using the four cognitive processes of forethought,intentionality, self-reactiveness, and self-reflectiveness as outlined by Bandura [6], [11]. The studyby Yoon [25] used the personal agency constructs to examine the relationship between agency,vocational identity, and career decision self-efficacy workforce education and development forundergraduate students broadly. Our search yield no new literature on the development of personalagency measures. Yoon [25] also claimed that before his study, no scale using Bandura’s personalagency constructs had been developed.We used survey items from Yoon’s [25] original scale, making modifications and changes toseveral items. However, we did not adopt Yoon’s [25] survey items for the latent construct self
the level of utility value students perceived could explain approximately half of thevariance (51%) in their intention to pursue an engineering career. [6]. Based on this research, we expect thatsuccess and usefulness will both predict empowerment.The success factor of the MUSIC inventory measures the perception that one can succeed in the relevantacademic work if they put forth the effort [1]. Several different human motivation theories serve as thefoundation for this factor. Bandura’s now-classic self-efficacy theory [7], Covington’s self-worth theory [8],goal orientation theory [9], and expectancy-value theory [10] all address achievement motivation and success/competency. In fact, competency is now considered to be a basic human need [11
scale measuredthe extent to which students believed that their academic performance was dependent on factorsthey controlled, such as the amount of their study or effort (e.g., ‘‘If I try hard enough, then I willunderstand the course material’’). The eight items of the self-efficacy scale measured the extentto which students believed that they were competent in terms of task-related abilities and skillsand had a high likelihood of a successful academic performance (e.g., ‘‘Considering thedifficulty of this course, the teacher, and my skills, I think I will do well in this class’’). The fiveitems of the test anxiety scale assessed the extent to which students experienced discomfort orhad negative thoughts that could interfere with their test
into not only the predictive nature of these characteristics, but the predictivepossibilities of their interaction in attrition within engineering.Data collection and Instrumentation The sample in this study included 1,523 incoming first-year engineering students (292females, 1,231 males) at a large Midwestern university during the 2004-2005 academic year.Ethnicity was as follows: 2.05% African American, 0.51% American Native, 10.18%Asian/Pacific Islander, 2.64% Hispanic, 82.43% Caucasian, 2.20% Other. The students’ non-cognitive measures were collected across eight scales (completed priorto the freshman year): Leadership (20 items), Deep vs. Surface Learning (20 items),Teamwork (10 items), Self-efficacy (10 items), Motivation (25 items
studentsevaluation of teaching (SET) survey was conducted by CSU Chico Department of InstitutionalResearch which captured students’ attitude regarding self-efficacy using a Likert-type scale from1 to 5. This paper discusses the outcomes of this survey.Tags: Energy Conservation Measure, Engineering Thermodynamics, Energy Efficiency, EnergySavings, Central Utility Plant, Field Trip I. IntroductionPublic policy is a key driver of energy efficiency investment in the United States. State policiesthat support ratepayer-funded energy efficiency programs, federal and state low-incomeweatherization efforts, energy efficiency programs administered by state energy offices, andbuilding codes and standards have been major contributors to the increase in energy
STEM careers butthe question remained how much was attributable to the EPICS experience itself. An instrument9based in Social Career Cognitive Theory10 was developed to assess change in self-efficacy,outcome expectations, and personal interest in engineering amongst high school students whoparticipated in the EPICS High program. It was comprised of survey questions and open-endedresponses. In addition to the focus on self-efficacy, outcome expectations, and interests, thesurvey addressed perceived attributes of an engineer, student understanding of scientists versusengineers, changes in grades, college and major goals, and contextual supports. More detailsabout the full instrument have been published previously9, and the analysis of the data
