overarching goals that are the focus of all Creative Design sections.Areas of common measurement included; (1) Creative Self-Efficacy and Creative Role-Identity,(2) Ideation Capacity and (3) Creativity in Engineering Design (Artifacts).Creative self-efficacy is one’s belief that they are able to design creative products6. Researchcompleted by Tierney and Farmer reported that creative self-efficacy is a predictor of creativedesign performance. The Creative Self-Efficacy and Creative Role-Identity Scale was identifiedas an appropriate instrument to measure student growth through a pretest/posttest researchdesign.7 Surveys completed in Fall 2016 and Spring 2017 indicated that students from educationschool majors (n=33) have the lowest reported average
persistence in engineering as part of the CAEE’s (2007) Academic PathwaysStudy (APS), which identified 21 variables for persistence in engineering. The instrument’soverall goal was to collect data utilizing relevant questions from each survey instrument onindividuals’ experiences and perceptions during their K-14 and academic careers and to create acomprehensive picture of the culture-sharing group – in this case, the young women that hadpersisted in the research site’s College of Engineering.The MSLQ questions were used to identify within the results a measure of motivationalorientation for college engineering students (Pintrich et al., 1991) and examine women’s feelingsof self-efficacy to determine if patterns existed among the women in the
will have adirect and positive effect on grade performance.2.0 Study OverviewThis study is intended as a pilot study of the measures of social belonging in an engineeringclassroom. Data were collected from an introductory level solid mechanics class at a privateuniversity in the United States. Most student respondents were beginning their engineeringacademic careers, mostly as sophomore students taking their first-ever engineering specificcourse. The instrument used to measure engineering self-efficacy was developed by our researchteam. The instruments used to measure social belonging, engineering identity and interpersonalcloseness have strong research pedigrees but have never been used in this novel combination.2.1 Measuring Social Belonging
Appendix A.2.Innovation Self-Efficacy (ISE.5) – This self-efficacy construct involves specific behaviors thatcharacterize innovative people and is designed to measure a students’ confidence in his/herability to innovate. The included items are adapted from Dyer, Gregersen, and Christensen(2008). The original Dyer items were piloted and factor-analyzed as part of the EMS surveydevelopment process. The emergent five factors corresponded to Dyer’s innovative behaviordomains of questioning, observing, experimenting, and idea networking, as well as the relateddomain of associative thinking. These items each have a Likert scale of (0-4), have an acceptableCronbach 𝛼 (.78), and have been averaged to form the ISE.5 construct variable (Schar,Gilmartin
exploration as a theme, and the other used micro controllers as thefoundation for activities. The goals of this research are as follows: 1. Develop effectivecurricula for improving student self-efficacy in CT, 2. Develop a reliable and effective wayof measuring student self-efficacy in CT, and 3. Enforce the notion that CT is not problemsolving (PS), but a component of cognition.Background and Related Work“Computational thinking involves solving problems, designing systems, and understandinghuman behavior, by drawing on the concepts fundamental to computer science”26. However,computational thinking (CT) is not intended to be equated to computer science; rather theessence of CT comes from thinking like a computer scientist when faced with problems
completing design tasks to be the same. Additionally, it was found thatstudents had a significant increase in their development of this combined confidence-success factorover the course of a semester (p-value = .002). Based on extensive research by Godwin et al.13,measures of self-efficacy (presented as performance-competence), alongside subject interest andrecognition by others, have shown to be an important factor to students’ development ofengineering identity. It is suggested then that active learning may allow students to develop anengineering identity11.Initial qualitative work from Major & Kirn11 found five emerging themes: 1) students discovereddesign tasks they were competent in or not competent in, which lead to motivation to complete
(3), 175-213.19. Mamaril, N. A., Usher, E. L., Li, C. R., Economy, D. R., & Kennedy, M. S. (2016). Measuring undergraduate students' engineering self-efficacy: A validation study. Journal of Engineering Education, 105(2), 366-395. doi: 10.1002/jee.2012120. Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2013). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461-481. doi: 10.1007/s11165-013-9389-321. Jackson, A., Mentzer, N., Kramer, R., & Zhang, J. (2017, June). Enhancing student motivation and efficacy through soft robot design. Paper presented at the 2017 ASEE Annual Conference & Exposition, Columbus, OH.
