joined the faculty at SDSU in 2009. He teaches courses in thermodynamics, fluid mechanics, heat transfer, and energy systems. His main research interests lie in the areas of thermal management of electronics and two-phase heat transfer.Dr. Ross Peder Abraham, South Dakota State UniversityDr. Richard Reid P.E., South Dakota State University c American Society for Engineering Education, 2018 1 Reflections of CSEMS and S-STEM Faculty Mentors Suzette R. Burckhard Joanita M. Kant Gregory
Paper ID #21568Professional and Personal Use of Reflection by Engineering Faculty, Students,and PractitionersDr. Adam R. Carberry, Arizona State University Dr. Adam Carberry is an associate professor at Arizona State University in the Fulton Schools of En- gineering, The 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. Dr. Carberry was previously an employee of the Tufts’ Center for Engineering Education & Outreach.Dr. Trevor Scott Harding, California
education research through doctoral education programs; two developedengineering education knowledge and practices through exposure as part of our doctoral andpost-doctoral program work; and one of us developed the knowledge and practices while in afaculty position. In our new faculty positions, we represent both tenure and non-tenure trackroles and have positions that are within a range of programs.To examine the impact of institutional context on our agency, we selected and implementedaspects from both collaborative autoethnography and collaborative inquiry to study theexperiences of our research team [14, 15]. Throughout the first two years of our positions, wewrote weekly, monthly, pre-semester, and post-semester reflections to capture and make
. James John Bale Jr., University of GeorgiaDr. Nicola W. Sochacka, University of Georgia Nicola W. Sochacka is the Associate Director of the Engineering Education Transformations Institute (EETI) in the College of Engineering at the University of Georgia. Dr. Sochacka’s research interests span interpretive research methods, STEAM (STEM + Art) education, empathy, diversity, and reflection. She holds a Ph.D. in Engineering Epistemologies and a Bachelor of Environmental Engineering from the University of Queensland.Dr. Joachim Walther, University of Georgia Dr. Joachim Walther is an Associate Professor of engineering education research at the University of Georgia and the Founding Director of the Engineering Education
scenarios,describing how educators systematically explore problems and promising solutions in their dailywork.This paper presents a case study of the cognitive heuristics used by a cross-functionalinstructional design team as they modified a second-year embedded systems course for electrical,computer, and software engineering students. In this study, we conducted a qualitative analysisof 15 transcripts (over 17 hours of audio) of meetings during which the team following acollaborative instructional model for course design. Interviews, reflections, design artifacts, andinformal conversations supplemented and contextualized the primary data. Through weeklymeetings and course interventions, the team aimed to promote design thinking, systems thinking
activitiesrequire some low-level processing on the part of the student, reflecting tasks such assummarizing information, interpreting graphs, and collecting data. Level 3 tasks generallyrequire students to apply content and skills they have learned to complete activities such asanalyzing data, explaining using course concepts, and revising work. Last, Level 4 tasks such assynthesizing, designing, and reflecting on one’s own learning require the highest level ofcognitive engagement. In addition to these nuances of student activity, the protocol also capturesthe instructor’s stated learning objectives for the class and the observer’s judgment of thealignment between the objectives and the classroom activities.The ELCOT and Existing Observation Protocol
generation processes. For example, an interview question may be wordedin such a way that it reflects the experiences and worldview of somebody who speaksAppalachian English versus African American English. To offset this possibility, the researchteam should consult with people who are familiar with the language and culture of the researchparticipants and ask them to evaluate data generation protocols as well as early collected data. Insummary, researchers can enact several validation procedures to increase the likelihood that theirdata generation methods are culturally responsive and result in a fit between a social reality andthe research report, rather than a deficit view. These steps include: • Recognize subtle (or non-subtle) linguistic
executionAccording to Bringle and Hatcher [1], service-learning is defined as a “course-based, creditbearing educational experience in which students (a) participate in an organized service activitythat meets identified community needs, and (b) reflect on the service activity in such a way as togain further understanding of course content, a broader appreciation of the discipline, and anenhanced sense of personal values and civic responsibility” (p. 112).” Service-learning has beenproven to benefit students in many ways. More specifically, service learning has been found toenhance students’ collaboration skills [2], civic engagement, interpersonal skills [3], [4], andtheir ability to apply knowledge to problem-solving [5].Our service-learning course was
