calculations, answers to intermediate calculations will be provided tospeed up the process of error checking. Fourth, taking a different tack, inclusion of open-endedconstructivist activities (both virtual and physical) will be investigated for inclusion into the labsas a way to strengthen student self efficacy. As well, the injection of systems level activities,such as constructing small but practical physiological measurement circuits, will be explored fortheir potential to better contextualize and engage students in their exposure to the challenginganalytical concepts. These modifications will take place during the next phase of the project,which will also focus on encouraging the dissemination of these online circuits lab materials toother college
-cognitive factor are included.In the few studies that attempt to combine non-cognitive factors alongside cognitive ability in aneffort to explain college GPA, it has been shown that non-cognitive factors such as study skillsand effort explain significant variance in college GPA beyond cognitive ability[8], [9]. One studyhas shown that learning skills and study strategies alone can provide a 10% increase in predictivevalidity when added to cognitive-only models of academic performance[9]. Similarly, a recentmeta-analysis showed that non-cognitive factors such as conscientiousness, test anxiety, andacademic self-efficacy can explain as much variance in college GPA as high school GPA andSAT scores [10]. While these studies provide intriguing results
gender equity, we focused onsupporting the behaviors (e.g. the climate variables discussed above) to promote equity. Wewanted to see how this indirect dual agenda approach impacted faculty beliefs about their 11department’s ability to achieve gender equity, as well as their perceptions of other key aspects ofdepartmental climate.Our research addresses an issue raised by Acker: “Does the sex composition of change agentgroups make a difference in the success of projects?” (p. 627)4 Our goal was to see if there weredifferential impacts of the Dialogues process on departmental climate measures among academicdepartments that vary in the percent of
engineering education research as a psychometrician, program evaluator, and institutional data analyst. As a psychometrician, she revised the PSVT:R for secondary and undergraduate students, developed the TESS (Teaching Engineering Self-efficacy Scale) for K-12 teachers, and rescaled the SASI (Student Attitudinal Success Inventory) for engineering students. As a program evaluator, she evaluated the effects of teacher professional development (TPD) programs on elementary teachers’ attitudes toward engineering and students’ STEM knowledge through a NSF DRK-12 project. As an institutional data analyst, she is investigating engineering students’ diverse pathways to their success.Dr. Teri Reed, Texas A&M University Teri
instructionalsoftware emphasized lower-level cognitive processes,9 but a larger number report learning gainswhen implementing technology in the classroom through virtual experiments or onlineinstruction.10-13 Additionally, incorporating simulations into the classroom can increasevisualization and problem-solving processes,14,15 as well as show positive gains in student self-efficacy with respect to engineering skills.16Virtual experiments offer an opportunity to provide students with valuable experience at a lowcost (no laboratory space or consumables, only computer facilities, required), high flexibility(can be performed outside of class, does not require direct supervision, safety is not a directconcern), and great breadth (some disciplines may have
apply what they learn immediately in the context of the project. Theseelements of just-in-time learning and increased emphasis on a “discipline project” level projectbased learning strategy were added to the course in an attempt to increase student motivation toapply these fundamental design concepts in a manner that would improve their ability to applythem and transfer this knowledge into other contexts14-16.To assess the effectiveness of these changes, the students’ knowledge of the design processneeds to be evaluated. Different instruments are available in the literature to assess differentaspects of the engineering design process. For example, Carberry et al.17 developed aninstrument to measure engineering design self-efficacy. McKenna and
who start as freshmen in engineering complete theirbaccalaureate degrees in engineering1. Reasons for this attrition among engineering studentshave been studied for many years. Seymour and Hewitt2 found two main reasons for departuresfrom the sciences: disinterest or disappointment in field, and poor academic performance withsubsequent loss of self-efficacy. Haag et al.3 also found that poor academic advising,unapproachable faculty, and complicated engineering curricula were important institutionalcontributors to student attrition. Although poor academic performance may motivate somestudents to leave engineering, other students persist despite these academic setbacks. In thispaper, rather than focusing on students who leave engineering, we
