makerspaces blend new manufacturingtechnologies like 3-d printing and laser cuttings with more traditional woodworking andmachine shop tools. Little data exist, however, about what the impact of universitymakerspaces is on the students who choose to participate in those spaces. In order to betterunderstand this impact of university makerspaces, our research team is conducting a multi-university longitudinal study.To measure the impact of making environments, this study looks at different metrics such asGPA, design self-efficacy, retention, and idea generation ability and how these metrics areaffected by different levels of involvement in university makerspaces. Preliminary results (twoof four years are completed) from the longitudinal studies raised
programsurvey was used to probe participant ‘s abilities/confidence in research. Their results indicateddirect relationships between research skills and research self-efficacy. These researchers alsofound that research skills and self-efficacy were good predictors of career aspirations.8 However,the measures used to assess research self-efficacy were not ideal. For example, items such as “Ihave the ability to have a successful career as a researcher,” and “I have a strong interest inpursuing a career as a researcher” are reflective of the student’s career goals, but may not reflecttheir beliefs in their current research capabilities. This concern about the quality of self-efficacyitems for assessing the gains in REU programs was highlighted earlier by
Camp Wilson, T. Telling more than we can know: Verbal reports on mental processes. Psychological Review 84(3)., 231-259, 1977.19. Eraut, M. Informal learning in the workplace. Studies in Continuing Education 26(2), 247- 273, 2004.20. Lave, J., & Wenger, E. Situated learning: Legitimate peripheral participation. Cambridge, England: Cambridge University Press, 1991.21. Knowles, M. The adult learner: A neglected species (3rd Ed). Houston, TX: Gulf Publishing, 1984.22. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 191-215, 1977.23. Carberry, A., Lee, H., & Ohland, M. Measuring engineering design self-efficacy. Journal of Engineering Education 99(1), 71-79, 2010.24
in the project: identification and self-efficacy. Further,it presents results responses from approximately 2,000 first-year engineering students at a largepublic institution. The paper addresses two questions: 1) How do engineering students respond totwo scales related to identity frameworks; and 2) What has been learned by giving these twoscales to first-year engineering students.IntroductionThe importance of increasing the number and diversity of B.S. graduates with degrees in science,technology, engineering, and mathematics (STEM) has been highlighted in several nationalreports1,2 . Increasing retention of students, including retention of students traditionallyunderrepresented in engineering is one approach to addressing this challenge
). After the completion of the summer program, teachers completed a post-survey (n =7-8 ) ontheir self-efficacy for teaching engineering during the Fall to measure any perceived changes inbeliefs as a result of the summer program. The results of the pre-post survey are found in Table3.Table 3: Teachers reported self-efficacies in teaching engineering pre-post summerprogram. Strongly Moderately Slightly Slightly Moderately Strongly Disagree Disagree Disagree Agree Agree Agree 1. I can discuss PRE how given 20% 10% 20% 0% 40% 10% N=10
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
“weed-out” course for students in theengineering program.The two-year project described in this paper will be designed and implemented over threeiterations (alpha, beta, and gamma), using a quasi-experimental design that includes a treatmentcourse and control course for comparison, and employing an outcome-focused approachconsistent with the tenets of design-based research [13]-[16]. This project employs experimentalmeasures which past researchers have designed and validated [17]-[20]. These measures assessclassroom climate [17], engineering identity [18], self-efficacy [19], and classroom practices[20]. For both the alpha (Spring 2017) and beta (Fall 2017) iterations, the project team will givepre-post assessments to the students, conduct
statistical analysis of the pre- and post- measures ofscientific communication self-efficacy. Therefore, the results can only be interpreteddescriptively. Mean scores improved by a standard deviation or more on the Writing, Presenting,Speaking, and Total Scales, as shown in Table 1.Table 1. Pre- and Post-SCSE Means (Standard Deviations) Mean (SD) Baseline Post Writing Scale 35.5 (4.3) 39.8 (4.7) Presenting Scale 12.3 (3.3) 16.10 (2.5) Speaking Scale 27 (6.6
thatone of the key indicators of a successful summer research experience is early contact betweenthe student and the faculty mentor and/or graduate student mentor prior to the start of theresearch experience, and regular contact thereafter. We also determined that for purposes ofengagement, it is important to provide hands-on activities from the beginning (in parallel withresearch training that supports the later phases of the summer project), even if these hands-onactivities do not bear directly on the longer-term research goals. Finally, we found that exposureto professional development activities involving industry and technology transfer themes resultedin increased self-efficacy related to the ability to innovate in students’ chosen field. A
