Conference Session

ERM Technical Session 14: Thinking about the Engineering Curriculum

Collection

2019 ASEE Annual Conference & Exposition

Authors

Atsushi Akera, Rensselaer Polytechnic Institute; Sarah Appelhans, University at Albany; Alan Cheville, Bucknell University; Thomas De Pree, Rensselaer Polytechnic Institute; Soheil Fatehiboroujeni, Indiana-Purdue University; Jennifer Karlin, Minnesota State University, Mankato; Donna M. Riley, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

consider quantitative accreditationstandards. In an era when a majority of engineering schools did not yet have extensive offeringsin engineering science, quantitative standards were the quickest way of getting U.S. engineeringschools to accommodate the perceived curricular needs of the Cold War era [23].EC 2000’s OriginsThe Cold War consensus favoring the engineering sciences generally held into the 1970s.Nevertheless, as concerns about U.S. manufacturing productivity and national competitivenessgrew during the 1970s and 1980s, there emerged a sense that the U.S. was winning one front ofthe Cold War, only to be falling behind on the other. While not all U.S. colleges and universitiesembraced the engineering sciences as strongly as others, there

Conference Session

System 1 in Engineering Education and Research

Collection

2018 ASEE Annual Conference & Exposition

Authors

Elif Miskioglu, Bucknell University; Kaela M Martin, Embry-Riddle Aeronautical University, Prescott

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

seek to gather data from large sample sizes that provide strong evidence for possible trends.We recognize that our current methodology is not feasible for a larger-scale study implementedby course instructors nationwide, as it requires work on the part of the instructor. We aredeveloping standardized problems and an accompanying questionnaire that can be easilyintegrated as a homework problem in the appropriate course(s). We will use online datacollection, and point-of-collection consent, to minimize any work for the course instructor.To further support standardization, we will not be using previous simulation tools such asGMAT but rather are developing simulation tools that can be run on software commonly used byengineering students, such as

Conference Session

Building Communities for Engineering Education Research

Collection

2006 Annual Conference & Exposition

Authors

Robin Adams, Purdue University; Philip Bell, University of Washington; Cheryl Allendoerfer, University of Washington; Helen Chen, Stanford University; Larry Leifer, Stanford University; Lorraine Fleming, Howard University; Bayta Maring, University of Washington; Dawn Williams, Howard University

Tagged Divisions

Educational Research and Methods

2006-1740: A MODEL FOR BUILDING AND SUSTAINING COMMUNITIES OFENGINEERING EDUCATION RESEARCH SCHOLARSRobin Adams, Purdue University Robin S. Adams is an Assistant Professor in the Department of Engineering Education at Purdue University. She is also leads the Institute for Scholarship on Engineering Education (ISEE) as part of the Center for the Advancement of Engineering Education (CAEE). Dr. Adams received her PhD in Education, Leadership and Policy Studies from the University of Washington, an MS in Materials Science and Engineering from the University of Washington, and a BS in Mechanical Engineering from California Polytechnic State University, San Luis Obispo. Dr. Adams' research is

Conference Session

Problem-based and Challenge-based Learning

Collection

2012 ASEE Annual Conference & Exposition

Authors

Angela van Barneveld, Purdue University; Johannes Strobel, Purdue University, West Lafayette; Greg Light, Northwestern University

Tagged Divisions

Educational Research and Methods

deep learning in students and; an integrative rather than anadditive approach to the inclusion of new content or to meet accreditation requirements. Page 25.1272.16 [First Authors Last Name] Page 16 ReferencesABET. (2009). Criteria for Accrediting Engineering Programs. Retrieved from http://www.abet.org/Linked%20Documents- UPDATE/Criteria%20and%20PP/E001%2009-10%20EAC%20Criteria%2012- 01-08.pdf.Ahlfeldt, S., Mehta, S., & Sellnow, T. (2005). Measurement and analysis of student engagement in university

Conference Session

Medley of Undergraduate Programming and Pedagogies

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Calvin Sophistus King, MCET; Venugopalan Kovaichelvan, TVS Institute for Quality and Leadership

