Conference Session

NSF Grantees Poster Session

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

S. Patrick Walton, Michigan State University; Lisa Linnenbrink-Garcia, Michigan State University

Tagged Topics

NSF Grantees Poster Session

psychological processes(students’ feelings of belonging, their motivation in engineering (self-efficacy, value, cost), andtheir development of an identity as an engineer) and how these processes are in turn associatedwith persistence in engineering. We are studying these research questions in the context of theCoEng at Michigan State University (MSU). Figure 1: Conceptual Model of Research Design. The project is examining which early (first-year) and later institutional supports predict students’ growth of important psychological processes and whether such growth mediates improvements in student persistence.Procedure and Data Collection: To date (including work prior to the current RIEF project), wehave collected longitudinal data from six cohorts of

Conference Session

Engineering Professional Development for K-12 Teachers

Collection

2011 ASEE Annual Conference & Exposition

Authors

Hui-Hui Wang, University of Minnesota; Tamara J Moore, University of Minnesota, Twin Cities; Gillian Roehrig, University of Minnesota; Mi Sun Park, University of Minnesota, Twin Cities

Tagged Divisions

K-12 & Pre-College Engineering

in this study. Page 22.1469.8Data analysis The paired sample t-test is a statistical technique that used to compare two populationmeans in the case of two samples that are correlated. Generally, it used when measurements aretaken from the same subject before and after the treatments37. Therefore, to compare the impactof the STEM PD, the paired samples t-test were conducted to analyze the pre and post surveys,teachers’ self-efficacy of teaching science/mathematics within engineering context. On the other hand, in order to standardize the answers of the open-ended question in theEngineering design cycle survey, a coding framework was

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Ignatius Fomunung, University of Tennessee at Chattanooga; Christopher Silver, University of Tennessee at Chattanooga; Marcy Porter, University of Tennessee at Chattanooga

of Belonging and Self Efficacy Sense of Belonging* Self-Efficacy** Pre-Program Mid-Program Pre-Program Mid-Program First-Year Average 3.7/5 3.8/5 4.0/5 3.8/5 (N=32| 32% response rate) Mentor Average (N=7| 23% 4.1/5 4.4/5 4.4/5 4.6/5 response rate)*Average of responses to three peer-reviewed sense of belonging questions, measured on a five-pointLikert scale. (Ex: “I feel loke an important member of my school community”)** Average of responses to three peer-reviewed sense of belonging questions, measured on a five-pointLikert scale

Conference Session

Learning Mechanics Through Experimentation

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Ethan Hilton, Louisiana Tech University

Tagged Divisions

Mechanics

engineeringprofessionals all had a generally positive attitude towards the inclusion of project-based learningin curriculum. Furthermore, the inclusion of project-based learning has been found to have severalpositive impacts. Even though these projects generally take away from the amount of timededicated to lectures, these tradeoffs do not detract from the understanding of course content, andstudents even gain a better ability to adapt their knowledge to new situations [4]. These types ofcourses have also been found to improve performance and retention at all levels of education [5–7]. Working on these types of projects has been shown to boost self-efficacy and careeraspirations [8]. Self-efficacy (or a person’s belief in their ability to complete a task

Conference Session

Engineering and ET Relationships & Professional Development

Collection

2008 Annual Conference & Exposition

Authors

Kenneth Reid, Indiana University-Purdue University-Indianapolis; P.K. Imbrie, Purdue University

Tagged Divisions

Engineering Technology

relate to typical cognitive measures of incoming students (high schoolGPA, standardized test scores, etc.)Those characteristics with slight differences include constructs related to the (self reported)learning style and academic ability of the student (with the exception of self-efficacy).Engineering students show a propensity more towards deep learning and away from surfacelearning and a slightly higher self-reported metacognitive ability. One of the subfactors of GoalOrientation, “Classroom Mastery Goal Structure” shows a similar slightly higher value orengineering students and seems related to these constructs.Leadership, found to be slightly different, showed some subfactors (“planning” and“motivation”) to be comparable to Teamwork; other

Collection

2025 ASEE -GSW Annual Conference

Authors

Araceli Martinez Ortiz, The University of Texas at San Antonio; Gabriela Gomez, The University of Texas at San Antonio; Patricia Rodriguez Ann Rodriguez, The University of Texas at San Antonio

