. Victoria Bhavsar about obtaining the required IRB for this research. Theviews and conclusions contained herein are those of the authors. They should not be interpretedas necessarily representing the official policies or endorsements, either expressed or implied, ofCSU or Dr. Bhavsar.9. References[1] M. C. Low, C. K. Lee, M. S. Sidhu, S. P. Lim, Z. Hasan, and S. C. Lim, “Blended learning toenhanced engineering education using flipped classroom approach: An overview.” ElectronicJournal of Computer Science and Information Technology, vol. 7, no. 1, 2021. Available athttps://doi.org/10.52650/ejcsit.v7i1.111[2] S. Hrastinski, “What do we mean by blended learning?” TechTrends, vol. 63, no. 5, pg. 564-569, 2019. Available at https://link.springer.com
. 2023 ASEE Annu. Conf. Expo., Baltimore, MD, 2023.[2] G. D. Bruce, “Exploring the value of MBA degrees: Students’ experiences in full-time, part-time, and executive MBA programs,” J. Educ. Bus., vol. 85, no. 1, pp. 38–44, 2009, doi:10.1080/08832320903217648.[3] S. K. Gardner and B. Gopaul, “The part-time doctoral student experience,” Int. J. DoctoralStud., vol. 7, pp. 63, 2012.[4] M. A. Cohen and S. Greenberg, “The struggle to succeed: Factors associated with thepersistence of part-time adult students seeking a master's degree,” Contin. Higher Educ. Rev.,vol. 75, pp. 101–112, 2011.[5] J. C. Yum, D. Kember, and I. Siaw, “Coping mechanisms of part‐time students,” Int. J.Lifelong Educ., vol. 24, no. 4, pp. 303–317, 2005.[6] R. Darolia, “Working
✓ ✓ ✓Of the 12 programs surveyed in this research, six have no lectures of any kind in fourth year; theremainder have fewer than six designated lectures, all of which are in the first term of fourthyear; and seven programs include a limited number of modules/workshops on specific topics(typically fewer than five). The primary means of instruction in these courses is mentorship,informal advice, or feedback to student teams through assessment(s). Across all 12 programs, theprimary means of assessment are written reports and formal presentations by the student teams,most frequently at the middle, and end of each term, though regular meetings in between thesemajor milestones are not uncommon.Three of the 12 programs surveyed include at least one
constant data in the MEM stage. The data memory is separated from the instructionmemory, with a pair of read and write ports only accessible to the MEM stage. To supportvariable data widths requested by LB/SB, LH/SH and LW/SW, the data memory is split into fourone-byte wide sub-memory components. A store pre-processing component examines the type ofthe store instruction and determines which one(s) of the sub-memory components should beactive during the store operation. A load post-processing module examines the type of the loadinstruction and determines which byte(s) should be connected to the load data bus. One issue isreported in [6] about the difficulty to merge synthesized memory into their pipeline as IntelQuartus Prime can only create two
at Allan Hancock College (AHC), a California community college between UC Santa Barbara and Cal Poly San Luis Obispo. At AHC, he is Department Chair of Mathematical Sciences, Faculty Advisor of MESA (the Mathematics, Engineering, Science Achievement Program), has served as Principal/Co-Principal Investigator of several National Science Foundation projects (S-STEM, LSAMP, IUSE). In ASEE, he is chair of the Two-Year College Division, and Vice-Chair/Community Colleges of the Pacific Southwest Section. He received the Outstanding Teaching Award for the ASEE/PSW Section in 2022.Dr. Jean Carlos Batista Abreu, Elizabethtown College Jean Batista Abreu earned his Ph.D. and M.S.E. at the Johns Hopkins University, M.S. at
. (2019). Using learning analytics to develop early- warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20. 4. Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review. IEEE Access 5. Seidel, E., & Kutieleh, S. (2017). Using predictive analytics to target and improve first year student attrition. The Australian Journal of Education, 61(2), 200–218 6. Yu, C.-C., & Wu, Y. (Leon). (2021). Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks. Sustainability, 13(22
. [Online]. Available: https://peer.asee.org/46523.[6] S. Coşkun, Y. Kayıkcı, and E. Gençay, "Adapting engineering education to industry 4.0 vision," Technologies, vol. 7, no. 1, p. 10, 2019.[7] A. Ustundag, E. Cevikcan, S. Cevik Onar, A. Ustundag, Ç. Kadaifci, and B. Oztaysi, "The changing role of engineering education in industry 4.0 era," Industry 4.0: managing the digital transformation, pp. 137-151, 2018.[8] B. Bordel Sánchez, R. P. Alcarria Garrido, and T. E. Robles Valladares, "Industry 4.0 paradigm on teaching and learning engineering," International Journal of Engineering Education, vol. 35, no. 4, pp. 1018-1036, 2019.[9] B. Y. Ekren and V. Kumar, "Engineering education towards industry 4.0