Tagged Divisions

Educational Research and Methods

education. Rigorous implementation of stagegate process with active involvement of industry persons in the design, development,deployment and evaluation of a freshmen’s course titled “Introduction to Engineering” hasshown the extent of course refinement and improvement possibilities in learning outcomes ofstudents.References:[1] https://indicators.report/targets/4-3/ accessed 6th March 2021[2] https://facilities.aicte-india.org/dashboard/pages/angulardashboard.php#!/graphs accessed6th March 2021[3] https://facilities.aicte-india.org/dashboard/pages/aicte_nba.php accessed 6th March 2021[4] National Board of Accreditation, Annual Report 2018-19, NBA New Delhi, April 2019.[5] Ashok, S. S., Rama, K. C., Sanjay, A. and Upendra, P., “Examination Policy

Conference Session

Approaches to Curriculum and Policy

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Hadi Ali, Arizona State University, Polytechnic campus

Tagged Divisions

Educational Research and Methods

target letter in a nonsearch task. Perception & Psychophysics, 16, 143-149.Eriksen, C. W., & Hoffman, J. E. (1973). The extent of processing of noise elements during selective encoding from visual displays. Perception & Psychophysics, 14(1), 155-160.Fox, E., Russo, R., Bowles, R., & Dutton, K. (2001). Do threatening stimuli draw or hold visual attention in subclinical anxiety? Journal of Experimental Psychology: General, 130, 681–700.Gazzaniga, M. S. (1987). Perceptual and attentional processes following callosal section in humans. Neuropsychologia, 25, 119-133.Gharajedaghi, J., & Ackoff, R. (1985). Toward Systemic Education of Systems Scientists. Systems Research, 2(1), 21-27.Hastings, D

Conference Session

Classroom Practice I: Active and Collaborative Learning

Collection

2016 ASEE Annual Conference & Exposition

Authors

Stephanie Butler Velegol, Pennsylvania State University, University Park; Sarah E. Zappe, Pennsylvania State University, University Park

Tagged Divisions

Educational Research and Methods

the flipped course in this study, the due dates for allhomework and the dates for all quizzes were established at the beginning of the semester. When the sameinstructor taught using the lecture-based approach, the pace was not as predictable. This may lead toconfusion about what is expected in the course. It is the author’s (and Instructor 1’s) opinion that thisincrease in organization of the course is one of the main benefits of the flipped classroom.Finally, we found that, given the same instructor, the averages are higher for the Involvement subscale(2.85 vs. 2.44). Involvement is an indicator of active learning as it measure how involved the students arein their own learning. This is confirmation that a flipped classroom will increase

Conference Session

Assessing Social Responsibility & Sustainability

Collection

2015 ASEE Annual Conference & Exposition

Authors

Mark H Minster, Rose-Hulman Institute of Technology; Richard A House, Rose-Hulman Institute of Technology; Patricia Brackin P.E., Rose-Hulman Institute of Technology; Corey M. Taylor, Rose-Hulman Institute of Technology

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods, Engineering Ethics, Liberal Education/Engineering & Society

Futurity: Essays on Environmental Sustainability and Social Justice, A. Dobson, Ed., Oxford: Oxford UP, 1999, pp. 21-45..11. H. Farley and Z. Smith, Sustainability: If It's Everything, Is It Nothing?, Abingdon: Routledge, 2014.12. R. Norgaard, "Transdisciplinary Shared Learning," in Sustainability on Campus: Stories and Strategies for Change, Barlett, P. and G. Chase, Eds., Cambridge, MA, MIT Press, 2004, pp. 107-20.13. P. Barlett and G. Chase, Sustainability on Campus: Stories and Strategies for Change, Cambridge, MA: MIT Press, 2004.14. P. Barlett and G. Chase, Sustainability in Higher Education, Cambridge, MA: MIT Press, 2013.15. P. Jones, D. Selby and S. Sterling, Sustainability Education: Perspectives and

