these beliefs are shaped by mastery experiences, socialpersuasion, vicarious experiences, and physiological experiences. In turn, these beliefs impactcognitive processes, motivational processes, affective processes, and selection processes [12].Related specifically to this study, self-efficacy can be explained as a measure of how confidentstudents are in their ability to complete their engineering coursework and become an engineer,with implications ranging from how they feel when they are working on their engineeringcoursework to whether or not they ultimately continue to pursue the field. Related to thephysiological experiences component of self-efficacy, stress can impact student’s self-efficacyand has been found to be a concern specifically
Texas A&M UniversityAbstractThis paper presents the progress made in the first two years of a five-year NSF ER2 (Ethical andResponsible Research) project on ethical and responsible research and practices in science andengineering undertaken at a large public university in the southwestern United States. Overallobjectives of the project include: 1) conduct a survey of incoming freshmen college students toassess their ethical research competency and self-efficacy at the beginning of their tertiaryeducation and during their senior-level capstone course; 2) evaluate the ethical researchcompetency and self-efficacy of university students and identify any significantly contributingfactors to develop an intervention plan to improve their ethical
area involvesuniversities with small proportions of URMs. Thus, continued study of the impact of thesefactors on more diverse student populations is also necessary to better capture the calculusexperience of URM engineering majors. The purpose of the study was to examine student andclassroom-level factors that influence course performance measured by course grade. This studyfocused on two engineering-related psychosocial factors: (1) engineering self-efficacy and (2)engineering sense of belonging, and three mathematics-specific psychological factors which werefer to as math motivators, (1) math interest, (2) self-concept, and (3) anxiety. Classroom levelfactors included active engagement practices, proportion of females, proportion of
overall planning, organizing,and time management. With that desire, we have reason to research if these project managementskills and concepts are being taught effectively enough to prepare students for senior-levelcapstone courses and future careers. Degree programs that do not heavily focus on managementprinciples may impact students' abilities to obtain manager-style roles. Outside the classroom,there are opportunities to obtain this experience, such as through internships and studyingabroad. Data collected stem from a self-efficacy questionnaire administered to 811 students andvoluntarily completed by 361. The survey was issued at the beginning of the semester for ninefall courses through 15 different majors and intended to take approximately
item-difficulty. SD P(i) = standard deviation of item-difficulty. Md P(i) = median of item-difficulty.In result, only one item (V13), with item-difficulty P(13) = .79, is in the desired value-range todifferentiate between participants. The other items are agreed to unilaterally throughout, meaningthat all participants show very high ratings in teaching self-efficacy.4.2.2. Corrected item-total correlationsThe part-whole-corrected item-total correlation r(i,total-i) of an item i indicates how much theitem i measures the same psychological construct as the other items combined (total-i). Valuesbetween 0.4 and 0.7 are preferred [15]. Table 4 gives an overview of item-total correlations ofthe 18 items taking the sub-scales and the aggregate scale
different demographic groups.ResultsThe lowest reliability within this data set, seen in Table 2, is observed in the ‘Test Anxiety’ and‘Help Seeking’ scales. This could suggest that these are less important within the Southeast Asiancontext. Data represented by Pintrich [20] align with the ‘Help-Seeking’ aspect, displaying asimilar alpha coefficient of 0.52. However, on the ‘Test Anxiety’ scale, there is a significantdifference between the study’s 0.56 and Pintrich’s 0.80. That could suggest that test anxiety is notimportant within this region or it has become less important over the last 30 years since theappearance of the MSLQ. Self-efficacy is shown to be a reliable construct, with a measured 0.96alpha coefficient, which is higher in comparison