This need for a stronger STEM workforce is a function of education andawareness at all levels of student education, but it has been documented that choosing STEMmajors is largely decided by an early interest in STEM disciplines.4 As such, one of the nationalgoals set forward by the National Science and Technology Council Committee on STEMEducation is to increase participation in authentic STEM experiences for K-12 students in orderto provide students the opportunity to develop and deepen their interests in STEM as well as tobuild student self-efficacy regarding their ability to participate in STEM.1Summer camps are commonly offered as a mechanism for exposing K-12 students to STEMmajors and careers, often with the direct goal of recruiting
thinking. Luster-Teasley et al14 investigated the use offour case studies in a lab course to introduce sustainability and environmental engineeringlaboratory concepts using a modified-flipped classroom method. Students were given a casestory related to the class experiment and asked to research the topic. The in-person lab classstarted with a discussion of the case and the student’s research finding and then studentsconducted the lab exercise. Pre and post survey data indicated increased self-efficacy for ABETcriteria skills and learning gains. A problem-based learning (PBL) approach was used for an environment engineeringlaboratory component to provide an applied context to traditional experiments by Hill andMitchell15. Two problems were
. Washington, DC: Association of American Colleges (Report of the Project on the Status and Education of Women).5 Morris, L. K., & Daniel, L. G. (2008). Perceptions of a chilly climate: Differences in traditional and non- traditional majors for women. Research in Higher Education, 49(3), 256-273.6 Pascarella, E. T., Nora, A., & Terenzini, P. T. (1999). Women's perceptions of a “chilly climate” and cognitive outcomes in college: Additional evidence. Journal of College Student Development, 40(2), 163- 177.7 Malicky, D. (2003). A literature review on the under-representation of women in undergraduate engineering: Ability, self-efficacy, and the" chilly climate”.age, 8, 1.8 Haines, V. A., Wallace
faculty perspectives, and surveying perceptions of culturalintelligence among students at strategic points in the curriculum. The first part of the assessmentinvolved surveys of the faculty to identify types and amount of learning activities related toglobal learning. The key finding from the survey was the common narrow perception of globallearning as study abroad and international education activities. The second part of the assessmentinvolved surveys of faculty and students in the Fall 2015 and Spring 2016 terms to exploreperceptions about students and their self-efficacy associated with global learning. The studentssurveyed ranged across the curriculum from freshman to graduate students. Interestingly, resultsindicate that students’ self
studying earlier and outline the chapters for review). Self-knowledge also includes aspectsof motivation for learning. For example, is the learner pursuing the learning through an intrinsic(“this is interesting”) or extrinsic (“I want a good grade”) orientation, and what about thelearner’s self-efficacy? Research over the past 40 years has conclusively demonstrated the effectiveness of learningaccompanied by metacognition [see, for example: refs.17, 18, 19, 20]. Although few of these studieshave been based in engineering or science, the evidence seems clearly extendable to theselearning environments. As Pintrich13 states, “Because metacognitive knowledge in general ispositively linked to student learning, explicitly teaching metacognitive
, and crosscutting concepts1. Even ifdeveloped tomorrow, it would still take years for most districts to adopt and implementthis new curriculum in elementary classrooms. Curriculum adoption and revisionrequires many levels of professional development, pilot study implementation, anddistrict/board approval. In the meantime, teachers are left to work with the curriculumthey currently have and attempt to meet the demands of the NGSS. Research has shown that, given their limited preparation for teaching science,elementary teachers rely heavily on their science curriculum materials7, 8. This reliancestems from a combination of factors including (1) teachers’ reported low self-efficacy forteaching science9, (2) their reported lack of deep
thermodynamics instructions by someresearchers. This method trains students to tackle ill-defined, ill-structured problems as found inthe real world.4 Studies have shown that this learning method results in more positive students’attitudes, a deeper conceptual understanding and improved retention of knowledge.12 Thesuccess of problem-based learning depends to some extent on students’ self-efficacy and thedegree of collaboration among peers. In problem-based environments, learners practice higherorder cognitive skills (analysis, synthesis and evaluation), and constantly engage in reflectivethinking.34 Lape35 presented tiered scaffolding techniques to bridge the gaps in high-cognitive-load problem-based learning in thermodynamics. In a problem-based
% 42% 34% 32% Customer development 21% 17% 22% 8% * Economic development 22% 27% 14% 31% * Self-efficacy 27% 27% 23% 25% Endurance 30% 26% 16% 18% Need for autonomy 13% 14% 10% 17% Social orientation 12% 8% 10% 3% * = p < 0.05What’s changed since 2012‘Creativity’ is an even stronger associative characteristic for I&E