Conference Session

Professional Skills and the Workplace

Collection

2008 Annual Conference & Exposition

Authors

Betty Harper, Pennsylvania State University, University Park; Patrick Terenzini, Pennsylvania State University

Tagged Divisions

Educational Research and Methods

students spend in these activities. Precisely whythis relation exists remains to be explored. It may be that these faculty members encourageparticipation more than their non-industry counterparts, or it may be that programs with a largeproportion of such faculty tend to offer more opportunities for students to engage in suchactivities. While the reason(s) for this relationship deserves further attention, the implication Page 13.1223.9remains. Faculty members' industry experience can positively effect student participation indesign competitions and activities and should be a consideration in the recruitment of newfaculty. Contrary to our

Conference Session

Assessment

Collection

2008 Annual Conference & Exposition

Authors

Michael Collura, University of New Haven; Samuel Daniels, University of New Haven

Tagged Divisions

Educational Research and Methods

) s = standard deviationEffect size is generally used in studies which employ a well-defined control group forcomparison with the experimental group. In such cases, the standard deviation of the controlgroup is used. Boud’s recommendation for studies which compare student to instructorassessment is to use the standard deviation of the instructors assessment.This statistic is useful in determining how well the students’ self-assessment reflects theperformance of the class as a whole. A value of zero indicates perfect agreement, while apositive value indicates that the students overestimate their proficiency. Boud suggests thatvalues of 0.2 are considered small, values of 0.8 are considered large.A correlation coefficient can be used to

Conference Session

Knowing Our Students, Faculty, and Profession

Collection

2009 Annual Conference & Exposition

Authors

Ashlyn Munson, Colorado School of Mines; Barbara Moskal, Colorado School of Mines; Alka Harriger, Purdue University

Tagged Divisions

Educational Research and Methods

significant accomplishments,the students still wanted younger speakers. This may be accomplished by including collegestudents who are majoring in IT as part of the summer workshop, linking high school and college Page 14.1104.10with a career in IT. A similar approach is likely to be appropriate to other high schoolinterventions which share similar goals. Even without these changes, the SPIRIT workshopsappear to be accomplishing their goals with respect to the participating student groups.Bibliography1. Patterson, D. A. (2005). “Restoring the popularity of computer science”. Communication of the ACM, Vol. 48(9),pp. 25-28.2. Reges, S. (2006). “Back to

Conference Session

Predicting Student Success

Collection

2017 ASEE Annual Conference & Exposition

Authors

Seyedhamed Sadati, Missouri University of Science & Technology; Nicolas Ali Libre, Missouri University of Science & Technology

Tagged Divisions

Educational Research and Methods

of semester was incorporated for development of a prediction tool. Linearregression analysis was incorporated to establish correlations between the early semesterperformance and the end-of-semester score as suggested in Equation 4. 𝑛 𝐹𝑖𝑛𝑎𝑙 𝑆𝑐𝑜𝑟𝑒 = 𝛼0 + ∑ 𝛼𝑖 𝐻𝑊𝑖 + 𝛽𝑀𝑇1 + 𝛾𝐵𝑄 (4) 𝑖=1Where 𝛼𝑖 , β and γ are the regression coefficients, 𝐻𝑊𝑖 s are the scores corresponding to each ofthe homework assignments, 𝑀𝑇1 is the score obtained from the first mid-term exam, and 𝐵𝑄 is thescore obtained from in-class performance, i.e. bonus questions.Initially, 80% of the available data points were randomly selected and used for

Conference Session

Instrument Development

Collection

2017 ASEE Annual Conference & Exposition

Authors

Timeri K. Tolnay, Colorado School of Mines; Sam Spiegel, Colorado School of Mines; Jennifer Zoltners Sherer, University of Pittsburgh

Tagged Divisions

Educational Research and Methods

2015. Golden, CO: Colorado School of Mines.Eddy, S. L., Converse, M., & Wenderoth, M. P. (2015). PORTAAL: A Classroom Observation Tool Assessing Evidence-Based Teaching Practices for Active Learning in Large Science, Technology, Engineering, and Mathematics Classes. Cell Biology Education, 14(2), ar23-ar23. doi:10.1187/cbe.14-06-0095Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. doi:10.1073/pnas.1319030111GORP Tool: UC Davis Center for Educational Effectiveness. (n.d.). Retrieved

