Paper ID #38149Engineering CAReS: Measuring Basic Psychological Needs in theEngineering WorkplaceProf. Denise Wilson, University of Washington Denise Wilson is a professor of electrical and computer engineering at the University of Washington, Seattle. Her research interests are split between technical research in sensors and sensor systems and engineering education with an emphasis on the role of self-efficacy, belonging, and other non-cognitive aspects of the engineering classroom and engineering workplace.Dr. Jennifer J. VanAntwerp, Calvin University Jennifer J. VanAntwerp is Professor of Engineering at Calvin University
STEM education for future researchers. He is currently participating in an NSF-funded grant (#1923452) to spearhead research into middle school students’ digital literacies and assessment. Recently, Dr. Hsu has received a seed grant at UML to investigate how undergradu- ate engineering students’ digital inequalities and self-directed learning characteristics (e.g., self-efficacy) affect their learning outcomes in a virtual laboratory environment during the COVID-19 pandemic. Dr. Hsu’s research interests include advanced quantitative design and analysis and their applications in STEM education, large-scale assessment data (e.g., PISA), and engineering students’ perception of faculty en- couragement and
factors or sub-constructs commingle to form the self-concept of a student inengineering undergraduate education is the crux of this study. To accomplish that, a systematicreview was performed over recent studies, related to engineering education, that assessed self-concept as part of their methodology.This paper first introduces self-concept and self-efficacy, the two constructs that are often usedinterchangeably in literature, followed by a database search for recent studies measuring self-concept. Based on the results this study enlists the variables assessing either of the constructs thatwere introduced. Then a detailed analysis of the differences between the two constructs isprovided. Extensions to the current structure of self-concept and
teams,undergraduate research, and service-learning organizations. The first phase of this study,reported in this paper, involves the implementation of an electronic survey to measure the impactof engineering-focused extra-/co-curricular activities on students’ academic achievement andself-efficacy. Academic achievement is measured using questions from the Statics ConceptInventory [1], and self-efficacy is measured using a series of questions from self-efficacy surveyitems [2] that ask students to rate on a six-point Likert scale their capability in (a) specificengineering skills such as working with machine and engineering design, and (b) generalengineering coursework. Based on the results from the survey administered to junior and
and a possible solution,(Conradty, Sotiriou & Bogner, 2020). Self-report measures of design self-efficacy also tend toreflect subject domains such as science (self-efficacy for designing experiments; Hushman &Marley, 2015) and the arts (designing in a visual arts environment; Catterall & Peppler, 2007).Notably, we did not find a self-report measure for problem-finding, ingenuity, or inventivenessthat could be used in elementary and middle school settings.1 Models of the invention process are analogous to the pedagogical guidance provided in models of the engineeringdesign process or the scientific method.Rationale for the Study Using Inventive Mindset measure data from 252 elementary and middle school agedchildren, Garner
profession.DiscussionWe developed a questionnaire to measure student attitudes toward and perceptions ofengineering using items adapted from two previously vetted questionnaires. The instrument’sreliability and validity were confirmed through item analysis and an iterative EFA in which weexplored four models, with the number of factors ranging from three to six. Most adequate wasthe 6-factor structure, which assesses students’: (1) academic self-confidence and self-efficacy;(2) sense of belonging in engineering; (3) attitudes toward persisting and succeeding inengineering; (4) understanding of the broad nature of engineering; and perceptions of theimportance of (5) non-technical and (6) technical skills in engineering.Although the scree plot (Figure 1
] concluded that a fully-flipped statistics course for engineers enabled more personalizedlearning and instruction than a partially-flipped classroom. A study led by Motamedi [19]indicated that a flipped and “modified instructor-guided” pedagogy for a data analysis coursefor engineers yielded higher computational understanding and theoretical and statistical self-efficacy than a problem-based learning approach. However, problem-based learning tended toresult in higher self-efficacy for using data analysis software. Similarly, Huang et al. [20]found that students in a project-based learning intervention were more likely than those in anonline course to talk about the connection between statistics and their disciplines but notthemselves. They posited
and co-moderated a Birds of a Feather session at SIGSCE 2022 virtually entitled: Mentoring a Women in Computing Club: The Good, The Bad and The Ugly. Dr. Villani presented a paper at ASEE 2022 in Minneapolis, MN entitled: Designed A (Re)Orientation Program for Women Computing Students at a Commuter College and Measuring its Effectiveness. Fall 2023 a paper entitled: An Early Measure of Women-Focused Initiatives in Gender-Imbalanced Computing programs were presented at CCSC Eastern Conference. Dr. Villani has been a Grace Hopper Scholarship reviewer, Dr. Villani was awarded the Chancellor’s Award for Teaching Excellence in 2013. Prior to joining FSC, Dr. Villani had a fifteen-year Computer Consulting Career in the