discussion and collaborative leaning, they could get problem solutions and deepen theircognitive understanding and thus develop the abilities of critical thinking and professionaljudgment.According to the results of the experiment, the peer evaluation has the lowest score amongthe three evaluation methods because of the competition among peers, while the self-evaluation and the expert evaluation share a similar score. Additionally, the analysis of thelearning behaviors show that most of the students with low creativity read and downloadedinformation in the learning system and interacted with peers in the platform to have diverseviews and enhance their abilities of self-efficacy analysis; the students with high creativitywere willing to seek, explore
. Pintrich, P. R. A Motivational Science Perspective on the Role of Student Motivation in Learning and Teaching Contexts. J. Educ. Psychol. 95, 667–686 (2003).18. Hagemeier, N. E. & Murawski, M. M. An instrument to assess subjective task value beliefs regarding the decision to pursue postgraduate training. Am. J. Pharm. Educ. 78, (2014).19. Artino, A. R. & McCoach, D. B. Development and Initial Validation of the Online Learning Value and Self- Efficacy Scale. J. Educ. Comput. Res. 38, 279–303 (2008).20. Garcia, T. & Pintrich, P. R. Assessing students’ motivation and learning strategies in the classroom context: The Motivated Strategies for Learning Questionnaire. Altern. Assess. Achiev. Learn. Process. prior
than either of the twoeffects alone.”[21] In his study, Henson[21] suggests that we may be able to predict outcomes notbased on a person’s past aptitude or grade point average, but rather, on their self esteem,dogmatism, and intrinsic or extrinsic motivation to be successful.[21] Evidence of the use of performance comparisons in efficacy belief formation is supportedby other research and supports the claim of self-efficacy theory that vicarious experiences aremore influential on students who have little experience in a particular area such as in comingfreshman engineering students.26 Yet, another study stated that individuals “who are lessconfident, experience negative interactions with peers and instructors, and hold
, and others. 81% of the population was male and 19% was female. Noattempt to oversample women or minorities was made in collecting this sample. 8.2% of thesample were freshman or sophomores, 59% were juniors, and 33% were seniors (including fifthyear seniors).C. InstrumentsThe questions analyzed in this study were included in a survey that included basic demographicsand affective indicators including self-efficacy, task value, belonging, and job values that maymediate or otherwise influence the way in which the primary indicators grow and evolve over theundergraduate years. The primary indicators included various measures of sustainability values(e.g. social responsibility, consumer responsibility), and five short answer questions related
,encouraging teacher-student dialogue, improving student motivation and self-esteem, bridgingthe gap between current and expected performance, and ultimately improving teaching.32Narciss33 identifies two components of feedback – the evaluative part, which assesses the qualityof the answer, and the informational part, which provides direction for progress. Shute1 reviewsa similar model, according to which feedback contains both verification and elaborationcomponents. A more informative feedback is found to be related to better performance, and insome cases, better motivation33. Whether or not more information in the feedback improvesstudent motivation depends on the student’s confidence in their own abilities (or self-efficacy)34.Different frameworks
better understanding of their early career work. Drawing from the PEARS data,Brunhaver4 showed that engineering graduates who were non-engineering focused four yearsafter earning their degree were different from their engineering focused peers in terms of certainundergraduate experiences (e.g., they were less likely to have participated in an internship or co-op) and level of technical interests. Moreover, while women and men graduates in this samplewere not different in terms of their current position (engineering or non-engineering), they weredifferent in terms of future plans. Women tended to have lower technical self-efficacy andinterests than did men, which helped to explain why they were more non-engineering focused intheir
North Carolina State University. She earned a B.S. in Biological Engineering from North Carolina State University and an M.S. and Ph.D. in Biological Systems Engineering from Virginia Polytechnic Institute and State Uni- versity. Dr. Baldwin’s primary focus is working across the Colleges of Engineering and Education on engineering education related initiatives. She teaches undergraduate courses in the First Year Engineering Program and in the Department of STEM Education. Dr. Baldwin’s research interests include self- efficacy, motivation and persistence of underrepresented populations in STEM and engineering design in K-12.Dr. Lina Battestilli, North Carolina State University Lina Battestilli is Teaching
