, atmospheric aerosols, air pollution, and atmosphere-biosphere interactions.Dr. Olusola Adesope, Washington State University Dr. Olusola O. Adesope is a Professor of Educational Psychology and a Boeing Distinguished Profes- sor of STEM Education at Washington State University, Pullman. His research is at the intersection of educational psychology, learning sciences, and instructional design and technology. His recent research focuses on the cognitive and pedagogical underpinnings of learning with computer-based multimedia re- sources; knowledge representation through interactive concept maps; meta-analysis of empirical research, and investigation of instructional principles and assessments in STEM. He is currently a Senior
McElhaney is Senior Research Scientist in STEM & CS Education with the Learning Sciences Research group at Digital Promise. He holds a B.S. in Materials Science and Engineering from Stanford University, an M.S. in Materials Science and Engineering from Northwestern University, an Ed.M. in Teaching and Curriculum from Harvard University, and a Ph.D. in Science Education from UC Berkeley. He conducts design and implementation research on K-12 teaching, curriculum, and assessment across the science, engineering, and computer science disciplines. Previously, he conducted research on electronic materials at Intel Corporation and taught high school mathematics and science in California and Missouri
Pittsburgh Dr. Mary Besterfield-Sacre is Associate Dean for Academic Affairs and Nickolas A. DeCecco Professor in Industrial Engineering at the University of Pittsburgh. She is the Founding Director for the Engineer- ing Education Research Center (EERC) in the Swanson School of Engineering, and serves as a Center Associate for the Learning Research and Development Center. Her principal research is in engineering education assessment, which has been funded by the NSF, Department of Ed, Sloan, EIF, and NCIIA. Dr. Sacre’s current research focuses on three distinct but highly correlated areas – innovative design and entrepreneurship, engineering modeling, and global competency in engineering.Dr. Wendy Carter-Veale, University
richdescriptions of lived experience, is also the main limitation of this research approach. A singleinterview transcript may be 20 to 50 pages or more and require hours of qualitative data analysis.This limitation makes traditional approaches to narrative analysis inherently unsuitable forcapturing large numbers of stories in real-time, and examining changes across a system overtime. SenseMaker offers a way to overcome this limitation through “link[ing] qualitative andquantitative data that can be assessed in parallel” [2].Complex systems theoryAccording to Van der Merwe, et al. [2]: “The patterns that emerge in the narratives, heuristics, and memes of individuals, groups, or organizations are avenues for systemic meaning-making that enable
on the NSF-funded Engineering For Us All (E4USA) project. Dr. Klein-Gardner serves as the chair of the American Society for Engineering Education Board of Director’s Committee on P12 Engineering Education and is a Fellow of the Society.Dr. Adam R Carberry, Arizona State University Dr. Adam Carberry is an associate professor at Arizona State University in the Fulton Schools of Engi- neering Polytechnic School. He earned a B.S. in Materials Science Engineering from Alfred University, and received his M.S. and Ph.D., both from Tufts University, in Chemistry and Engineering Education respectively. His research investigates the development of new classroom innovations, assessment tech- niques, and identifying new ways
NIFA grant, and is currently co-PI on three NSF-funded projects in engineering and computer science education, including a Revo- lutionizing Engineering Departments project. She was selected as a National Academy of Education / Spencer Postdoctoral Fellow and a 2018 NSF CAREER awardee in engineering education research. Dr. Svihla studies learning in authentic, real world conditions; this includes a two-strand research program fo- cused on (1) authentic assessment, often aided by interactive technology, and (2) design learning, in which she studies engineers designing devices, scientists designing investigations, teachers designing learning experiences and students designing to learn.Dr. Susannah C. Davis, Oregon
her predominately White institution. While Aubrey was assessing the frequencyof her interaction with Black people, Taylor, a first-year Ph.D student in operations research,actually counted the number of Black students and faculty. Taylor frankly queried “where AREall the Black people” (“are” is capitalized and italicized for emphasis) in both the student andfaculty body when she stated, “We have 180 students in our department, which is huge, and likeRunning Head: RACIALIZED ISOLATING INTERACTIONS 1460 faculty members. And we have two Black students and no Black faculty. Like none. Where arethey?” Taylor’s response described how she recognized