Conference Session

Cognitive Skills Development

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Hannah Smith, Queen's University; Brian M. Frank, Queen's University

Tagged Divisions

Educational Research and Methods

Education, Champaign, IL: National Institute for Learning Outcomes Assessment, 2012, pp. 24–30.[3] International Engineering Alliance, “Celebrating international engineering education standards and recognition,” Washington, 2014.[4] S. Borwein, “The great skills divide: A review of the literature,” Toronto, Ontario, 2014.[5] National Association of Colleges and Employers, “Career Readiness Competencies: Employer Survey Results,” 2014. [Online]. Available: https://www.naceweb.org/knowledge/career-readiness-employer-survey- results.aspx?terms=employer survey skills. [Accessed: 07-Aug-2019].[6] J. Trevelyan, “Reconstructing engineering from practice,” Eng. Stud., vol. 2, no. 3, pp. 175–195, 2010.[7

Conference Session

ERM Technical Session 9: Persistence and Retention

Collection

2019 ASEE Annual Conference & Exposition

Authors

Johnny C. Woods, Jr., Virginia Tech; Tahsin Mahmud Chowdhury, Virginia Tech; Homero Murzi, Virginia Tech; Michelle Soledad, Virginia Tech, Ateneo de Davao University; David B. Knight, Virginia Tech; Jacob R. Grohs, Virginia Tech; Scott W. Case, Virginia Tech; Natasha Smith, Virginia Tech

Tagged Divisions

Educational Research and Methods

teaching, learning, and retention of first-year students,” Journal of Faculty Development, vol. 21, no. 1, pp. 5–21. 2007[5] E. Bettinger, C. Doss, S. Loeb, A. Rogers, and E. Taylor, “The effects of class size in online college courses: Experimental evidence,” Economics of Education Review, vol. 58, pp. 68–85, Jun. 2017.[6] R. Zaurin, “Preparing the Engineering Student for Success with IDEAS: A Second Year Experiential Learning Activity for Large-size Classes,” in 2015 IEEE Frontiers in Education Conference (FIE), Camino Real El Paso, El Paso, TX, USA, 2015 p. 21.[7] S. Huang and E. Pierce, “The impact of a peer learning strategy on student academic performance in a fundamental engineering course,” in 2015

Conference Session

Classroom Practice III: Student-Centered Instruction

Collection

2016 ASEE Annual Conference & Exposition

Authors

Evelyn Hanna Laffey, Princeton University; Maria E. Garlock, Princeton University; Aatish Bhatia, Princeton University

Tagged Divisions

Educational Research and Methods

Foundation under Grant No. NSF 14-32426,14-31717, and 14-31609. Any opinions, findings, conclusions or recommendations expressed in the materialsprovided are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  understand and assess the students’ STEM affect. Each component of the theoretical frameworkis described in the following paragraphs.STEM-literacy for the 21st Century is multifaceted and includes content knowledge and habits ofmind5. For the purpose of this study, we refer to STEM-literacy as the union of students’understanding of STEM content and their ability to reason critically about structures using civilengineering principles. The STEM content relevant to the Structures course was

Conference Session

Broadening Participation in Engineering

Collection

2015 ASEE Annual Conference & Exposition

Authors

Coleen Carrigan, Cal Poly San Luis Obispo; Eve A. Riskin, University of Washington; Jim L Borgford-Parnell, University of Washington; Priti N Mody-Pan, University of Washington; Dawn Wiggin, University of Washington; Sonya Cunningham, University of Washington

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

interview participants. This work was supported by aNational Science Foundation Research Initiation Grant in Engineering Education (RIGEE) grant.Any opinion, finding, and conclusion or recommendations expressed in this material are those ofthe author(s) and do not necessarily reflect the views of the National Science Foundation.References 1. Wyner, J. S., Bridgeland, J. M., & DiIulio Jr, J. J. (2007). Achievement Trap: How America is Failing Millions of High-Achieving Students from Lower-Income Families. Jack Kent Cook Foundation and Civic Enterprises. 2. Strutz, M., Orr, M., and Ohland, M. (2012). Low Socioeconomic Status Individual: An Invisible Minority in Engineering. In Engineering and Social Justice: In the University

