engineeringidentity, sense of belonging, and self-efficacies.The survey instrument was designed with validated scales to measure engineering self-efficacy[19], design self-efficacy [20], and students’ sense of belonging [21]. The first survey also askedstudents to self-report demographic items, such as gender identity, sexual orientation,race/ethnicity, nationality, status as first-generation college students, estimated family income,plans to work during the academic year, and if they would identify as having a disability. Theinstrument also asked students what forms of making they had previous experience with, forexample, woodworking or making with textiles. Students’ perceived attitudes towardmakerspaces were also collected through the form of Likert-type
PS5 1 Sense of Community, Self-Efficacy of Engineering Students, Grade Point Average 2 3 (Overall and Gateway 8), 4 Success Measures (Various), 5 Program SatisfactionThe Engineering Self-Efficacy survey (Frantz, Siller & Demiranda, 2011) measures students’judgments concerning their academic performance in engineering courses and an engineeringprogram, their expectations about an engineering career, and their persistence in pursuing anengineering education. In addition, at the end of each academic year, students participated in afocus group to discuss their personal experiences in the program and offer suggestions forchange
innovation by analogy and reflection in their career pathways project. The objective isfor students to learn about the engineering design process and to apply it to their academicchallenges by analogy. This prepares students with meta skills to help solve future problems intheir academic path, and at each iteration, the students transform themselves, hence the use of theterm self-transformation (also referred as “self-innovation”). Data collected from pre and postsurveys will be presented to measure self-efficacy in engineering design, grit, motivation tolearn, and STEM identity. Participant interviews provide a qualitative insight into theintervention. This project is funded by NSF award 2225247.IntroductionIn recent years, the transition of
not sign the consent letter to participate in the IRB-approved evaluationresearch.Evaluation MethodsEvaluation of the project consisted of a pre-post survey instrument focused on perceived self-efficacy in universal teamwork and research skills. This instrument was an adaptation of theResearch Self-efficacy scale [9]. Questions focused on things like the perceived ability to“engage in effective team practices,” “follow ethical principles of research,” “identify my ownstrengths within a team setting,” and “present research ideas in oral or written form.” This pre-post survey was augmented by weekly surveys aimed at understanding fellow engagement in theprogram. A final focus group was held with the project evaluator to further elucidate the
the median for each EVT prompt and mean for each of the EVT constructs werecalculated. Paired T-tests were used to determine if there were any statistical significance of thedifferences (p value equal or less than 0.05) between the motivation constructs (i.e., interest, attainment, 2utility, self-efficacy, and cost) of students in the pre-writing and writing phase. Lastly, Cohen’s d wasdetermined to measure the effect size of the possible differences.LimitationsThe greatest limitation in this study is the low number of participants due to the limited number ofstudents enrolled in the CEE program under study. The sampling distribution
, document, observe, and quantify the development of a student’s EM during hands-on experiences in an REU. his work-in-progress paper describes the successful implementation of concept mapping as anTanalytical tool to measure student learning outcomes in the non-traditional learning environment of an REU. Furthermore, this paper describes a work in a current study to explore the development of research self-efficacy and engineering identity development of early career engineering students who participate in a 10-week interdisciplinary research experience and community-building activities through the Engineering Grand Challenges Scholars REUp rogram. This paper illustrates the key role of the
sketching fundamentals inperspective to engineering students that was developed at Texas A&M University [6-11]. Thesystem has been deployed at three universities for 4 years in undergraduate and graduatemechanical engineering and design graphics courses. It has also been used by undergraduateinstructors outside of engineering. Students receive real-time feedback on their speed, precisionand smoothness and also an additional tip to help them improve. SketchTivity has repeatedlyimproved the sketching self-efficacy of students along with sketching skill development. Asurvey instrument that measures the self-efficacy of students was developed and validated as partof the project that helped us [12].A few years ago, an instructor who used
trustworthiness of the findings. The final codebook was developed, and categorieswere organized into factors (internal and external) and dimensions (systemic/programmatic)based on emergent participant responses.FINDINGSThe findings from this study revealed that the engagement of STEM faculty in EEPs was shapedby an interplay of internal and external influences, which related with systemic andprogrammatic dimensions relevant to entrepreneurship and EEPs. We define internal influencesas interactions within the individual, such as identity and self-efficacy. External influencesinvolved interactions found in the structures or setting outside of the individual, such as theiracademic setting or family structures. Programmatic dimensions focused on aspects