from a single university instead of multiple institutions. Including more data from differentuniversities would give more validity to the results and increase the generalizability of the study.A second shortcoming was that due to small sample sizes, only two races were included in thestudy – White and Black. Other races/ethnicities, such as Hispanic, Asian or Pacific Islander, andAmerican Indian/Alaskan Native, were not included as they collectively represented less thanfive percent of the total population of participants. Furthermore, the data used did not containvariables such as marital status, SES, self-efficacy, and transfer credit/dual enrollment. Otherstudies have indicated that these variables may have an effect on first year grades of
introduce skills, tools, and some engineering basics, followed by 8 weeks forstudent teams to design, build and demonstrate a prototype device. The authors noted that thechoice of project had a pivotal role in the student experience, with overly challenging orunconstrained projects having a negative impact on student interest in engineering.In an effort to acquaint freshmen with the various areas of mechanical engineering at TheCitadel, Rabb et al.12 modified an Introduction to Mechanical Engineering course to combineindividual and teamwork projects and assignments, many of which were small, hands-onactivities. Following the opinion of Vogt13 that “student self-efficacy had very strong effects oneffort and critical thinking where academic
numerous national and international conferences in the fields of education and women’s studies (AERA, AESA, & NWSA). In 2009, Beckett served as a Program Evaluator for the world renowned Apprenticeship in Ecological Horticulture at the Center for Agroecology and Sustainable Food Systems (CASFS) at UC Santa Cruz. She co-authored an evaluation of two decades of the apprenticeship program (Perez, Par, & Beckett, 2010). She served as the Program Evaluator for Apprenticeships in Sustainability Science and Engineering Design (ASCEND) at UC Santa Cruz in the 2014-2015 academic year, where she collaborated with the Program Director to build new assessment to measure STEM learning through ”audio diaries,” and piloted an
altogether.These include concerns over the ability to earn a degree, self-efficacy, or the effort required toattain a degree.10 Another study revealed the desire for a student to fit in the standard definitionof an engineering student or self-identify as part of the program.11 Others have addressedparental pressure or financial need as issues for wanting to study engineering.12 Since many ofthese negative motivators are less apt to be addressed by curriculum changes, this paper willfocus on positive motivators.Positive motivations can be further classified into a spectrum of altruistic reasons. Evidence hasshown that social responsibility can play a large role in students motivation to choose a major.13The most common responses from students was a desire
and Exposition,Seattle, Washington. 10.18260/p.246415 Riley, Donna. Engineering and social justice. Chapter 2 “Mindsets in Engineering” Synthesis Lectures onEngineers, Technology, and Society 3.1 (2008): 33-45.6 Bandura, Albert. (1977). “Self-efficacy: Toward a unifying theory of behavioral change.” Psychological Review,Vol 84(2), Mar 1977, 191-215.Acknowledgment: This material is based upon work supported, in part, by the National ScienceFoundation, under grant 1256529. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.For more on content of the course, see Riley, D., Grunert, J., Jalali, Y., Adams, S.G
., & Tarule, J. (1986). Women’s Ways of Knowing: The Development of Self, Voice, and Mind. New York: Basic Books.11. Sprague, J., & Massoni, K. (2004). Student Evaluations and Gendered Expectations: What We Can’t Count Can Hurt Us. Sex Roles, 53(11-12), 779-793.12. Bailey, J. G. (1999). Academics’ Motivation and Self-Efficacy for Teaching and Research. Higher Education Research and Development, 18(3), 343-359.13. Schuster, J.H., & Finkelstein, M.J. (2006). The American Faculty: The Restructuring of Academic Work and Careers. Baltimore: Johns Hopkins University Press.14. Winslow, S. (2010). Gender Inequality and Time Allocations Among Academic Faculty. Gender & Society, 24(6), 769-793.15. Hart, J., & Cress, C. M
0.1 0.0 Persisted in Discontinued Engineering Figure 2: Bernoulli persistence data for 2012 cohort.While the NFS version has a higher persistence percentage, the statistical significance of thisdifference needs to be assessed. For this analysis, n1 = 71, p1 = 0.437, n2 = 86, and p2 = 0.384.The statistical significance depends on the z score for the difference p1 − p2 . The null hypothesissays this difference should be zero. The z score measures how many standard deviations awayfrom zero the observed difference is. The null hypothesis analysis also depends on the persistencefraction for both
-environmental engineering. Educational areas of interest are self- efficacy and persistence in engineering and development of an interest in STEM topics in K-12 students.Dr. Chris Geiger, Florida Gulf Coast University Chris Geiger is an Associate Professor and Chair of the Department of Bioengineering in the U.A. Whitaker College of Engineering at Florida Gulf Coast University. He received his M.S. and Ph.D. de- grees in Biomedical Engineering from Northwestern University in 1999 and 2003, respectively, and his B.S. in Chemical Engineering from Northwestern University in 1996.Ms. Kimberly A. Reycraft, Florida Gulf Coast University Kim Reycraft earned a Bachelors degree in Environmental Science and Policy and worked in that field