postings, programsshould consider regularly reviewing and updating their outcomes to better prepare their graduatesfor the skills expected for doctoral entry-level positions. Our findings complement existingresearch documenting that for early-career EngE researcher job postings exist for a variety ofroles at various institutional types [26], emphasizing the need for EngE programs to equipgraduates with a broad range of skills to prepare them for the job market.We demonstrated that embeddings and cosine similarity are valuable techniques to augmentqualitative data analysis. These methods are valuable in aggregate, rather than looking atindividual similarity scores and comparing them. For example, if program 1’s outcomes have asimilarity score of
. The challenge becomes stark; 20% of thedepartment faculty each year has little or no prior teaching experience, many have only earned aMasters’ degree and may not have the depth of knowledge needed to teach the undergraduatecourse they are assigned to teach, yet they are expected to perform at a very high level almostimmediately.In response to the challenge described above, a new faculty orientation program (NFO) wasdeveloped in the early 2000’s to train the new faculty. It is unclear from historical documents ifthe NFO and SOTL programs were started together. The goal of the program is to producefaculty prepared to teach and enthusiastic about the art and science of teaching. It is led by oneor more experienced senior faculty members each
Antonio, Texas: ASEE Conferences, Jun. 2012, p. 25.538.1-25.538.10. doi: 10.18260/1-2--21296.[13] A. Bandura, “Self-efficacy: Toward a unifying theory of behavioral change,” Adv. Behav. Res. Ther., vol. 1, no. 4, pp. 139–161, Jan. 1978, doi: 10.1016/0146-6402(78)90002-4.[14] M. Sherer, J. E. Maddux, B. Mercandante, S. Prentice-Dunn, B. Jacobs, and R. W. Rogers, “The Self- Efficacy Scale: Construction and Validation,” Psychol. Rep., vol. 51, no. 2, pp. 663–671, Oct. 1982, doi: 10.2466/pr0.1982.51.2.663.[15] R. E. Wood and E. A. Locke, “The relation of self-efficacy and grade goals to academic performance,” Educ. Psychol. Meas., vol. 47, no. 4, pp. 1013–1024, Dec. 1987, doi: 10.1177/0013164487474017.[16] S. Brown, “Student
act of being empathetic.Figure 1 [12], [13] shows a redesigned model of empathy in engineering that mixes theconstructs of empathy with the work that went into Walther, et al.’s Model of Empathy inEngineering [14]. The model breaks up the practice of empathy into three dimensions: empathyas a learnable skill, as an orientation of practice, and as a professional way of being [13], [14].Empathy as a learnable skill encapsulates the portions of empathy that can be taught. These skillsinclude affective sharing, self and other awareness, perspective taking, emotion regulation, andmode switching. Empathy as an orientation of practice in this model is described as “howengineers choose to utilize their various skill sets, and what course of thought
. Perrucci, “Minority status and the pursuit of professional careers: Women in science and engineering,” Soc. Forces, vol. 49, no. 2, pp. 245–259, 1970, doi: 10.2307/2576524.[6] B. M. Vetter, “Women scientists and engineers: Trends in participation,” Science, vol. 214, no. 4527, pp. 1313–1321, Dec. 1981, doi: 10.1126/science.7313688.[7] J. G. Robinson and J. S. McIlwee, “Women in engineering: A promise unfulfilled?,” Soc. Probl., vol. 36, no. 5, pp. 455–472, 1989, doi: 10.2307/3096812.[8] H. Blackburn, “The status of women in STEM in higher education: A review of the literature 2007–2017,” Sci. Technol. Libr., vol. 36, no. 3, pp. 235–273, Jul. 2017, doi: 10.1080/0194262X.2017.1371658.[9] “Empowering
will offer afaculty workshop on graduate-level writing support in STEM locally and broadcast via a virtualmeeting platform in the final year of the award. This workshop will feature the grant’s findingsand materials.7. Acknowledgements This work has been funded by a National Science Foundation Innovations in GraduateEducation grant, award number 2224967.8. References[1] S. Simpson, R. Clemens, D. R. Killingsworth, and J. D. Ford, “Creating a Culture of Communication: A Graduate-Level STEM Communication Fellows Program at a Science and Engineering University.,” Across the Disciplines, vol. 12, pp. 167–187, 2015.[2] D. Allison, L. Cooley, J. Lewkowicz, and D. Nunan, “Dissertation writing in action: The development of a
