Conference Session

Student Attitudes and Perceptions

Collection

2010 Annual Conference & Exposition

Authors

Tuba Yildirim, University of Pittsburgh; Mary Besterfield-Sacre, University of Pittsburgh; Larry Shuman, University of Pittsburgh

Tagged Divisions

Educational Research and Methods

iterations and updates to their solution methodology (process). A student with highlevels of self-efficacy should, in theory, persist longer in modeling iterations and perform betterin creation of conceptual and calculational models. In contrast, low self-efficacy may inhibit thestudent’s effort even when the skill is present leading to discouragement.A common approach to measure self-efficacy, particularly in the context of student work, hasbeen to ask students to what extent they believe they can perform a certain task. However, asself-efficacy is task dependent and there is no common single method to measure it, we proposethat a separate scale needs to be developed for modeling. This is particularly true forengineering students; as how self

Conference Session

New Learning Paradigms II

Collection

2010 Annual Conference & Exposition

Authors

Chandra Austin, Utah State University

Tagged Divisions

Educational Research and Methods

has confidence in his orher ability to engage in occupational and educational decision making 17. Career decision self-efficacy, which was originally defined by Taylor and Betz 18, is measured in terms of self-appraisal, occupational information, goal selection, planning, and problem-solving 19. Qualityexploration of career development is the basis for career decision self-efficacy 16. Research hasused the Social Cognitive Career Theory (SCCT)20 and outcome expectations to predictbehavioral influences in careers. Ojeda et al. 21 reported that high levels of confidence are relatedto positive career behaviors and outcomes. Thus, there is no debate that behavior stronglyinfluences career decision self-efficacy. The interest comes when one

Conference Session

Measurement Tools

Collection

2010 Annual Conference & Exposition

Authors

Adam Carberry, Tufts University; Matthew Ohland, Purdue University; Chris Swan, Tufts University

Tagged Divisions

Educational Research and Methods

writing self-efficacy assessment: Greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development, 2001. 33: p. 214-221.32. Comrey, A.L. and J.W. Osborne, Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, & Evaluation, 2005. 10(7).33. Gorsuch, R.L., Factor analysis. 2nd ed. 1983, Hillsdale, NJ: Earlbaum.34. MacCallum, R.C., et al., Sample size in factor analysis. Psychological Methods, 1999. 4: p. 84-99.35. Rummel, R.J., Applied factor analysis. 1970, Evanston, IL: Northwestern University Press.36. Stevens, R., K. O'Connor, and L. Garrison. Engineering student identities in the navigation of the

Conference Session

Special Session: Impacts of Service in Engineering

Collection

2010 Annual Conference & Exposition

Authors

Trevor Harding, California Polytechnic State University; Lynne Slivovsky, California Polytechnic State University; Nina Truch, California Polytechnic State University

Tagged Divisions

Educational Research and Methods

the psychological constructs are shown in Table 2. Eachconstruct was measured with at least one instrument, but in some cases several such instrumentswere used. For example, we defined self-efficacy as the belief that one is capable of performinga certain task or tasks in order to achieve a desired goal. From this definition, it is apparent thatany measure of self-efficacy would be highly dependent on the tasks and goals in question. AsTable 1 indicates, we were interested in assessing students’ self-efficacy surrounding both designand life-long learning. A review of the literature failed to produce validated instruments foreither objective. Consequently, we developed our own instrument based on the work of AlbertBandura.9 As Table 2 shows

Conference Session

Classroom Engagement

Collection

2010 Annual Conference & Exposition

Authors

Tamara Floyd-Smith, Tuskegee University; Denise Wilson, University of Washington; Ryan Campbell, University of Washington; Rebecca Bates, Minnesota State University, Mankato; Diane Jones, University of Washington; Donald Peter, Seattle Pacific University; Melani Plett, Seattle Pacific Univ; Elaine Scott, Seattle Pacific University; Nanette Veilleux, Simmons College

Tagged Divisions

Educational Research and Methods

the conceptual model, Table 1 provides examples of Likert-scale items and the inputs oroutputs that they measure. The items are measured on a scale of 1 to 5 where 1 is stronglydisagree and 5 is strongly agree. Figure 2: Conceptual Model of Outcomes of Engagement Table 1: Examples of Likert-scale Survey Items Input/Output Likert-scale Item University PSC There is a social atmosphere on campus. Classroom Belonging I feel accepted in class. Locus of Control Persistence and hard work usually lead to success. Self Efficacy I expect to do very well in this

Conference Session

Student Learning

Collection

2010 Annual Conference & Exposition

Authors

Dianne Raubenheimer, North Carolina State University; Eric Wiebe, North Carolina State University; Chia-Lin Ho, North Carolina State University

Tagged Divisions

Educational Research and Methods

all) to 4 (Very valuable).a The pre- and post-test means of all computing capabilities are significantly different from eachother at p < .05.The authors also examined the impacts of the course intervention on (a) students' self-efficacyabout learning in the discipline of engineering/computer science (9 questions), and (b) on theself-efficacy of using computers (7 questions). The results, aggregating across questions in eachscale, are shown in Table 8. No change in either of the scales was found after implementing thecourse interventions.Table 8: Self-reported engineering/computing self-efficacy and computer self-efficacy Pre-test Post-test Scale