and equipping faculty with the knowledge and skills necessary to create such opportunities. One of the founding faculty at Olin College, Dr. Zastavker has been engaged in development and implementation of project-based experiences in fields ranging from sci- ence to engineering and design to social sciences (e.g., Critical Reflective Writing; Teaching and Learning in Undergraduate Science and Engineering, etc.) All of these activities share a common goal of creating curricular and pedagogical structures as well as academic cultures that facilitate students’ interests, moti- vation, and desire to persist in engineering. Through this work, outreach, and involvement in the commu- nity, Dr. Zastavker continues to focus
. Second, they work regularly with the course instructor as a member ofthe instructional team to better understand the content that they will deliver in class. Third, theyfacilitate active learning in classes of near peers, and reflect on their learning and practice inwriting. LAs have become widely used in science courses at many universities and there isresearch evidence that the programs effectively enhance the success of the students in LA-facilitated courses and of the LAs themselves [6], [7]. To date, the implementation and researchabout engineering LA programs is sparse.At a large public university, we identified specific logistical barriers and educational goals in theCollege of Engineering and adapted the LA Program developed in the
proposed tobe widely adopted in engineering education because prior research have suggested its effectivenessin improving students’ problem-solving skills, collaboration skills, and academic achievement [1].By converting lecture-based courses into a project-based learning environment, students learn tocollaboratively solve multidisciplinary, complex problems.Moreover, it has been reported that students’ participation in PBL activities could be beneficial fortheir epistemological development [2]. Personal epistemology refers to students’ reflections on “thelimits of knowledge”, “the certainty of knowledge”, and the “criteria for knowing” [3]. Expertengineers demonstrated higher level of epistemological development than novices [4]. Priorresearch
E X Q28 to focus on.] (-) When I have a big decision to make... [I try to think of all the possible G 1 Q82 options.] When I have a big decision to make... [I consider the pros and cons of each E 1 Q83 option.] Q93 [I often reflect on my decision after implementing it and seeing the outcome.] L 3 [I often reflect on my decision PROCESS after implementing it and seeing
arguments in the class Individual & team research assignments, culminating in a projectResearch & Inquiry report that includes attention to context and related approaches. Written and oral reflections on the experiences of takingReflection different perspectives, learning from sources, listening to various stakeholders 5Student Learning OutcomesDefining specific and measurable learning outcomes for such
faultystrategy. Usually, their responses only reflect what a client has already seen, known and becomecomfortable with or else the responses are hopelessly vague on the order of: “Give me somethingI’ve never seen before.”The obstacles for innovation in such situations should be obvious. This situation in softwaredesign has its direct analogue in architectural engineering design. The architect or engineerreceives a program with space sizes and relationships, perhaps as well a statement oforganizational goals, and then is expected to turn these parameters into a design concept. Thelarge gap between the demands of basic functionality and the evolution of an artistically unifieddesign response make the conceptual and schematic phases of the design process
not only was this exhausting, but that it worked against theirmastery of the concepts: Having a whole day of lectures, theoretically, allows students to focus on the work and ask questions in a ordered, consecutive manner. Unfortunately, owing to the long days [specifically in CHE3005W] this was not achieved practically as the long hours is exhausting for the student and the lecturers. Additionally, it was difficult to not really know anything about the topic at 10h00 and then by 18h00 essentially finishing two weeks worth of information. If one did not understand a concept or if one needs time to reflect on the work to fully understand it, meridian was the only time to do so to ensure that one
American Society for Engineering Education, 2018 Rewards of an Engineering Pre-Requisite AssignmentAbstractThis evidence-based practice paper describes a proposal for an assignment in an introduction toengineering course designed to help students become aware of just what it takes academically toobtain an engineering degree. In an effort to promote this awareness, the authors have institutedan assignment that is designed for the students to explore various universities, their engineeringprograms, and the prerequisites for those engineering programs. The qualitative data gatheredthrough the assignment reflections were analyzed using criteria-based content analysis.Students have, to a significant degree, found this assignment to be
