electrical at higherrates than traditional students (McNeil, Ohland, & Long, 2016). This paper focused on thestickiness measure for NTS students, and other statistical tests of prediction were outside thescope of this paper. Further research is needed to explore why NTS’ stickiness follows adifferent trend than traditional students. 5 ReferencesAlvord, C. J. (2004). First-time freshman graduation rates Fall 1980-Fall 1997 entering classes (Biennial Report). Retrieved from http://ms7.dpbwin2k.cornell.edu/documents/ 1000024.pdfAstin, A. W., & Astin, H. S. (1992). Undergraduate science education
perceptionsof doing engineering work, regardless of occupational title. We also believe that a sequentialregression model will show that engineering belief measures predict a significant proportion ofvariance in perceptions of having jobs “related to” engineering, over and above SCCT variables.AcknowledgementsThe authors would like to thank the Purdue University Davidson School of Engineering, whosePipeline Center funded this project. This work was also supported by the NSF (DGE-1333468).Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the National Science Foundation.References[1] E. Cech, “The Self-Expressive Edge of Occupational Sex Segregation
assignment was due for MAE 434W,which could have influenced questions 8 and 11. Based on the instructors’ feedback, Expertizawas updated between semesters and the scores from the spring semester suggest the studentsfound the newly adjusted system easier to use.Table 2. Average Survey Results per Class from the Fall and Spring Semesters. Survey Question Fluid Mechanics Capstone Design 1. The reviews I received addressed F 3.41 F 3.63 the questions/concerns I had about S 3.79 S 3.43 my work. 2. The reviews I received gave me F 3.50 F 3.63 new insight into my work. S 3.80
’ actual work has been found poor.15In light of these issues, many researchers have defined engineering retention as simply thenumber of engineering graduates who report being employed in an engineering occupation.16-21By this measure, as of 2008, an estimated 1.2 million out of 2.5 million individuals withengineering as their highest degree were retained in engineering.22 Nonetheless, an obviouslimitation of counting engineers in this way is that, unlike using degree-job relatedness, “it willnot capture individuals using S&E knowledge, sometimes extensively, under [other]occupational titles”.21 In other words, defining engineering based on occupational classificationdoes not capture the full range of career paths that engineers take.14,23
makerspaces critically calls attention to the practices of makerspaceswhich may be inequitable. However, makerspace practitioners rarely engage or are engagedin this type of work. There is an opportunity to bring together the generous and the critical tosupport the design of more equitable university makerspaces.Different stakeholders within engineering education have different definitions of equitywhich are drawn from their lived experiences. The purpose of our framework is not to putforth a definition of equity we believe everyone should use, rather we believe the frameworkcan help us structure conversations on equity in makerspaces through a shared understanding.Against this backdrop, our research is informed by Vossoughi et al.’s definition
Paper ID #13574I Like Therefore I Learn! Engineering Student Motivation to Learn in TheirLeast and Most Favorite CoursesDr. Louis Nadelson, Utah State University Louis S. Nadelson is an associate professor and lead researcher for the Center for the School of the Future in the Emma Eccles Jones College of Education and Human Services at Utah State University. He has a BS from Colorado State University, a BA from the Evergreen State College, a MEd from Western Washington University, and a PhD in educational psychology from UNLV. His scholarly interests include all areas of STEM teaching and learning, inservice and
) Robin S. Adams is an Associate Professor in the School of Engineering Education at Purdue University and holds a PhD in Education, an MS in Materials Science and Engineering, and a BS in Mechanical Engineering. She researches cross-disciplinarity ways of thinking, acting and being; design learning; and engineering education transformation.Dr. Jie Chao, The Concord Consortium Jie Chao is a learning scientist with extensive research experience in technology-enhanced learning en- vironments and STEM education. She completed her doctoral and postdoctoral training in Instructional Technology and STEM Education at the University of Virginia. Her past research experiences ranged from fine-grained qualitative mental process
influencing their post-graduation career plans? RQ2. What areas of thinking related to junior and senior engineering students’ career plans are influenced by socializers? RQ3. What areas of thinking related to junior and senior engineering students’ career plans are influenced by specific socializers?To answer these questions, we examined interviews with 62 engineering juniors and seniors fromsix different universities in the U.S. To frame our study, we used Eccles et al.'s Expectancy xValue Theory of Achievement Motivation as this framework provides concrete examples ofways that socializers influence student outcomes.5-7Background Literature and Theoretical FrameworksAlthough research shows that socializers
