. The ultimate goal of her research is to facilitate the majority of students in classrooms in developing images of science consistent with current practice, and in understanding what science is and what science is not, and the relevancy of science to society.Aida Alibek Aida Alibek is a PhD student in Mathematics Education at the University of Georgia. Her research interests are in undergraduate mathematics education, especially entry-level math courses for STEM majors, learning environments and students' learning experiences. Before getting into education research Aida Alibek did mathematics research in the area of mathematical logic and model theory. She got her Master of Science in mathematics from the Al-Farabi
mastered. The similarity in the milestones that define acquisition is essential, as this can be a useful metric to measure the proficiency of the learner. Programming language learning stages Natural language learning stages Acquiring: the learner is focusing on Silent: the learner spends time learning ba- declarative knowledge to understand sic vocabulary and getting accustomed to the the basic concepts of the language language as a whole. Consolidating: a leap between declar- Early production: the learner has some vo- ative and procedural knowledge is cabulary, and it starts to form basic gram- done, so the learner establishes their matical structures. knowledge Tuning
discrete convolution fromthe perspective of the input signal to the students encountering it for the first time. It was alsopossible to teach this topic in a shorter time while enabling the students to master its concepts.The approach that was adapted and implemented by the author is as given below. The softwaretool that was designed and implemented by the author is described and discussed thereafter. Discrete ConvolutionDiscrete Convolution as a Mathematical OperationOne of the most basic mathematical operations in any field is the operation of addition. Theoperation of addition (denoted by an operation symbol +) is a mathematical operation that takesany two numbers (a and b) and produces a third number (c = a
educational reformer, with a focus on experiential and situated learning strategies. He is a graduate of Fordham’s master’s program in Educational Leadership, where he studied change management and leadership theories. He is also a skilled administrator and system builder, and is adept at using technology to automate and streamline workflow.Dr. Raymond K.F. Lam, City University of New York, Queensborough Community College Assistant professor of Engineering Technology Department of Queensborough Community College in Bayside, New York. He holds a Doctor of Science degree in Materials Science & Engineering from Massachusetts Institute of Technology, and a Master of Scienc ©American Society for
results will 10be applied to further develop and enhance program education; prove that the qualitativeresults of these assessment schemes can demonstrate the school’s task and teaching objective.Each program is required to constantly conduct self-evaluation of teaching objective andprocess via appropriate and normative assessment tools which need to be refined andimproved. Quality assurance in engineering education not only requires teachers to put moreeffort in teaching, but also master the specialized knowledge and skills needed for continuousimprovement. Engineering teachers use diversified assessment methods of program includingconventional assessment methods of learning outcomes, develop new
series of NSF REU site grants and supplements.Student participants were recruited by and supported through a Materials Research Science andEngineering Center (MRSEC) which itself has been a partner in two NSF-PREM programs since2015. Each summer, students from both PREM institutions and those who apply directly to theREU from institutions across the country are invited to participate in the MRSEC-REU. Thetypical student cohort comprises a diverse range of undergraduate and masters level science andengineering students, all with a stated interest in materials research. The diversity of theparticipant population is by design, due to both the nature of the NSF-REU solicitation, whichrecommends focusing recruitment efforts on students from
]. With this behavior, those with high self-efficacy have an increased likelihood of success [3].Studies have shown that self-efficacy is a positive predictor for a person’s ability to achieve theirgoals [3-8]. In fact, research has found that these efficacy beliefs can better predict achievementof future accomplishments than examining the previous accomplishments alone [9, 10]. Whentrying to help students learn and develop, it is important to consider their beliefs about their skillsin addition to their ability to master the skill. Students’ self-efficacy is highly related tomotivation and achievement, and is a predictor for academic success [3, 6-8, 11-13].Within engineering specifically, engineering self-efficacy has been found to be
