suggested that teacher’s believed some of themore useful conversations, especially regarding classroom use, occurred among participants.Any PL experience should encourage such conversations, and ensure that there is ampleopportunity for teachers to engage in constructive dialogue with each other and only guided bythe PL instructors. Teachers also described confusion or perceived challenges about translatingsome of the material to classroom use. Any PL should ensure that it is clear who the audience ofthe PL is (e.g., is the material intended to be presented to students as is, or is it for teacherlearning purposes?) and formulate a plan for describing ways in which complex material can betaught and assessed with classrooms of 20 or more students. As
. Vanderlinded and E. Kim, "A Multi-level Assessment of the Impact of Orientation Programs on Student Learning," Research in Higher Education, p. 320–345, 2010.[7] K. J. Nelson, C. Quinn, A. Marrington and J. Clark, "Good practice for enhancing the engagement and success of commencing students," Higher Education, pp. 83-96, 2012.[8] A. M. Williford, L. C. Chapman and T. Kahrig, "The university experience course: A longitudinal study of student performance, retention, and graduation," Journal of College Student Retention: Research, Theory and Practice, pp. 327-340, 2001.[9] M. Karp, S. Bickerstaff, Z. Rucks-Ahidiana, R. Bork, M. Barragen and N
thinking, data modeling, communication, reproducibility and ethics [11]. In a similar study [13], researchers monitored trends across Europe in order to assess thedemands for particular Data Science skills and expertise. They [13] used automated tools for theextraction of Data Science job posts as well as interviews with Data Science practitioners. Thegoal of the study [13] was to find the best practices for designing Data Science curriculum whichinclude; industry aligned, use of industry standard tools, use of real data, transferable skill set,and concise learning goals. The best practices for delivery of Data Science Curriculum includemultimodality, multi-platform, reusable, cutting-edge quality, reflective and quantified, andhands-on. In
, shop apprenticeship [21]. As training in these areas was replaced by coursesin the fundamental sciences and math during the postwar era, educators and practicing engineersworried that practical design skills "began to slip away" from engineering ([21], p. 295).Following the curricular changes, an industry demand for graduates with “hands-on designtalent” increased, which spurred American engineering programs to reorient towards designeducation ([22], pg. 50).Changes in the review and assessment of engineering programs reflected the same shift towardsdesign. Engineering design became a required student learning outcome for ABET accreditationin the United States, and the accreditation systems of other countries [23-25]. Design coursesbecame
ethical training and data acumen of data scientists, integrating program assessment methodsinto the curriculum processes from design to operation, and continuing to innovate based onemerging needs in industry and application areas.Based on an understanding of the needs of industry within the state of Arkansas and the growingimportance of multi-disciplinary research that addresses high impact societal issues, theuniversity decided to invest in the development of a multi-college, multi-disciplinary,undergraduate program in data science. In the next section, we discuss the process fordeveloping the program, the program’s desired outcomes, and the resulting curriculum structureand operating methods.Program Development and DescriptionIn this section
and the U.S. about their preservation and change orientations duringthe process of creative ideation and validated our hypothesis. We have shown that students fromdifferent backgrounds could have very different cultural motivations associated with creativedesign. Japanese engineering students are more instigated by preservation-oriented problemstatements and are less motivated by changing situations. We hope the current work wouldstimulate reflections on principles and practices of creative design that are widely applicable, aswell as to uncover assumptions about design that are culturally specific.ReferenceAmabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the workenvironment for creativity. Academy of
. heavy course load, particular ways of assessment andinstruction, projects that depend on teamwork, different starting levels in programming) that addlevels of stress. Learning to learn inevitably involves transformational change in the learner.Adults have a role to play in enhancing students’ resilience through failures and disappointments.In order to make engineering education an available option for a high school graduate, similaradult support and knowledgeability may reasonably be expected. John explains why an earlyplanning is essential: […] if you want to go into engineering, you had to think of this like years in advance, you had to start taking the courses, the right level courses, so if you want to go to college or
your everyday life. Lastly, evaluate how these values may be helpful to you during the course of this class. Your response should be at least 250 words.Students were provided a list of different “core values” that they could choose to write about, suchas acceptance, courage, organization, creativity, stability, status, etc. and these values were usedto focus their discussion. Data were collected via the Concept Warehouse (Koretsky, 2020;Koretsky et al., 2014), an online platform designed to help students gain conceptual understandingthrough a range of formative and summative assessments. For this research, we utilized a tool thatallowed student responses to remain anonymous throughout the quarter but that the instructorcould
, 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