engineering careers and on developing theircontent knowledge in select grade-appropriate science and mathematics content areas. Pre-posttesting was conducted with sixty-five students of diverse backgrounds in grades six through eightto measure their self-reported engineering-related self-efficacy, knowledge of engineering careers,and motivation to pursue future engineering classes and careers. In addition, interviews wereconducted to examine any changes in middle school camp participants’ affective characteristics ofmotivation, self-efficacy, and self-determination.Introduction The attraction and retention of students in science, technology, engineering, andmathematics (STEM) disciplines along the full length of their education is a national
, to estimate the expected total numberof delayed months, including: 1-3 months, 4-6 months, 7-9 months, 10-12 months, and morethan one year. In terms of the career outcome, we evaluated students’ job search self-efficacy byasking three questions [25]: “Since the COVID-19 outbreak occurred, how confident have youbecome in finding (1) the job for which you are qualified? (2) a job in a company/institution thatyou prefer? (3) the job for which you are prepared?” The 5-point Likert scale was from -2 (muchless confident) to 2 (much more confident). The Cronbach’s alpha for these three job search self-efficacy items is .906. The measure for mental health outcome, which focused on symptoms ofdepression and anxiety, asked students if in the last 7
state finals in Spring, 2016. Allteachers were invited to participate. Components of this survey relevant for the current workinclude demographics, information about teachers’ backgrounds, and also several constructs:self-efficacy for teaching engineering, self-efficacy for teaching entrepreneurship, and teacherperceptions of the program’s effects on students. Some of these constructs were assessed throughvalidated instruments, while others were measured with internally developed items. Teaching Engineering Self-Efficacy Scale Self-efficacy for teaching engineering was measured with the Teaching Engineering Self-Efficacy Scale (TESS), which was developed and validated by Yoon Yoon et al., 201411. Theseauthors “define teaching
303L in the fallsemester, and students who did not participate in FIG and 1.2 Communication measurementare enrolled in BME 303L in the spring semester. These Section 3 of the survey measured the students’ scientificdata will be used to optimize advising and curriculum for communication self-efficacy, which is related to ABETfirst year students and improve engineering outcomes for Student outcome g: an ability to communicate effectively.all students. Future surveys are planned for sophomore and This included 15 Likert-scale questions adapted from ajunior years as well. validated self-efficacy in scientific communication
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
possible foci.Next, participants’ self-efficacy was measured with 7 items (alpha = .92), each measured on a 7point scale with Likert response options “Strongly disagree”, “Disagree”; “Somewhat disagree”;“Neither agree nor disagree”; “Somewhat agree”; “Agree”; “Strongly agree”. : “I am doing wellin the course”; “I am doing poorly in the course” (reverse-scored); “I feel like I can successfullycomplete the course with a C or higher”; “I’m not sure that I can pass the course”(reverse-scored); “I’m thinking of dropping the course” (reverse-scored); “It is possible for me tosucceed in this course”; “I’m confident that I can get the grade I want in this course”.Participants were asked to indicate how much they agreed with each statement as they
module of the rotation-based course RQ3: Does the rotation-based course impact career ambitions? RQ4: Do students in the rotation-based course see themselves as Computer Scientists and/or Engineers?4 Methodology & Data Sources4.1 Data SourcesA pilot study was crafted to monitor the impact of the rotation-based course on identified outcomes of interestduring the Fall 2020 semester using a pre-post-survey design. We build upon the work of a prior study (Erdil &Ronan, 2019) that tested the applicability of the SCCT theoretical framework and tested survey items measuringstudents’ career intentions (pre and post) and course satisfaction (post). Desiring to measure additional internalmediators related to self-efficacy and outcome
4.27 4.33 4.72 4.36 4.25 iPhone project 4.10 4.65 3.81 4.20 4.23 Sterling Engine kit 4.26 4.60 4.75 4.06 4.60Student Survey Results: Self-Efficacy: One of the goals of SEI is to improve student self-efficacy in succeeding as an engineering student. To determine the impact of SEI on student self-efficacy, the Baldwin Confidence Survey Form was used in 2014.8 In this survey, which wascreated to measure self-efficacy in STEM, participants respond to statements on a five-pointscale, ranging from strongly disagree to strongly agree. Statements are phrased both positivelyand negatively (items
, intrinsic value, and test anxiety. The second andthird surveys also asked students to identify the most helpful and challenging aspects of the course. Students’ demographic information, test scores, homework scores, and prior learning outcomes(cumulative graduate point average and ACT math scores) were collected after the semester wasover. Participation in the study was voluntary, and participants received a small cash compensationfor the time that they spent completing surveys.Measures in Self-reported Surveys Self-efficacy, intrinsic value, and test anxiety were measured by 22 items adopted from the Mo-tivation Beliefs Questionnaire (Pintrich & De Groot, 1990). The engagement measure consisted ofthree subscales: behavioral
of technology use. Mishra and Koehler23 used a surveyto track changes in teachers’ perception of their TPACK understanding over a course thatincorporated educational technology. Moreover, Archambault and Crippen47 developed 24 surveyquestions to measure teachers’ understanding of various instructional and conceptual issues. Thiseffort adapted a widely used self-efficacy TPACK instrument46,48 for our PD program, whichemploys robotics to teach classroom science and math. Moreover, in our study, we reformulatedthe TPACK survey instrument,46,48 guided by the self-efficacy research,49,50 to establishparticipant’s confidence, motivation, outcome expectancy, and apprehensiveness for each of theseven components of the TPACK framework.3. Research