Tagged Topics

Diversity

the Appendix.The Interest in STEM construct included questions focused on students’ enthusiasm and aspirationsin STEM fields, including items such as “I am interested in STEM studies/careers” and “STEM willbe useful for my future career.” The Self-Efficacy construct evaluated students’ confidence in theiracademic abilities and effort, with items like “I feel better prepared to succeed in the next schoolyear” and “I worked to my fullest potential in PREP.” The Collaboration construct assessedstudents’ ability to work effectively in group settings, using questions such as “I was able to sharemy thoughts, questions, and ideas with my group” and “I was able to work together in a team.” Forthe Academic engagement construct, items measured students

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2014 ASEE Annual Conference & Exposition

Authors

Imelda Olague-Caballero, New Mexico State University; Delia J. Valles-Rosales, New Mexico State University

Tagged Divisions

Industrial Engineering

team; communicate effectively; and knowledge of contemporaryissues while building students' self-efficacy through direct interactions with industryprofessionals. This model will increase the students' employability by facilitating the creation ofmeaningful connections to the real world of work, and will develop the students' ability tonavigate and negotiate the social, political, and practical dimensions of a workplaceThe model allows teams of 4 students to participate in this experience; they work under thesupervision and guidance of a graduate students acting as peer mentor, who is responsible toassist and support the team during the completion of their project. It is required that the teamspend two hours a day during twelve weeks

Conference Session

Case Studies in Entrepreneurship

Collection

2013 ASEE Annual Conference & Exposition

Authors

Mark Schar, Stanford University; Sheri Sheppard, Stanford University; Samantha Ruth Brunhaver, Stanford University; Mark Cuson; Michelle Marie Grau, Stanford University

Tagged Divisions

Entrepreneurship & Engineering Innovation

• Content Questions: The student respondents completed four on-line surveys in fall quarter, which were administered before and after the Longboard - Trucks and Longboard - Deck experiences. In the winter quarter, two on-line surveys were completed, after each of the two lab experiences. Each survey contained 10 content questions (5 engineering and 5 entrepreneurship), which were unique to each lab, resulting in 20 total pre-post content measures (10 engineering and 10 entrepreneurial). The content questions had between 4 and 5 multiple choice answers, which were presented in random order. Demographic, career intent, and self-efficacy questions were asked before the first lab and after the second lab; lab experience

Conference Session

NSF Grantees Poster Session

Collection

2011 ASEE Annual Conference & Exposition

Authors

Steven W. Villachica, Boise State University; Donald Plumlee, Boise State University; Linda Huglin, Boise State University; Drew Borresen, Boise State University

Tagged Topics

NSF Grantees

), as a “person’s beliefs about their ability to produce desired effects” (p. 614). Huang et al. (2005) also use Bandura’s definition of self-efficacy. Despite the sources used to define self-efficacy, all of the definitions point in the same direction and explain the same concept using different words. All of these studies found a significant relationship between self-efficacy and knowledge sharing, which indicates that this factor must be included when measuring knowledge sharing.6. Common Knowledge In order to gain knowledge, and ultimately acceptance among a group, you must enter

Conference Session

Research to Practice: STRAND 4 K-12 Engineering Resources: Best Practices in Curriculum Design (Part 2)

Collection

2015 ASEE Annual Conference & Exposition

Authors

Ayora Berry, Boston University; Don DeRosa, Boston University

Tagged Divisions

K-12 & Pre-College Engineering

ECD Self-efficacysurvey measures teachers’ engineering curriculum design self-efficacy. This new scale consistsof eight subscales that are rated on a 6-point Likert type agreement scale (Strongly Disagree,Disagree, Slightly Disagree, Slightly Agree, Agree, Strongly Agree). A three step process wasused to develop and validate the survey. First the constructs and associated items were defined.A literature review resulted in the selection of eight factors that are pertinent in engineeringcurriculum design: (1) K-12 Engineering Content, (2) Industry Engineering Content, (3)Engineering Design Process, (4) Project-based Learning, (5) Student Learning, (6) IntegratedLearning, (7) Teaching Coherence and (8) Curriculum Planning (see Appendix A for

