the student body is receiving an education that approaches critical thinking in aholistic manner (e.g., formulating problems, working in a laboratory setting, mastery ofgraphical/written/verbal communication). Institutions collect a series of assessments targetingthese individual student outcomes (SOs) with the goal of determining how well the student bodycan achieve the goals prescribed by ABET. This process provides a thorough overview ofstudent attainment in the SOs from the perspective of the institution and its individual faculty,but it lacks any substantive measure of student self-efficacy.Self-efficacy is a term used to describe how well an individual believes they can accomplish atask [1]. Self-efficacy in a higher learning setting
, fostering diverse learningenvironments, and promoting multi-disciplinary teams. We will also investigate the potential ofmaker spaces to positively influence females and minorities and thereby broaden participation inengineering.Impact will be measured through engineering design self-efficacy; retention in the engineeringmajor; and idea generation ability. Impacts will be measured at two levels. The first level of theproject will use a randomly assigned experimental design to assess the impact of early makerspace engagement on females and minorities through longitudinal measurements. In the secondlevel, we compare segment snapshots and longitudinal measurements between extensive makerspace users and those with minimal exposure. We will also
project. Importantly, thisscholarship program aims to increase the number of engineers in the state and nation, reachingout to those students who have an interest in the field but who are unable to pursue the educationnecessary to acquire a degree.IntroductionIn order to understand the unique needs of the transfer student, an intensive questionnaire wasdeveloped to assess the Pathway to Success program effectiveness. The questionnaire has severalcomponents, including: demographic information, beliefs about self-efficacy in engineering,anticipated and experienced hurdles throughout the program, and scholarship programassessment. Many of the questions posed aimed to better understand the distinctive challengesfaced by transfer students so that the
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
employed two constructs from the InterpersonalReactivity Index [7] - Empathic Concern and Perspective-Taking - and two constructs from arecent validation study in engineering - Interpersonal Self-Efficacy and Emotion Regulation [42].This correlation analysis is a form of external validity [29], as it identifies if our novel constructsalign with extant empathy constructs in expected ways. Empathic Concern can be considered another-oriented affective empathy concept and thus should theoretically correlate with ouraffective empathy in design constructs. Similarly, Perspective-Taking is a cognitive empathyconcept which primarily includes other-oriented items. Thus, we anticipated significant positivecorrelations between this construct and the
as cognitive and/or affective.We also wanted to contextualize our measure of expansive empathy to design, much as measuresof self-efficacy are commonly contextualized to mathematics, science inquiry, engineering, etc.[17-19]. Adopting this situative approach is common when we expect affective and dispositionalstances to develop in tandem with cognitive aspects and disciplinary practices, as is the case withempathy in design [20].Thus, research on how others have measured empathy in design provides key guidance in ourreview of existing decontextualized or general-purpose measures. For instance, in research ondesign thinking, feedback-seeking has been considered a proxy for empathy [21, 22]. In suchsurveys, questions focus on considering
World Academy of Science and Engineering Volume 28. ISSN 2030-37409. Irizarry, R. (2002). Self-efficacy and motivation effects on online psychology student retention. USDLA Journal, 16(12). Retrieved September 5, 2008, from http://www.usdla.org/html/journal/DEC02_Issue/article07.html.10. Joy, Ernest H. and Garcia, Federico E. Measuring Learning Effectiveness: A New Look at No-Significant- Difference Findings. Journal for Asynchronous Learning Networks, Vol. 4, Issue 1, pp. 33-39, June 2000.11. Nitsch, W. B. (2003). Examination of factors leading to student retention in online graduate education. Paper – ED 7212 Administration and Leadership of Distance Education Programs. Retrieved September 5, 2008, from http
]. Specificattitudes and cognitive skills are often attributed to adaptive experts. In 2001, Fisher andPeterson [2] developed a model around attitudes toward continuous learning and epistemology,as well as metacognitive skills. Pierrakos et al. [8] proposed a modified version of the Fisher andPeterson model by adding the constructs of innovation and conceptual understanding, whileFerguson et al. [10] added self-efficacy and resilience. Bransford et al. [11] proposed an AEmodel where innovation and efficiency were two orthogonal dimensions, with novices at thelower end of both scales and adaptive experts at the higher end of both scales.The framework adopted for this study is the original model by Fisher and Peterson [2], whichdecomposes adaptive expertise
theirbachelor’s degrees in engineering. We focus on these individuals due to the scarcity of researchon their experiences and the relevance of their perspectives to engineering education.29-31Implications of this work will focus on recommendations for educational research and practice.Framework and LiteratureThe overall EPS project is broadly situated in social cognitive career theory (SCCT) which positsthat a variety of factors influence career choice including self-efficacy beliefs, outcomeexpectations, and learning experiences.32 SCCT has been used extensively in the study ofengineering students’ career choices.33-37 A main goal of our study has been to identify theschool and workplace factors related to the career choices made by engineering