, and the practicesetting. 1,2Magnusson, Krajcik, and Borkos (1999) proposed a refined model of PCK for science teaching.Their model includes the following five components: 1) orientations toward science teaching; 2) knowledge and beliefs about science curriculum, 3) knowledge and beliefs about student understanding of specific science topics, 4) knowledge and beliefs about assessment in science, and 5) knowledge and beliefs about instructional strategies for teaching science” (p. 97).3An overarching component of this model is that a science teacher‟s knowledge is stronglyinfluenced by the stance or generalized orientation a teacher may take within his/her ownpractice. Teachers‟ orientations have also been described as
Paper ID #34730Guided Learning Sequences as an e-Learning Enhancer During COVID-19Emergency ConditionsDr. Gibr´an Sayeg-S´anchez, Tecnologico de Monterrey Dr. Gibr´an Sayeg-S´anchez is professor – consultant in the Science Department in Tecnologico de Mon- terrey, Puebla campus. He studied a PhD in Financial Science in EGADE Business School (2016), a MSc in Industrial Engineering in Tecnologico de Monterrey (2011), and a BEng in Industrial and Systems En- gineering in Tecnologico de Monterrey (2006). Dr. Sayeg-S´anchez has more than 10 years of experience in teaching statistics, mathematics, and operations research; and
students’ knowledge about the task-related discipline(s) [24], [25]. In thisstudy, we only focus on the implicit and explicit aspect of task interpretation. This study views task interpretation as an integral part of self-regulation. Self-regulatedlearning (SRL) is a complex, iterative, and situated goal-directed learning process [5], [8], [26].SRL is comprised by the student, learning environment, and learner’s engagement with theenvironment and is affected by student’s emotion and motivation [7], [9], [26]. Student’sengagement starts with task interpretation. Task interpretation is followed by (a) developing aplan based on the task understanding, (b) enacting the plan, (c) monitoring the progress andapproach, and (d) making any
about performance, and then code, gave students visual and textual practice more. The goal is to feedback about the code’s results, and improve performance in particular allowed students to retry or move to a concepts/skills over time. harder level (Chaffin et al., 2009). Gamified academic Students perform common A board game where students answered activity classroom learning task(s) with multiple-choice questions about the task-irrelevant game mechanics learning content to correctly to move (e.g., points, rewards, moving around the
always proven to be verysuccessful. Even today, a large percentage of the deaf community has reading comprehensionand writing deficits and this has not changed much over the past 30 years.3When deaf or hard of hearing students arrive at college, they have high expectations ofthemselves for completing bachelor‟s and graduate degrees.4 The research led by Cuculick andKelly has shown through statistical analysis that only about 17% of incoming deaf students atNTID, 2001 and 2002 had the requisite reading and language skills to enter a baccalaureateprogram in their first year. Also, with the same data, it indicated that at NTID it takes longer forthe deaf students to complete Associate of Occupational Studies (AOS), Associated of AppliedScience (AAS
Statistics [8], first-generation college students were characterizedas students’ whose parents did not have postsecondary educational experience. Another studystated, “first-generation college students include students whose parents may have some college,postsecondary certificate(s), or associate’s degree, but not a bachelor’s degree” and this definitionclosely aligns with the definition set forth by the Federal TRiO program (i.e., outreach and studentservice programs created to serve students from disadvantaged backgrounds) [9, p. 8]. There areinconsistencies and numerous ways in defining first-generation college students, so much so thatWhitley et al. [10] found at least six different definitions. However, regardless of how first-generation
skills.The testing will be done with students from varied backgrounds to assess how individuals studyingin a variety of domains are impacted by their beliefs about knowledge and their own abilities.Subsequently, the researchers will develop interventions that are applicable in existing curricula.Such interventions will be informed by the knowledge that designing and building are correlatedwith a high level of spatial skills.Bibliography1. Martín-Dorta, N., Saorín, S. J., & Contero, M. (2008). Development of a fast remedial course to improve the spatial abilities of engineering students. Journal of Engineering Education, 97(4), 505-513.2. Kell, H., Lubinski, D., Benbow, C., & Steiger, J. (2013). Creativity and technical innovation: Spatial