”). We excluded these because they do not appear to be directly measuring factors thatmight lead to the pursuit of STEM in the future. Another group of papers measured contentlearning that occurred during outreach (such as math skills or geophysics concepts). While thismay influence self-efficacy measures and/or better prepare students should they choose to enterSTEM, it is not directly measuring factors that most authors focus on as proxies for change toeducational and career paths. We have not included tests of content knowledge in thedescriptions of the outreach evaluation.Table 3: Examples of commonly referenced constructs in the papers, and our definitions.Construct DefinitionsAttitude What an individual
friendlyand easily accessible to teachers. This project also fosters strong collaborations between in-service teachers, and engineering and STEM education faculty.Program participants for yr2 included seven teachers, one male and six females. Of the seventeacher participants four identified as African American, two identified as white, and oneparticipant identified as biracial. Surveys instruments included the Project Knowledge Scale, thePatterns of Adaptive Learning Scales (PALS), Computer Self-Efficacy Scale, and the TeacherSelf-Efficacy Scale all used to measure teachers’ knowledge, attitudes, computer, and teachingself-efficacy changes pre and post surveys were disseminated to program participates during thesummer 6-week professional development
learning strategies. These strategies require further investigation as they areincreasingly important to integrate within the classroom, especially for challenging STEM-basedcourses. By specifically fostering motivation and SRL, students can engage more effectivelywith the material, leading to improved learning outcomes. To investigate these components of thelearning process in engineering, we collected self-report measures of achievement goalorientation (motivation), general self-efficacy (motivation), and motivated strategies for learning(SRL) for 146 undergraduate engineering students in Thermodynamics.To better understand (1) the interconnected nature of these constructs for students and (2) theself-regulatory and motivational profiles of
personnel, and others. In our approach, first, we devised course-specificmentoring objectives through literature surveys and pre-course surveys. To achieve theseobjectives, we created a set of mentoring activities. We also designed evaluation metrics to assesswhether there are any changes in students' perceptions toward computing programs and perceivedimpacts on students' self-efficacy and sense of belonging over time. Through these analyses, wetried to measure whether we need to design course-level-specific mentoring to help ourunderrepresented students attain their computing careers. We believe our mentoring should helpour underrepresented and predominantly major students who may hesitate to pursue a computingprogram or require enhanced self
SurveysThe PRISE assessments and surveys have been fully developed and administered via Qualtrics.The surveys on students’ engineering self-efficacy (Mamaril et al., 2016, self-coping efficacy(Concannon and Barrow, 2009), engineering interest measure (Henderson et al., 2002), andcareer outcome expectations (Concannon and Barrow, 2009) have been administered as our mainoverall program learning outcome. Data collection was at pre-intervention (before students’ firstyear), at mid-year (at the end of each fall semester), and post-intervention (end of each springsemester). Focus groups and/or individual interviews were used to evaluate scholars’ attitudesregarding their collegiate experience, impact of the program on their success and experience
, e.g., “I am confident that I canunderstand engineering outside of class”).Engineering self-efficacy was measured using a 5-item scale developed by Maramil et al. [32](e.g., “I can master the content in the engineering-related courses I am taking this semester”; “Ican do a good job on almost all my engineering coursework”).Engineering mindset was measured using a 3-item scale adapted from Hong et al. [33] (e.g.,“You have a certain amount of ability in Engineering, and you really can’t do much to changeit”).Intention to remain in the engineering major was measured using a 4-item scale adapted fromScott et al. [34] (e.g., “I have thought seriously about changing majors since I began inengineering”).Intention to pursue a career in engineering was
. Surveys included Likert-scale questions on self-efficacy, identity, and intent to persist that are supported by pre-existingliterature [13]–[15]. Additional questions on motivators, relevance of design challenges, andengineering skills were added for general instructor interest.Self-Efficacy Measured Across the Semester-Long CourseFour questions were asked to gauge self-efficacy (how certain are you that you can: identify adesign need, develop a design solution, evaluate and test a design, recognize changes needed fora design solution to work). Responses were collected on a Likert-scale, where 1 indicated“completely uncertain” and 7 indicated “completely certain.” Table 1 shows that responses to allfour questions could be grouped into one self