studied acrosseducation and psychology literature. As an example, Australian high school students’ academicself-efficacy is a significant predictor of academic resilience.27 Similarly, low-income Blackcollege students with high academic confidence who were also able to “bounce back” fromacademic challenges and setbacks in college (i.e., students labeled as “buoyant believers”)achieve greater academic success, as measured by grade-point average.29Using findings from the aforementioned study of low-income Black students, Strayhorn createdthe ‘buoyant believers’ framework. The framework positions students in four categoriesrepresenting the intersection of various degrees of academic self-efficacy and resilience. Thefour categories include (a
-surveys created and conducted throughQualtrics software, a set of nine items adapted from the Motivational Strategies for LearningQuestionnaire (MSLQ) (Pintrich & De Groot, 1990) were presented to students to quantitativelyreport their confidence regarding their future performance in and learning of cognitive andspectrum sharing radio communications. A mean score was calculated from an average of allitems, which were 7-point Likert scale questions. Analyses of results from the survey wereconducted in SPSS. It should be noted that the item pool for self-efficacy demonstrated a verystrong level of reliability in measuring the construct. The Cronbach’s alpha calculated for themeasure was 0.872 in the pre-tutorial survey and 0.925 in the post
-efficacy as described earlier (see Theoretical Background section). In the development of theEMS, this construct was adapted to capture a student’s confidence in his or her abilities ingenerating and gathering new ideas – labeled as Innovation Self-Efficacy. In a similar way, astudent’s confidence in his or her abilities to design and develop new technical prototypes,products or services was included and measured in a variable named Engineering task self-efficacy. For both types of self-efficacy, students were asked to rate their levels of confidencein several innovation- or engineering-related activities. All of those activities were measuredon a five-point Likert scale from “Not confident” (0) to “Extremely confident” (4). For eachtype, the
other courses includingvideo content and be less resistant to this form of instruction.To get insight into the effects of the courses focus on learning and applying design theory, aninstrument was used to measure participant engineering design self-efficacy. The instrumentwas designed and validated by Carberry et al [20]. The tool measures individual’s self-efficacytowards engineering design tasks. Self-efficacy is an individual’s belief in their ability tocomplete a specific task [21]. This instrument examines four aspects of an individual’s self-efficacy: 1) Confidence, 2) Motivation, 3) Expectation of Success and 4) Anxiety towardscompleting engineering design [20]. The instrument was administered at the beginning and endof the Hybrid2
). 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
evaluation instruments were built from psychometrically sound instrumentsand scales that include the Career Interest Questionnaire and Modified STEM Semantics Survey(Tyler-Wood et al., 2010), Entrepreneurial Self-Efficacy and Intention (Wilson et al., 2007),Student Attitudes toward STEM Survey (Mahoney, 2010); STEM Semantics Survey (Tyler-Wood et al., 2010), Sources of Self-Efficacy Scale (Britner & Pajares, 2006), and a 21st CenturySkills Assessment/Rubric. Specifically, the process evaluation was designed to measure both quality and intensity ofSTEM-Inc activities in order to monitor the short-term and formative results of activities andservices, validate program components, and determine whether activities were of sufficientquality and
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
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
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
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
(Meyer & Marx, 2014).Although the traditional response in addressing student preparedness is the strengthening of mathand science education at the K-12 level, additional individual factors have been found to play akey role in retention. In addition to aptitude factors, Big Five personality traits(Conscientiousness, Openness, etc.) and affective factors (attitudes, self-esteem, self-efficacy,etc.) have been proven to contribute to retention in engineering programs. According to Hall etal. (2015), “studies have shown internal locus of control, academic self-esteem, self-efficacy, andthe [Big Five] personality trait of Conscientiousness have contributed to retention inengineering” (p. 170). In other words, students that have exhibited higher
mathematics (STEM) disciplines, and engineering inparticular. These include systemic as well as personal barriers.An institution’s culture and climate are among several systemic barriers that exist to impedesuccessful matriculation of students with disabilities, particularly in engineering. Researchershave found engineering and law faculty members “were significantly less willing to provideaccommodations” than their counterparts in other academic units. Reluctance and negativeattitudes serve to foster environments that are counter to diversity and inclusion.Studies have shown that incorrect estimates of self-efficacy are among personal barriers thathinder student success. Some students with disabilities tend to have lower academic self-efficacy than