studies may uncover whether such networkconnectivity sustains even after the end of the semester. The study is also inconclusive on howsocial media interactions on a STEM topic may influence knowledge building. The study waslimited to the class of Construction Material and Methods; more efforts are needed to find outwhether such network growth patterns exist in different STEM courses.REFERENCES[1] S. Hasan, S. Ukkusuri, H. Gladwin, and P. Murray-Tuite, “Behavioral model to understand household-level hurricane evacuation decision making,” J. Transp. Eng., vol. 137, no. 5, pp. 341– 348, 2011, doi: 10.1061/(ASCE)TE.1943-5436.0000223.[2] A. M. Sadri, S. Hasan, S. V. Ukkusuri, and J. E. Suarez Lopez, “Analysis of social interaction
currently working at a start-up andperceives the climate to be much more positive than P0’s previous employer (a largecompany). P0 attributes this difference to the fact that it is a smaller company, and thuspeople are more apt to rely on and get to know each other.The interviewees used a variety of approaches to deal with their situations. P0 “never feltconnected with the Black [company employees]” and eventually left that company for asmall start-up. P1 did not expect to feel connected when first hired. Instead, P1’s approachwas to focus on the technical aspects of the job and “when I want to see Black folks I justdrive home.” P5 has decided,that I’m not pushing the envelope, I’m just sitting there collecting my paycheck…The less I dothe more
things in nature (e.g., butterflies, rocks) Page 26.1552.5 star Observed or studied stars and other astronomical objects group Participated in science groups/clubs/camps comp Participated in science/math competition(s) nonfic Read/Watched non-fiction science Abbreviation Reported Interest/Experience scifi Read/Watched science fiction game Played computer/video games prog Wrote computer programs or designed web pages talk Talked with friends or family about scienceResults and
of school enterprise cooperators,” Research on higher engineering education, no.4, pp.101-106, 2019.[6] S. R. Brunhaver, R. F. Korte, S. R. Barley and S. D. Sheppard, Bridging the gaps between engineering education and practice, Chicago: Chicago University Press, pp. 129-165, 2018.[7] V. Domal and J. Trevelyan. “An engineer's typical day: Lessons learned and implications for engineering education,” In 20th Annual Conference for the Australasian Association for Engineering Education, Adelaide, Australia,2009.[8] D. Vinck, “Engineering practices,” Revue d'anthropologie des connaissances,vol. 8, no.2, pp.a-s, 2014.[9] D. Jonassen, J. Strobel and C. B. Lee, “Everyday Problem Solving in Engineering: Lessons for
weekly and stored in Canvas Studio that could be streamed to thestudents on demand. The students could either view these lectures during the class time in thecourse schedule or at some other time workable for them in the same week. Offering suchflexibility could avoid the potential conflicts between the original class schedule and students’altered schedules during the public health emergency period. The video lectures were preparedusing a versatile note-taking app S Note that supports integration of multimedia files. The appruns on an Android tablet. The lectures presented on the tablet were recorded by a screenrecording app x-Recorder in the mp4 format that can be streamed online. Examination scores inthe on-demand course were compared with
goal of thiswork is to visualize and make meaning of CAIR-related assessment data. Our display design isinspired by concepts from the domain of human factors engineering. A low-fidelity conceptualdesign and walk-through of the display are provided and key scenarios and tasks the instructorcan achieve via using the display are explored. The display can inform the instructor on both thequality of the marking done by the assessor(s) and common problem-solving errors committedby the students across a problem, test, and so on.IntroductionMeeting the pedagogical goals of Constructive Alignment, Formative and outcomes-basedAssessment are deemed significant for learning [1]–[3]. Constructive Alignment promotes asocial negotiation and mapping between
the questionSpeaker A Insert expansion Fins rephrases questionSpeaker B Second Pair Part Sb answers questionSpeaker A Post-expansion Fpost asks a follow-up questionSpeaker B Post-expansion Spost answers follow-up questionThere were generally seven different iterations of this schematic found within the excerptsanalyzed for this study. The most common forms of talk are noted in Table 1. Notably, talkinclusive with post-expansions were most commonly found within the excerpts analyzed for thisstudy. Number of Excerpts that included parts of talk (Schegloff, 2007) F pre S pre Fb Sb SCT PCM F post S post 15
gap, this study aims to gain adeeper understanding of the faculty‟s experience with LTS. Herein, we present the thoroughdevelopment of the LTS Faculty Survey, designed with content and construct validationprocesses in mind and included quantitative and qualitative items, as well as key findings fromsurveyed LTS faculty experts (N=25). The survey enabled us to measure characteristics of LTScurricular and extracurricular efforts, perceived barriers faced by faculty, motivations forimplementing LTS efforts, attitudes about LTS, etc. all from a faculty perspective. Key findingssuggest that major barriers for LTS implementation are (1) faculty time/workload, (2) problemscoordinating with the community, and (3) the lack of policy on the role of LTS