to understand andembrace, but once we did, we knew there was no going back” 30. This acknowledgement ofstudents’ emotional experiences changes the direction for reform efforts from the narrow scopeof pedagogy and curricular support to a broader conversation that includes student engagementand the development of a supportive community. Efforts to understand student self-efficacy haveincluded studies of identity, or whether students think of themselves as engineers 31,32, anddefining what is meant by “continuing motivation,” other than simply staying in a degreeprogram 33.Some efforts should concentrate, then, on creating supportive environments within engineeringto help retain students, while others focus on developing courses and projects
one.Identifying these shifts is key to discovering strategies for students to help themselves orinstructors to help students get students on positive pathways that contribute to an improvementin global affect towards engineering. We also plan to undertake a comparison of the results fromour survey analysis to interview data about emotional pathways, as a step towards validation ofthe instrument. Looking forward, the study of affective pathways may also have connections toand impacts on other important factors for retention and student experience such as engineeringidentity, self-efficacy, and mindset.References[1] J. Swenson, A. Johnson, T. Chambers, and L. Hirshfield, “Exhibiting Productive Beginnings of Engineering Judgment during Open-Ended
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
CT awareness among leaders andpractitioners, builds traction by relating CT to local goals, educational initiatives, or reformefforts, connects teachers to help them explore grade-appropriate implementation, and createsopportunities to practice CT learning activities.Related WorkMalallah investigated complications associated with adopting a U.S.-based STEM outreachprogram into the Kuwaiti educational system. The program focused on teaching CT viaArduino and Scratch to students in grades 6–9. Malallah used pre-post self-efficacy surveys todetermine increased CT awareness. Survey results revealed that, although students wereconfused about some CT concepts, their overall CT knowledge improved after the STEMoutreach program [19]. In a
the content against bothprior analysis and relevant literature. Content validity through expert review We drafted materials for expert review, including a 1-page definition of framing agency and its sub-constructs, a version of the survey, and a scoring sheet. Given the relatively novel nature of the construct (e.g., as compared to developing a scale for self-efficacy in a new domain), we were concerned about the possibility of inclusion bias (i.e., in not having true expertise due to the newness of the construct, would experts tend to rate every question as relevant?). We developed 17 distractors to evaluate experts’ tendency to include constructs that may be interesting but not included as
Paper ID #37099Development of a Longitudinal Method to Measure AttritionIntentionsKyeonghun Jwa Kyeonghun Jwa is a Ph.D. candidate in the Department of Mechanical Engineering at The Pennsylvania State University. He earned his Bachelor’s degree and Master’s degree in Mechanical & Automotive Engineering from the University of Ulsan in South Korea. His research interests include doctoral engineering attrition, international graduate students’ academic literacy, and adjustment experiences in the U.S.Catherine Berdanier Catherine G.P. Berdanier is an Assistant Professor of Mechanical Engineering at Pennsylvania
Research, 16, 235-239.Atman, C., Adams, R., Cardella, M., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners, Journal of Engineering Education 96(4), 359-379.Atman, C. J., & Bursic, K. M. (1998). Verbal protocol analysis as a method to document engineering student design process. Journal of Engineering Education, 87(2), 121-132.Ball, L. J., Ormerod, T. C., & Morley, N. J. (2004). Spontaneous analogizing in engineering design: A comparative analysis of experts and novices. Design Studies, 25(5), 495-508.Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28, 117-148
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
space, the teams were able to use them efficiently and createand test multiple prototypes in a short period of time and make the necessary adjustment to theirdesign such that it better meets the identified requirements. As shown in Figure 3, theperformance of the final prototypes for both teams was tested using press test method and it wasobserved that both designs increase the weight-bearing limit of the patient as much as 12-15pounds. Figure 2: Prototyping in the maker space Figure 3: The final prototype was tested using press test methodSurvey InstrumentThe Engineering Design Self-Efficacy tool (Carberry et al, 2010) was used to measure anychanges in the students
pilot study shows that students' self-efficacy for specific cross-disciplinary team learning objectives was influenced by participation on team projects withothers from different disciplines. Further data collection will help better understand how teamcomposition, stage of project design, and individual factors such as year in school and priorexperience with similar projects impacts confidence levels.II. Development of cross-disciplinary team functioning measuresThe team also attempts to develop and measure teamwork in cross-disciplinary projectteams. Such teams consist of members with different functional experiences and abilities, andwill likely come from different departments within the organization 13. Many believe that inorder for
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