between authentic engineering learning and student engagement [35],professional identity or learning interest [36] , student-perceived learning outcomes [37], reasonableassumptions and problem-solving abilities [32], engineering learning self-efficacy [38] and so on.RESEARCH PURPOSEThe current study was situated in the engineering learning in communities of practice. Communities ofpractice were seen as an effectively collaborative learning situations with a group of learners sharingprofessional knowledge and common career enthusiasm. In our previous study, we found community ofpractice is an important engineering learning context and engineering learning happening in communitiesof practice usually focused on solving the authentic engineering
of the relationship between the student and the student’s advisor andthe support received from this advisor. Some of these factors are productivity, self-efficacy, andcommitment. The second category, Student experience, grouped the factors related to theExpectancy Value Theory [8], such as the perceived cost, the intrinsic and extrinsic motivation,and the sense of belonging. The Faculty-Student interaction refers to factors that come from thefaculty professors, besides the advisor. The latter category includes factors such as receivingadvice, mentorship, or special attention from a professor. Finally, the academic support categoryincluded factors that are related to the institution, for example, participation in research projectspreviously
outcomes [14, 15]. In somecases, self-efficacy is seen as a significant predictor of academic outcomes [16-18]. However,just as in other areas, a universal measure of self-efficacy is not appropriate to determine ethicsself-efficacy [19, 20]. Some domain specific self-efficacy scales include general engineering [21]and software engineering [22]. This work presents a survey instrument that attempts to measureethical self-efficacy.Whereas a general self-efficacy instrument would contain questions such as, “I can alwaysmanage to solve difficult problems if I try hard enough” or “I can solve most problems if I investthe necessary effort” [23], an instrument related to the design domain would include questionssuch as “I can identify a design need
we take a different tack, wanting to identify the nexus, or common ground, ofInnovative and Entrepreneurial self-efficacies, and Innovative and Entrepreneurial behaviors.Thinking about common ground is a useful lens with which to look at the intentional or focusedcreativity of engineers, whether they are working in new or existing enterprises. First, we showthe development of this intersectional/nexus concept (which we call Embracing New Ideas, ENI)in terms of measures of self-efficacy (ENI-SE; consisting of six items, with a Cronbach’s Alphaof .85) and behavior (ENI-B; consisting of five items, with a Cronbach’s Alpha of .80). Thenbased on Social Cognitive Career Theory (SCCT), we model ENI-B (our dependent variable) asa function of ENI-SE
.” American Educational Research Journal, vol. 29, no. 3, pp. 663–676, 1992.[7] N. A. Mamaril, E. L. Usher, C. R. Li, D. R. Economy, and M. S. Kennedy, “Measuring Undergraduate Students’ Engineering Self-Efficacy: A Validation Study.” Journal of Engineering Education, vol. 105, no. 2, pp. 366–395, 2016.[8] B. W. Smith, J. Dalen, K. Wiggens, E. Tooley, P. Christopher, and J. Bernard, “The Brief Resilience Scale: Assessing the Ability to Bounce Back.” International Journal of Behavioral Medicine, vol. 15, no. 3, pp. 194—200, 2008.Appendix A: Survey InstrumentPart 1: Please read the following 12 statements regarding advising functions and select the mostrelevant response option from the 6-point scale in the drop-down box to indicate
. Students access the active learning modules through an online learning managementsystem. Modules consist of ten units that engage students through relatable examples andpractices of foundational principles and applications of engineering graphics. The team took self-efficacy and academic success measurements, which were then analyzed using paired t-tests. Results support previous findings that there are significant differences in self-efficacyand academic success, including students' mental rotation abilities, when instructors providesupplemental materials. The data also supports that students at risk of non-matriculation benefitfrom the combination of active learning modules and additional video tutorials in the realms ofself-efficacy
within thefirst two weeks of class and the post-survey was administered two weeks before final exams.MeasuresThere were three items measuring outcome expectations for engineering adapted from Lent et al.[13], six items measuring intentions to stay in engineering adapted from Lent at al. [13], threeitems measuring self-efficacy adapted from Lent et al. [13], and five items measuringengineering identity adapted from Chemers et al. [33] & Estrada et al. [34]. Table 1 provides thesample survey items for all four surveys used in this study. Table 2 provides the summary ofdescriptive statistics of continuous predictors and categorical variables. The Cronbach’s alphacoefficients across all subscales were also estimated with values ranging from 0.85
