incourses related to the AIMS certificate programs. Project-based activities such as AIMS-relatedworkshops offered by the university will be assessed to measure learning outcomes associatedwith engineering self-efficacy, judgment, and leadership skills.Engineering self-efficacy (ESE) is an individual’s belief in their capability to act in the waysnecessary to reach specific goals. Judgment about one’s abilities can influence behavior and goalattainment. We hypothesize that the groups’ self-guidance during the hackathon will improvetheir ESE related to applying AIMS concepts. Next, engineering judgment (EJ) is an individual’sability to make and justify decisions and predict the resulting consequences. EJ is developed inparallel with engineering
SystemsTheory recognizes that variation in individuals’ development “exists across time within contexts,and across contexts within time;” as a result, “differences in time and place constitute vitalcontributors to plasticity across the life span” [13]. Given the variations by time and place, weexpect a diverse range of pathways of individuals who are on their way to the engineeringprofession.The Social Cognitive Career Theory (SCCT) [14] posits that one’s learning experiences caninfluence their self-efficacy and outcome expectations, which in turn influences their interests,goals and, ultimately, career choice actions; these learning experiences are affected by personinputs (such as predispositions, gender and race) and contextual affordances (such as
-efficacy by asking four items on a 5-point scale (1= notconfident at all; 5=completely confident): “how confident are you in your ability to engage in thefollowing activities related to being an entrepreneur or an individual who starts a company, eitheralone or with others? 1) successfully identify new business opportunities, 2) create new products,3) think creatively, and 4) start a business with a new idea. The items were averaged to calculatethe entrepreneurial self-efficacy variable.Entrepreneurial identity aspirations We asked six items to assess the entrepreneurial identity aspirations measure on a 5-pointscale (1 = strongly disagree; 5 = strongly agree): “I think I can become an entrepreneur,” “I cansee myself as an entrepreneur
practices focused on team- and project-based learning. ©American Society for Engineering Education, 2023 Student perspectives on engineering design, decision-making, adaptability, and support in capstone designAbstractThis study analyzed how students’ sense of support from industry mentors and teammates in acapstone design course was related to their perceived learning regarding engineering design andadaptability when controlling for design self-efficacy and preparedness. An end-of-course surveyprovided the data for this study and included Likert-type items to measure these six factors aswell as open-ended questions regarding students’ experience in capstone design. An explanatory
national Ph.D. programs.The scope of this work is to develop a baseline of the data within a single Hispanic servinginstitution. The analysis completed to this point validates the survey instrument in measuring theidentified constructs. This validation is necessary so that this study may be expanded to a largersurvey population.Research QuestionsThis research investigates several factors that are believed to impact the identity of engineeringstudents as researchers. We seek to assess the role of research self- efficacy, researcher identity,and cultural compatibility on research persistence intentions. These variables were selected asthey have been determined to be relevant factors in prior identity studies [16], [29]–[35].Students that self
(e.g., ARS-30) tend to measure resilience as aprocess by which persons overcome adversity. However, resilience also enables studentsto achieve their goals and improve their learning outcomes. Factors indicative of thisprocess, such as self-efficacy, adaptive coping, exploration, and willingness to changelearning approaches when needed, are not measured in the ARS-30 or other currentresilience scales. The proposed Values Resilience Scale (VRI) under study measuresresilience as a process that enables one to overcome academic adversity so as to achieveone’s fullest academic potential. Such a measure would allow educators to identifystudents who may be hindered from reaching their utmost potential through their lack ofacademic resilience, and help
barriers facultyexperience in providing encouragement to students. Additionally, the creation and validation of atool to measure faculty perceptions of providing encouragement can be used by institutions toidentify critical areas to strengthen how we teach in engineering.Guiding FrameworkAn extensive literature review showed the Academic Encouragement Scale (AES) and theFaculty Encouragement Scale (FES) as the best instruments to guide this research [20, 21]. Bothmeasure students’ perceptions of receiving encouragement in academic settings. Findings fromboth studies indicate that receiving encouragement increases students’ self-efficacy and outcomeexpectations.The Social Cognitive Career Theory (SCCT) guided the development of the survey