systems, conducting research inengineering education domains focused on interaction-dominant phenomena, and meeting critical datacollection and analysis needs, complex systems research can provide important insights for theengineering education community. ReferencesAnylogic (2016) Retrieved from: http://www.anylogic.com/Benson, L., Kirn, A., & Faber, C. (2013, June). CAREER: Student motivation and learning in engineering. In ASEE Annual Conference Proceedings.Berggren, K. F., Brodeur, D., Crawley, E. F., Ingemarsson, I., Litant, W. T., Malmqvist, J., & Östlund, S. (2003). CDIO: An international initiative for reforming engineering education. World Transactions on Engineering and
can be seen that the input from the instructorshelped reshape the format of the workshop between the years but the same underlying principlesexisted: collaboration, interest in student understanding, and material development. With thesecore principles remaining the same across the workshops, we can then compare how theinstructors’ attitudes and beliefs changed throughout this timeframe.Theoretical FramingFor this research, the Concerns Based Adoption Model (CBAM) has been utilized to compareand contrast how the instructors’ beliefs and attitudes towards the innovation changed over time2.CBAM is a well-researched educational model created in the 1970’s ad 1980’s that helps depictthe change process in an educational setting. There are three
relate to forces and creating free-body diagrams [6]. Moments (of a force)have also been identified as a particular area of confusion for students both because ofconflicting terminologies [3] and their role as “intermediate quantifier[s] of the rotational effectof interactions [between bodies]” [7]. That is, while the net force is the quantity proportional to amass’s translational acceleration, the moment is proportional to the mass’s angular acceleration.That moments build on the already difficult concept of force likely only complicates learning.This work in progress paper describes an early pilot of a study to investigate the process ofconceptual change related to moments in an engineering statics course. Preliminary results fromthe pilot
., Ciarallo, F.W., Klingbeil, N.W. (2014). Developing the Academic Performance- Commitment Matrix: How measures of objective academic performance can do more than predict college success. Proceedings 121st ASEE Annual Conference and Exposition, Indianapolis, IN, June 2014.Brown, S. D., Tramayne, S., Hoxha, D., Telander, K., Fan, X., & Lent, R. W. (2008). Social cognitive predictors of college students’ academic performance and persistence: A meta- analytic path analysis. Journal of Vocational Behavior, 72(3), 298-308.Burnham, J.R. (2011). A case study of mathematics self-efficacy in a freshman engineering mathematics course. (unpublished master’s thesis). Washington State University, Pullman, WA.Connor, M. C., & Paunonen, S. V
consider the engineering course they took in theprevious semester that was the most relevant to their current course and to indicate their priorexperience with four of the most commonly used types of instruction in engineering course.These types of instruction include: “listen to the instructor lecture during class,” “answerquestions posed by instructor during class,” “brainstorm different possible solutions to a givenproblem,” and “discuss concepts with classmates during class.” If a student had been exposed tothis type of instruction in the prior course, s/he was also asked how s/he typically responded to itusing four classroom engagement constructs of value, positivity, participation, and distraction(Table 1; DeMonbrun et al., 2017; Fredricks
Page 12.561.4characteristics, there was a broader range of characteristics listed and therefore a longer list ofthemes.Table 1. Technical and Tinkering Skills Themes Technical Skills Themes Tinkering Skills Themes Knowledge/background Knowledge/background Technical Technical Problem(s) Problem(s) (How things) work (How things) work Think/reason Think/reason Tool(s) Tool(s) Creative Creative Analytical Analytical Interest Interest Hands-on Hands-on Curious/inquisitive Curious
Education, Washington, DC: ASEE, 2012.6 See https://www.nsf.gov/pubs/2014/nsf14602/nsf14602.htm7 Douglas, E., private communication, January 31, 2017.8 Jordan, S. and M. Lande, “Additive innovation in design thinking and making,” International Journal of Engineering Education, 32(3B), pp. 1438-1444, 2016.9 McKenna, A., N. Kellam, M. Lande, S. Brunhaver, S. Jordan, J. Bekki, A. Carberry, and J. London, “Instigating a Revolution of Additive Innovation: An Educational Ecosystem of Making and Risk Taking,” 2016 ASEE Annual Conference & Exposition, New Orleans, LA, June 2016.10 Kellam, N., B. Coley, and A. Boklage, “Story of change—Using experience-based critical event narrative analysis to understand an engineering program’s