformance throughout the day, over the course of prolonged cognitive tasks, and even within thefirst few questions on single
scale between“not true of me” and “very true of me.” The Cronbach’s alpha value for this measure for the pre-survey data was 0.91 and for the post-survey data, it was 0.94. Example question from thesurvey: “Compared with others in this class, I think I'm a good student.”Self-Efficacy – Task. Students were surveyed to gauge their self-confidence in comprehendingand mastering the content covered in the fundamental circuits course, encompassing areas suchas linear resistive circuits, 1st order circuits, sinusoidal steady-state circuits, ideal transformers,and semiconductor circuits, including diodes and transistors. The data collected was based on arating from 0-10, with 10 being most confident and 0 being no confidence. The dataset'sreliability
Institution Type Community college 1 17 17 Baccalaurate/Masters 1 17 34 Research 1 University 4 66 100 Immigration PhD Status International Student 3 50 50 Domestic Student 3 50 100 N=6 Figure 1: Descriptive Statistics of Our Sample of Teaching-Focused Faculty Our study’s sample size in terms of engineering TFF is relatively small in terms of aphenomenological
citizenship]. It's just the, the stereotype that they immediately are drawn to. Yeah. It's not just – it's not just Mexicans. It's a lot of Europeans [that have DACA status]… If I tell them, “oh, I'm a DACA recipient,” their response is “oh, what is that? Explain that.” Or, “what do you mean you can't do that,” you know? So, I think it's just having to constantly explain it because that gets tiring, and you have this script in your head [to answer their questions]. That's the frustrating part.It is important to note here that racialization itself was initiated because only those“unmarked” are considered white. That is, individuals who are perceived as not belonging tothe “master category” [40] or the default
undergraduate students struggle to actualize the amount of writingand other forms of communication that engineering careers often require.Solution: Interview-based podcast that focuses on the communication aspects of engineeringEducational Environment. I am a faculty member in Virginia Tech’s Mechanical Engineeringdepartment where I also serve as the director of the department’s Technical Communication Program(TCP). The department awards an average of 400 undergraduate, 40 Master of Science, and 30Doctoral degrees annually (Virginia Tech, 2023). As TCP Director, I offer writing workshops, guestlectures, and other similar events to help students gain different skill sets outside of the classroom.Purpose. After attending a National Humanities Center’s
Paper ID #43872Poetry Writing as a Creative Task to Enhance Student LearningEmma S Atherton, University of Florida Emma S. Atherton is an incoming Management Consultant and a recent graduate from the University of Florida with a Master of Engineering in Industrial and Systems Engineering, with a concentration in Production and Service Operations. She additionally received her Bachelor of Science in Industrial and Systems Engineering from the University of Florida, with a minor in Sales Engineering.Prof. Elif Akcali, University of Florida Dr. Elif Akcali is an Associate Professor in the Department of Industrial and
community engagement workshop, studentswere introduced to a four-stage model for initial transdisciplinary collaboration [13]. To modeltransdisciplinary applications to community-engaged solution-seeking to environmentalchallenges, the students simulated a case study scenario involving an action plan for climatechange. In the first stage of the model, students individually read a document that simulated acommunity stakeholder - in this case, a city agency requesting assistance in drafting a cityaction plan to combat climate change issues in a coastal community. The students individuallyhighlighted lexical items of significance in the document. Subsequently, they worked in smallgroups to create one master list of focal themes that emerged in the
entrepreneurship topics 20 business.”** indicate codes mentioned by at least 50% of students.Table 4: Responses to the question (2): “What could the instructor do to improve?” Percent of Example respondents Codes (n=20) “To improve, consider giving a master list of assignment deadlines at the start of the semester, so that students can plan their work Instructor-specific
programs face challenges in increasing the representation of students from low and middle-income countries, and engineering spaces can continue to be hostile environments, where students of color represent 21% and 14% of engineering and science masters and doctorates, respectively[36]. Future studies will delve into how inherent privileges and common practices of socially dominant individuals may complicate and pose challenges to a student's capacity for social justice activism. inally, given the educational aims of HE programs and the lack of studies on HE students’Fgrowth as social justice engineers, future work will characterize how learning experiences in HE programs influence graduate students to advocate
Paper ID #43159Optimizing Database Query Learning: A Generative AI Approach for SemanticError FeedbackAbdulrahman AlRabah, University of Illinois Urbana-Champaign Abdulrahman AlRabah is a Master of Science (M.S.) in Computer Science student at the University of Illinois at Urbana-Champaign. He holds a Graduate Certificate in Computer Science from the same institution and a Bachelor of Science in Mechanical Engineering from California State University, Northridge. He has experience in various industries and has served in multiple roles throughout his professional career, including in oil and gas and co-founding a food &
Information Literacy Teams: Bridging the Fluency Divide Judy Collins, Beverlee Kissick, Jung Oh, Alysia Starkey Kansas State University-Salina Introduction "The quality and quantity of information needed to function effectively in society and the workplace continues to increase. Individuals...must be able to master rapidly changing information technology and possess the information literacy skills to act independently in this information rich environment1."Information Literacy and the Fluency DivideAccording to futurists, in the next decades, the amount of information will be doubling everyeleven minutes. Yet
inexperienced and maydisregard safety procedures or considerations out of haste, ignorance, or distraction. Students arestill trying to master technical detail and have limited exposure to what can go wrong, what canbreak, and how to assess the reliability of a design. Industry addresses the issue of new engineersby assigning senior engineer mentors, having careful design reviews, and developing anunambiguous culture of safety where all stakeholders are on the lookout for unsafe practices.Before we delve into a specific senior design case study, we want to outline a general frameworkfor incorporating safety into such a project: a. Define specific safety-related technical requirements for the project definition presented to the students; b