Mechanical Engineering at Purdue University, joining Purdue in August 2014. He has been teaching mechanics for nearly 20 years, and has worked extensively on the integration and assessment of specific technology interventions in mechanics classes. He was one of the co-leaders in 2013-2014 of the ASEE Virtual Community of Practice (VCP) c American Society for Engineering Education, 2017 Paper ID #20484 for mechanics educators across the country. His current research focuses on student problem-solving pro- cesses and use of worked examples, change models and evidence-based teaching practices in engineering
unavailability. Moving forward, we will compile more data toensure that our activities have been effectively and successfully implementing ABET outcomestaught and assessed in the capstone course.When students were asked about the most challenging aspect of the process, majority of themexpressed that finding the ideal project was a toughest task. Most students felt that the ideationstage helped them tremendously to formulate a practical problem and determine alternative 10solutions. Furthermore, many students found the customer discovery stage very rewarding andhelpful in terms of receiving meaningful feedback.In addition, at the end of each
programs weresurveyed to assess how they learned about our programs and to identify which mechanism wasmost influential in convincing them to apply to Lehigh University. Thus far, 188 students (42%)have completed the survey. The distribution of survey responders according to M.Eng. programis shown in Figure 4. The large majority of responders (177) completed their program full-time,while only eleven of the responders completed their program part-time. Which professional master's program did you complete? 42 41 188 Total Responses Energy Systems
4 Voices of our Studentsparticipants due to the size of the program at the time the study took place. We are also mindfulto protect the identity of the faculty member teaching the course.In preparation for the program launch, the faculty team reviewed the literature and studied thecurricula of similar programs. We visited the Boulder, CO Engineering PLUS program andsought expert input from a respected peer from Olin College. We engaged in a backward designapproach developing program and course outcomes [35] to frame the development of thecurricular content and assessment methods. We explained to the students that the course was a“design challenge” and that they had
collection including a screening questionnaire,artifact elicitation interviews, and critical incident interviews. This paper, part of a larger work inprogress by the authors, will expand on the collection methods used in order to inform others ofpossible approaches for understanding the skills learned and pathways taken by a sector of theadult community who embody many of the qualities vital to the engineer of 2020. In addition,by exploring the life pathways of makers, we can begin to see how classically trained engineersrenew their passion for engineering and how adult non-engineers learn and engage withengineering skills and knowledge. By presenting a method for assessing the skills learned byMakers along with descriptive examples of adults
Frameworkidentified and described the range of leadership behaviors exhibited within teams.Thematic coding of the ECT transcripts produced 11 categories of leadership behaviors: IdealBehavior, Individual Consideration, Project Management, Technical Competence,Communication, Collaboration, Motivating Others, Training & Mentoring, Delegation, Problem-Solving, and Boundary-Spanning (Table A). To assess the relative importance of these concepts,team members mentioning behaviors in each category were counted (Table B).Table A. Definitions of behavioral categories. Behavioral Category DefinitionIdeal Behavior Behaving as a role model for team members.Individual Consideration Recognizing that each team
AC 2007-2868: AN ANALYSIS OF MULTI-YEAR STUDENT QUESTIONNAIREDATA FROM A SOFTWARE ENGINEERING COURSEValentin Razmov, University of Washington Valentin Razmov is an avid teacher, interested in methods to assess and improve the effectiveness of teaching and learning. He is a Ph.D. candidate in Computer Science and Engineering at the University of Washington (Seattle), expected to graduate in 2007. Valentin received his M.Sc. in Computer Science from UW in 2001 and, prior to that, a B.Sc. with honors in Computer Science from Sofia University (Bulgaria) in 1998. Page 12.198.1© American Society for
