Khorbotly, “Machine Learning: An undergraduate Engineering Course”, 2022 ASEE 2. Introduction to Deep Learning: A First Course in Machine Learning 3. Niklas Lavesson. Learning machine learning: a case study. IEEE Transactions on Education, 53(4):672–676, 2010. 4. Maja J. Mataric “Robotics Education for All Ages”, Proceedings, AAAI Spring Symposium on Accessible, Hands-on AI and Robotics Education, Palo Alto, CA, Mar 22-24, 2004. 5. Sergeyev, A., Alaraje, N., “Partnership with industry to offer a professional certificate in robotics automation”, ASEE Annual Conference & Exposition (ASEE 2010), AC 2010-968 6. Sergeyev, A
the Institute of Networked Autonomous Systems at the University of Florida, Gainesville where he focused on the research and development of small, autonomous aerial and underwater vehicles, sensors and actuators. He received a BS and MS degree from the Aerospace Engineering Sciences department at CU Boulder in 2010 and 2011 respectively.Nathan Eric Whittenburg, University of Colorado Boulder Nathan Whittenburg is currently pursuing a degree in Aerospace Engineering with a minor in Computer Science at the University of Colorado Boulder. He serves as a Lab Assistant in the Aerospace department, where his responsibilities include employing Large Language Models and Natural Language Processing to enhance educational
TechnologyTom McKlinMr. Douglas Edwards, Georgia Institute of Technology Douglas Edwards is a K-12 Science Technology Engineering Mathematics (STEM) educational researcher with the Georgia Institute of Technology. His educational experience in the Atlanta area for the past twenty years includes high school mathematics teachiRafael A. Arce-NazarioJoseph Carroll-MirandaIsaris Rebeca Quinones Perez, University of Puerto Rico, Rio PiedrasLilliana Marrero-SolisJason Freeman, Georgia Institute of Technology Jason Freeman is an Associate Professor of Music at Georgia Tech. His artistic practice and scholarly research focus on using technology to engage diverse audiences in collaborative, experimental, and ac- cessible musical
their helpful feedback on earlier drafts of this paper. This material isbased upon work supported by the Strategic Instructional Innovations Program in the GraingerCollege of Engineering at the University of Illinois Urbana-Champaign.References [1] M. Hertz, “What do CS1 and CS2 mean? investigating differences in the early courses,” in Proceedings of the 41st ACM Technical Symposium on Computer Science Education, ser. SIGCSE ’10. New York, NY, USA: ACM, 2010, p. 199–203. [Online]. Available: https://doi.org/10.1145/1734263.1734335 [2] G. Marceau, K. Fisler, and S. Krishnamurthi, “Measuring the effectiveness of error messages designed for novice programmers,” ser. SIGCSE ’11. New York, NY, USA: Association for Computing Machinery
computer science course that teaches programming or requires programming as a prerequisite.” • Offered informal learning opportunities: Variables scq17a through scq17k code yes/no responses to various informal computer science learning opportunities like after-school clubs and summer camps.2.2.2 American Community SurveyThis study also uses public data from the American Community Survey (ACS), an annualsurvey conducted by the United States Census Bureau. We draw from the 2022: ACS1-Year Estimates Subject Tables which provide estimates presented as population countsand percentages on a variety of topics and aggregated by demographic and geographicfactors [16].Sample Design Each year the census selects approximately 3.4 million
Inclusive Delivery Method for Course Content in Higher EducationAuthors: Vijesh J. Bhute*, Ellen L. Player, and Deesha ChadhaAffiliation: Department of Chemical Engineering, Imperial College London, SouthKensington Campus, London, SW7 2AZ, UK*Corresponding Author: Dr. Vijesh J. BhuteAddress: Room 1M17A, ACE Extension Building, Department of Chemical Engineering,Imperial College London, South Kensington Campus, London, SW7 2AZ, UKEmail: v.bhute@imperial.ac.ukAbstractCourse books containing mathematical equations and images when delivered as physicalcopies, scanned ebooks or PDFs are not screen reader accessible. Current frameworks forclassification of learning resources assume ‘equal’ access and ‘uniform’ engagement
, The Behrend College. Dr. Ashour received the B.S. degree in Industrial Engineering/Manufacturing Engineering and the M.S. degree in Industrial Engineering from Jordan University of Science and Technology (JUST) in 2005 and 2007, respectively. He received his M.Eng. degree in Industrial Engineering/Human Factors and Ergonomics and a Ph.D. degree in Industrial Engineering and Operations Research from The Pennsylvania State University (PSU) in 2010 and 2012, respectively. Dr. Ashour was the inaugural recipient of William and Wendy Korb Early Career Professorship in Industrial Engineering in 2016. Dr. Ashour’s research areas include data-driven decision-making, modeling and simulation, data analytics, immersive
of Illinois Urbana-Champaign Dr. Tomasz Kozlowski is a Professor and Associate Head for the Undergraduate Programs in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois Urbana-Champaign. He received a Ph.D. in Nuclear Engineering from Purdue University in 2005. He has been an active member of the American Nuclear Society (since 1997) and the American Society for Engineering Education (since 2016). He is also a member of the OECD/NEA Nuclear Science Committee Working Party on Reactor Systems (NSC/WPRS) (since 2010) and is serving on the Scientific Board and Technical Committee of the OECD/NEA Benchmark for Uncertainty Analysis in Best-Estimate Modelling for Design
science in the digital age. Running on Empty, 2010. URL https://api.semanticscholar.org/CorpusID:220884923. [8] E. B. Witherspoon, C. D. Schunn, R. M. Higashi, and R. Shoop. Attending to structural programming features predicts differences in learning and motivation. Journal of Computer Assisted Learning, 34(2):115–128, 2018. doi: 10.1111/jcal.12219. URL https://doi.org/10.1111/jcal.12219. [9] S. Marwan, G. Gao, S. Fisk, T. W. Price, and T. Barnes. Adaptive immediate feedback can improve novice programming engagement and intention to persist in computer science. In Proceedings of the 2020 ACM Conference on International Computing Education Research, pages 194–203. ACM, August 2020.[10] Ismaila Temitayo Sanusi and Sunday