persistence as a manifestation of motivation,while Graham et al [6] view motivation as a driver of student engagement. Self-efficacy orconfidence is one among several constructs underlying motivation. Programs that have beensuccessful in improving the persistence of college students in STEM deploy threeinterventions, which include: 1) early research experiences, 2) active learning, and 3)membership in STEM learning communities.3. Literature ReviewStrategies to improve knowledge retention and student interest in Computer ScienceProblem-based Learning (PBL) is an instructional model that may prove a good fit forcomputer science education due to the problem-solving basis that is also a quality shared withthe nature of many STEM careers. Problem solving
. National Science Foundation, Science and Engineering Indicators 2010, 2010, NSF.4. Marra, R.M., et al., Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engineering Education, 2009. 98(1): p. 27-38.5. Atkinson, R.D. and M.J. Mayo, Refueling the US Innovation Economy: Fresh Approaches to Science, Technology, Engineering and Mathematics (STEM) Education. 2011.6. Huang, G., N. Taddese, and E. Walter, Entry and Persistence of Women and Minorities in College Science and Engineering Education. Education Statistics Quarterly, 2000. 2(3): p. 59-60.7. Berenson, S.B., et al., Voices of women in a software engineering course
are poorpredictors of students’ learning gains due, in part, to students’ inability to accurately assess theirlearning as socio-cognitive elements such as a students’ self-efficacy beliefs may distort theirperceptions of their own learning, causing some to overestimate their learning gains while others,with lower self-efficacy beliefs, underreport their learning gains (Lattuca, 2023).We contend that this issue is particularly important in computer science (CS) education, whereautograded assignments are a growing approach to delivering students, instructors, andresearchers feedback on written coding assignments (Haldeman et al., 2018). That is, autogradersmay falsely suggest that students who have developed and implemented working code
remote controlled aircraft. While influencing longterm educational goals is a primary focus of the STEPS camps, the experiences andactivities are also designed to promote self esteem, self confidence, and demonstrate thebenefits of teamwork and collaboration. Based upon this positive message, Grand ValleyState University began offering STEPS camps in the summer of 2002, and the popularityhas ensured full capacity participation every year thereafter.Pre and post assessments measuring content knowledge, self efficacy about success inmathematics and science, and the likelihood of pursuing STEM related fields are used toevaluate the camps yearly. Results indicate that increase in content knowledge amonggirls in the 2007 program increased from 10.5
applied to two different drivers on the same track.With this metric, areas for driver improvement could be identified and potentially be used toguide an event-specific driver selection process or personalize driver training.Student learning objectives linked to ABET outcomes are described in the context of how theyare assessed in this course. Results from student self-efficacy surveys and student achievementon assignments are presented and discussed as they apply to ABET outcomes b, g, i, and k.IntroductionAuthentic engineering experiences, such as student competitions, sponsored projects, designclinics, and project-based learning modules have been incorporated broadly within theundergraduate curricula to enhance student learning. The challenges
; Williams, Smiley, Davis, & Lamb, 2018). Non-cognitivefactors are defined as unobservable traits and latent skills related to students academicachievement (Yoon et al., 2014).The Student Attitudinal Success Instrument (SASI; Immekus, Imbrie, & Maller, 2004; Immekus,Maller, Imbrie, Wu, & McDermott, 2005; Reid, 2009; Reid & Imbrie, 2008; Yoon et al., 2014)was developed to quantify non-cognitive characteristics of first-year engineering students beforeentering colleges or universities. The original SASI consisted of 161 items assessing ninespecific non-cognitive constructs: 1). intrinsic motivation, 2). academic self-efficacy, 3).expectancy-value, 4). deep learning approach, 5). surface learning approach, 6). Problem-solvingapproach, 7
] 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
). “The role of interest in understanding the career choices of female and male college students,” Sex Roles, vol. 44, pp. 295-320. 2001.National Academy of Engineering. (2004). “The Engineer of 2020: Visions of Engineering in the New Century,” National Academies Press, Washington, D.C, 2004.Ponton, M. K., Edmister, J. H., Ukeiley, L. S. & Seiner, J. M. (2001). “Understanding the role of self- efficacy in engineering education,” Jnl of Engineering Education, vol. 90, no. 2, pp. 247-251, 2001.Priniski, S. J., Hecht, C. A. & Harackiewicz, J. M. (2017). “Making Learning Personally Meaningful: A New Framework for Relevance Research,” The Jnl of Experimental Education, vol. 86, no. 1, October 18, 2017