research/teaching topic. The best practices also address how to maximize theprogram benefits both individually and for the institutions and ideas of how tosustain the benefits. Through first person testimonials from the contributingauthors, the paper presents personal experiences from Fellow and what was doneby Fellows, what we would do again and what we would do differently. The paperconcludes by describing how to get involved.IntroductionThe challenges in STEM education are well documented.1,2,3 For example, theOrganization for Economic Co-operation and Development (OECD) Program forInternational Student Assessment evaluated and ranked 31 countries in theirperformance of math and science education. This study found that the US ranked#19 in
that can be used to structure engineeringprogram evaluation/accreditation process and assess the capability of the program. The modelbeing developed is a five level model, called Engineering Education Capability MaturityModel23 (EECMM). It maps the activities that need to be undertaken to achieve accreditation tothe appropriate level of capability maturity of the engineering program. An engineering programthat has reached level 3 could be regarded as producing competent engineers. While one that hasreached level 5 has documented that it is producing competitive engineers. Page 12.754.11The second effort LACCEI has undertaken related to
using wireand batteries. They then create a speaker using the coil, by attaching it and magnet to a yogurtcontainer. By running wires off the coil, students are able to hook the speaker up to a radio andlisten. The activity can be extended to include equations for advanced students. Students mayvary the amount of current and monitor the changes in the speaker’s movement. They shouldthen check if the results match what is expected using the equation of a coil.Assessment MethodologyIn order to aid in the assessment of program outcomes, the Institute participants were asked tofill out an evaluation survey at the conclusion of the Institute. The participant survey wasdeveloped based on previous post-institute surveys, but also included specific
semesters of 2005 andFall semester of 2006. The process of developing this model will be discussed as well as projectoutcomes to date. Topics to be discussed are as follows: 1. Cases used at each school 2. Projects developed from the cases 3. Infusement processBrief Case Summaries From UniversitiesThe following summaries where submitted from Universities and are listed to show how theentrepreneurship cases were used and to provide some qualitative assessment. Only EIA casesare reported in this paper.Vanderbilt UniversityProfessor: Dr. R. Wilburn ClouseWhat part did the case play in the course? 20%HOD 2760Number of Students 90UndergraduateReason for using the cases1. Introduction to entrepreneurship +2. Critical thinking about entrepreneurship
testing and critical review (20). Class participation: participation in discussion of questions supplied before class (24), instructor’s subjective assessment contribution to the group discussions (16). Modelled after criteria from LEGO 375 (Table 1, M and N).Journal Format:electronic Regardless of how originally generated (e.g., typed, scanned, screen dumps, digital images), journal entries were required to be submitted in PDF format. Freeware multi-platform authoring tools were provided.Extant Journals: All student journal files (PDFs) are extant.3.2 Inquiry-Based Approaches to Autonomous Robotics (BIOL 803b)In Summer 2004, BIOL 803 began a 3-year transition from its traditional
and reliable knowledge in anethical manner” and validity and reliability can be approached through careful attention toconceptualization, data procedures, and findings presentation. Triangulation leads to credibilityby using different sources. To assess credibility, different methods to collect data for this studywere used: interviews, observations, and documents. In addition, peer debriefing was used toprovide an external check of the inquiry process, to discover biases, to clarify interpretations, andto discuss possible future directions. Finally, the respondents have an opportunity to review thedata gathered and provide or modify the information. This member checking technique isdescribed by Lincoln and Guba21 as the most important in
sample of the students at the four institutions (n=842). These studentshad not previously taken the longitudinal survey and represented a comparable sample ofstudents from these institutions. Data analysis for each of the methods is ongoing.I: Survey Questions on Group IdentificationA series of questions (items) designed to assess group identification with engineers andengineering students was administered to the longitudinal cohort of students twice, once in thefirst year and then again in the sophomore year. Four constructs comprised a number of items; afull list is given in Table 1. Three of the constructs used to explore specific dimensions ofengineering identity are based on constructs found in the Multidimensional Inventory of Black