decreasing technical capability. 2) Attributes of holistically-thinking engineers are measureable via combined assessments of technical skills and self-efficacy, identity, attitudes, and other psychosocial factors. 3) Extracurricular LTS efforts, such as EWB, and curricular LTS efforts provide the same benefit; i.e., there is no discernible difference in impacts from different forms of LTS. 4) Underrepresented students are attracted to, retained in, and persist through engineering programs at higher levels when engaged in LTS.In brief, the research effort consists of a longitudinal study performed at four target institutions.These institutions are diverse in size, type, mission, and student socio-economic conditions(Figure 1
basedon 259 Israeli samples and 304 American samples. CIP theory emphasizes the knowledge of the occupation for one to make career decision.CIP theory also mentions the importance of interest to career decision making.Social Cognitive Career Theory (SCCT) Among other theories, social cognitive career theory (SCCT) 12,13 is a robust frameworkfrequently used to investigate career and academic behavior in both college and high schoollevels in sample of STEM field (engineering:1,14;15; computing:16). According to SCCT, self-efficacy affects outcome expectations; self-efficacy and outcome expectations are bothprecursors of interests 17; and interests, self-efficacy, and outcome expectations predict choicegoals jointly12. Hackett et al
learning material and computing abilities are the most influential ones in boosting engineering students’ self-efficacy (Hutchison, et. al., 2006). This measurement instrument has also indicated that factors like teamingskills, availability of help and ability to access the help, ability of completing assignments, problem solvingskills, enjoyment, interest and satisfaction in learning, and grades are strongly correlated with positive self-efficacy. Many research results have also indicated that there are statistically significant differences in self-efficacy between gender and ethnic groups. More details will be discussed the demographic category later.Learning styles. Student learning temperament types are found to have significant correlation
. In addition, some PhDstudents have extensive prior teaching experiences while others have none.While a career in academia typically requires research, teaching, and service, most doctoraldegrees in the United States are conferred at research intensive universities, where researchaccomplishments are prioritized over instructional training for future faculty members [4].However, as some engineering PhD students wish to pursue a more teaching-focused career at aprimarily undergraduate institution, these future faculty members eventually find they did not feeladequately prepared for their career [5].Further investigation on the self-efficacy regarding instruction for engineering PhD students isneeded. Specifically, there is a need to better
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
interest inventories (also referred to as measured interests) assess interests forschool subjects, occupational titles, work-related activities, and vocational activities.Students with science and engineering-related measured interests choose related majors ata higher frequency, and are more persistent in those majors. A recent meta-analysissuggests that interests begin to stabilize in early adolescence (Low, Yoon, Roberts &Rounds, 200510) indicating potential for early career intervention. It also appears thatSTEM-related self-efficacy beliefs are important co-determinants of college major choiceand performance. Self-efficacy beliefs are developed through a number of psychologicalmechanisms – the most influential being personal performance
an undergraduateengineering program at a large southwestern university. Students were invited to respond toonline surveys using a link sent to their university email address. Participants were surveyedthree times during their first year: prior to entering the engineering program (Survey 1), at theend of their first semester (Survey 2), and at the end of their second semester (Survey 3).Students were given time during summer orientation and during class to complete these surveys.In total, a sample of 2473 participants was used to develop and validate a 5-item engineeringidentity measure, with Surveys 1, 2, and 3 consisting of 1900, 1083, and 481 respondents,respectively.MeasuresEngineering identity and engineering self-efficacy, the belief
) USB-6008 DAQ and NI LabView.The tremor motion was simulated using a circuit designed to output 3 differentfrequencies to a vibration motor, resulting in motion in the range of human tremors.This project used two different data analysis methods: Empirical Mode Decomposition(EMD) and Auto-Regressive (AR) process of order p. Preliminary results show that eachof these methods performed in a satisfactory manner. Page 13.1065.6From educational perspective, this project has provided invaluable graduate researchexperience. The skills and self-efficacy gained from this project have stimulated thegraduate student’s research interests and his desire of
moving fromconcrete experiences into reflective observation is essential for learning.This learning was assessed by direct assessment of students’ performance on an in-lab exam thatassessed both theoretical and experimental skills, surveys of self-efficacy administered beforeand after the treatment, coding student answers to reflection questions in the lab manuals, andcounting the number of answers to interactive questions to determine compliance.Significant results from the experiment indicated that students in the treatment group took longerto complete the lab, felt greater time pressure, performed more poorly on the in-class evaluation,and had fewer metacognitive gains than the control group. The treatment appears to haveincreased the