a Ph.D.12. Thesurvey uses attitudinal scales to measure undergraduate students’ attitudes toward graduatestudies, their engineering skills self-efficacy, and their level of school-related self-confidence.Development of the Attitudes toward Graduate Studies SurveyThe Attitudes toward Graduate Studies Survey was modeled after the Attitudes to EngineeringSurvey6-10. Several drafts of the survey were reviewed and revised based on feedback fromengineering faculty and interviews with a group of students who took the survey. Students areasked to indicate the degree to which they agreed or disagreed with a total of 30 statementsabout careers in engineering, the benefits and or disadvantages of graduate studies, their desireto pursue graduate
new interest inunderstanding what attracts students to careers in science and engineering and what teachingstrategies can maintain interest in those careers. Validating and testing accurate measures ofstudents’ FTP can ultimately lead to meaningful contributions in this area of study. Futureresearch will reveal how well FTP predicts important outcome variables such as academicpersistence and study strategies among engineering students as well as how other well-established psychological constructs such as self-efficacy and work engagement interact withFTP. In the end, larger and more comprehensive models of motivation can be constructedthat will provide insight not only into the structure of FTP as a motivational construct, butalso into
rationale for alarger study.Future InstrumentsWith the excitement of getting 3, 4, and 5 year old children to tinker has lead these facultymembers to the pursuit of future research in the area of developing a range of tools, models, andresources for use by K-12 STEM teachers that will increase student awareness and interest intechnology as an academic pursuit and career opportunities, with a particular focus on girls.Utilizing real world applications and examples for the students to find relevance in the lessonswill increase the self-efficacy of both the teachers and their students. The goal is to assist theteachers without adding additional work, but increasing student interest in STEM.Research has shown that girls and women are particularly
. Page 22.454.7 5. Pajares, F., Hartley, J., & Valiante, G. (2001). Response format in writing self-efficacy assessment: Greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development, 33, 214-221. 6. American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association. 7. Carminer, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Thousand Oaks, CA: SAGE Publications. 8. Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp
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
, almost never), thisscale reflects participants’ awareness of their mindfulness with higher scores indicated they areless mindfulness in the daily life events.Core Self-EvaluationsJudge, Erez, Bono & Thoresen stated, “core self-evaluations is a basic, fundamental appraisal ofone’s worthiness, effectiveness, and capability as a person.” [7] There are four traits that make upthe core self-evaluations: self-esteem, generalized self-efficacy, neuroticism, and locus of control.[16] These traits can be measured to predict people’s satisfaction with their job, job performanceand life situation. [17] In addition, this inventory was validated [7] using both corporate employeesand university students. It asks participants how strongly they agree or
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
, J. A. Zeiber, P. Sullivan, S. Stochaj, “Using multi-disciplinary design challenges to enhance self-efficacy within a summer STEM outreach program, “ in Proceedings of the 2018 ASEE Southwest Section Conference, Austin, TX, 2019.5. A. Reynolds Warren, K. Harp, N. Ben Aissa, E. Specking, “Responding to COVID-19: Insights into making summer camps virtual,” in Proceedings of the 2021 ASEE Midwest Section Conference, Virtual, 2021.6. J. J. Rogers, T. G. Ganesh, J. Velez, “Engineering virtual design competition – a solution for high school summer outreach during the pandemic and beyond,” in Proceedings of the 2021 ASEE Annual Conference, Virtual, 2021.7. M. E. Foltz, and S. Koloutsou-Vakakis, “Can online summer camps work
online at http://caeeaps.stanford.edu/phpESP/admin/manage.php.[20] LAESE (Longitudinal Assessment of Engineering Self-Efficacy) survey versions 3.0 (copyright 2006) and 3.1 (copyright 2007), which are products of AWE (Assessing Women and Men in Engineering), available online at www.aweonline.org.[21] DeVellis, R. F. (1991). Scale Development: Theory and Applications. Newbury Park, California: Sage Publications.[22] Armstrong, J.B., and Impara, J.C. (1991). The impact of an environmental education program on knowledge and attitude. Journal of Environmental Education, 22(4):36-40.[23] Barrow, L. H., and Morrisey, J. T. (1987). Ninth-grade students' attitudes toward energy: A comparison between Maine and New Brunswick. Journal of