Conference Session

STEM Issues

Collection

2019 ASEE Annual Conference & Exposition

Authors

Anthony E. Sparkling, Purdue University; Anne M. Lucietto, Purdue Polytechnic Institute; Aayushi Sinha, Purdue University; Trenton Thomas Hasser, Purdue Polytechnic Institute

Tagged Topics

Diversity

Tagged Divisions

Engineering Technology

were also investigated based on high school preparedness, path to CM as amajor, self-efficacy, institutional and curriculum satisfaction, and future career plans. Parentaleducational level (i.e., completed a bachelor’s) is used as a measure of first-generation college student.The measure of high school preparedness evaluates students’ math and science experience. For instance,students respond to semester of math in high school, math/science course completed, whether advancedplacement courses were offered, and perceived college math preparedness. Students indicated their pathstudents followed to CM major, institutional and curriculum, and future plans. Most of the measuresused multiple choice survey options while others, such as self-efficacy

Conference Session

Entrepreneurship & Engineering Innovation Division Technical Session 7

Collection

2016 ASEE Annual Conference & Exposition

Authors

Christina S. Morton, University of Michigan; Aileen Huang-Saad, University of Michigan; Julie Libarkin, Michigan State University

Tagged Topics

Diversity

Tagged Divisions

Entrepreneurship & Engineering Innovation

current approach to entrepreneurship education. As engineering educationseeks to recruit and retain diverse groups of students, it is important to consider the influence ofentrepreneurship education environments on women. To date, the few entrepreneurship education studies specific to engineeringentrepreneurship programs are usually multi-institutional and focus on individual studentparticipant characteristics, attitudes, outcomes,12 and interests13. Individual characteristics, suchas a person’s sense of self-efficacy and agency, certainly contribute to one’s interest andcapability for success in entrepreneurship and innovation. Yet, the nature of the environment onechooses to participate in also plays a critical role in initial student

Conference Session

NSF Grantees Poster Session

Collection

2017 ASEE Annual Conference & Exposition

Authors

Robin G. Tuchscherer, Northern Arizona University; Ron Gray, Northern Arizona University; Christine Allison Gray, Northern Arizona University

Tagged Topics

Diversity, NSF Grantees Poster Session

“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

Conference Session

Addressing the NGSS, Part 2 of 3: Supporting K-12 Science Teachers in Engineering Pedagogy and Engineering-Science Connections, Part 2 of 3

Collection

2014 ASEE Annual Conference & Exposition

Authors

Christine Schnittka, Auburn University; George Edward Turner Jr., Auburn University; Randall William Colvin, Auburn University; Mary Lou Ewald, Auburn University

Tagged Divisions

K-12 & Pre-College Engineering

mathematical concepts in the context of engineering design challenges, teacherswork in teams on design projects that involve constraints, optimization, and predictive analysis.In this study, we measure not only changes in science content knowledge, but changes inattitudes toward engineering and changes in self-efficacy to teach engineering. Theoretical Framework Learning is not an individual, isolated process; it involves the interchange of ideas Page 24.106.6   5  between teacher and student and among peers

Conference Session

Design in Engineering Education Division (DEED) - Engineering Design and First-Year Education

Collection

2024 ASEE Annual Conference & Exposition

Authors

Elizabeth Marie Starkey, Pennsylvania State University; Nicolas F. Soria Zurita, Pennsylvania State University; Sarah C. Ritter, Pennsylvania State University; Matthew B. Parkinson, Pennsylvania State University

Tagged Divisions

Design in Engineering Education Division (DEED)

: 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.

Conference Session

Problem Solving and Misconceptions

Collection

2008 Annual Conference & Exposition

Authors

Mica Hutchison, Northwestern University; Ann McKenna, Northwestern University

Tagged Divisions

Educational Research and Methods

AC 2008-865: UNDERSTANDING STUDENTS’ USE OF INNOVATIVE LEARNINGSTRATEGIESMica Hutchison, Northwestern University Mica A. Hutchison is a CASEE postdoctoral fellow at Northwestern University. She received a B.S. in Chemical Engineering from the University of Idaho in 2002, a Masters in Chemistry from Purdue University in 2006, and a Ph.D. in Engineering Education from Purdue in 2007. Her research interests include engineering and design education and the retention of engineering students. She investigates these areas using self-efficacy theory and the adaptive expertise framework.Ann McKenna, Northwestern University Ann McKenna is the Director of Education Improvement in the Robert R. McCormick

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Catherine G. P. Berdanier, Pennsylvania State University; Jacqueline O'Connor, Pennsylvania State University; Karen A. Thole, Pennsylvania State University

Tagged Topics

Diversity, NSF Grantees Poster Session

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.