institution in theSoutheast United States. Given the exploratory nature of the study, a novel survey tool wascreated that focused on: residual time, club participation, design skills before and after clubparticipation, design self-efficacy, and demographic information, see Appendix A. This researchstudy was approved by the IRB at Duke University (protocol #2023-0178). 1) Survey DesignFor the purpose of transparency, we defined engineering clubs as a subset of clubs whosemembership is primarily engineers, the subject matter is technical, and/or they are a pre-professional organization for engineers. The engineering school at Duke University gives clubsthis designation. We divide engineering clubs into three categories: competition design teams
psychosocial construct values forFeelings of Inclusion, Coping Self-Efficacy, and Belonging Relative to Community are nearlyidentical (standard deviation of .04 or less) from spring of 2020 through spring of 2022. The labfocused skill efficacy for Design and Experimental skills, also maintained steady average valueswith a standard deviation of .04 or less. These values imply, that for the students who completedthe survey, the sudden switch to a virtual format was successful in supporting these constructs.Table 2. Constructs measured in surveys to date Construct Average value of mean per semester (S.D
attended the Bridge remotely, still found the experiencetransformational. In a case study interview conducted by Ruxton Consulting, one student attributedtheir success to the Bridge saying, “I really think I wouldn't be here. I wouldn't be studyingengineering without the creation of the Bridge program.” (Ruxton Consulting Evaluation Reportpresented to the PI, 2022).Students also reflected on how their effort, within the structure of the Bridge, contributed to theirimproved self-efficacy in math. As one student shared, “It's not a test of your finances, or yourbrains. It's a test of how hard you can work, and I think that's a great factor to measure someoneby.” Another student acknowledged how much work was ultimately needed in order to be readyfor
structured interviewdata collected through an extracurricular student project. We investigated three key aspects ofgraduate school, particularly experiences with 1) work-life-balance, 2) imposter syndrome, and3) burnout. To develop the survey and interview instruments, we developed a pool of memes andgraduate student oriented advice columns then used thematic analysis to identify 9 thematicquestions about the graduate student experience. For this work, the data set was abbreviated toconsider only the 3 most salient topics. We found that students generally disagreed with thenegative themes identified and that memes tended to exaggerate these features of graduatestudent experience. However, emergent themes of self-efficacy in our analysis demonstrated
(survey questionnaires) were collectedsimultaneously, and the analysis of those data was completed separately.Participants completed pre-test and post-test surveys. The pre-test survey, completedprior to their departure to Brazil, was a 30 minute online instrument that used a five-pointrating scale to evaluate baseline values of the measures studied: (1) research self-efficacy; (2) research skills; (3) knowledge of water management issues; (4) attitudestoward technology and sustainable development; (5) global competency and interculturalknowledge, attitudes, skills, and awareness; (6) teamwork skills; (7) perceptions ofenvironmental engineering community relevance; (8) attitudes toward interdisciplinaryresearch; and (9) behavioral intentions for
and measure their course preparedness. Welch's t-test was also used to determineif there was any difference in the students' performance on the common final exam.For the test anxiety Likert questions, a score ranging from 5 to 20 was obtained by summing thescores for all five questions, with 1=almost never and 4=almost always. Paired t-tests wereperformed for both grading methods to identify any changes over the semester after taking thecourse. In the case of the self-efficacy questions, scores for each category (e.g. masteryexperience, vicarious experience, social persuasion, and physiological state) were averaged afterbeing set on a scale of 1=definitely false to 6=definitely true. Paired t-tests were performed forboth grading methods to
. 1997, New York: W.H. Freeman and Company.15. Kolar, H., A.R. Carberry, and A. Amresh. Measuring computing self-efficacy. in 2013 ASEE Annual Conference & Exposition. 2013.16. Carberry, A., M. Ohland, and H.S. Lee. Developing an instrument to measure engineering design self-efficacy: A pilot study. in ASEE Annual Conference and Exposition, Conference Proceedings. 2009.17. Yildirim, T., M. Besterfield-Sacre, and L. Shuman. Scale development for engineering modeling self efficacy. in 2010 Annual Conference & Exposition. 2010.18. Baker, D., S. Krause, and S. Purzer. Developing an instrument to measure tinkering and technical self efficacy in engineering. in 2008 Annual Conference &
active learning activities. Finally, they were asked about the barriers that theyfaced when trying to implement active learning in their classrooms. Instructor and student surveys were aligned so that we could learn how student andinstructor perceptions are comparable for each individual class. The student survey asked abouttheir instructor’s use of active learning and if their instructor used different strategies forimplementing active learning. Additionally, we measured the student response to active learningincluding their affective and behavioral responses. Finally, we asked questions about theirfeelings of belongingness in their STEM classes as well as their self-efficacy in these courses.Preliminary Findings To understand
