multidisciplinary project presented in this paper brings together the fields of structuralengineering and computer science to address an existing shortcoming in seismic reconnaissance.Presently, expert engineers are required to manually filter and tag post-earthquake images ofdamaged civil infrastructure (acquired from engineering inspection teams or other formal/socialmedia platforms); the collaborative research team is attempting to automate these time intensiveand technically challenging tasks by developing a robust deep learning (DL) algorithm.The research team is based out of California Polytechnic State University – San Luis Obispo, apredominantly undergraduate public university located on the West Coast. As a reflection of thisacademic environment
electronic Lab Notebooks (eLN), we tested two no-cost implementations ofeLN and compared to the traditional paper-based LN. This paper describes the background,method, and result of the comparison test.Lab Notebooks ReviewMany groups of instructors and researchers studied LN-related topics. For example, Berland etal. [2] noted many forms and purposes of engineering notebooks in colleges and high schools,and identified two general aspects: process-based versus product-based. The distinction,according to them, is based on the primary audience and the timing of reflection and feedback.Process-based notebooks are “for recording, reflecting upon, and receiving feedback on works-in-progress”, including “preliminary ideas, personally relevant questions
adoption of RBIS, iscalled instructional change [4]. Facilitating instructional change in engineering educationrequires a different approach, one that understands academia as a complex system [5] and usessystems thinking to understand how everything is connected to everything else [6] instead of thetraditional approach that is based only on faculty reflection and intuition drawn from theirteaching experiences [2]Academia is a complex system, and as such, it does not have isolated drivers or root causes thatare individually capable of generating change [6]. Instead, multiple interactions and feedbackloops exist that reinforce or balance decisions, motivators, and actions of agents in the system[7]. Academia is a system with strong historical roots
,yet, it is clear the model is applicable among many disciplines. Part 1 of the model specifies thefive-core components of interdisciplinary collaboration: 1) interdependence, 2) newly createdprofessional activities, 3) flexibility, 4) collective ownership of goals, and 5) reflection on theprocess [17]. Part 2 outlines the influences on interdisciplinary collaboration: professional role,structural characteristics, personal characteristics, and a history of collaboration [17]. Figure 1describes Bronstein’s [17] model and serves as the framework for the remainder of this paper. Professional Role Structural Characterisics - Holding values and ethics specific to each - Manageable
give feedback and guide students towards higher learning, or they may be with peers in “jointdialogues” [20, p. 82] where two or more students co-construct learning by reflecting on the other’sperspective. The active-constructive-interactive taxonomy classifies pedagogies through their taskfeatures, the activities which learners do, and the cognitive processes they use. The three levels ofactivity describe how engaged students are with a task, depending on expectations of behavior,dialogue, and producing outputs. Another model of student engagement was proposed by Smith and colleagues [11] calledthe pedagogies of engagement model. This model is based on interactions among teams or groupsof students, and it describes the dynamics of
them the upper hand with industry recruiters.Competitions sanctioned by SAE International (formerly the Society of Automotive Engineers)generally occur at the end of the school year (May/June), thereby making the summer months acritical time for student teams to reflect on their previous designs and to start proposinginnovations for the subsequent season. The Formula SAE (FSAE) team at The Cooper Union inNew York City has used this time to immerse high school students in this real-world activity intheir college’s summer STEM program.This 6-week intensive summer program is separated into two main modules. The first modulefocuses on teaching students the fundamentals of engineering experimentation that culminate inoral presentations detailing
Wind by William Kamkwamba and Bryan Mealer, about a boy inMalawi who built a windmill to power his community. In 2017, the book selection was TheImmortal Life of Henrietta Lacks by Rebecca Skloot, which focuses on ethics and issues of classand race within science.During the fall semester, students participate in a 1.5-hour discussion session led by two upper-level College of Engineering students. These discussions focus on important themes in the bookand how these relate to engineering and the experiences of a first-year student. The sharedexperience is intended to encourage community-building and promote a sense of belongingamong the students. This discussion also prompts reflection about what it means to be anengineer, including the