Conference Session

Modeling Student Data

Collection

2010 Annual Conference & Exposition

Authors

P.K. Imbrie, Purdue University; Joe Jien-Jou Lin, Purdue University; Kenneth Reid, Ohio Northern University

Tagged Divisions

Educational Research and Methods

factors andthe outcome of student’s retention after one year.A. Data CollectionIndependent Variables: The students’ non-cognitive measures were collected acrossnine scales in a self-reported online SASI survey completed prior to the freshman year5,6.These scales are: Leadership, Deep vs. Surface Learning Types, Teamwork, Self-efficacy, Motivation, Metacognition, Expectancy-value, and Major decision.The following eleven cognitive items were also collected: overall GPA and core GPAfrom high school, standardized test results, average high school grades in mathematics,science, and English classes and the number of semesters taking mathematics, science,and English

Conference Session

Knowing our Students, Faculty, and Profession

Collection

2010 Annual Conference & Exposition

Authors

Katherine Winters, Virginia Tech; Holly Matusovich, Virginia Tech; Ruth Streveler, Purdue Universtiy

Tagged Divisions

Educational Research and Methods

between our current classroom practices and students’ needsfor autonomy, competence and relatedness. With regard to autonomy, students do not feelsupported. They do not feel in control of their own learning or have the clarity instructions,functioning laboratory equipment, etc. that they need to successfully complete assignments andlearn the content. With regard to competence, across all four years students are not feelingsupported by faculty in their efforts for competence and mastery. We know from recent researchthat competence related constructs, including self-efficacy, are important to students‟, andparticularly women students‟, success. 26-31 The news for faculty is better with regard torelatedness. Other than the third year, students do

Conference Session

Educational Research & Methods Poster Session

Collection

2010 Annual Conference & Exposition

Authors

Ming-Chien Hsu, Purdue University; Monica Cardella, Purdue University; Senay Purzer, Purdue University; Noemi Mendoza Diaz, Purdue University

Tagged Divisions

Educational Research and Methods

Learning (INSPIRE). She received a Ph.D. and a M.A in Science Education, Department of Curriculum and Instruction from Arizona State University. Her creative research focuses on collaborative learning, design & decision-making, and the role of engineering self-efficacy on student achievement.Noemi Mendoza Diaz, Purdue University Page 15.449.1© American Society for Engineering Education, 2010 Assessing Elementary Teachers’ Perceptions of Engineering and Familiarity with Design, Engineering and Technology: Implications on Teacher Professional DevelopmentAbstractSixty-nine elementary

Conference Session

New Learning Paradigms II

Collection

2010 Annual Conference & Exposition

Authors

Ming-Chien Hsu, Purdue University; Monica Cardella, Purdue University; Senay Purzer, Purdue University

Tagged Divisions

Educational Research and Methods

received a Ph.D. and a M.A in Science Education, Department of Curriculum and Instruction from Arizona State University. Her creative research focuses on collaborative learning, design & decision-making, and the role of engineering self-efficacy on student achievement. Page 15.200.1© American Society for Engineering Education, 2010 Development of an Instrument to Assess Elementary Teachers’ Design Process Knowledge: Findings from a Pilot TestAbstractAs more states are adding engineering to their teaching and learning standards, teacherprofessional development activities are necessary to foster

Conference Session

New Learning Paradigms II

Collection

2010 Annual Conference & Exposition

Authors

Juyeon Yun, Purdue University; Monica Cardella, Purdue University; Senay Purzer, Purdue University; Ming-Chien Hsu, Purdue University; Yoojung Chae, Purdue University

Tagged Divisions

Educational Research and Methods

Senay Purzer is an Assistant Professor in the School of Engineering Education at Purdue University. She is also the Co-Director of Assessment Research for the Institute for P-12 Engineering Research and Learning (INSPIRE). She received a Ph.D. and a M.A in Science Education, Department of Curriculum and Instruction from Arizona State University. Her creative research focuses on collaborative learning, design & decision-making, and the role of engineering self-efficacy on student achievement.Ming-Chien Hsu, Purdue University Ming-Chien is a doctoral student of Engineering Education and a research assistant for the Institute for P-12 Engineering Research and Learning (INSPIRE) at Purdue

Conference Session

Special Session: Impacts of Service in Engineering

Collection

2010 Annual Conference & Exposition

Authors

Linda Barrington, University of Massachusetts, Lowell; John Duffy, University of Massachusetts Lowell

Tagged Divisions

Educational Research and Methods

understanding of subject matter. They found that service-learning is moreeffective over four years and that the messiness inherent in helping solve real community-basedproblems enhances the positive effects  (Eyler & Giles, 1999).  Astin et al. found with longitudinal data of 22,000 students that service-learning had significantpositive effects on 11 outcome measures: academic performance (GPA, writing skills, criticalthinking skills), values (commitment to activism and to promoting racial understanding), self-efficacy, leadership (leadership activities, self-rated leadership ability, interpersonal skills),choice of a service career, and plans to participate in service after college. In all measures exceptself-efficacy, leadership, and