Biomedical Engi- neering.Mr. Francisco Cima, Old Dominion University Francisco Cima is a Ph.D. student in Engineering Management and Systems Engineering at Old Dominion University. He obtained his Masters in Business Planning and Regional Development from the Techno- logical Institute of Merida. His areas of interest are innovation practices in organizations, communication technology in organizations, knowledge management, and team processes.Dr. Orlando M Ayala, Old Dominion University Dr. Ayala received his BS in Mechanical Engineering with honors (Cum Laude) from Universidad de Ori- ente (Venezuela) in 1995, MS in Mechanical Engineering in 2001 and Ph.D. in Mechanical Engineering in 2005, both from University of
centrality. Arizona State University and Virginia Tech followedPurdue in this ranking. After linking our network with the Carnegie classification data, we foundthat the top universities based on degree and betweenness centrality are most likely to bedoctoral or masters universities with high research activity. The collaboration network density(the number of existing collaborations relative to all possible numbers) is low and equals 0.001.Dense networks are more important for control than for information.Table 1: Top institutions with Carnegie classification based on degree and betweennesscentrality. (15: Doctoral/Research Universities—Extensive, 16: Doctoral/ResearchUniversities—Intensive, 18:Master's L: Master's Colleges and Universities (larger
courses, where she provided extra help to any student in need. This experience also allowed her to assist the professor in the implementation of modules within the class, and learn how to interact with various students.Dr. Devina Jaiswal, Western New England University Dr.Devina Jaiswal completed Masters of Science degree in Biomedical Engineering from Pennsylvania State University in 2010. She completed her Ph.D. from University of Connecticut in 2017 where she worked on creating nano and micro devices that could interact with micro-tissue and cells. Her research interest lies in fabrication of micro-electronic devices that can be used to understand biological patterns and apply them to the field of tissue engineering
Academy. He was commissioned a General Unrestricted Line Ensign through NROTC at the University of Notre Dame after receiving his Bachelors of Science in Electrical Engineering. He achieved a Masters of Science in Electrical Engineer- ing from the University of Illinois at Urbana-Champaign. He has completed the Bettis Reactor Engineer- ing School and was granted an Associates Degree in Italian from the Defense Language Institute Foreign Language Center. He has worked as an Engineer, Instrumentation and Control Division, NAVSEA 08K (Office of Naval Nuclear Propulsion), Crystal City, VA; as Assistant Operations Officer and Alfa Company Commander with U.S. Naval Mobile Construction Battalion FIVE, Port Hueneme, CA
on the Advisory Board for the Journal of Engineering Education. He was selected as a Fellow of ASEE in 2008 and of ASME in 2012. He holds a B.S. in Nuclear Engineering from Penn State, an M.Eng. in Mechanical Engineering from RPI, and a Ph.D. in Mechanical and Aerospace Engineering from Princeton.Ms. Cathy J. Holsing, Pennsylvania State University Cathy Holsing is the Director of the Office for Digital Learning in the College of Engineering at Penn State which promotes innovative approaches to engineering education on campus and online. She has over 20 years of experience in the field of online and digital education, and holds a Masters in Education Degree from Penn State. American
Engineering focused on Mechatronics, Robotics and Automation. She went through engineer- ing pathways herself, completing master electrician degree when completing Technical School in Uzice, Serbia, focusing on pre-engineering program on high power voltage systems and maintenance of electro- mechanical systems. Her research is focuses on engineering pathways, career and technical education, digital thread, cyber physical systems, mechatronics, digital manufacturing, broadening participation, and engineering education. She is a Director of Mechatronics and Digital Manufacturing Lab at ODU and a lead of Area of Specialization Mechatronics Systems Design. She worked as a Visiting Researcher at Commonwealth Center for Advanced
Construction Management at the Kennesaw State University (KSU). He earned both his Master in Architectural Engineering and Ph.D. in Civil Engineering from the University of Napoli ”Federico II”, Italy. Before joining KSU in the Fall 2016, he worked as postdoctoral associate at both the University of Miami and Georgia Institute of Tech- nology. He has conducted research across different disciplines with particular focus on novel construction materials and structural performance evaluation. His research activity focuses on: the advancement of high strength/high performance cementitious materials and steel composite (SC) structures; the develop- ment of advanced composites-based systems for repair and strengthening existing