Conference Session

Works in Progress: Classroom Practice

Collection

2016 ASEE Annual Conference & Exposition

Authors

M. Austin Creasy, Purdue University (Statewide Technology)

Tagged Divisions

Educational Research and Methods

. The actions that a student takes ina learning cycle are not normally provided for assessment in a traditional setting, but theprocedures explained here allows those actions to be recorded.References1. Butler, D. L., and Winne, P. H. (1995) Feedback and Self-Regulated Learning: A Theoretical Synthesis, Review of Educational Research 65, 245-281.2. Shute, V. J. (2008) Focus on Formative Feedback, Review of Educational Research 78, 153-189.3. Nicol, D. J., and Macfarlane‐Dick, D. (2006) Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice, Studies in higher education 31, 199-218.4. Thurlings, M., Vermeulen, M., Bastiaens, T., and Stijnen, S. (2013) Understanding

Conference Session

Motivation, Attitudes, and Beliefs

Collection

2018 ASEE Annual Conference & Exposition

Authors

Allison Adams, Kansas State University; Amy Rachel Betz, Kansas State University; Emily Dringenberg, Ohio State University

Tagged Divisions

Educational Research and Methods

experience may lead them to share or disclose information they maynot have, potentially leading the interview process. The process of developing and validating aninterview protocol has proved to be an excellent opportunity to introduce engineering researchersto qualitative, educational research.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.#1738209. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. ReferencesAmerican Academy of Arts & Sciences. (2017). The future of undergraduate education, the future of

Conference Session

Understanding Student Behavior and Experiences

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Nancy Nelson, University of Calgary; Robert William Brennan, University of Calgary

Tagged Divisions

Educational Research and Methods

to be successful. A set of forced-choice questions was used to rank strategies related to class time, completing assigned work,note taking, studying, and overall work ethic. Responses were validated using a set of relatedLikert scale questions, and a set of open ended questions allowed students to identify strategiesthey believe contribute to, or impede their success. Correlational analysis and predictiveclassification were used to determine the key behaviour indicator(s) of student success, and thespecific behavioural factors associated with different levels of academic success.Findings indicate that the key behavioural indicator of student success is actually doing theassigned work. This is also the most important predictor of students who

Conference Session

Assessment and Evaluation in Engineering Education I

Collection

2007 Annual Conference & Exposition

Authors

Mysore Narayanan, Miami University

Tagged Divisions

Educational Research and Methods

and Mills’ ideas.A comparison between Dr. Boylan’s research and author’s data is shown in Appendix G.[Copyright for VARK version is held by Neil D. Fleming, Christchurch, New Zealand andCharles C. Bonwell, Green Mountain, Colorado, USA]. Page 12.289.10APPENDIX B (Rubrics courtesy of W S U, Pullman, WA) Rubrics based on Likert Scale5 Has demonstrated excellence. Has analyzed important data precisely. Has provided documentation. Has answered key questions correctly. Evidence of critical thinking ability. Has addressed problems effectively. Very good performance

Conference Session

Issues in Advising and Mentoring

Collection

2013 ASEE Annual Conference & Exposition

Authors

Gillian M. Nicholls, University of Alabama in Huntsville

Tagged Divisions

Educational Research and Methods

hour completionpercentage, number of courses with D or F grades as of Fall midterm, and credit hours attemptedin the spring term. The predictive results showing at-risk students are used to make interventionattempts. Raimondo22 described analysis at the University of Michigan to assess within classperformance by students and offer guidance via a digital resource called “E2Coach”s to assistthem in improving their performance trajectory. McKay23 has used E2Coach to interact withphysics students predicted to be at risk of not succeeding and provide tailored feedback to allenrolled students that they can use to adjust their strategy in the course.Universities have constrained resources including enrollment capacity, faculty, staff, lab space,etc