. Nextwe incorporate sklearn 40 so students can execute and explore the results of machine learningalgorithms. To prepare for machine learning content students watch bots videos 14 and they arealso assigned some ethics reflection prompts in response to Cathy O’Neil’s TED Talk 35 .The common thread across topics is the problem-solving heuristics shown in Figure 1. Weintroduce these early on and revisit them with each topic and explicitly point out when we areusing a strategy, or trying several of them, to solve a problem. For example we point out the useof concrete examples for solving encoding problems, developing algorithms, and initially usinghard-coded values in incremental web development. Another example is how students areexposed to
knowledge gains, interest in the degreeprogram, and ability to function as a professional engineer. The mobile boards have also beenutilized in other disciplines such as mechanical engineering using two experiments developedand tested in a class. [5-12].Connor et. al. observed that to successfully adopt and incorporate innovative educational devicesinto curricula within and across multiple institutions, understanding the potential advantages isessential, but understanding the barriers that can occur is just as important to ensure theeffectiveness of implementation. For the Mobile Studio project, they identified barriers as: (i)reflected experience of both students and instructor, (ii) the use and the development ofsupporting resources to the device
canbe influenced to change the moral choices that are made through rational thinking, but in themoment the moral decision will be intuitive. While Social Intuitionism acknowledges rare caseswhere reasoning or reflection can change an intuitive moral action [8], Dual Process Theoryexplicitly merges aspects of the two theories. Specifically, the theory holds that most moraldecisions occur over a time where there can be a dialogue between fast intuitive and slowerrational processes. Therefore, in the moment of making a moral decision one can rationalize aresponse that differs from the intuitive reaction.Pfieffer and Billiar [10] note that different well-developed ethical theories can result in equallyvalid opposing decisions. They recommend the
helpful in refining this specific OEMP assignment and developing generalguidelines for writing OEMPs on any topic. If multiple students are not making reasonable, well-justified assumptions, this suggests that the problem should be redesigned to provide morescaffolding that helps students make more realistic assumptions or more explicitly prompts themto write out their justifications. Second, having students metacognitively reflect on their ownassumptions is an important factor in their development of engineering judgment. Byunderstanding what assumptions students are making and the impact these have on design,instructors can highlight productive beginnings of engineering judgment and help studentsunderstand when they have made assumptions that
identitiesrelated to a specific subfield within their major (e.g. “I see myself as a mechatronics person, butnot a fluids person”) and therefore we expect to find differences in responses between coursecontexts for the same student.We measured motivation and attitudes towards learning in a cohort of students simultaneouslyenrolled in three upper-division mechanical engineering courses. We adapted portions of theMotivated Strategies for Learning Questionnaire (MSLQ) into two surveys: an online surveyasking students to reflect on all of their mechanical engineering courses (“cohort context”), and apaper survey delivered during class in each of the three courses (“course context”). Thecohort-context survey included questions related to intrinsic motivation
institution, in a student’s decision toremain there. However, culture and student perspective should also be valued and considered.Institutions that are more agile in doing this may be more successful at maximizing retention andsuccess for wider numbers of students, with a range of backgrounds related to race, ethnicity,socioeconomic status, environment, and/or the intersectionality of these and others. For example,students from backgrounds that reflect first-generation college attendance can also face a rangeof similar (though not identical) challenges. While there can be various approaches to enhancingretention for students of all backgrounds, first-semester GPA may help better predict andencourage graduation for students [11-13].STRIDE: A Cohort
andwere seen in prior turnover studies as well. Especially important were the interpersonal support networks,as networks lead to increased productivity, inclusion, and efficiency [15]. Holtom et al. [7] also found thatnetwork groups can improve social embeddedness and lower turnover intentions. This social embeddednesswas defined as access to mentoring and social inclusion, which is further reflected in the welcomingenvironment, close-knit groups, and social events that managers identified in this research. Additionally,social embeddedness involves how the newcomer fits with the team in social aspects, which is supportedby Cloutier et al. [1] and could be considered part of the close-knit groups and social events found in thisresearch. However