“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
for engineers who understand the fundamentals ofsystems engineering. This paper has presented an effort to improve mechanical engineeringstudents’ systems engineering skills through the redesign of a sophomore design course. Studentswere exposed to primers and case studies that covered essential steps in the systems engineeringprocess and completed a semester-long project that required integration of various subsystems.The effectiveness of the intervention was assessed through a newly designed systems thinkingskills survey and through a course satisfaction survey. Students showed a statistically significantimprovement in self-efficacy for all measured skills, but showed a statistically significant gainover the control group only for the skill
, but were used for overall program evaluation. The three remaining scales included measures of creative self-efficacy, identity, and expectation. Creative self-efficacy refers to the “belief that one has the ability to produce creative outcomes” (p. 1138).18 Creative self-identity refers to the “overall importance that a person places on creativity in general as part of his or her self-definition” (p. 248).19 Creative self-expectation refers to students’ perceived expectations that they need to be creative within the academic setting, in this case the REU. Descriptions of the items included in these scales are given in Table 1. All three instruments used Likert-type scales. The number of anchor points corresponded to the
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
teams of four and complete in-class homework and projectchallenges with their team. Teams are assigned using a survey (discussed later) in order tobalance out multiple individual characteristics such as gender mix and self-reported efficacy andprior learning. The exact ‘formula’ by which the team assignments are made varies slightly inyear, but generally uses the same categories of data later discussed in Table 1. The methodologyfor forming team attempts to pick a ‘ringer’ for each team, based on self-reported self-efficacy inprogramming. The ringer is chosen based on the reported programming skills, but is balancedacross the demographic factors mentioned earlier as well as ensuring a balance of experienced,somewhat experienced and novice
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
research instrument: self-efficacy, research skills, and scientificleadership. The sections below describe survey questions from each of these survey sections. Atotal of 17 questions are provided: 5 from General Self-Efficacy, one (1) from Research Skillsand Knowledge, and 11 from Scientific Leadership.General Self-Efficacy Feedback from students on general self-efficacy addresses student confidence in theirability to perform each of the activities listed in Table 5. Students select the rating that bestdescribe their degree of confidence by using the following scale: Strongly Agree (5), SomewhatAgree (4), Neutral (3), Somewhat Disagree (2), and Strongly Disagree (1).Table 5. General Self-Efficacy Student Survey 2015 Post Questions
(the degree to which the learning task is deemed to be ofimportance for present and future work and learning) and self-efficacy for learning (pertainingto the individual’s confidence in their ability to successfully complete assigned tasks). The fullscales are provided in Table 3. For each statement, the respondents rated themselves on a 6-point Likert scale ranging from 1 point, “Strongly disagree” to 6 points, “Strongly agree”. TheCronbach’s α of the two subscales was .91 and .89.Table 3. Questionnaire of Learning MotivationItems Questions 1. I am very interested in the content area of this course. 2. I like the subject matter of this course.Task value 3. It is important for
compared totraditional robot design experiences. This development and study is contextualized in a courserequired of many 9th grade students called Foundations of Technology, which is the freshman-level technology and engineering education course provided by the Engineering byDesign coreprogram. It is taught in over 270 school districts across 23 states to about 100,000 studentsannually. The study will employ a design research framework to develop the 8-hour unit andstudy its implementation in 11 classrooms in two school districts. Measures include theSituational Motivation Scale and the Engineering Self-Efficacy Scale. In Year 3, the project willimplement an efficacy study to compare results of the soft robotics unit with the unitimplemented
theintegrated STEM lessons to measure lasting effects. The students of the participant teachers arealso being assessed using STEM content knowledge tests and surveys to measure attitudestoward STEM learning and career interest.Data Collection Instruments and Methods Several instruments are being used to collect data from teachers. The Science TeachingEfficacy Belief Instrument (STEBI) is designed to assess teachers’ perceptions of theireffectiveness for teaching science with 25 questions using a 5 point Likert scale, with 1 being“Strongly Disagree,” to 5 being “Strong Agree” (Riggs & Enochs, 1990). The Teacher Efficacyand Attitudes toward STEM (T-STEM) survey measures changes in teachers’ self-efficacy andconfidence in STEM subject content