basicthermodynamics concepts and principles [32]. Van Meter, for example, designed andimplemented an intervention to improve students’ conceptual understanding of and reasoning onintroductory thermodynamics problems [33]. From an SRL perspective, the results of thesestudies suggest that early undergraduates have difficulty developing accurate and completeunderstandings of thermodynamics problems. The SRL literature documents several evidence-based teaching strategies that are purported to enhance students’ self-regulation skills [34]–[36].Self-evaluation is one example of such an instructional approach. During self-evaluation,students are commonly provided problem solutions and asked to reflect on their own problem-solving approaches or results. Self
research assistant observes a series of lectures in a particular class, and fills out a classroom observation protocol with categories such as content; instruction, student cognitive engagement, and student behavioural engagement with space for examples and comments under each category) [9]; Individual or group think-aloud sessions (where a research assistant records students while they work on homework assignments or laboratory reports, and where the students are expected to verbalize out loud their thinking process); Self-reflections (similar to the individual think-aloud sessions but done in writing by the students as opposed to being recorded by the research assistant); Minute papers on muddiest concepts (where
toconduct tasks. Similarly, competence describes a student’s belief in their ability tounderstand content. Performance and competence are closely linked. In later quantitativestudies of identity, these factors were combined into one performance/competence factor,thus reflecting student’s self-perception of performance as linked to their actualperformance. Recognition describes how parents, relatives, friends, and instructors seethe student in a given context. This framework was expanded by Hazari, Sonnert, Sadler,and Shanahan (2010) in their quantitative analysis of physics identity with the addition ofinterest to the framework. Interest describes one’s enjoyment in learning or interest inlearning about engineering. The PCIR framework refers to the
are derived primarily throughthe use think-aloud protocols, have little association with one another. Correlations between thetwo types of measure typically range from -.07 to .31 (Veenman, 2005). Several explanationshave been proposed for these low correlations: • Verbal reports obtained during task performance may lack reliability and would not validly reflect people’s cognitive or affective states; • responses to questionnaires typically reflect people’s beliefs or perceptions about their general learning and do not capture specific learning tasks; or • questionnaires and think-aloud protocols measure different kinds of metacognition.The first of these explanations has been addressed by several researchers, most
-level electrical and computer engineering course. The primary source ofdata was 21 transcribed audio recordings of design meetings and is supplemented withinterviews, reflections, and course artifacts. Thematic analysis revealed 10 themes that representconnections and disconnections between the process used and a common five-stage designthinking process (empathize, define, ideate, prototype, and test). These themes demonstrate someof the opportunities and challenges related to design thinking within an engineering coursedesign setting. In particular, they suggest that engineering course design is a relevant context fordesign thinking, but one to which design thinking methods do not always naturally translated.Future work should focus on better
participants were male, reflecting the demographics of the schoolat the time of the formal meetings.Samples of this size are commonly accepted in qualitative studies investigating social andexperiential phenomena. This size also seems appropriate because we were able to engage almosteveryone who shared the experiences in question. Even in cases where the target population islarger, scholars of qualitative and phenomenological research recommend limiting the sample size.This is done to allow the researcher to delve deeply into the phenomenon and the data. For instance,Dukes (1984) recommended a sample size of 3-10 for phenomenology (cited in Creswell, 2007).A literature review by Guest, Bunce, and Johnson (2006) identified recommendations
” Mechanical EngineeringResearch quality was considered throughout the data collection and analysis process, based onthe Qualifying Qualitative Research Quality (Q3) framework by Walther, Sochacka, and Kellam[17]. The belongingness responses from each student were coded using in vivo codes [18]. Invivo codes brought richness to the analysis and reflected the exact words used by the students[17]. Multiple coding and theming passes, as well as a constant comparative method, were usedacross interviews to tightly link the themes to the data [19]. Authors had ongoing conversationsabout emergent results and addressed borderline cases. Memos were kept throughout theanalytical process to document and make apparent the researchers’ perspectives.The qualitative