Efficacy Scale (TSES) survey is a set of questionnaires developed byTschannen-Moran at College of William and Mary and Woolfolk Hoy at the Ohio State University[4]. It is designed to help people gain a better understanding of the kinds of things that createdifficulties for teachers in their school activities. Similarly, teachers are asked to indicate theopinion about each question by marking from 1 to 9. There are two forms of this survey. The longform has 24 questions and the short form has 12 questions. These questions measure efficacy inStudent Engagement, Instruction Strategies, and Classroom Management. TSES has been used inmany teachers’ self-efficacy studies. 2.2 Bandura’s Instrument Teacher Self-efficacy Scale Bandura’s instrument on
in our courses to see students' attitudes towardengineering and analyze the engineering course progress.As the assessment team (authors), we develop new learning models and assessment methodsspecifically tailored to the LED program. These methods allow us to measure the effectivenessof the program in promoting engineering understanding and attitudes among students. Byanalyzing the results of our assessments, we provide instructors and researchers with valuableinsights into how the LED program can be improved and how it compares to other engineeringeducation programs. We are particularly interested in examining the influence of the LEDprogram on students' self-determination, motivation, and self-efficacy, as these factors haveshown to be
29 8.8 Total 328 100.0B. SurveyThe measurement instrument was built out of other investigations having a similar purpose tothat of this work [6, 22-26]. This version of the instrument included more statements thatenabled further probing on student sense of belonging, in its various aspects, such as social,academic and general interactions within the institution; given that the other investigationsplaced their emphasis on items more related to other factors, such as self-efficacy, identity,attitudes, behavior, among others, and secondly, with fewer probing on items relating to asense of belonging. During the survey validation process, a Cronbach's Alpha of 0.878 wasattained
thatthe M-EDSI is reliable for measuring students’ EDTE.DiscussionWhile previous research explores the topic of engineering teaching efficacy, the present studyoffers a novel perspective by specifically addressing Engineering Design Teaching Efficacy 5(EDTE). This is important because engineering design is a major part of the NGSS [3] and islinked to students’ enhanced learning [20]. The findings show that the intervention did not justsignificantly improve participants’ EDTE but also their EDE. Mastery experiences is a primarysource of self-efficacy development [21]. Therefore, PSTs’ improved EDE could be attributed totheir active engagement in
students' engineering social cognitions (self-efficacy and outcomeexpectations), this paper investigates students' confidence in their ability to learn andtheir instructor's ability to teach across 6 engineering courses. A group of 6 facultyformed a learning community focused on improved teaching strategies for their classes.The faculty chose selected strategies and implemented them in their classes. Surveysasked students to rank their confidence level in "their ability to learn" the specific classmaterial and the instructor's "ability to teach" the class material using a sliding bar scalefrom 0-100. Surveys were conducted before and after the improvements to the teachingstrategies at both the beginning and end of the semesters. The results of the
.[13] P. Yantraprakorn, P. Darasawang, and P. Wiriyakarun, “Enhancing self-efficacy through scaffolding,” Proceedings from FLLT, 2013.[14] A. Bandura, “Self-efficacy: toward a unifying theory of behavioral change.,” Psychological review, vol. 84, no. 2, p. 191, 1977.[15] R. M. Klassen and E. L. Usher, “Self-efficacy in educational settings: Recent research and emerging directions,” The decade ahead: Theoretical perspectives on motivation and achievement, vol. 16, pp. 1–33, 2010.[16] M. J. Scott and G. Ghinea, “Measuring enrichment: the assembly and validation of an instrument to assess student self-beliefs in CS1,” in Proceedings of the tenth annual conference on International computing education research, 2014, pp. 123
belonging and self-efficacy items were adapted from a study on the self-efficacy of women engineering students and a dissertation (Marra et al., 2009; Jordan, 2014). Identity, teamwork self-efficacy, and community involvement items were adapted from a study that investigated how underrepresented students’ self-efficacy and identity impact their science career commitment (Chemers et al, 2011). Items about college life experience were adapted from the National Survey of Student Engagement (Kuh et al., 2011). The six factors we measured are as follows: ● Self-efficacy: Confidence in the participant’s own ability to complete a degree and succeed in an engineering or computing career. ● Sense of belonging: Feeling part of the engineering or
SketchTivity?A Drawing Self-Efficacy Instrument was used to measure the pre and post self-efficacy of studentswho practiced using SketchTivity[25]. The instrument consisted of 13 items and the average ofdrawing self-efficacy score was calculated for each student.B. ParticipantsThe participants in this study consisted of undergraduate and graduate students enrolled in fourcourses at three different institutions. Out of a total of 138 students enrolled in three courses atthree institutions, 137 students responded to Q1 and Q2; 109, 88, and 65 participants respondedTable 1: Demographics of the participants Participant demographcis Percentage Men 76.09% Women 18.84% First-generation 10.14