Conference Session

Student Learning, Problem Solving, & Critical Thinking 1

Collection

2014 ASEE Annual Conference & Exposition

Authors

Sean Moseley, Rose-Hulman Institute of Technology; Rachel McCord, Virginia Tech

Tagged Divisions

Educational Research and Methods

Talk about Salient Problem Features. Journal of Engineering Education, 2010. 99(2): p. 135-142.3. Litzinger, T.A., P.V. Meter, C.M. Firetto, L.J. Passmore, C.B. Masters, S.R. Turns, G.L. Gray, F. Costanzo, and S.E. Zappe, A Cognitive Study of Problem Solving in Statics. Journal of Engineering Education, 2010. 99(4): p. 337-353.4. Chi, M.T.H., P.J. Feltovich, and R. Glaser, Categorization and representation of physics problems by experts and novices. Cognitive Science, 1981. 5(2): p. 121-152.5. Brown, J., A. Collins, and S. Newman, Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. Cognition and instruction: Issues and agendas, 1989: p. 453-494

Conference Session

Research Informing Teaching Practice II

Collection

2012 ASEE Annual Conference & Exposition

Authors

Renata A. Revelo Alonso, University of Illinois, Urbana-Champaign; Michael C. Loui, University of Illinois, Urbana-Champaign

Tagged Divisions

Educational Research and Methods

Outside EngineeringIntroductionAssessing the state of engineering education within the larger community of educators, theNational Science Foundation has highlighted the need for an understanding of engineering infields outside of engineering and “attention to STEM literacy for the public at large”1. In the1995 NSF report Restructuring Engineering Education: A Focus Change2, one of thesuggestions to address such a need was to offer engineering courses to non-engineering students.Consequently, in the late 1990’s and early 2000’s, engineering departments slowly began to offercourses for students who did not plan to major in engineering. Because few such generaleducation courses were offered in the past, little is known about the long-term student

Conference Session

Educational Research and Methods Potpourri I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Sohum Sohoni, Oklahoma State University; Donald P. French, Oklahoma State University; YoonJung Cho, Oklahoma State University

Tagged Divisions

Educational Research and Methods

the new measure of GTA‟s need assessment can be used as a reliable and valid toolacross institutions.IntroductionConcerns about recruitment and retention of students in engineering disciplines have resulted innumerous calls for reform in engineering education[1-3]. Regardless of the chosen response tosuch calls, it is clear that quality education requires the presence of instructors who have learnedto teach effectively. Unfortunately, because we often rely on “on-the-job” training, facultybecome skilled at teaching after receiving their doctoral degrees and “practicing” on students.For this reason, institutions commonly establish teaching effectiveness centers dedicated tofaculty development. Moreover, and of greater concern to us, much

Conference Session

Engineering Education Research in K-12

Collection

2011 ASEE Annual Conference & Exposition

Authors

Karen A. High, Oklahoma State University; Melanie C. Page, Oklahoma State University; Julie Thomas, Oklahoma State University

Tagged Divisions

Educational Research and Methods, K-12 & Pre-College Engineering

education. Journal of Engineering Education,309-318.4. Halpern, D.F., Benbow, C.P., Geary, D.C., Gur, R.C., Hyde, J.S., & Gernsbacher, M.A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest. 8(1), 1-51.5. Walters, A.M., & Brown, L.M. (2005). The role of ethnicity on the gender-gap in mathematics. In A.M. Gallagher & J.C. Kaufman (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 207-219). New York: Cambridge University Press.6. Catsambis, S. (1995). Gender, race, ethnicity, and science education in the middle grades. Journal of Research in Science Teaching, 32, 243-257.7. Margolis, J. & Fisher, A. (2002

Conference Session

Engineering Education During the COVID-19 Pandemic

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Patricia R. Backer, San Jose State University; Laura E. Sullivan-Green, San Jose State University; Maria Chierichetti, San Jose State University