institutions. She previously managed the Enterprise Performance Excellence center in a healthcare system. Dr. Furterer received her Ph.D. in Industrial Engineering with a specialization in Quality Engineering from the University of Central Florida in 2004. She received an MBA from Xavier University, and a Bachelor and Master of Science in Industrial and Systems Engineering from The Ohio State University. Dr. Furterer has over 25 years of experience in business process and quality improvements. She is an ASQ Certified Six Sigma Black Belt, an ASQ Certified Quality Engineer, an ASQ Certified Manager of Quality/Organizational Excellence, an ASQ fellow, and a certified Master Black Belt. Dr. Furterer is the Vice Chair of
= 0.007, respectively), and (2) “Agree” responses to Learn5(p-value = 0.007). No significant trends were observed in Learn4.Figure 3. Students' Perceptions of Online Classes: Learning3.4. Students' Self-EfficacyThe final set of questions explored students' self-efficacy. Five questions were included in thissection: To what extent do you agree or disagree with the following statements about yourself: SE1 – I can master the content in the engineering-related courses I am taking this semester. SE2 – I can master the content in even the most challenging engineering course. SE3 – I can do an excellent job on engineering-related problems and tasks assigned this semester. SE4 – I can learn the content taught in my engineering
for a 70 million-dollar National Institutes of Health funded center based at the University of Washington. She has experience assessing student learning and other outcomes for K-12 and higher education populations, including both two and four- year college environments all over the country, and ensuring programs have strong evaluation plans and the necessary data for evidence based decision-making.Mr. Germain Degardin, New Mexico State University Germain graduated from New Mexico State University with a Bachelor in Economics, a Master in Busi- ness and Administration, a Master in Curriculum and Instruction, and a secondary education teaching license. Germain currently works for the Southwest Outreach Academic
, University of Florida Lilianny Virguez is a Instructional Assistant Professor at the Engineering Education Department at Uni- versity of Florida. She holds a Masters’ degree in Management Systems Engineering and a Ph.D. in Engineering Education from Virginia Tech. She has work experience in telecommunications engineer- ing and teaches undergraduate engineering courses such as engineering design and elements of electrical engineering. Her research interests include the intersection of core non-cognitive skills and engineering students’ success. American c Society for Engineering Education, 2021 Combining a Virtual Tool and Physical Kit for Teaching Sensors
) 9.09% (1) 11 .89 3.45 academic work I really wanted this course 0% (0) 0% (0) 54.55% (6) 18.18% (2) 27.27% (3) 11 .86 3.73 regardless of who taught it When this course began, I believed I could master its 0% (0) 0% (0) 54.55% (6) 27.27% (3) 18.18% (2) 11 .77 3.64 content My background prepared me well for this course’s 0% (0) 0% (0) 9.09% (1) 81.82% (9) 9.09% (1) 11 .43 4.00 requirementsThe data in Table 8 indicate that students felt the amount of work in the course was average, butmost students felt the difficulty level was above average. There was a mixture of perceived effortlevels
Paper ID #34413Cultural Dimensions in Academic Disciplines, a Comparison BetweenEcuador and the United States of AmericaDr. Homero Murzi, Virginia Polytechnic Institute and State University Homero Murzi is an Assistant Professor in the Department of Engineering Education at Virginia Tech with honorary appointments at the University of Queensland (Australia) and University of Los Andes (Venezuela). He holds degrees in Industrial Engineering (BS, MS), Master of Business Administration (MBA) and in Engineering Education (PhD). Homero has 15 years of international experience working in industry and academia. His research
Paper ID #34066Development of a Structural Loadings Course for ArchitecturalEngineering StudentsProf. Christina McCoy P.E., Oklahoma State University Professor McCoy is a licensed Structural Engineer and Architect. She holds a Bachelor Degree in Archi- tectural Engineering and a Bachelor in Architecture from Oklahoma State University. She holds a Masters of Science in Architecture from the University of Cincinnati and Masters of Civil Engineering (Structural Emphasis) from the University of Kansas. She worked in the structural engineering profession for 10 years before joining the full-time faculty at Oklahoma State
Paper ID #33730Enhancing Preservice Teachers’ Intention to Integrate Engineeringthrough a Multi-Disciplinary Partnership (Evaluation)Mr. Francisco Cima, Old Dominion University Francisco Cima is a Ph.D. student in Engineering Management and Systems Engineering at Old Dominion University. He obtained his Masters in Business Planning and Regional Development from the Techno- logical Institute of Merida. His areas of interest are innovation practices in organizations, information and communication technology in organizations, knowledge management, and team processesDr. Pilar Pazos, Old Dominion University Pilar Pazos is an