test their solution to the event’s problem. The last half-dayconsists of demonstrations and presentations of their design to their classmates and the teachingteam. In most implementations, these final presentations are also assessed, often forcommunication, to decouple the success of the physical prototype from the presentation andcourse grade. In several implementations, students have also completed some preparatory workin advance, and reflections on their experience afterwards. See Appendix A for full schedules ofMech, Tron, and ECE Days.While many of the Engineering Design Days implementations have some competitive aspect, theprizes are often merely bragging rights. The problems posed to students are carefullyconstructed to ensure most
forPerformance-Approach (Revised) (5 items), Performance-Avoidance (Revised) (4 items), andMastery Goal Orientations (Revised) (5 items) from the Patterns of Adaptive Learning Scales(PALS) [10]. To measure self-regulation, we used the Metacognitive Self-Regulation scale (12items) and the Time and Environment scale (8 items) from the Motivated Strategies andLearning Questionnaire (MSLQ) [11].For qualitative data, we collected all course assignments: Reaction Papers, Reflection Papers,Strategy Documents, and Final Papers. Students wrote Reaction Papers to document theirthoughts on TEDTalks and readings that were assigned as homework. Students wrote ReflectionPapers to document their thoughts after in-class discussions and after reflecting on the
achievementHowever, most evaluation tools are developed by instructors. As such, the desired behaviorsas listed are top-down rather than bottom-up. How the students themselves are perceivingtheir own learning environment is vitally important to their persistence in engineering[12][14]. A second study suggests that, though many behaviors overlap, some aspects ofteammate behavior viewed as important to students are not reflected in most instructor-created peer assessments. This study lists eleven behavior components important toteammates in engineering education settings. The more unexpected components of poor teambehavior include expecting teammates to contribute beyond their “fair share”, beingunwilling to take on tasks beyond clearly articulated
project to reflect on anddiscuss progress, brainstorm additional ideas related to project implementation, problem-solve,identify potential fields and faculty for potential inclusion or expansion of the communities, anddiscuss research and evaluation. The second community was the community of leaders (LC) forthe leaders of the discipline-based faculty development communities. The CLC was led by thePIs, with all members of the research team as participants. The CLC afforded an opportunity forthe community leaders to become oriented to a faculty learning community and a safe space todiscuss successes and areas for potential growth for their own teaching and as leaders of theirown communities. The third community was the teaching development
aligned to the learning outcomes (includes the use of formative and summative assessments, • strong task design, • support for diverse learners, and; • refining course instructional sequence and design to increase coherence in the learning progression and content. • Create a student-centered syllabus and course map for the revised course. • Design rigorous learning experiences for the targeted course that actively engage students to achieve or exceed the course learning outcomes. • Develop new approaches and repertoire of research-based practices to more effectively implement the course design. • Develop reflective practitioner skills to enact continuous improvement
process because of the nature of the reflections (e.g., describing what they ate in considerable detail).ParticipantsThis paper describes the first stage of analysis in this project. For this stage, we used data fromthe 2016 cohort of RSAP, which included 91 students who participated in three different tracks:Europe (Italy, Switzerland, and Germany), China, and the Dominican Republic. Demographicinformation for this cohort is in Tables 2 and 3. In general, the program has larger representationof women and underrepresented students than the population of the College of Engineering(CoE), and the 2016 cohort is no different. All participants signed consent forms agreeing toparticipate
knowledge – higher level learning skills which are nottraditionally emphasized in the undergraduate classroom. Therefore, these higher levellearning skills become not just purely aspirational goals but need to be actualized in order tomake the KI based pedagogy effective. This is where an active learning model can prove veryeffective. This paper describes such an active learning model developed and implemented in2017 for the introductory electronics course in the junior year. This learning model consists ofthree key components which are described in details - the concept introduction or pre-workcomponent, the concept exploration or classwork component, and the concept reflection orpost-work component. In addition, new assessment techniques tailored