Conference Session

K-12 Students and Teachers

Collection

2011 ASEE Annual Conference & Exposition

Authors

Elsa Head, Tufts University; Morgan M. Hynes, Tufts University

Tagged Divisions

Educational Research and Methods

aboutengineering design, and engineering design pedagogical content knowledge, or a amalgamateknowledge of engineering design, students and how the two interact, was measured using ahands-on think-aloud interview tasks that asked teachers to reflect on a hypothetical studentdesign and observations of a STOMP classroom. To examine self-efficacy, an online engineeringdesign self-efficacy survey was administered to teachers enrolled in STOMP and to teachers notenrolled in STOMP as a comparison group for analysis.With the support of STOMP, it is possible that teachers develop knowledge of engineeringdesign and feel more comfortable using engineering design in the classroom. Preliminaryevaluation of this program shows that teachers feel STOMP helps them learn

Conference Session

Learning Outside the Classroom

Collection

2011 ASEE Annual Conference & Exposition

Authors

Adam R. Carberry, Arizona State University; Christopher W. Swan, Tufts University

Tagged Divisions

Educational Research and Methods

. Page 22.454.7 5. Pajares, F., Hartley, J., & Valiante, G. (2001). Response format in writing self-efficacy assessment: Greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development, 33, 214-221. 6. American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association. 7. Carminer, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Thousand Oaks, CA: SAGE Publications. 8. Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp

Conference Session

Educational Research and Methods Potpourri I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Erin Crede, Virginia Tech; Maura J. Borrego, Virginia Tech

Tagged Divisions

Educational Research and Methods

DiscussionDuring instrument development, sections of questions were developed using data gathered fromthe ethnographic observations and interviews in combination with SCCT themes. Thesequestions, combined with those from the literature, form the basis of the following sections ofanalysis: student self efficacy, outcome expectations, goals, and barriers and support. In anothersection of the survey (separate from the Likert items), we asked students to indicate the variousreasons to attend or not to attend graduate school by selecting applicable items from a list. Adiscussion of these results will follow at the conclusion of this section.A. Student Self Efficacy Regarding Graduate SchoolSeveral items were developed to measure student self efficacy as it

Conference Session

They're Not "Soft" Skills!

Collection

2011 ASEE Annual Conference & Exposition

Authors

Brook Sattler, University of Washington; Ashley Ann Thompson, University of Washington; Jennifer A. Turns, University of Washington; Deborah Kilgore, University of Washington

Tagged Divisions

Educational Research and Methods

to measure their progress against their own goals as well asagainst their peers’ progress, which in turn impacts the types of goals they set.Positive Relationship between Goals and Self-EfficacyIn engineering education, self-efficacy is important when considering issues of recruitment andpersistence of students, especially underrepresented students.15 Students with higher self-efficacytend to have higher academic achievement, because they set higher goals.14 Bandura defines self-efficacy as ―the conviction that one can successfully execute the behavior required to produce theoutcomes.‖ 16 Relative to goal setting and monitoring, social learning theory articulates a causalrelationship between self-efficacy and goals since ―goals increase

Conference Session

Understanding Our Students

Collection

2011 ASEE Annual Conference & Exposition

Authors

Ida B. Ngambeki, Purdue University, West Lafayette; Demetra Evangelou, Purdue University, West Lafayette; Matthew W. Ohland, Purdue University, West Lafayette; George D. Ricco, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

’ interest inengineering, students’ social orientation and motivation, the barriers and supports theyencounter, their self-efficacy, and their satisfaction with their major.Students’ satisfaction with their major was measured using Nauta’s validated Major SatisfactionScale27 that contains items such as “I often wish I hadn’t gotten into this major” and “I feel goodabout the major I have selected.” Chen’s General Self-efficacy Scale28 tailored to engineeringwas used to measure students’ self-efficacy. This scale contains items including “Compared toother people, I can do most tasks very well” and “I am confident in my ability to solveengineering problems”. Social influence was measured using the Social Influence Scale 29. Theother scales used were

Conference Session

Educational Research and Methods Potpourri I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Hyun Kyoung Ro, Pennsylvania State University

Tagged Divisions

Educational Research and Methods

make plans to leaveengineering after earning an undergraduate degree 11, there is a need to examine what factorscontribute engineering students’ post-graduate plans using large scale data sets. Such studentsmay help undergraduate engineering programs design interventions to keep engineering studentsin the engineering graduate programs and profession.Students’ Self-assessments of Abilities and Graduate School Plans Most research identifies academic preparedness in mathematics and science at an earlyage as one of the most salient factors influencing engineering student choice of graduate schoolin engineering5. However, Bandura argued that students aspire to careers based on not only theirqualifications but also their self-efficacy in