self-efficacy mayavoid developing better practices in exactly those areas in which they need the mostimprovement.16,17 Additionally, teachers who believe in the efficacy of their teaching onstudent learning have a profound effect on their classrooms, exhibiting longer-lastingconfidence and persistence, offering more productive feedback, and providing betteracademic focus.17,18 Self-efficacy can be measured for different subjects, so that ateacher who exhibits low self-efficacy for teaching English may have higher self-efficacyfor teaching science, and even one’s self-efficacy toward learning science and teachingscience may differ.19 Along this reasoning, Riggs developed an instrument to measurespecifically elementary school teachers’ beliefs
participants to report these findings. The remainder of theanalyses focused on gender.Similar rates of persistence existed for women and men, even though when they began theprogram there were statistically significant difference between mean scale scores for freshmenwomen and men on some measures of self-efficacy. For the Self-Efficacy Scale II, t(66) = 2.63,p = .011; Career Success Scale, t(66) = 3.03, p = .004, and Math Scale t(66) = 2.49, p = .015,men averaged higher scores than women (see Table 2 for averages). Although men scored higherthan women on the Self-Efficacy I Scale and Coping Self-Efficacy Scale, these results were notsignificantly different. Women and men scored similarly on the Inclusion Scale. The means onself-efficacy scales at the
, orconcentration—a “learning experience”—and other SCCT constructs: Innovation Self-Efficacy(ISE) and Career Goals: Innovation Work (CGIW).The EMS institutional sample represents a stratified quasi-random sample of ~350 U.S.engineering schools. Schools were stratified on the basis of: 1) research university or non-research university, 2) size of engineering school as measured by number of engineering degreesawarded, and 3) presence of an undergraduate business major on campus. This resulted in a2x2x2 scheme. Institutions within each stratification “cell” were “quasi” randomly sampled inthat schools were flagged as “Epicenter affiliated” or “not Epicenter affiliated”, and, wherepossible, roughly equal numbers were randomly selected from each group
faculty as having better-informed opinions in their areas of expertise and as being Page 12.1020.3 able to teach students techniques for evaluating the quality of evidence underlying conclusions. 4The self-efficacy or Perceived self-efficacy is another framework or operative construct that hasrelevance to Lifelong learning studies. A student’s self efficacy is related to subsequentbehavior and that is ultimately the intent of ABET’s inclusion of Life-long learning as aoutcome6. We should strive to create Engineering graduates who have adequate self-efficacyand therefore the motivation to never stop learning. The construct of
particularly enable a more diverse group of students to leveragecreativity and innovation toward success in engineering careers; 2) discover specific learningmodels that involve both STEM university students and pre-service teachers in order to developteamwork, self-efficacy, communication, and identity formation in the Maker environment; 3)pilot instruments to measure the impact of such programs on students’ self-efficacy,communication, and identity formation and 4) understand to what extent students who use themaker space for a class project become regular users of the space. This paper reports on theprogress and findings from the first year of implementation. Maker Space user log in data will beanalyzed as will preliminary results of student
– the Engineering Majors Survey (EMS) developed by the National ScienceFoundation (NSF)-funded National Center for Engineering Pathways to Innovation (Epicenter)and a survey developed by BRAID. Additional items were also created to explore issues andquestions not addressed by the EMS and BRAID instruments.The Engineering Majors Survey (EMS) (Gilmartin, et al., 2017) draws upon psychologicaltheories of career choice to ask students about their "innovation self-efficacy", their expectationsfor the outcomes of innovative behaviors, their innovation interests, and their goals around doinginnovative work in their early careers. Designed to measure a comprehensive range ofundergraduate learning experiences that may influence students' beliefs about
1.4 Missing 4 .8Instrumentation The 2014 version of the STEM IQ consisted of 71 items. 13 were demographic, 58attitudinal items related to students’ experiences, teamwork, learning experience, hands on activelearning, and career. The study focuses on only attitudinal items with a high reliability Cronbachalpha (α=.83). The items in the questionnaire are based on 100 scale. Further, the attitudinalitems are divided into three components. First component aims to measure STEM-related self-efficacy, like a student’s ability to complete academic milestones and to overcome performancehurdles. Students are asked to indicate their confidence to perform successfully
workforceonce they have arrived.Self-efficacy, grit, and leaning in; are these the keys that distinguish female students who willthrive in ECS not only as college students but also as career professionals? Arguably, Bandura,Duckworth, and Sandberg were a perfect storm of women’s empowerment theories. In ourSPARK assessments, we decided to quantify the first two items and qualitatively assess how –and if – our students were “leaning in”.In addition to measuring self-efficacy, we administered surveys to assess the students’ grit,perseverance, ambition, problem-solving abilities, resilience, self-confidence, and GPAs. Weincluded questions about their satisfaction with the various SPARK activities we organizedthroughout the years. We also asked open ended