year. Another important finding was that expectancy beliefs werepositive predictors of academic achievement in the form of higher GPAs, while value beliefswere predictors of more concrete career plans. Another study that found differences based on sexwas looking into intelligence beliefs and social comparisons [50]. The results of this papershowed that strong self-efficacy, which relates to expectancy measures, was more beneficial tofemales than males regarding final course grades. Another interesting, and concerning, findingwas that intelligence growth mindsets had no correlational effect on self-efficacy for women andthat students concerned with social comparisons were just as detrimental to self-efficacy for bothmales and females. Social
Experience: A large design oriented clinical immersion course based in emergency departments. 2016.13. Carberry, A. R. and Lee, H., Measuring Engineering Design Self-Efficacy. Journal of Engineering Education, 2010. 99(1) :71-79.14. Aultman Website “Student Experiences” [Online]. Available:https://aultman.org/education/students-at-aultman-hospital/#/ [Accessed April 14, 2023].15. Aultman Website “Healthcare Observations” [Online].Available:https://aultman.org/education/students-at-aultman-hospital/ah-job-observation-2/#/[Accessed April 14, 2023].16. Infobase Website [Online]. Available https://infobase.com/ [Accessed April 14, 2023].Appendix I: Career Aspiration QuestionnaireSurvey/Questionnaire No
identities, epistemologies and values. Volume 2 : engineering education and practice in context. Cham, Switzerland ; Heidelberg, Germany : Springer International Publishing, 2015.[29] Y.-h. Liu, S.-j. Lou, and R.-c. Shih, "The investigation of STEM self-efficacy and professional commitment to engineering among female high school students," South African Journal of Education, vol. 34, no. 2, pp. 1-15, 2014.[30] D. Kiran and S. Sungur, "Middle School Students' Science Self-Efficacy and Its Sources: Examination of Gender Difference," Journal of Science Education and Technology, vol. 21, no. 5, pp. 619-630, 2012, doi: 10.1007/s.[31] T. P. Robinson, "THE DEVELOPMENT OF AN INSTRUMENT TO MEASURE THE SELF
investigated. Demographic information for thetotal analytic sample is as follows: 76% self-identified as men, 95% White, 50% were onEngineering Track 1, 30% were on Engineering Track 2, and 20% were on Engineering Track 3.Measures Engineering Self-Efficacy. Students’ confidence in their ability to complete necessarysteps for obtaining their engineering degree was measured using a three-item instrumentdeveloped by Lent and colleagues [45]. The items were rated on a 5-point Likert scale (1-noconfidence to 5-complete confidence) where participants indicated their level of confidence intheir ability to complete each step necessary to obtain their engineering degree. Engineeringself-efficacy scale scores were derived as the average of all items
unique. This restructuring would also allow students to work in an industry-like environment where teams have specific tasks and communication is critical. The particularuse case presented in this paper is to create a remote-sensing application for vital signmonitoring. Some details will not be included to avoid IP infringement with the sponsor of thisproject.The assessment plan is to evaluate if this new team structure improves learning outcomescompared to a traditional team. The two outcomes being compared in this study are ABETstudent outcome 3 and 5 by measuring student's communication and self-efficacy relative toother team structures (e.g. other capstone section). ABET 3 (Communication) relates to theability to communicate effectively with
] and measured to what extent students felt included,valued and respected. We used this scale with the purpose of exploring students’ sense ofbelongingness, specifically in CS, and modified the items to include “in computing.” Adefinition of computing was also included, “Computing is defined as doing things like making anapp, coding, fixing a computer or mobile device, creating games, making digital music, etc.”Sample questions then asked students to indicate the extent to which they agreed with statementssuch as, “I feel comfortable in computing” and “Compared with most other students at myschool, I know how to do well in computing.”Self-Efficacy: Self-efficacy captures students’ beliefs that they can accomplish designated tasks[38] related to
job seekers. The system, called VirtualInterview (VI)-Ready, offers an immersive role-play of interview scenarios with 3D virtual agentsserving as hiring managers. We applied Bandura’s concept of self-efficacy as we investigated: 1)overall impressions of the system; 2) the impact on students’ job interview preparedness; and 3)how internal perceptions of interview performance may differ from external evaluations by hiringmanagers. In our study, we employed a convergent parallel mixed methods approach.Undergraduate and graduate students (n = 20) underwent virtual job interviews using theplatform, each interacting with one of two different agents (10 were randomly assigned to each).Their interactions were video recorded. Participants then
communities of practice. This case study was completed as part of courseevaluation and feedback processes, in order to identify improvements to how the course kits andtools were implemented and supported. All processes were completed under the supervision andwith the approval of the course instructors. The survey questions, shown in Appendix 1 in Table2, included open-ended questions to explore students’ feedback on the benefits of kits and theirvalue in supporting their learning, and any barriers they experienced in using them. Questionswith Likert scale rating for students to rate an item on a 1-to-5 scale [12], were used fordetermining level of student engagement and measuring students’ self-efficacy in developingdesign, experimentation, analysis