Conference Session

Persistence and Retention I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Qu Jin, Purdue University, West Lafayette; P.K. Imbrie, Texas A&M University; Joe J.J. Lin, Purdue University, West Lafayette; Xingyu Chen, Purdue University

Tagged Divisions

Educational Research and Methods

decision, deep learning, self efficacy, surface learning, team, and motivation. The highschool history matrix includes SAT/ACT scores, high school core GPA, high school math,English, and science grades, and number of semesters taken. Outcomes of the model can beretention in engineering and academic performance through students’ undergraduate study.In this study, only seven of the affective measures were included. Also, as a starting point, weonly focused on retention and GPA after one year. The results of this study will help determinethe potential of using neural networks to model a larger list of outcomes in the future. Page 22.70.3

Conference Session

They're Not "Soft" Skills!

Collection

2011 ASEE Annual Conference & Exposition

Authors

Susan M. Lord, University of San Diego; Candice Stefanou, Bucknell University; Michael J. Prince, Bucknell University; John Chen, California Polytechnic State University; Jonathan D. Stolk, Franklin W. Olin College of Engineering

Tagged Divisions

Educational Research and Methods

information available in the environment in combination withwhat they already know, (b) learners can control and regulate aspects of their thinking, motivation,and behavior and in some instances their environment, (c) learners compares their progress toward agoal against some criterion and this comparison informs the learner of the status of progress towardthe goal, and (d) self-regulatory mechanisms mediate between the person, the context, andachievement (pp 387-388). Zimmerman emphasized that in addition to metacognitive skill,students need a sense of self-efficacy and personal agency for success in self-directedenvironments. 16 From these descriptions, it is clear that self-regulation involves many forms ofautonomy.Based on this description of

Conference Session

Engineering Education Research in K-12

Collection

2011 ASEE Annual Conference & Exposition

Authors

Gamze Ozogul, Arizona State University; Martin Reisslein, Arizona State University; Amy Marcelle Johnson, University of Memphis

Tagged Divisions

Educational Research and Methods, K-12 & Pre-College Engineering

-427.[14] Dunsworth, Q., & Atkinson, R. K. (2007). Fostering multimedia learning of science: Exploring the role of an animated agent’s image. Computers and Education, 49, 677-690.[15] Yung, H.I. (2009). Effects of an animated pedagogical agent with instructional strategies in multimedia learning, Journal of Educational Multimedia and Hypermedia. 18(4), 453-466.[16] Murray, M., & Tenenbaum, G. (2010). Computerized pedagogical agents as an educational means for developing physical self-efficacy and encouraging activity in youth. Journal of Educational Computing and Research. 42(3), 267-283.[17] Moreno R., Reisslein, M., & Ozogul, G. (2010). Using virtual peers to guide visual attention during learning: A test

Conference Session

Educational Research and Methods Potpourri I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Sohum Sohoni, Oklahoma State University; Donald P. French, Oklahoma State University; YoonJung Cho, Oklahoma State University

Tagged Divisions

Educational Research and Methods

Relationship of Prior Training and Previous teaching Experience to Self-Efficacy among Graduate Teaching Assistants. Research In Higher Education, 1994. 35: p. 481-497.15. M. Anderson, Impostors in the Temple. 1992, New York, NY: Simon & Schuster.16. J.M. Civikly and R. Hidalgo, Ta Training as Professional Mentoring, in Preparing Teaching Assistants for Instructional Roles: Supervising Tas in Communication, J.D. Nyquist and D.H. Wulff, Editors. 1992, Speech Communication Association: Annandale, VA. p. 209-213. Page 22.1097.1217. D.G. Herrington and M.B. Nakhleh, What Defines Effective Chemistry Laboratory

Conference Session

Engineering Education Research in K-12

Collection

2011 ASEE Annual Conference & Exposition

Authors

Karen A. High, Oklahoma State University; Melanie C. Page, Oklahoma State University; Julie Thomas, Oklahoma State University