, 82-91 (2000).28 Zimmerman, B. J. Self-efficacy and educational development. Self-efficacy in changing societies, 202-231 (1995).29 Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychological review 84, 191 (1977).30 Bandura, A. & Walters, R. H. Social learning theory. (1977).31 Schunk, D. H. Self-efficacy and achievement behaviors. Educational psychology review 1, 173-208 (1989).32 Kirton, M. Adaptors and innovators: A description and measure. Journal of applied psychology 61, 622, doi:http://dx.doi.org/10.1037/0021-9010.61.5.622 (1976).33 Diamond, A. & Lee, K. Interventions shown to aid executive function development in children 4 to 12 years old. Science 333, 959-964 (2011).34
curriculuminvolves instruction on techniques such as sketching in both isometric and perspective spaces,shading, and ray-tracing.This paper observes the impacts of a modified curriculum in and engineering graphics course onstudents’ ability to sketch, self-efficacy in engineering design, and spatial visualization skills.Impact was measured using pre- and post-course assessments and surveys. The pre-to-postcomparisons of the groups of students taught using different methods showed equalimprovements in the spatial visualization of the students. The improvements in sketching abilityof the students in the modified perspective curricula were found to be significantly higher thanthe improvements experienced by students in the traditional curriculum. These
, Honolulu, Hawaii: ASEE, 2007-2972.[11] De-Juan, A., Fernandez Del Rincon, A., Iglesias, M., Garcia, P., Diez-Ibarbia, A. & Viadero, F., (2016). Enhancement of mechanical engineering degree through student design competition as added value. Considerations and viability. Journal of Engineering Design, 27 (8), 568-589.[12] Seth, D., Tangorra, J. & Ibrahim, A., (Year). Measuring undergraduate students' self- efficacy in engineering design in a project-based design courseed.^eds. Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEEIEEE, 1375-1382.[13] Hadim, H.A. & Esche, S.K., (Year). Enhancing the engineering curriculum through project-based learninged.^eds. Frontiers in Education, 2002. FIE 2002
of Missouri. His main research interests are program evaluation and education policy. c American Society for Engineering Education, 2017 The Role of High School Math and Science Course Access in Student College Engineering Major Choice and Degree AttainmentI. IntroductionPrevious research has documented numerous factors that impede the progress of women andunderrepresented minorities in engineering fields, which can be broadly categorized into sixfactors: “classroom and academic climate, grades and conceptual understanding, self-efficacy andself-confidence, high school preparation, interest and career goals, and race and gender” (Geisingerand Raman, 2013). While high school
[Portions of this paper in the review of the literature and research design have been reprintedfrom the 2016 ASEE Poster Session Papers, which provide preliminary material for the reader.]1There is a growing national concern over decreases in science achievement in middle and highschool. Paired with it are challenges associated with workforce declines in STEM-relatedcareers. In response, in a recent PCAST report2 recommendations for recruitment of scienceand engineering students and corresponding recommendations for increased attention to strategicSTEM-related instruction and teacher professional development have emerged. A significantchallenge facing urban science teachers is a low sense of self-efficacy in teaching STEMcontent.3 Additionally, a
. Specifically, there seems tobe a misalignment between teachers’ lessons and what the STIR is intended to measure, namely, afull scientific investigation. Furthermore, our observations also highlighted the challenge that highschool STEM teachers’ face in integrating nanotechnology into their classroom. While each of theclassroom lessons that we observed included a nano-component, the teacher’s primary focuscorresponded with something students were expected to know per state mandates and with respectto state tests. More time spent on nanotechnology, especially a full nano-lab would, we think,detract from what the teachers were expected to cover.Third, we did not find any changes in students’ STEM self-efficacy as measured by the S-STEMconstructs
faculty and administrators will require a cognizant understanding ofwho these students are, -- the challenges they face, how they handle stress, their levels of self-efficacy, and their development of an engineering identity, -- if they are to successfully designand implement programs specifically targeted at this demographic.The semistructure interview and design protocols have resulted in large amounts of datacollected. Work continues to explore the intricacies of who these students are. The aim is to havelarge enough numbers that results can be generalized and broadly applied. Future work willdwell into adult learners’ level of preparedness and their student-faculty relationship.AcknowledgementsThis material is based upon work supported by the
Study 4 and Study 5 into a singleprotocol. See below.Study 5: Frame-of-reference training makes participants better team membersPurpose of study: This study explores the effect of cognitive model development (measured by aknowledge test as in Study 2) on team performance and team-member effectiveness. Trainingmembers of teams to develop a more accurate cognitive model of teamwork should increaseteam performance, team cohesion, team self-efficacy, and satisfaction, and reduce team conflict.Status of study: Participants were recruited to the experimental and control groups at UNCCharlotte and Purdue University for lab studies, and the results of that work are being published.A significant research protocol was designed, developed, and launched at