confirmatory factor analysis on the pre- and post-survey datarespectively, we will validate and test the reliability of the surveys to measure self-efficacy, senseof belonging, perceptions of the curriculum, and perceptions of DEI.Fig. 1: Three levels of DEI student interventionData AnalysisThe pre-survey data was collected in the Fall 2023 semester, and is currently being analyzedusing SPSS and R softwares. After data cleaning, results of the pre-survey indicated that therewere 10 students in the S-STEM scholarship program, 91 in just the ELC, and 135 in neither.This represents a 100%, 84%, and 90% response rate for these populations, respectively. Asexpected, comparisons between these groups did not yield significant differences. However, weexpect
clinical immersion course impacts students from historicallymarginalized groups in race, ethnicity, first-generation status, gender, and age. Comparisonswere made between historically marginalized groups such as African Americans, Hispanics,women, and first-generation college students and their dominant counterparts. Pre- and post-course Likert scale questions were used to analyze the student’s sense of self-efficacy, abilities tomake connections and create value, and general interests in engineering. Preliminary dataanalysis indicated that there are discrepancies in the results of different demographic groups.Further analysis will be conducted to reveal the intricacies of the relationship betweenhistorically marginalized groups and their
higher self-efficacy in using ChatGPT as a learning tool in comparison with othergender identities. Furthermore, Freshmen engineering students tend to have high perceptions onusing ChatGPT as a learning tool, while junior engineering students have the lowest. Finally,freshmen engineering students tend to have high perceptions on ease of accessing ChatGPT, whilesophomore engineering students have the lowest.Keywords: ChatGPT, concerns with ChatGPT, ethical considerationsIntroductionEngineers working in Open Artificial Intelligence (OpenAI) developed the language model ChatGenerative Pre-Trained Transformer (ChatGPT). It's a kind of artificial intelligence (AI) systemthat can produce text responses to a variety of questions and prompts that seem
understanding of its structure and purpose. Below is a detaileddescription of the rubric that has been recontextualized from its original application inmanufacturing to its broader use in inclusive STEM education. The rubric is structured into threeprimary sections—Head, Heart, and Hands—each representing critical facets of the learningexperience and corresponding to cognitive engagement, emotional engagement, and activeparticipation. Our application of the 3H model[1] is rooted Piaget’s constructivist learningtheories[2], Vygotsky’s Zone of Proximal Development[3], brain-based learning like that ofSmilkstein[4], self-efficacy[5], and cultural responsive teaching[6].Head (Cognitive Engagement): This section of the rubric focuses on self-efficacy
Paper ID #42415Latina Engineering Student Graduate Study Decision Processes—Developmentand Initial Results of a Mixed-Methods InvestigationDr. Bruce Frederick Carroll, University of Florida Dr. Carroll is an Associate Professor of Mechanical and Aerospace Engineering at the University of Florida. He holds an affiliate appointment in Engineering Education. His research interests include engineering identity, self-efficacy, and matriculation of Latin/a/o students to graduate school. He works with survey methods and overlaps with machine learning using quantitative methods and sequential mixed methods approaches.Dr. Janice
significant increase in self-efficacy of the participants in the development of STEM education manuscripts. There was a 67%increase in academic publications among graduate students. The dominant theme in the qualitativeinvestigation was the "supportive and collaborative environment." Insufficient time managementpresents a barrier. Additionally, through the writing accountability group structure, there was asignificant increase in trainees’ scholarship productivity. Faculty advisers and administrators canprioritize writing groups as a cost-effective and impactful intervention to enhance academicproductivity. Further research is required to identify the most effective implementation strategies;however, integrating a writing collaboration approach seems
theirstatus in engineering.Course data is used to assess academic performance and institutional data is used to tracepersistence. Enrollment data is collected to track students in the upper-level engineering coursesequence as well as major declaration compared to non-PLTL groups. Focus groups, peer leaderjournals, classroom observations, and faculty surveys are used to assess commitment toengineering and engineering identity. Non-cognitive factors that have a promising effect oncommitment such as motivation, self-efficacy, and confidence are measured using pre- and post-survey instruments [7].Peer Leader TrainingThe first step in this work was the development of a 10-hour self-paced peer leader trainingcourse. This course consists of three modules