1980s.THE SMART goal framework, published by George Doren, states that goals should be Specific,Measurable, Attainable, Realistic, and Timely [17]. This overlaps with Latham and Locke’s goal-setting theory but is much more detailed and seems to diverge from their suggestion that goals bedifficult, rather than stating that goals should be both attainable and realistic instead of lofty ordifficult. If we are to follow Bandura’s self-efficacy model, students need “mastery experiences,”which should be somewhat challenging but attainable. The ideal degree of difficulty is likelyindividualistic, but the experience itself can be small or large.Several papers have noted the effect of goal setting on students and engineers [16-22]. However,only two papers
study evaluates the use of entrepreneurial design projectsin a first computer aided design (CAD) course. The study quantifies changes in affectivecapacities in terms of Need for Achievement (nAch), Generalized Self-Efficacy (GSE), andTolerance for Ambiguity (ToA). Surveys deployed at the start and conclusion of the CADcourse provide the data needed to evaluate these changes. A paired sample t-test for those whoresponded to both entry and exit surveys (N=14) shows an absence of significant change for anyof the measured affective capacities. However, a small number of individual students exhibitednoteworthy, though not statistically significant, changes for one or more of the three measures.This outcome points to the value of conducting larger
. 2005.[6] R.W. Lent, S.D. Brown, and K.C. Larkin, “Self-Efficacy in the Prediction of AcademicPerformance and Perceived Career Options,” J. Couns. Psy., vol. 33(3), pp. 265-269, Jan. 1986.[7] A.R. Carberry, H.S. Lee, and M.W. Ohland, “Measuring Engineering Design Self-Efficacy,”J. Eng. Ed., vol. 99(1), pp. 71–79, Jan. 2010.[8] J.S. Mullin, “Developing Technical Self-efficacy through a Maker-inspired Design Project,”at At Home With Engineering Education: ASEE’s Virtual Conference, June 22-26, 2020.[9] A. Jackson, N. Mentzer, J. Zhang, and R. Kramer, “Enhancing Student Motivation andEfficacy through Soft Robot Design,” at 2017 ASEE Annual Conference and Exposition,Columbus, OH, USA, June 24-28, 2017.[10] L. Murray, J. Ekong, S. Niknam, and M.J
populations.As the institution being studied, the junior-level MSE lab courses have robust computational modelingand simulation curricular content. Our findings therefore suggest a strong positive impact that frequentuse of simulation tools in MSE courses can have on students’ attitudes toward these tools in the contextof engineering work. However, because we did not directly measure students’ actual competency, butonly their self-efficacy, it is not clear whether their lack of confidence with these tools accurately reflectsa low level of proficiency or whether it reflects a greater level of appreciation of the complexity of thesetools, which novices would not appreciate. It would be valuable for a future study to examine therelationship between actual
employed instruments for self-efficacyand engineering identity, and conducted interviews with focus groups. To measure the impact,qualitative and quantitative methods are used. This content analysis helped the project teamidentify challenges, difficulties, and gains of adopting this approach to the engineering programand provide an appraisal of student outcomes, including cognitive and affective responses. In thisposter, the project team will share their results from Fall 2021 semester.Major ActivitiesTo understand the impacts of the intervention on self-efficacy and engineering identity,contemporary industry-relevant problems were designed, introduced to the targeted course,instruments for self-efficacy and engineering identity were developed and
engineeringstudents at two Midwest universities, the University of Illinois at Urbana Champaign and theUniversity of Illinois at Chicago. The goal is to gain a comprehensive understanding of theinformation sources and decision-making strategies used by these students, with the hope ofimproving the major selection process for all students.Theoretical FrameworkThe study is rooted in the Social Cognitive Career Theory (SCCT), which posits that students'evolving career interests are shaped by their self-efficacy expectations. This theory has beensupported by multiple research studies, which have established a positive correlation betweenself-efficacy and career interests. [2][3][4]. SCCT asserts that self-efficacy acts as a driving forcefor career choice.To
) framework to provide undergraduate students with morepractice in tissue characterization. The framework involves structuring a multi-week lab thatintegrates theoretical foundations, bioinstrumentation background, experimental design, and dataanalysis. The goal of the framework is to enhance lab-based learning by providing opportunitiesfor students to incorporate multiple levels of Blooms Taxonomy. By consolidating theseopportunities into a multi-week module, we hypothesized that students would experience morereinforcement and thus self-efficacy with these experimental methods. For this study, we focusedon the development of a TDA module to measure apoptosis in tissue constructs using real-time,reverse transcription polymerase chain reaction (RT-PCR