study is an adaptation of the Laanan-transfer students' questionnaire (L-TSQ)1,2,3,4 plus a compilation of survey items extracted from the following multi-institutionalresearch studies that investigated transfer student experiences in STEM: Prototype to Production:P2P5 and Measuring Constructs of STEM Student Success Literacy: Community CollegeStudents’ Self-Efficacy, Social Capital, and Transfer Knowledge.6,7The final survey instrument, the “Engineering Transfer Student Survey”, was developedspecifically for this project and is comprised of six sections that include a mix of multiple choiceand open-ended questions. Multiple survey items are embedded in 16 of the 45 questions. Ahigh level summary for each section of the survey is provided as
interventions to create change.Background – Utility Value Theory Research in social psychology has continually shown that students’ expectancies for success(e.g., self-efficacy) and the perceived value of a particular career predicts motivation to pursuethat career. Classic work within this Expectancy-Value framework (e.g., Eccles et al., 1983) hasexamined this relationship for decades on primarily non-engineering students (e.g., math andbiology, Eccles, 1984; Wigfield & Eccles, 1992; Sullins, Hernandez, Fuller, & Tashiro, 1995).Until relatively recently, the focus of expectancy-value research has centered predominately onthe “expectancy” side of the theory (and has extended into other theories such as social-cognitivecareer theory, Lent
and four-year colleges influence student identity, belonging, self-efficacy,and encouragement. The “STEM culture” a student experiences shapes their awareness andunderstanding of standards, expectations, and their sense of belonging in STEM. Moreimportantly, the encouragement or lack thereof within the “STEM culture” of the departmentand/or institution can support or undermine their performance and persistence through their self-concepts and beliefs and their feelings of community and belonging in STEM fields.3-5For historically underrepresented students, such as black students, views of the way race andethnicity function in their college environment are especially important in their social andacademic adjustment.25 Experiencing a college
methods attempted to improve retention. The majorcauses of attrition are reported to be (1) an unwelcoming academic climate, (2) conceptualdifficulty with core courses, (3) lack of self-efficacy or self-confidence, (4) inadequate highschool preparation, (5) insufficient interest or commitment to engineering or a change in careergoals, or (6) racism or sexism within the field. The SEECS program already has programmaticfeatures which address three of these stated attrition factors, namely (1), (3), and (5).Furthermore, the selection of students for participation in SEECS in part eliminates factor (4).SEECS does, however, suffer attrition related to factor (2), conceptual difficulty in foundationalcourses. In particular, the SEECS faculty members
of our research. These data, along with a careful review ofdocuments and websites available from each community college and applicable higher educationliterature as a comparison informed the refinement of the CPPI which was developed, and testedin our previously described STEM community college study.5The Refined College Pedagogical Practice Inventory (CPPI-R): Refinement, testing, and use ofthe CPPI has been informed by measurement research of educational psychologicalresearchers.33 Specifically, the inventory was initially designed with the intent of enabling us toexplore relationships among the dependent and independent variables associated with collegepedagogical practices and to determine potentially predictive factors that relate to
and self-efficacy in the new generation of women STEM scholars", Women in engineering, science and technology: Education and career challenges, 97-114. 14. Grisselle Centeno, Susana Lai-Yuen, Iman Nekooeimehr, Audra Banaszak, Ashley Ishak, “The Impact of Healthcare-Related Pedagogical Interventions on Student Diversity, Motivation and Retention”, Proceedings of the 2016 Industrial and Systems Engineering Research Conference
participated in our training programsboth before and after their participation in the RET program. As teachers were required to havebeen in a program only before the RET and not after, only a limited dataset from eight teachersqualified for this analyses. The STEBI instrument is comprised of two subscales, the personalscience teaching efficacy and science teaching outcome expectancy. Although the dataset issmall, teachers made a significant gain of 6% in their outcome expectancy (p<0.05). This is apromising result as some studies have shown that teachers’ perception on the outcomeexpectancy measure, or their ability to affect actual student outcomes, is often unchanged afterprofessional development.42-43 This suggests that assessing teachers’ self
modified the curriculum to the needs of scholars.Week 1 instruction focused on algebraic concepts and dimensional analysis. Week 2 addressedcalculus concepts. The course received excellent evaluations from students and data analysisshowed measurable gains in knowledge as assessed by pre- and post-tests. All but one student inCohort 1 achieved growth during Math Boot Camp. The mean post-test score across all threecohorts was 81/100 (SD = 15), versus a mean pre-test mean of 52/100 (SD = 29). Using a pairedt-‐test, we found that growth was statistically significant, t(26) = 6.376, p < 0.0001. Besidesmaking virtually all students feel like they had improved their mathematics skills, students alsoreported that they had increased confidence in other