of studying engineering, self-efficacy, and contingencies of Academic Competence, Academic Competence subscale. Example items and references for each of these scales are provided in Table 1, and 5. Retention, defined as enrollment in the engineering school in the fall of the second year.AnalysisTwo machine learning techniques were investigated in this work: a neural network and a decisiontree. A neural network works to learn patterns via an iterative process of trial and error to classifydata into categorical outputs [11], and the results are black box (it is not possible to tell why aclassification was made without the aid of explainable methods). For the neural network analysis, Table 1: EVT
for the games included in the curriculum. Figure 1. Example of the hardware settingTheoretical FrameworkWe developed a conceptual framework for the PICABOO hardware curriculum that reflected ourteam’s shared vision for the structure and the outcomes of our curriculum. Specifically, we aimto promote engineering identity and persistence by gamifying the learning experience to fostersituational interest [7] and to support students’ self-efficacy for engineering [8]. Additionally,educators' self-efficacy also influences their confidence in teaching hardware concepts [9]. Therelationships between these theoretical foundations are illustrated in Fig. 2 and are incorporatedinto the design and development of the modules
students were invited to complete the survey a secondtime early in the fall quarter of their second academic year, thus bounding their first-year collegeexperience with pre and post survey administrations. This process of survey data collection wasrepeated for each new cohort of incoming students over the course of the study. The instrumentused was an adapted version of a survey developed by the Studying Underlying Characteristicsof Computing and Engineering Student Success (SUCCESS) project [18-19], which includesitems drawn from previously validated measures of self-efficacy, identity, and sense ofbelonging related to engineering [1, 11].Unfortunately, at least in part due to impacts of the COVID-19 pandemic, response rates werelower on the post
end-of-semester presentations with direct feedback from mentors. Based on thefeedback from Fall 2021, the implementation was redesigned and introduced in Spring 2022.Two problems were assigned in Spring 2022 along with mentor interactions and students’presentations.Instrument Development and EmploymentThe study used two survey instruments to measure self-efficacy and engineering identity, whichwere chosen based on literature and piloted in two different courses. The surveys wereimplemented at the beginning and end of the Spring 2021, Fall 2021 and Spring 2022 semesters.Additionally, the study conducted interviews with randomly selected students, stratified bygender, at the beginning and end of both semesters, as well as with two mentors and
2021 were given to women [3], but they constitute 16% of work-ing professionals in the field [4]. Additionally, Hispanic and Black populations are under-represented in the engineering/STEM workforce relative to the general workforce: 11% ofthe total workforce is Black, but only 5% of the engineering workforce, and 17% of the totalworkforce is Hispanic, but only 8% of the STEM workforce [3]. Addressing the issues thatcreate these disparities is multifaceted, but beginning with educational interventions for stu-dents that enhance their self efficacy for further pursuing engineering post graduate is a start.Prior research has indicated the need to increase self efficacy in engineering students, es-pecially from these diverse backgrounds, to help
with sixsubscales measuring six competencies: Maintaining Effective Communication (4 items),Aligning Expectation (4 items), Assessing Understanding (3 items), Fostering Independence (3items), Addressing Diversity (3 items), Promoting Professional Development (4 items). Therevalidated scale is called MCA-21 to distinguish it from the original MCA-26 [36].Newly Developed Instruments for Added Modules in EM As the NRMN Mentor Training Core expanded the EM curriculum by adding additionaltraining modules, they developed scale items to assess the training outcomes of these modules.For the self-efficacy training module, the instrument (Promoting Mentees’ Self-Efficacy – table1), which consisted of five items on a 7-point Likert scale, aims to
. ©American Society for Engineering Education, 2023 Clinician-engineer self-concept in biomedical engineering students and its relationship to race, first-generation status, and mode of deliveryIntroduction and abstractRetention, recall, comprehension, and measurable skills are mainstays of the scholarship ofteaching and learning, and yet they represent only a fraction of what engineering educators hopeto achieve through education. The development of self-efficacy, for example, is a common goaland is often measured as a psychological construct. Less commonly measured constructs that arenonetheless commonly valued by educators are the development of creativity, perseverance(grit), and self-concept.Self-concept is particularly interesting in