Conference Session

High School Engineering Programs, Curriculum, and Evaluation

Collection

2011 ASEE Annual Conference & Exposition

Authors

James C. Baygents, University of Arizona; Jeffrey B. Goldberg, University of Arizona; Jane Hunter, University of Arizona

Tagged Divisions

K-12 & Pre-College Engineering

Engineering Self-Efficacy (LAESE) — High School Version survey is theprimary instrument for evaluating student self-efficacy, feelings of inclusion and outcomesexpectations.10 The LAESE undergraduate instrument has been tested and validated on male andfemale engineering students and measures self-efficacy of undergraduate students studyingengineering or high school students. 10 LAESE survey instruments are available through theAssessing Women and Men in Engineering web-site: www.AWEonline.org. LAESE covers thefollowing aspects of self-efficacy:10 • Student efficacy in “barrier” situations • Outcomes expected from studying engineering • Student expectations about work load • Student process of choosing a major • Student coping

Conference Session

Entrepreneurship & Engineering Innovation Division Technical Session 2

Collection

2018 ASEE Annual Conference & Exposition

Authors

Leon Szeli, Stanford University; Shannon Katherine Gilmartin, Stanford University; Helen L. Chen, Stanford University; Sheri Sheppard, Stanford University

Tagged Divisions

Entrepreneurship & Engineering Innovation

) patterns of predicted external correlates, and (d) convergence betweenself and observer ratings“[1]. The TIPI is asking the following question on a 7-point likert-scale(1 = disagree strongly; 7 = agree strongly): I see myself as… • Extraverted, enthusiastic. • Critical, quarrelsome. • Dependable, self-disciplined. • Anxious, easily upset. • Open to new experiences, complex. • Reserved, quiet. • Sympathetic, warm. • Disorganized, careless. • Calm, emotionally stable. • Conventional, uncreative.For Entrepreneurial Self-Efficacy, we used the 4-item measure (α = .792) developed by Zhaoet al. [14], which is asking the participants how

Conference Session

First-year Programs Division Technical Session 8: Project-based Learning and Cornerstone Courses

Collection

2015 ASEE Annual Conference & Exposition

Authors

Wei Zheng, Jackson State University; Yanhua Cao, Jackson State University; Jianjun Yin, Jackson State University

Tagged Topics

Diversity

Tagged Divisions

First-Year Programs

outcomes of their project-based communityservice learning based on collected students’ learning data, this paper reveals impacts of thescaffolding through different delivery approaches on students’ perceptions on creativeproblem solving, self-efficacy, identity, and application of creativity strategies. It alsoconfirms the correlation among application of prompts and students’ learning process andlearning outcomes, and compares the available results of data analysis from twoimplementation years. The results from data analysis indicate that scaffolding creativeproblem solving through freshmen’s project-based service learning may in general enhancestudent’s self-efficacy, strategies application, and interest in engineering. Among threeintervention

Conference Session

Methodological & Theoretical Contributions to Engineering Education 2

Collection

2014 ASEE Annual Conference & Exposition

Authors

So Yoon Yoon, Texas A&M University; P.K. Imbrie, Texas A&M University; Joe J.J. Lin, Purdue University, West Lafayette; Kenneth Reid, Ohio Northern University

Tagged Divisions

Educational Research and Methods

to persistence, and academic self-efficacy andachievement motivation were the best predictors of cumulative GPA over pre-college cognitiveindicators, such as standardized achievement test scores and high school GPAs. This implies thatsolely depending on traditional cognitive measures may not be sufficient to predict collegestudents’ performance, so embracing noncognitive measures may increase the predictive powerof students’ persistence and future performance in college.As students’ noncognitive attributes have gained more attention in academic performance andretention studies in higher education, this study describes a validation procedure for the extendedversion of the Student Attitudinal Success Inventory (SASI) to assess engineering