and Equity Research (PEER), The Urban Institute, Washington, DC, 2005.[47] M. T. Jones, A. E. L. Barlow and M. Villarejo, "Importance of Undergraduate Research for Minority Persistence and Achievement in Biology," The Journal of Higher Education, vol. 81, no. 1, pp. 82-115, 2010.[48] M. W. Ohland, C. E. Brawner, M. M. Camacho, R. A. Layton, R. A. Long and e. al., "Race, Gender, and Measures of Success in Engineering Education," Journal of Engineering Education, vol. 100, no. 2, pp. 225-252, 2011.[49] J. A. Raelin, M. B. Bailey, J. Hamann, L. K. Pendleton, R. Reisberg and e. al., "The Gendered Effect of Cooperative Education, Contextual Support, and Self-Efficacy on Undergraduate Retention," Journal of Engineering
Education Conference (FIE), 2016 IEEE.Smith, K. A., Sheppard, S., Johnson, D. W., & Johnson, R. T. (2005). Pedagogies of engagement: Classroom‐based practices. Journal of Engineering Education, 94(1), 87- 101.Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and individual differences, 16(1), 1-12.Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015). Investigating How Student's Cognitive Behavior in MOOC Discussion Forums Affect Learning Gains. International Educational Data Mining Society.Weinstein, C. (1986). The teaching of learning strategies
by self-efficacy andoutcome expectations derived from learning experiences. Limited exposure to biomedicalengineering topics and engagement in exploration could lead to students not having a well-developed individual interest [9] or finding interests that endure into a career choice, resulting inattrition from the field. To put this more concretely, if students’ exposure to biomedicalengineering is only focused on prosthetics, that might be initially interesting to them; but if thatinterest is lost, then interest in biomedical engineering as a whole is compromised. Withoutexposure to the many areas associated with biomedical engineering, students cannot proceedfrom triggered situational interest to maintained situational interest; meaning
Paper ID #42246Scoping Review of Instruments for Measuring Doctoral Students’ MentoringRelationships with Advisors or MentorsTerkuma Stanley Asongo, University of Massachusetts, Lowell I hold a degree in science education from the University of Agriculture Makurdi in Nigeria. Following that, I completed coursework for a master’s program in research, measurement, and evaluation at the University of Nigeria, Nsukka. I also earned a master’s degree in biomedical science from the Moscow Institute of Physics and Technology. Currently, I am pursuing a Ph.D. in research and evaluation at the University of Massachusetts Lowell
reduction of facultytime. To enhance reliability, we worked with instructional designers to develop an online, self-paced training.Introduction and research purposeThe idea of using evidence to inform instruction undergirds faculty development anddepartmental change initiatives, many of which include threading team design challengesthrough core courses. While there are assessments that measure conceptual understanding andsurveys that measure perceptions (e.g., design beliefs, engineering identity, design self-efficacy,team skills, etc.), these provide an incomplete understanding of student individual progress ondesign problem framing ability. Students typically get a lot of practice solving problems, butcomparatively little practice framing
. Amelink is the Director of Graduate Programs and Assessment in the College of Engineering Virginia Page 26.506.1 Tech and affiliate faculty in the Department of Engineering Education and the Department of Educational Leadership and Policy Studies at Virginia Tech. c American Society for Engineering Education, 2015 Developing the Postsecondary Student Engagement Survey (PosSES) to Measure Undergraduate Engineering Students’ Out-of-Class Involvement Abstract A large body of literature focuses on the importance of student involvement in all aspects ofcollege for achieving
beparticularly noticeable in historically underserved populations. Some universities attempted tosolve this problem by allowing students to remotely log into expensive software programs usingthe university VPN. However, this often led to overload of the VPN and did not solve theproblem of individual students not having high quality Internet at home [11]. Some authors,while reporting successful achievement of student learning goals, saw that there were increaseddifficulties with teamwork and communication between students in the virtual mode [9].Additionally, some researchers reported a decrease in the amount of student learning, eventhough they gained some self-efficacy skills from the experience [12].Like the author, many educators attempted some sort