(URCAD) enablestudents to explore and comprehend the essence of research.Undergraduate students participating in research experiences show to enhance many of theirtechnical and professional skills [2], [3]. Communication and critical thinking, careerclarification and even further aspirations to continue to graduate school have been documentedbecause of a research experience for a student [2], [4], [5]. Another key and important element,especially at UMBC, is the impact on diversity. These experiences demonstrate increasing self-efficacy in students who are working to complete a STEM degree, especially women andmarginalized populations [5], [6], [7], [8].However, not all students have the chance or find themselves in a position to pursue such
integrations, and belonging seminars.Although community colleges (CC) implement several FYEs -- retention, and student successcan be improved. Wright College, one of the City Colleges of Chicago, a Hispanic Servingcommunity college, developed a framework that holistically and programmatically supportengineering students through admission, transfer, and degree completion (associate andbachelor). This framework resulted in a 75% transfer rate to 4-year engineering programs withintwo years. The course provides excellent first-year experience, measures belonging and self-efficacy, and tracks the success of engineering students.The ESS is a three-credit hour seminar incorporating Introduction to the Engineering Profession,College Success, and Professional
Asian Black White Agree Agree Agree Agree Self-Efficacy I am confident that I will be 4.7 ± 0.7 4.7 ± 0.6 4.6 ± 0.7 4.5 ± 0.9 4.7 ± 0.6 4.8 ± 0.6 4.7 ± 0.5 4.7 ± 0.7 able to transfer to a 4-year institution. Self-Efficacy I am aware of the 3.9 ± 1.1 3.9 ± 1.1 3.9 ± 1.2 3.7 ± 1.2 3.9 ± 1.1 4.0 ± 1.0 4.1 ± 1.0 3.5 ± 1.3 procedures involved in transferring to a 4-year institution. Self-Efficacy I know how I can get more 4.2 ± 1.0 4.2 ± 1.0 4.2 ± 1.0 4.1
research article. Outcomes aremeasured at every end-of-semester. The generated data allow for evaluating the efficacy ofUSTEM2 versus USTEM1 and the parametric characterization of trends across semesters. In thisreport, we present preliminary results generated from five completed measurement occasions (M1-M5) for Cohort 1 (at M1: USTEM1, n=22; USTEM2, n=19) and Cohort 2 (at M1: USTEM1,n=12; USTEM2, n=17) vis-à-vis five PSO indicators: 1) academic self-efficacy in STEM(ASESTEM; an average of 3 items), 2) self-efficacy in performing STEM tasks (STEMTaskSE;an average of 4 items), 3) sense of belonging in STEM (STEMSB; an average of 18 items), 4)STEM self-identity (STEMSI; an average of 4 items), and 5) sentiments about staying in a STEMmajor
preparedness10. Workshop(s) on product commerciali- 1 2 - 2 3 4 2zation Table1: Ratings of the overall summer bridge experienceStudent self-efficacy was assessed using the Engineering Skills Self-Efficacy Scale [6]. The scalewas developed to assess the different dimensions of self-efficacy for undergraduate studentsacross various engineering-related disciplines. The measure reports three sub-scales:Experimental Skills, Tinkering Skills, and Design Skills. To assess the effectiveness of theadditive manufacturing project-based experiences, the project evaluator wrote four itemsmodeled on the existing items on the Engineering Skills Self-Efficacy Sub-Scales
, 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
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
’ workplace behaviors. The same Likert scale was used.Job roles: A few variables were included to measure job roles, including: working for a medium-or large-size business (relative to all the alternatives, coded as a 0-1 dummy variable), andmultiple choice questions about specific work functions (e.g., working in R&D, Design,Manufacturing, or Management roles) and career choices (e.g., Startup career).Self-efficacy measures: Self-efficacy measures people’s perceived confidence in their ability tosuccessfully perform tasks and activities in certain domains, and have shown to be importantpredictors of their work outcomes [74]. We use pre-established scales as detailed in [61] tocapture participants’ beliefs about their personal efficacies in four
: https://sites.psu.edu/learningfactory/students/edsgn-100-cornerstone/[28] D. Baker, S. Krause, and S. Purzer, “Developing an instrument to measure tinkering and technical self efficacy in engineering,” presented at the 2008 Annual Conference & Exposition, 2008, pp. 13–392.[29] E. Anderson, “The white space. Sociology of Race and Ethnicity, 1 (1), 10-21,” 2015.
development of a measure of engineering identity. In ASEE AnnualConference & Exposition. 2016.[16] V. L. Bieschke, K. J., Bishop, R. M., & Garcia, “The utility of the research self-efficacy scale,” J.Career Assess., vol. 4, no. 1, pp. 59–75, 1996.