Conference Session

Laboratory and Research Skill Development

Collection

2024 ASEE Annual Conference & Exposition

Authors

Vanessa Svihla, University of New Mexico; Madalyn Wilson-Fetrow, University of New Mexico; Stephanie G Wettstein, Montana State University, Bozeman; Catherine Anne Hubka, University of New Mexico; Jennifer R Brown, Montana State University, Bozeman; Eva Chi, University of New Mexico

Tagged Divisions

Chemical Engineering Division (ChED)

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

Conference Session

Educational Research and Methods Division Poster Session

Collection

2019 ASEE Annual Conference & Exposition

Authors

Brittany Bradford, Rice University; Margaret E. Beier; Michael Wolf, Rice University; Megan McSpedon, Rice University; Ann Saterbak, Duke University

Tagged Divisions

Educational Research and Methods

nationally, particularly for students who tookless rigorous STEM courses in high school, a population that disproportionally comprisesunderrepresented minorities. The authors developed an 11-item measure of STEM-specific studystrategies, termed the STEM Study Strategies Questionnaire. We explored STEM-specificidentity, self-efficacy, and career aspirations, as well as perceived utility of attaining a STEMdegree, using a model based on Eccles and Wigfield’s (2002) expectancy-value framework ofachievement. An exploratory factor analysis found a four-factor solution to the newly developedscale: Group Work in STEM, Active STEM Learning, Interactions with STEM Professors, andSTEM Exam Familiarity. The authors found significant moderate to strong

Conference Session

NSF Grantees’ Poster Session

Collection

2014 ASEE Annual Conference & Exposition

Authors

Jennifer E. LeBeau, Washington State University; Denny C. Davis, Washington State University; Noah L. Schroeder, Washington State University; Brian F. French, Washington State University ; Shane A. Brown P.E., Washington State University; Howard P. Davis, Washington State University

Tagged Topics

NSF Grantees Poster Session

Tagged Divisions

Division Experimentation & Lab-Oriented Studies

, critical thinking assessments,and metacognition measures. Approximately 72 instruments comprise the Attitudes domain.Thirty (30) instruments are classified in the Behavior domain, including instruments related tomotivation, engineering design self-efficacy, and team effectiveness. The Professional Skillsdomain is comprised of 33 instruments related to critical thinking, writing, teamwork, anddesign. Nine instruments are related to Learning Environment, and four instruments fall underthe Institutional Data domain. Certain instruments, such as the Achievement MotivationInventory, are categorized in more than one domain.Within ASSESS, instruments are searchable by domain as well as by other filtering criteria,including ABET Student Learning Outcomes

Conference Session

Robot Mania in Precollegiate Engineering Education

Collection

2009 Annual Conference & Exposition

Authors

Linda Hirsch, New Jersey Institute of Technology; John Carpinelli, New Jersey Institute of Technology; Howard Kimmel, New Jersey Institute of Technology; Ronald Rockland, New Jersey Institute of Technology; Levelle Burr-Alexander, New Jersey Institute of Technology

Tagged Divisions

K-12 & Pre-College Engineering

between science, mathematics and real-worldengineering. Survey instrument were developed to measure Teachers’ Attitudes to Engineeringand Knowledge of Engineering Careers and Students’ Attitudes to Mathematics, Science andEngineering, Knowledge of Engineering Careers and self-efficacy for engineering skills.To help more students appreciate the role of technology and engineering in today’s society, andincrease the number of students interested in pursuing careers in STEM fields, particularly thosein generally underrepresented populations, the Center for Pre-College Programs has developed anew program centered on the unifying topic of robotics using biomedical engineeringapplications. Engineering design activities are powerful tools for the