member in both the College of Engineering and Computer Science and the College of Science at FAU. Her research interests include understanding and designing curricular models to advance meaningful learning in complex domains and the role of socio-psychological factors in student academic success in STEM fields. She is currently Co-PI on several major NSF grant projects (e.g., NSF S STEM, NSF Cybercorps SFS, and NSF RAPID). She is PI on FAU’s Title III Hispanic Serving Institution (HSI) STEM Articulation grant project with two large, urban community colleges. ©American Society for Engineering Education, 2025 Advancing AI Education: Curriculum Development in Florida's Two-Year State
., 2018.[2] D. J. McCarthy, W. J. McFadden, and M. L. McGinnis, "5.3.2 Put Me in Coach; I'm Ready to Play!: A Discussion of an Evolving Curriculum in Systems Engineering," INCOSE International Symposium, vol. 13, no. 1, pp. 493-501, 2003, doi: https://doi.org/10.1002/j.2334-5837.2003.tb02635.x.[3] S. Goodlass, "5.2.1 Can Systems Engineering be taught at Undergraduate Level?," INCOSE International Symposium, vol. 14, no. 1, pp. 945-955, 2004, doi: https://doi.org/10.1002/j.2334-5837.2004.tb00547.x.[4] G. Muller and G. M. Bonnema, "Teaching Systems Engineering to Undergraduates; Experiences and Considerations," INCOSE International Symposium, vol. 23, no. 1, pp. 98-111, 2013, doi: https://doi.org
/CBO9780511803932.[12] National Postdoctoral Association, “NPA Core Competencies.” [Online]. Available: https://www.nationalpostdoc.org/page/CoreCompetencies[13] B. S. C. Kwan, H. Chan, and C. Lam, “Evaluating prior scholarship in literature reviews of research articles: A comparative study of practices in two research paradigms,” Engl. Specif. Purp., vol. 31, no. 3, pp. 188–201, Jul. 2012, doi: 10.1016/j.esp.2012.02.003.[14] S. A. Crossley, D. R. Russell, K. Kyle, and U. R mer, “Applying Natural Language Processing
teaching interventions.References:[1] F. Mosteller, “Broadening the Scope of Statistics and Statistical Education,” Am. Stat., vol. 42, no. 2, pp. 93–99, May 1988, doi: 10.1080/00031305.1988.10475536.[2] A. Carberry, S. Krause, C. Ankeny, and C. Waters, “‘Unmuddying’ course content using muddiest point reflections,” in 2013 IEEE Frontiers in Education Conference (FIE), Oct. 2013, pp. 937–942. doi: 10.1109/FIE.2013.6684966.[3] L. P. Snead, “The Effect of Using the Muddiest Point Technique in a Large General Chemistry Class,” Master of Science, Drexel University, Philadelphia, Pennsylvania, United States, 2016. doi: 10.17918/etd-7381.[4] M. J. Prince and R. M. Felder, “Inductive Teaching and Learning Methods: Definitions
research [3]. Industry-universitypartnership is a requirement of this model, which calls for concerted participation of industryexperts in the training of students through identification of industry-relevant research problems,co-advising about how to approach their practical solutions, and training for other non-technicalskills that are crucial for success in industry. An assessment of student demand and their experience with P3’s non-traditional features,support of university administration, and the challenges felt by interested faculty advisers duringits implementation at Lehigh University were presented previously [3, 4]. This paper completesP3 program’s assessment by analyzing the feedback provided by industry scientists who haveserved
Methods to Inform Criteria for Broadening Participation in Institutions and Organizationsintroduction2022’s Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act [1]mandates efforts to “ensure collaboration and coordination across federal agencies, the privatesector, and with state and local governments to facilitate timely and effective reviews of allfederally funded projects.” The 4b requirement includes “measures of the institution’s ability toattract and retain a diverse and nontraditional student population in the fields of science,technology, engineering, and mathematics, which may include the ability to attract women,minorities, and individuals with disabilities.” To retain the workforce enabled by this act
outcomes,” presented at the ASEE Annual Conference and Exposition, Conference Proceedings, 2012. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85029080569&partnerID=40&md5=e0961e206913bf6956d610a54a631bac[10*] I. J. Paredes, R. Li, S. Kwak, C. Woods, and D. R. Krusniak, “Creation of a Workshop Series on Inclusive Teaching and Design Practices for Engineering Undergraduate Teaching Assistants,” presented at the 2024 ASEE Annual Conference & Exposition, Jun. 2024. Accessed: Nov. 26, 2024. [Online]. Available: https://peer.asee.org/creation-of-a-workshop- series-on-inclusive-teaching-and-design-practices-for-engineering-undergraduate-teaching- assistants[11*] G. Zhang, “Support