supports students in building self-efficacy in their abilitiesas electricity and electronics students. 1IntroductionActive learning is a teaching pedagogy which has gained traction in higher education as aneffective method for engaging learners in the process of attaining new knowledge [1]. It movesthe student from a passive role in hearing and absorbing information, to an active participant inconstructing new knowledge, typically through hands-on exercises. Active learning is an umbrellaterm used to describe many different types of practices, including role playing activities, pairprogramming, project-based learning, and many others [2].Many introductory electricity and electronics courses are ripe
dropoutrates and improving student success.Keywords: AI, data mining, dropout, engineering, first-year students, higher educationIntroductionOver the years, many studies have been conducted to understand why students leave theirstudies in Science, Technology, Engineering, and Mathematics (STEM) disciplinesprematurely. Research has delved into sociocognitive factors that play a critical role in studentpersistence in university. For instance, sense of belonging [1, 2], self-efficacy [3, 4], identity[5, 6], and intrinsic motivation [7], which are vital to student persistence in university. Forinstance, Andrews et al. [8] researched how the incorporation of makerspaces impactsstudents' self-efficacy and sense of belonging concerning design, engineering
. J. Environ. Res. Public Health, vol. 19, no. 23, p. 16284, 2022.6. Renshaw, T. L., & Bolognino, S. J. (2016). "The College Student Subjective Wellbeing Questionnaire: A brief, multidimensional measure of undergraduate’s covitality." Journal of Happiness Studies, 17(2), pp. 463-484.7. Heslin, P. A., & Klehe, U. C. (2006). "Self-efficacy." Encyclopedia Of Industrial/Organizational Psychology, SG Rogelberg, Ed., vol. 2, pp. 705-708.8. Maddux, J. E., & Meier, L. J. (1995). "Self-efficacy and depression." In Self-Efficacy, adaptation, and adjustment, pp. 143-169. Springer, Boston, MA.9. Honicke, T., & Broadbent, J. (2016). "The influence of academic self-efficacy on academic performance: A
Act [3]. The RET program included a 6-week paidinternship in multiple integrated circuit (IC) design labs at Oklahoma State University for highschool and community college teachers to learn about semiconductors and chip designfundamentals. After the RET program, teachers were also required to translate their researchexperience into new curriculum modules. The RET program is also mutually beneficial to the USsemiconductor industry and teachers. It benefits the industry by encouraging teachers andstudents to become familiar with new technologies. Teachers gain from enhanced self-efficacy atthe same time [4].However, it is challenging to measure the progress of teachers in acquiring semiconductorknowledge. In contrast to other aspects of
Small and Big-C creativity in Poland,” The International Journal of Creativity &Problem Solving, vol. 19, pp. 7-26, 2009.[45] J. C. Kaufman and R. A. Beghetto, “Beyond big and little: The four c model ofcreativity,” Review of general psychology, vol. 13, no. 1, pp. 1-12, 2009.[46] M. Karwowski, I. Lebuda, and E. Wiśniewska, “Measuring creative self-efficacy andcreative personal identity,” The International Journal of Creativity & Problem Solving, 2018.[47] P. Tierney and S. M. Farmer, “Creative self-efficacy development and creative performanceover time,” Journal of applied psychology, pp. 96, no. 2, 2011.[48] A. Bandura, Social foundation of thought and action: A social cognitive theory, EnglewoodCliffs, NJ: Prentice-Hall, 1986.[49] T
researcherswill address alignment of data formats to the research questions. The research team will be usingthe Civic Assessment Survey Instrument with pre-, mid-, and post-field tests to assess the impactof the curricula.FUTURE RESEARCH: PILOT STUDYAfter the curriculum is completed, we will conduct a pilot study for 40 teachers (20 treatment, 20control), totaling about 800 students. The Expectancy-Value-Cost for Professional Developmentscale (EVC-PD)[9] will be used to gauge teacher motivation to implement modules. The teamwill measure effect of modules on student civic purpose, knowledge and skill, empathy and self-efficacy. After the curriculum is implemented in classrooms, we will measure if the interventionsupports academic success as measured by
in today will be important for my future goals”. Interest wasdefined as interest in the subject material. An example of Interest is “I found fulfillment in doingengineering ”.Self-perceptions and definitions were operationalized as students’ personal and social attributeswhile learning. Two underlying factors were used to measure self-perception and definitions:Self-efficacy (3 items; α= .83; ω=.86) and Self-concept (3 items; α= .73; ω=.78) [26-28]. All self-perceptions and definitions questions were listed in one block with the following prompt “Pleaseconsider how confident you were today in the camp”. Self-efficacy was defined as students’ self-assessment in solving content related problems. An example of Self-efficacy is “I
concepts Compare students who took HCE courses with those on the concepts than students on standard prerequisite pathway. the standard prerequisite pathway.4. Sense of Correlate self-efficacy and perceptions as measured by the Key activities, support belonging adapted version of the Longitudinal Assessment of mechanisms, and programs Engineering Self-Efficacy survey (AWE, 2009) to activities, identified. support mechanisms, and programs that students participated in (self-report and tracking of certain programs such as First-Year Summer