inductive processfor extracting relationships and models and is suited to areas of inquiry that are not wellresearched. The data was coded using a commercial software package for qualitative contentanalysis, Atlas.ti (www.atlasti.com).Children expressed varying levels of self-efficacy, the self-perception of one’s ability tocomplete a given task. Although self-efficacy was not measured directly, based on Bandura’s17social cognitive theories, positive, affirming, motivated, and confident statements wereinterpreted as indicative of high self-efficacy and instances of negative, pessimistic, disengaged,anxiety-related statements were associated with low self-efficacy.The most significant gender-related findings were: (1) of equally skilled girls and
advised section was perhaps due torepetitive brainstorming during the weekly team meetings, while the advised section wasdeprived of this activity. Work by others suggests that repetitive brainstorming improves self-efficacy 8,9 – people’s beliefs in their capacities to produce desired effects through their actions 10.We hypothesize that the construct of self-efficacy is covariate with actual process knowledge,and that process knowledge is improved through brainstorming. The latter may be tested bymeasuring whether knowledge of processes other than engineering design can be covertlyinfluenced by brainstorming, but without overt instruction on that process.These data are consistent with a previous study showing that the mode of instruction
(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
, depression, and anxiety) and personal resources (self-efficacy, engagement, and motivation) using an online survey. Students also provided permissionto record their grades on course assignments for analysis. Following the end of the semester,participating students’ scores were recorded for the following: (1) Average of scores forhomework assignments; (2) Average of scores on quizzes; (3) Average of scores for each of threephases of the term project; (4) Average of scores for three midterm exams; (5) Score for classparticipation. Data will be analyzed using multiple regression models. The proposed paper willdescribe the course structure and design of the course assignments, which differ in their level offlexibility, as well as the results and
students.In Texas, students were measured over a six-year period. From 2006 – 2010, enrollmentquadrupled and participants increased 18,686 individuals (4498 in 2006 to 23184 in 2010)9.Female participation increased 586% and Hispanic students increased 507%. This study alsoshowed a high impact on students enrolling in higher education (62.1%) compared to their non-PLTW counterparts (58.4%)9. In addition, post-secondary enrollment was slightly greater forfemales (63.5%) compared to their non-PLTW peers (63.1%).Several studies have examined self-efficacy of females for math and science subjects whenparticipating in PLTW10,11,12. Exposure to engineering through PLTW has shown to havesignificant impact on self-efficacy and underrepresented students10. The
REU affect students’ self-efficacy of making decision about graduate school and success therein? 3. How does the REU affect students’ preferences on research type? 4. How does the REU change participants' perceptions of their research knowledge, skills, and engineering career path?II. MethodA. SettingObjectives of the REU Program. The specific objectives of the REU program at the universitywere to (a) engage a minimum of 10 undergraduates annually; (b) prepare the students forgraduate school through workshops on the Graduate Record Examinations (GRE), increasingawareness of graduate opportunities, strengthening of resumes by publishing research, andimproving written and oral communication; and (c) measure the effectiveness of
. 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 &
structure previously determined through exploratory and confirmatory factor analysisrevealed five latent variables that align with a framework proposed by Fila et al. [1] for teachingengineering within a humanistic lens to help students develop a sense of belonging and theirengineering identity. Our SEM analysis showed that for all students, academic self-confidenceand self-efficacy and a broad understanding of engineering both have a significant positiveinfluence on their sense of belonging, which in turn has a significant influence on their attitudestoward persisting and succeeding in engineering. Appreciating the importance of non-technicalskills in engineering had no significant influence on most students’ sense of belonging with theexception
affective measures related to increased interest in andawareness of careers related to photonics or other STEM fields. We were particularly focused on Page 11.1055.11reaching underrepresented ethnic/cultural groups and females. Research suggests that the barriersto greater involvement in STEM careers for underrepresented minority groups and women arestrongly related to factors such as people’s beliefs about their competence in the science-relatedareas2,3,6. Specifically, low self-efficacy beliefs, lack of encouragement, and a lack of access tomaterials and resources together with other cultural, familial, and socioeconomic factors conspireto keep
through theimmersion of “creative work…[and] ‘deliberate practice’” [8]. One way to gauge one’s creativeability is through the measure of Creative Self-Efficacy (CSE) [30], [31]. CSE is a measure ofone’s belief in their creative ability and has been shown to be a predictor of future creative success.Not only is CSE important, but short creative activities have been shown to increase CSE.Many tools exist to help people brainstorm ideas such as: brainstorming, Design Heuristics Cards,SCAMPER, and C-Sketch [29],[32]. While product dissection has traditionally been used as alearning tool, it has also been investigated as a creativity tool [21]. Prior research has found thatboth virtual and physical product dissection have a positive impact on
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