7, allowing the student to fully master material they had slight issueswith. Then it went to the material where the student had deeper issues, eventually ending thegame once the student had mastered all 7 sections. Step Grade for Each Section Section Decision 1 1, 2, 2, 0, 0, 0, 1 1 2 2, 2, 2, 0, 0, 0, 1 7 3 2, 2, 2, 0, 0, 0, 2 5 4 2, 2, 2, 0, 2, 0, 2 6 5 2, 2, 2, 0, 2, 2, 2 4 6 2, 2, 2, 2, 2, 2, 2 EndTable 1: Example path chosen by the reinforcement learning system with the student’s grade
]emphasize the importance of course organization and presentation and suggest that online coursesmust have a clear and consistent structure which is vital to student success. As often observed,face-to-face courses transferred for online instructions are seldom effective because of the lack ofsuitability to online collaborative environments and the tendency of learning deficiency fromstudents’ disengagement. Online instructors would need to master the learning managementsystems (LMS) and design effective courses that communicate to the learners what is importantand how they will focus, select, organize, integrate, and apply content as they learn [21]. Instructorsmust plan, organize, and structure course components [22]. Online courses must not only
In someinstitutions, this service involvement has fueled the creation of courses and programs thatoffer Learning Through Service (LTS) which seems to attract a wider range of students toengineering. A growing body of evidence advocates that LTS may provide significantadvantages to engineering students, but studies to date are quite limited.11-15 Asuniversities play catch-up to these trends, a fundamental question remains unexplored:What motivates engineering students to be engaged in service?2. ObjectivesThis paper presents findings to the above question of student motivation from two LTSprograms at Michigan Technological University: (1) iDesign, an international senior-level capstone design program, and (2) Peace Corp Master s International
courses and of the program itself. Besides evidence that objectives andoutcomes are being met it also requires documentation of the process of continuous qualityimprovement. This entails an endless cycle of assessment and reassessment at both the courseand program level. Mastering this data streaming process and automating the tasks involved in the use of such dataare crucial to the survival of programs and the maintenance of the sanity of those involved. Thispaper presents one method that we have devised to be particularly easy to employ and a powerfultool for taking control of these tasks. Proceedings of the 2011 North Midwest Section Conference I. Introduction The ABET accreditation process is familiar to most US
Florida. She holds a Masters’ degree in Management Systems Engineering and a Ph.D. in Engineering Education from Virginia Tech. She has work experience in telecommunications engineer- ing and teaches undergraduate engineering courses such as engineering design and elements of electrical engineering. Her research interests include the intersection of core non-cognitive skills and engineering students’ success.Dr. Debarati Basu, University of North Carolina at Charlotte Dr. Debarati Basu is an Assistant Teaching Professor in the Department of Software and Information Sys- tems in the College of Computing and Informatics at the University of North Carolina at Charlotte. She earned her Ph.D. in Engineering Education from
Paper ID #33040A Curriculum on Naval Science & Technology for a Midwestern UniversityDr. James Buchholz, University of Iowa James Buchholz is an Associate Professor of Mechanical Engineering at the University of Iowa. He received the Bachelors and Masters degrees in Mechanical Engineering from the University of Alberta, and the Ph.D. degree in Mechanical and Aerospace Engineering from Princeton University. He teaches courses in fluid mechanics and conducts research in unsteady aerodynamics and hydrodynamics.Dr. Jae-Eun Russell, University of Iowa Dr. Russell serves as the Director of Research & Analytics Office of
: Learning objectives in ME 2010: Statics course. Students need to master C-level concepts beforecontinuing onto subsequent levels. Learning Objectives C1: Solve 2D equilibrium problems involving dry friction. C2: Solve 2D equilibrium problems involving frames and machines under point C-level loads and basic (uniform, triangular) distributed loadings. C3: Solve for internal shear / normal forces and internal bending moment of cantilever and simply-supported beams exposed to external point loads. B1: Solve 2D equilibrium problems involving basic machine elements (wedges, screws, rolling resistance). B2: Solve for centroids of
%respectively,and foreign-funded enterprises accounts for 2.42%. Figure 3 Industries of samplesThe basic characteristics of samples are shown in Table 2. As we can see, men accountfor 73.95% and women account for 26.41%. In terms of academic qualifications,undergraduates account for 35.04%, masters account for 41.6%, PhD accounts for14.81%, and junior colleges and others account for 8.55%. As for the time to learn TRIZ,41.93% respondents have learned TRIZ less than 5 years. In short, according to thebasic characteristics, the samples basically meet the demands for random sampling. Table 2 Basic characteristics of samples Variable Category Samples Proportion