tandem with the IV-Intervention emergent technologypresentation, and not during control semesters. These circumstances were beyond our control butaffected the pseudo-experimental design and represent a threat to the validity of the study.In addition, this study only reflects the behaviors and attitudes at CSU Chico. Replication acrossmultiple institutions would be necessary to generalize the conclusions. CSU Chico is also arecognized Hispanic Serving Institution (MSI) and enrolls disproportionately high percentage offirst-generation, low-income, and under-represented minorities (URM) in STEM. Our students’motivations and behaviors may or may not reflect those of the general population of softwareengineering students.Nevertheless, it is
improvement process. Student learning outcomes are developed with the consultation of the graduate faculty committee, Industry Advisory Board (IAB) and alumni, who are the constituents of the program. Data are collected every three years to assess the attainment of the learning outcomes. Analyzed data are presented to the graduate faculty committee to identify improvement needs. Approved improvements are implemented and assessed. The learning outcomes are periodically reviewed by the constituents to ensure that learning outcomes are still valid and relevant to reflect the needs of the industry. Student learning outcomes are developed with the consultation of the graduate faculty committee, Industry Advisory Board (IAB) and alumni, who
., 2016, p. 6). However, this dichotomy does not reflect the heterogeneity and blendof real engineering practice in industry, thus there is a tension that arises within the division oflabour. Women and minoritized individuals will assimilate valued forms of technical masculinityin the workplace in order to build positive professional identities. This techno-social dualism isused as a framework for analyzing engineering design discourses to deconstruct invisiblemessaging that may unintentionally create spaces that are not inclusive.MethodThis paper uses discourse analysis to review highly cited engineering education literature onengineering design and observe themes. The discourse analysis methodology and the datasetselection method are both
-year engineering students was acquired through open-ended surveys.As shown in Table 4, surveys were given to participants in Cohorts A and B at the end of thefirst-year of engineering study and at the start of the second-year of engineering study (to allowstudents time to reflect on their major discernment process and determine the certainty of theirmajor selections). In these Table 4. Open-Ended Student Feedback Opportunitiessurveys, the main questionasked participants to reflect Offered to Offered to Student Feedback Opportunitieson their first-year of Cohort A Cohort Bengineering
]. Similarly,the faculty of the pilot sections prepared a pre and post survey that all students taking EASC1107would be asked to complete. IRB approval was obtained, and students were asked to consentbefore completing surveys. The surveys had students create an anonymous identifier by whichwe were able to match their pre and post surveys while retaining student anonymity. Due to thechallenges of having all students complete the pre/post survey, as well as one faculty member notpassing out the post survey in time, the analysis is presented for 3 sections of makerspacecourses with 22 paired responses, and two sections of the traditional course model reflecting 25paired responses.The pre survey was passed out in the middle of the semester, just prior to
during the class session. For instance, these items focus on the subject matter being taught, and the ways in which the instructor includes key concepts. Procedural knowledge (content): These items measure how students engage with course materials. Specifically, these items are focused on assessing the ways that students talk about or characterize the phenomena being taught in the class and whether they are reflective about their learning in the course. Communicative interactions (culture): The communicative interactions section focuses on the types of interactions that occur in the classroom. These items examine if classroom culture is inclusive and what types of
others interested in the project to discuss skill sets and to make ageneral determination of their compatibility as teammates.During each lab time, up to 75 students mingled and placed sticky notes on up to 25 posters. Weallocated about 45 minutes for this mingling process. Students were encouraged to monitor thenumber of sticky notes, colors, and names on a particular project poster in order to gage the levelof interest and note which other students were interested in the project. Based on thisinformation, they had the opportunity to adjust their choices. Pictures of the activity as itprogressed are shown in Figure 5.After this first 45-minute round, we asked the students to stop and reflect: Did their first-choiceproject include people with
of performance indicators isthat, collectively, they span all competencies suggested by either the SO or any relevantdefinitions in the Criteria.The second observation applies to programs in transition from the former (pre-2019) SO a-k tothe current SO 1-7. ABET/EAC revised the SO’s and definitions in the new Criteria with intent,so simply “mapping” existing SO a-k performance indicators to the new SO 1-7 may not yieldthe best result. Indeed, it is a valuable exercise for the SO committee to “start fresh” and deviseperformance indicators that specifically integrate the new language of the Criteria, because thisactivity will compel the committee to collectively reflect on the implications of the SO and itsattendant implied competencies.The