given a survey at the beginning and end of the semester for a pre andpost-assessment. Students also complete written reflections after each lesson. Student surveyresults from the Fall 2023 cohort are used for this analysis.Survey items There are three factors from Godwin [7] on Engineering Identity: Recognition, Interest,and Performance/Competence, all previously defined. There are four factors of empathy fromHess et al. [8] based on the work of Davis’s [25] Interpersonal Reactivity Index (IRI):interpersonal self-efficacy, emotional regulation, perspective taking, and empathic concern.Interpersonal self-efficacy is defined as “the ability to successfully interact with others, includingothers who may have perspectives that diverge from
relationships.The research represents a preliminary analysis of data examining the role of students’ socio-academic relationships in their learning in undergraduate science and engineering education. Thebroader study also examines sociocognitive influences, such as self-efficacy beliefs andacademic adjustment, in students’ socio-academic experiences. While findings from thispreliminary analysis appear to undermine research that has consistently documentedunderrepresented minorities (URM) students’ negative experiences in STEM classroomsbroadly, and within engineering classrooms specifically, we intend to analyze these andadditional data using social network analysis, which we believe may be better suited forunderstanding students’ socio-academic
] illustrates the concept of engagement as a complex interplay between social contextsand individual experiences. Engagement is portrayed as a consequence and a predictor ofsignificant academic, social, and emotional outcomes. In this conceptualization, engagementbecomes a crucial factor influencing the causal relationships between students’ individualexperiences and their behaviors in school and beyond [17].Figure 1: The Various Aspects of Student Engagement [16]As opined by Bandura [18], one activity cannot fully address the complex chain of the cognitiveprocesses that make up motivation. Self-efficacy, or the conviction that one can bring aboutpositive results through one’s own decision-making is a key motivator [19]. Self-efficacy affectspeople’s
communication ofdesign information through technical sketching and computer-aided design (CAD)constraint-based solid modeling. Such an engaging course intends to enhance students’spatial visualization, modeling ability, and self-efficacy in applying related tools in thefuture. This sample consists of students who each enrolled in one semester from a totalof three semesters of participating students exposed to components of student-centeredlearning between the Spring of 2018 and Spring of 2019. The course consists of up to60 students per section. The data for this study comes from an NSF IUSE study measuring student self-efficacy in 3D modeling and academic success, including course grades and spatialvisualization skills [5]. The sample size is
wereadapted from the Motivated Strategies for Learning Questionnaire (MSLQ) [37, 38], to measureattitudes associated with learning. In this survey, the learner is asked to rate statements on a 7-point Likert scale (1 - “not at all true of me” to 7 - “very true of me”). The students rated their at-titudes toward intrinsic goal orientation, which is associated with a student’s perception that theyshould participate in the learning task because it is challenging, arouses their curiosity, and forcomplete understanding of the material. Further, the students rated their motivation to reengagewith the material and their fear of making mistakes. Finally, the survey also asked the students torate several self-efficacy constructs, where they are asked to judge
. Likewise, the infrastructure in place to facilitate the courses,whether software or physical resources, can impact the GTA experience in positive and negativeways.The interpersonal network for a EPICS GTA is complicated, with large variation in perceived‘rank’ of individuals that must be navigated by the GTA (Figure 2). These relationships can oftenconflict and create sources of stress for the GTA, who is likely already in an intense phase ofpersonal formation and building self-efficacy. A common cause of such conflict arises from having‘dotted-line’ management. GTA’s in general often balance multiple roles with differentsupervisors, including at minimum their direct supervisor for their TA position and their researchadvisor if applicable
(1994) usability inspection methods, usability testing will be done throughfocus groups to explore participants’ perceptions of the user interface design, identify designproblems, and uncover areas to improve the user interface and user experience in Ecampus andhybrid courses (RQ1). A heuristics evaluation [16, 17] of the user interface will be conducted toensure that usability principles are followed to provide a user interface with inclusivity andaccessibility (RQ2). A Likert scale will be adapted from Bandura’s (1989) MultidimensionalScales of Perceived Self-Efficacy [18] to explore participants' self-regulatory efficacy (RQ3).Planned InterventionThe proposed study will combine elements of both exploratory and quasi-experimental