Conference Session

Entrepreneurship & Engineering Innovation Division Technical Session 2

Collection

2022 ASEE Annual Conference & Exposition

Authors

Ying Wang, Georgia Institute of Technology; Joy Harris; Janece Shaffer

. 4, pp. 880–895, 2010, doi: 10.1037/a0019506.[19] H. Piesch, H. Gaspard, C. Parrisius, E. Wille, and B. Nagengast, “How can a relevance intervention in math support students’ career choices?,” J. Appl. Dev. Psychol., vol. 71, p. 101185, Nov. 2020, doi: 10.1016/j.appdev.2020.101185.[20] M. Hartwell and A. Kaplan, “Students’ Personal Connection with Science: Investigating the Multidimensional Phenomenological Structure of Self-Relevance,” J. Exp. Educ., vol. 86, no. 1, pp. 86–104, Jan. 2018, doi: 10.1080/00220973.2017.1381581.[21] J. E. McGee, M. Peterson, S. L. Mueller, and J. M. Sequeira, “Entrepreneurial Self– Efficacy: Refining the Measure,” Entrep. Theory Pract., vol. 33, no. 4, pp. 965–988, Jul. 2009, doi

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2023 ASEE Annual Conference & Exposition

Authors

Alexis Ortiz-Rosario, The Ohio State University

Tagged Divisions

Biomedical Engineering Division (BED)

unique. This restructuring would also allow students to work in an industry-like environment where teams have specific tasks and communication is critical. The particularuse case presented in this paper is to create a remote-sensing application for vital signmonitoring. Some details will not be included to avoid IP infringement with the sponsor of thisproject.The assessment plan is to evaluate if this new team structure improves learning outcomescompared to a traditional team. The two outcomes being compared in this study are ABETstudent outcome 3 and 5 by measuring student's communication and self-efficacy relative toother team structures (e.g. other capstone section). ABET 3 (Communication) relates to theability to communicate effectively with

Conference Session

Experimentation and Laboratory-Oriented Studies Division Technical Session 3

Collection

2019 ASEE Annual Conference & Exposition

Authors

Anas Chalah, Harvard University; Fawwaz Habbal, Harvard University; Michael Raspuzzi, Harvard University

Tagged Divisions

Experimentation and Laboratory-Oriented Studies

duringsummer programs2 and here we employed a similar assessment process.Pre-program questionnaires were sent to the students and were completed before the startof the program. This included asking the students to provide their backgrounds, relevantclasses they had taken, and their learning expectations from the program. During theprogram, assessments are completed at the end of each day and students are asked to reflecton the effectiveness in delivery of content, their self-efficacy ratings in material presented,instructors rating and any feedback for improvement. We anonymized the data daily andsent it to the instructors. Post-program evaluations covered feedback on the program,learnings from each student, and self-efficacy of the content. Students

Conference Session

Information Literacy, Computer Efficacy and Readiness

Collection

2013 ASEE Annual Conference & Exposition

Authors

Amy Renee Henson, Mineral Area College

Tagged Divisions

Two Year College Division

, younger students who are financially disadvantaged may lacksufficient computer skills. Computer technology changes rapidly; therefore, people who cannotafford updated equipment and broadband Internet services as well as new technologocial devicesoften have less proficiency because of limited access. If regular and personal access to suchdevices correlates to computer skills, those who cannot afford them may be academicallydisadvantaged.A significant factor related to task completion is self-efficacy. “Self-efficacy” refers to a person’sconfidence in his or her ability to perform a specific act. Consequently, a student’s personalbelief in his or her ability to complete computer-related tasks may affect results. Individualswith low self-efficacy

Conference Session

NSF Grantees: S-STEM 3

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Paul D. Adams, University of Arkansas; Xochitl Delgado Solorzano, University of Arkansas; Wenjuo Lo, University of Arkansas; Carol S. Gattis, University of Arkansas; Jennie S. Popp Ph.D., University of Arkansas Honors College

Tagged Topics

Diversity, NSF Grantees Poster Session

similar to the procedures that had been used in Study 1. We applied the samecriteria and one to five ratios to select our matched group, the final sample included 66 students.See Table 2 for their demographic information, ACT composite scores and high school GPA.MeasuresThese surveys involving eleven subscales (See Table 3 for details) were developed or adaptedfrom existing validated surveys. Two subscales (initial perceived social support and pre-collegeschooling) were surveyed only in the first semester, and two subscales (academic/socialintegration and institutional experiences) were only surveyed only in the second semester. Theremaining seven subscales (academic self-efficacy, career self-efficacy, self-regulation,perceived social support