slide showed only the questionstudents had to answer. Students had between one and two minutes to find the answer in thedatabase. The time limits were meant to keep the class moving rather than restrict students fromanswering, and extra time was granted when requested. On the following slide, students had 20seconds to choose their answer from multiple choice options. Most students were able to answerthe questions correctly in the amount of time given; however, some students experienced troublelogging into PollEverywhere, while others missed the explanation of the rules or took too long tosubmit their answers, so not all students who participated are reflected in the quiz numbersbelow. Generally, the librarians checked whether students were
’ Academic and Career PlansAbstractUndergraduate research experiences in engineering have recently received significant interest asmechanisms for attracting undergraduates to graduate-level work. In particular, the NationalScience Foundation’s Research Experiences for Undergraduates (REU) initiative aims to recruitstudents to careers in research. Our work employs a social cognitive theoretical framework toinvestigate how participation in a summer undergraduate research program influencesparticipants’ academic and career plans (specifically plans to pursue a Ph.D.) and their self-efficacy for future scientific research. A mixed-methods approach, incorporating surveyinstruments, interviews, and weekly self-reflective journal entries, was utilized to
0.00 4.79 critical thinking Treats all students in a consistent R12 92.86 7.14 0.00 0.00 0.00 0.00 4.93 manner R13 Exams reflect the material covered 85.71 14.29 0.00 0.00 0.00 0.00 4.86 Willingly assists students outside of R14 78.57 21.43 0.00 0.00 0.00 0.00 4.79 class R15 I found this class to be challenging 64.29 28.57 0.00 0.00 7.14 0.00 4.43 Item
point to students with a more dominant sequential style. Table 1: Summary of student and professor preferred learning styles. Students Professors Balanced Moderate Strong Total Total No. % No. % No. % No. No. Active 22 29.7 12 16.2 7 9.5 41 2 Reflective 22 29.7 8 10.8 2 2.7 32 3 Sensing 27 36.5 21 28.4 2 2.7 50 4 Intuitive 17 23.0 5 6.8 1 1.4 23
Eastern Europe4. These changes havebrought what some term as a new era5 or supply chain revolution6. This has also caused somecompanies to integrate supply chain management into every facet of their business. In manycases supply chain logistics design has become the means for companies to be more competitiveand advance themselves in the global marketplace. Consequently, supply chain management hasbeen a topic of intense interest for approximately two decades and has been widely examined inboth the trade and academic press.In spite of the attention it has received the field of supply chain is in a state of rapid change anddevelopment. Thompson7 notes that many of the courses in engineering management programsare often reflective of well
) Page 12.1316.7can be present in some information context and a subject makes a decision (yes or no) aboutwhether the signal is present. This decision is based on the amount of evidence perceived by thesubject. In our context the decision corresponds to whether an information element is relevant orirrelevant to solving a problem. The amount of evidence is considered to be a random variablewith a normal distribution. The probability distribution reflects the inherent noise (either in theinformation or a subject’s internal decision making process). The decision is modeled as twonormal distributions having the same variance. One distribution corresponds to pure noise (nosignal present) and the other distribution is the signal with noise. The model
it.Feminist pedagogy strives for a more egalitarian classroom where power is shared betweenteacher and student learners; this must include self-reflection of teachers, acknowledgingteachers and students as learners and knowers thereby seeing the role of the professor more asguide than expert and valuing the voices of individual students. Rather than serving as the all-knowing deliverer of truth, the teacher is a guide for student learning… While teachers are encouraged to enrich syllabi by choosing materials that will appeal to a wide range of students and that cover areas that include the students’ subjectivities, students are encouraged to comment upon and negotiate syllabi, course
Science Partnership conference onchallenging courses and curricula and the five strands of teaching for math proficiency (from theNational Research Council report, Adding It Up [4]), GBMP has arrived at a definition ofchallenging courses and curricula. For GBMP, there are four key aspects of challenging coursesand curricula: ‚ Deepening Knowledge of Important Mathematical Ideas ‚ Productive Disposition ‚ Inquiry and Reflection ‚ CommunicationDeepening knowledge of important mathematical ideas includes developing conceptualunderstanding, procedural fluency, and strategic competence. A productive disposition includesdeveloping a willingness to persist in working on mathematical problems and developingconfidence in one’s own ability