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

not promising for continued instruction online in the upcomingsemesters under the COVID-19 epidemic.References[1] Blaich, C. & Wise, K. (2020, September 14). Comparison of how faculty and staff have experienced their institutions’ responses to COVID-19. Higher Education Data Sharing Consortium (HEDS). Available: https://www.hedsconsortium.org/wp-content/uploads/2020.09.14-COVID-19-Survey-Faculty-v-Staff- Memo.pdf[2] The Chronicle of Higher Education (2020, October). ‘On the Verge of Burnout’: Covid-19’s impact on faculty wellbeing and career plans. Available: https://connect.chronicle.com/rs/931-EKA- 218/images/Covid%26FacultyCareerPaths_Fidelity_ResearchBrief_v3%20%281%29.pdf[3] Fox, K

Conference Session

Assessing Hard-to-Measure Constructs in Engineering Education: Assessment Design and Validation Studies

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Jiaqi Zhang, University of Cincinnati; P.K. Imbrie, University of Cincinnati

Tagged Divisions

Educational Research and Methods

education using cognitive and non-cognitive factors. Journal of Applied Research in Higher Education, 11 (2), 178–198.Aryee, M. (2017). College students’ persistence and degree completion in science, technology, engineering, and mathematics (STEM): The role of non-cognitive attributes of self-efficacy, outcome expectations, and interest (Unpublished doctoral dissertation). Seton Hall University.Asparouhov, T., & Muthén, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling: A Multidisciplinary Journal, 21 (4), 495–508.Bartholomew, D. J. (1980). Factor analysis for categorical data. Journal of the Royal Statistical Society: Series B (Methodological), 42 (3), 293–312.Bearden, W. O., Sharma, S., & Teel

Conference Session

Faculty and Student Perspective on Instructional Strategies

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Jennifer Jill Kidd, Old Dominion University; Krishnanand Kaipa, Old Dominion University; Samuel J. Sacks, Norfolk Public Schools; Stacie I. Ringleb, Old Dominion University ; Pilar Pazos, Old Dominion University; Kristie Gutierrez, Old Dominion University; Orlando M. Ayala, Old Dominion University; Lilian Maria de Souza Almeida, Old Dominion University

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

students of color to engineeringand computing. The research on this project is ongoing and will continue to add new insights tothis intervention.Figure 2. Items missed by the majority of engineering and education students ​ reservice teachers​ improved (#1)Figure 3. CS Quiz Item on which P ​ reservice teachers​ improved (#2) Figure 4. CS Quiz Item on which PReferences[1] D. M. Richter and M. C. Paretti, “Identifying barriers to and outcomes of interdisciplinarity in the engineering classroom,” ​European Journal of Engineering Education,​ vol. 34, no.1, pp. 29-45, 2009.[2] S. Tomek, “Developing a multicultural, cross-generational, and multidisciplinary team: An

Conference Session

Student Motivation and Faculty Development

Collection

2015 ASEE Annual Conference & Exposition

Authors

Jessie Keeler, Oregon State University; Bill Jay Brooks, Oregon State University; Debra May Friedrichsen, Unaffiliated; Jeffrey A Nason, Oregon State University; Milo Koretsky, Oregon State University

Tagged Divisions

Educational Research and Methods

Harvard-Danforth Center, 10-21. http://isites.harvard.edu/fs/docs/icb.topic771890.files/OTL3-Mosteller- Muddiest.pdf 5. Angelo, T. A., & Cross, P. K. (1993). Classroom assessment technique examples. In Classroom Assessment Techniques: A Handbook for College Teachers (2nd ed.) Retrieved from http://www.ncicdp.org/documents/Assessment%20Strategies.pdf 6. Hall, S. R., Wait, I., Brodeu, D. R., Soderholm, D. H., & Nasr, R. (2002). Adoption of active learning in a lecture-based engineering class. Frontiers in Education. doi: 10.1109/FIE.2002.1157921 7. Tanner, K. D. (2012). Promoting student metacognition. CBE—Life Sciences Education 11, 113– 120. doi: 10.1187/cbe.12-03-0033 8. Krause, S. J