high.However, the authors did not find a correlation between self-efficacy and exam grades. While theauthors attributed this to a small sample size, both troubleshooting and the measure of self-efficacy primarily focused on data collection and documentation during experiments (Domain 2).We wonder if high self-efficacy related to Domain 2 might be a weaker correlate of learning thanother domains, in part because students may experience what scholars have named “deceptiveclarity,” a phenomenon in which students underestimate how complex something is based onhaving completed a simplified version of the task [9]. The activities associated with collectingdata and monitoring during the experiment are somewhat more straightforward compared toactivities in
theoretical framework to identify the beliefs that mostaccurately predict behavior. In December 2021, a survey was conducted in the first-yearengineering program at a large public university with a predominantly White population (n = 452).The self-report survey instrument included measures of mental health help-seeking intention,attitude, perceived norm, personal agency, and outcome beliefs guided by the IBM. Respondentsexhibited high scores on scales measuring their attitude towards seeking help, perceived control,and self-efficacy. This suggests that, on average, first-year engineering students had positiveperceptions of their seeking help, felt in control of their decisions to seek help, and were confidentin their ability to seek help. Students
[1]. FET is a framework designed to evaluate ToLthrough the factors that impede or facilitate the transfer. In contrast with other methods that focuson determining the factors (see, for example, [9], [16], [17]), the FET model aims to assess them[1]. Furthermore, the FET’s framework encompasses evaluating multiple dimensions influencingthe ToL. Specifically, the FET model's categories include transfer dimensions, achieved learning,and intent to transfer. The transfer dimensions are: 1. Trainee, which includes factors related to the participants’ reactions to a training program, such as motivation of transfer, self-efficacy, and locus of control; 2. Training, that evaluates the training itself and its design, and includes factors
context of such available resources isof broad interest to the engineering community. This study sought to measure the effectivenessof a junior-level clinical observations course designed for a major land-grant, public universitywithout proximity to a medical school. We compared IP generation and pre- and post-classsurveys were used to quantify students’ self-efficacy, motivations, and ability to makeconnections to real-world problems. The total number of IP applications increased more thantwo-fold following the adoption of the course, and survey results indicated students’ collectiveimproving understanding of the design process and increased confidence in engineering-relatedskills. This study included a sample size of 75 undergraduate students
students' motivation topursue a career in microelectronics differ after this limited curriculum intervention?Literature ReviewThe Role of Interest in Career DevelopmentSocial Cognitive Career Theory (SCCT) [9] is an overarching conceptual framework that guidesall of the decisions of the Scalable Asymmetric Lifecycle Engagement (SCALE) project. SCCTemphasizes the role of relevant interests in career development. Within SCCT's Choice Modeland Interest Model, interest directly links self-efficacy, outcome expectations, and career-relatedchoices [9]. Because of this, many studies seeking to affect student's interest in engineeringcareers focus on increasing student self-efficacy and outcome expectations. In SCCT, interestsdirectly relate to choice
pass the course with at least a “C”. Students earning a “D” passed all the required Level 1skills, but did not regularly submit homework or daily class notes. Students earning an “F” did not passthe required Level 1 skills. All students earning an “F” did not regularly attend in-person meetings,submit homework, or sit for test assessments. Figure 2: Overall course grades for MBL system at UMU (2020) and ONU (2021-23).Student self-efficacy was measured by surveys administered during the 15th week of the semesterduring 2021, 2022, and 2023. The students ranked their competence with each skill on Likert Scale, andthen the student responses were compared across the course offerings (Fig. 3). Overall, the updatedMBL-approach resulted in
areconsistent with values measured for similar groups of students during the Fall 2022 semester atWMU [15]. Raw scores for mindset and self-efficacy responses can range from 1-6. Raw scoresfor ICOPPE responses can range from 0-10. With the exception of the Wellness Compositescore and the ICOPPE – Physical wellness score, average responses in Table 3 are in the upperhalf of each range for all groups.Table 3: Comparison of average responses on the start-of-semester survey for beginner students enrolled in ENGR2100, beginner students in the PREP sections of ENGR 2100, and beginner students not enrolled in ENGR 2100. Surveyed Beginner Surveyed Beginner Surveyed Beginner