quantitative approachesholds exciting possibilities to solve human-centered designs. Finally, interdisciplinarycollaboration between AI researchers, educators, and industry professionals can drive innovationand create new opportunities for integrating AI into industrial engineering education.References[1] Z. Lv, “Generative artificial intelligence in the metaverse era,” Jan. 01, 2023, KeAi Communications Co. doi: 10.1016/j.cogr.2023.06.001.[2] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Business and Information Systems Engineering, vol. 66, no. 1, pp. 111–126, Feb. 2024, doi: 10.1007/s12599-023-00834-7.[3] A. Borji, “Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and
understandable and separate parts. Each of these parts hasbeen designed with a specific purpose related to the system, and it looks at the system from aunique perspective. When put together, these three parts work hand in hand to describe thesystem completely. These parts of the same system also make it easy for students to divide theirthought processes into separate perspectives. These parts are (i) Project Concept Diagram/s, (ii)User Operational Flowcharts, and (iii) Functional Block Diagrams. Literature suggests that thesecharts/diagrams have a unique place in the System Engineering approach. However, in thispaper, a table is created with purpose, needed perspective, elements, format, and examples foreach part. Authors also point out connections
enrolled in the course have mirrored State University’s undergraduate population. The studentpopulations comprise significant income and racial gaps, in which 49% of students are racial and ethnicminorities and 36% of students are first-generation, meaning they are the first in their families to attendcollege (State University, 2023). A third of students interviewed were first generation college students,but due to anonymity concerns we offer this as a general distinction rather than identifying particularstudents in the Table 2. Institutional Review Board approval was obtained for this study (University IRBProtocol H23-0706).Table 2. Demographics of interviewed participants Label Major(s
VI. REFERENCES [1] Rose, S. J., Allen, D., Noble, D., & Clarke, J. A. (2017). Quantitative analysis of vocalizations of captive Sumatran tigers (Panthera tigris sumatrae). Method Accuracy False False Processing Bioacoustics, 27(1), 13–26. https://doi.org/10.1080/09524622.2016.1272003 Positives Negatives Time [2] X. Kong, D. Liu, A. Kathait, et al., "Behavioral-psychological motivations encoded in
was taken once a steady state was reached. For batch tests, thepressure was increased once the data for the desired pressure value had been taken. Heat Source I T1 I TH n n s s u T2 u l l a a Speciment
microcontroller-based EplayBot reconfigurable robotic kit inthree separate stages: a small-scale foosball table with a programmable ‘kicking leg’, anautonomous car, and a humanoid robot. We prepared a lesson plan and are in the process ofcreating an interactive video tutorial for use with the platform. We hope educational robotic kitslike this would be able to successfully spark the notion of creativity, skillfulness, and motivationin future roboticists.AcknowledgmentsThis work was supported in part by the Launch Grant and the School of Engineering Dean’sOffice at Wentworth Institute of Technology. The authors thank Doreen Cialdea and Tory Lamfor logistical support, and Nepali Class Boston where part of this work was tested.References[1] M. E. Karim, S
Fall2024 semester. Several faculty awarded minimal extra credit for completing the survey, othersposted the link as an announcement or assignment with no extra credit, and three facultydistributed the survey to all students in the program. Three hundred forty-two responses werereceived. Although it is possible that some respondents may have completed the survey multipletimes, the authors thought that would be unlikely given the timing, with the survey beingadministered in the last two weeks of the semester. Survey responses were gathered and analyzedas described below.Descriptor analysisThe survey asked respondents to “Think of a professor(s) who has or had a positive influence inyour life or education. Provide (3) words or phrases that describe
to our project and have been an active participant in both the lecture portions of the course, but also in our design work. I feel as if my contributions have represented a good understanding of design principles and different project management skills."References[1] J. Stommel, “How to ungrade,” in S. Blum, Ed., Ungrading: Why Rating Students Undermines Learning (and What to Do Instead), West Virginia University Press, 2020, pp. 25–41.[2] C. Pulfrey, C. Buchs, and F. Butera, “Why grades engender performance-avoidance goals: The mediating role of autonomous motivation,” Journal of Educational Psychology, vol. 103, no. 3, pp. 683–700, 2011. [Online]. Available: https://doi.org/10.1037