Tagged Divisions

Educational Research and Methods, K-12 & Pre-College Engineering

). Unlocking the clubhouse: Women in computing. Cambridge: MIT Press.8. Dweck, C.S. (2007). Is math a gift? Beliefs that put females at risk. In S.J. Ceci & W.M. Williams (Eds.),Why aren’t more women in science? Top researchers debate the evidence (pp. 47-55). Washington, DC: American Psychological Association.9. Pajares, F. (2005). Gender differences in mathematics self-efficacy beliefs. In A.M. Gallagher, & F. Pajares (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 294-315). New York: Cambridge University Press.10. Fryberg, S.A., Markus, H.R., Oyserman, D., & Stone, J.M. (2008). Of warrior chiefs and Indian princesses: The psychological consequences of American Indian mascots

Conference Session

Persistence and Retention I

Collection

2011 ASEE Annual Conference & Exposition

Authors

Cheryl A.P. Cass, Clemson University; Zahra Hazari, Clemson University, Department of Engineering & Science Education and Department of Mathematical Sciences; Philip Michael Sadler, Harvard-Smithsonian Center for Astrophysics; Gerhard Sonnert, Harvard-Smithsonian Center for Astrophysics

Tagged Divisions

Educational Research and Methods

generally characterize thedevelopment of self-efficacy as a mediator to career interests and goals. However, self-efficacyis constructed to examine highly task-specific self-perceptions, usually on the short-term. Wewere interested in implementing a macro-level framework that would allow us to assess howstudents view themselves over a long period of time, in a more global context. Our scienceidentity framework encapsulates the dimensions of recognition, interest, performance, andcompetence.29 As illustrated in Figure 1, recognition refers to perceived recognition by others asbeing a good science student while interest addresses a desire and/or curiosity to think about andunderstand science. Measures of performance refer to a belief in ability to

Conference Session

Knowing Ourselves: Research on Engineering Education Researchers

Collection

2011 ASEE Annual Conference & Exposition

Authors

Johannes Strobel, Purdue University, West Lafayette; David F. Radcliffe, Purdue University, West Lafayette; Prashant Rajan, Purdue University, West Lafayette; Sadia Nawaz, Purdue University, West Lafayette; Yi Luo, Purdue University; Jea H. Choi, Purdue University; Ji Hyun Yu, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

  curriculum   36.   self-­‐efficacy   project-­‐based  learning   37.   professional  skills   performance   38.   persistence   science   39.   collaborative  learning   mathematics   Page 22.1026.14 40.   stem   java  Table 2. Top 40 keywords most frequently occurring keywords in FIE_sample and FIE_totalFigure 2. Frequency distribution of keywords from FIE_sample: 2005 to 2010. Graph plotted ona semi-log scale to clarify location of the top 40 ranking keywords.Figure 3

Conference Session

Active and Inquiry-Based Learning

Collection

2011 ASEE Annual Conference & Exposition

Authors

Muhsin Menekse, Arizona State University; Glenda Stump, Arizona State University; Stephen J. Krause, Arizona State University; Michelene T.H. Chi, Arizona State University

Tagged Divisions

Educational Research and Methods

AC 2011-2394: IMPLEMENTATION OF DIFFERENTIATED ACTIVE-CONSTRUCTIVEINTERACTIVE ACTIVITIES IN AN ENGINEERING CLASSROOMMuhsin Menekse, Arizona State University Muhsin Menekse is pursuing a doctoral degree (PhD) in the Science Education program at Arizona State University concurrently with a MA degree in Measurement, Statistics and Methodological Studies. He had research experiences in the areas of conceptual change of nave ideas about science, argumentation in computer supported learning environments, and video game design to support students’ understanding of Newtonian mechanics. Muhsin is currently working under the supervision of Dr. Michelene Chi to develop and implement a classroom-based methodology with

Conference Session

K-12 Students and Teachers

Collection

2011 ASEE Annual Conference & Exposition

Authors

Tirupalavanam G. Ganesh, Arizona State University

Tagged Divisions

Educational Research and Methods

to ensure validity focused onensuring that the instrument was designed to measure what it was supposed to measure: didthe students’ drawings of a scientist depict specific characteristics that are stereotypical of ascientist’s image? To increase the validity of the subject produced drawings, researchersdeveloped coding schemes that attempted to standardize the identification of stereotypicalcharacteristics in the drawings. Codification schemes allow for human ‘raters’ or ‘coders’ tobe trained, and the use of inter-rater reliability measures among raters allowed researchers toeither modify the coding scheme or retrain the raters. Humans as ‘raters’ are fallible;therefore, the use of a score or statistical measure of homogeneity among raters