Paper ID #23384Early-career Plans in Engineering: Insights from the Theory of Planned Be-haviorTrevion S. Henderson, University of Michigan Trevion Henderson is a doctoral student in the Center for Higher and Postsecondary Education (CSHPE) at the University of Michigan. He recently earned his master’s degree in Higher Education and Student Affairs at The Ohio State University while serving as a graduate research associate with the Center for Higher Education Enterprise. Trevion also hold’s a Bachelor’s degree in Computer Science and Engineer- ing from The Ohio State University, where he served as a research assistant in
Paper ID #21635Understanding the Socializer Influence on Engineering Students’ Career Plan-ningRohini Abhyankar, Arizona State University Rohini Abhyankar is a second year graduate student at Arizona State University’s Engineering Education Systems and Design doctoral program. Rohini has a Master’s degree in Electrical Engineering from Syracuse University and Master’s and Bachelor’s degrees in Physics from University of Delhi, India. Rohini has over ten years each of industry and teaching experience.Dr. Cheryl Carrico P.E., Virginia Tech Cheryl Carrico is a part-time faculty Research Scientist for Virginia Tech and owner
college of engineering.When organizing a departmental or programmatic effort, logistical decisions can dominate andobscure the underlying organizing theory for the effort. Consistent with NSF’s calls for a greaterunderstanding of theories of change, we connect the explicit and implicit organizing philosophiesunderpinning the innovative approach to enacted institutional plans and approaches. We draw onHenderson’s theoretical models of Institutional Change in higher education to clarify the chosenapproach to transformation. We also draw on a complex systems perspective as a guidingphilosophy to conceptualize change in the interconnected human, institutional, and socialstructures of our engineering college, and on boundary spanning to address the
week’stopics. Students maintained Strategy Documents to plan and evaluate weekly academic goals(e.g., splitting up weekly problem sets into daily quotas). In lieu of a final exam, students wroteFinal Papers in the form of a letter to their high school self (or to a friend in high school). Theseletters included what students wished they had known before coming to college and whatstudents wanted to share from the “Engineering the Mind” course.Data analysisWe used multiple methods to analyze the data because we wanted to capture various nuances ofthe course. For quantitative methods, we calculated the average score of the items for each scalefor each student. For example, the mindset scale had eight items, each with a score ranging fromone to six (Likert
Paper ID #21415Rewards of an Engineering Prerequisite AssignmentDr. Cynthia Jane Wilson Orndoff Esq., Florida Southwestern State College Dr. Cynthia Orndoff received a J.D in 2014 from Ave Maria Law School and a B.S. in 1984, an M.S. in 1997 and a Ph.D. in 2001, all in Civil Engineering from University of Illinois, Urbana-Champaign. Prior to Florida SouthWestern College, she was an Associate Professor at Florida Gulf Coast University and an Assistant Professor in Civil and Environmental Engineering at the University of Missouri, Columbia. She has taught courses in infrastructure management, planning, transportation and
realized the distinctionsbetween theory and practice. Therefore, they demonstrated an awareness to connect theory topractice. For example, they would set extra time aside just in case for unexpected problems infeasibility tests, taking into account the gap between theory and practice. What’s more, theexperience of project-based learning can help students know more about the process of research.Based on these experiences, some students started to make plans for their future. Students reflecton the meaning of their major and the emphasis of their research orientation. Some students wonderabout whether they are going to receive further education or not. The thoughts about future plansreflected that students started to undertake major responsibilities
conducts consulting projects and professional development seminars for local industry on topics including forecasting, inven- tory control, production planning, project management, transportation logistics, procurement, and supply chain management.Dr. Leslie Pagliari, East Carolina University Dr. Leslie Pagliari serves as Associate Dean for Academic Affairs in the College of Engineering and Technology and Associate Professor in the Department of Technology Systems. Her research interests center on STEM initiatives, leadership, global supply chain issues, and new technologies in the distribu- tion and logistics sector. She was one of three professors in the United States recognized in an Inbound Logistics Article
. Three engineering-education collaborators were interviewed in dyads tounderstand conceptualizations of futures, values, systems, and strategic thinking in relation totheir joint research project(s). All three dyads provided specific examples of different ways ofthinking from their shared research efforts. Preliminary findings suggest that a ‘ways of thinking’framework could provide a useful guideline for engineering and education faculty planning tocollaborate for interdisciplinary research as well as the overall EER community.OverviewThe world today faces complex problems ranging from climate change to health issues.Numerous calls by prominent organizations have been made in light of these global,sociotechnical problems to transform
field and prior engineering identity studies. In particular, we seek tounderstand which factors may influence Hispanic students’ engineering identity development.We begin by answering the following research questions: 1. How do the engineering identity, extracurricular experiences, post-graduation career plans, and familial influence of Hispanic students attending a Hispanic Serving Institution (HSI) differ from those of Hispanic students attending a Predominantly White Institution (PWI)? 2. How do the same measures differ for Hispanic students attending a PWI from those of non-Hispanic white students at that PWI? 3. How do the same measures differ for Hispanic students attending an HSI from those of non-Hispanic
, personalconceptualizations and prior learning experiences related to the problem [11]–[13]. Taskinterpretation is broadly defined as students’ judgment about the required cognitive processes toanswer a problem [14]. Studies reported that people who can self-regulate appropriately (i.e.,engage in coherent planning, enacting, and monitoring activities) based on a correct andcomplete interpretation tend to be more successful in academia [15], [16], problem-solving [17]–[19], and engineering design [4], [20], [21].Task Interpretation in Self-Regulated Learning Task interpretation refers to one’s understanding of a problem, including knowledge ofthe required cognitive process to solve it [14]. Students’ interpretation of tasks is considered asan important work
intends to enact change [22].Evaluators use logic models to examine implementation fidelity, when logic models have beendeveloped as part of a program plan [23]. In addition, logic models can be used as a framework,to focus data collection on the specified program activities and expected outcomes, to determineappropriate methods for data collection, and to organize and interpret data in terms of aframework [22]. When no logic model exists, evaluators may develop a logic model to describethe program visually. Logic models can be a useful tool for communicating the nature of aprogram to stakeholders. The use of logic models has been found to contribute to clarity in goals,alignment of activities with goals, communication about the program, and
information gathering. Pertaining to thisgap between academia and industry, research is needed to explore characteristics of the problemsolving approaches of students and professionals to better understand what factors may influencethese approaches, and to gain insight into how to better teach undergraduate students how tosolve ill-structured problems. In order to extend the analysis of problem solving approaches to alarger group of participants, this study examines faculty members as well as students andpracticing engineers. It is hypothesized that these three groups of participants will differ bothquantitatively and qualitatively in their problem-solving processes.III. Methodology In this study, we plan to conduct a comparative analysis of
the main tenets of this theory is that writershold multiple processes at the same time, for example, composing text while also anticipating theaudience or the venue to which a manuscript will be submitted. Thedevelopment of this theory and model has extendedover time, to which aspects of technology have beenassumed into the model: Composing and revising ona computer is much different cognitively thancomposing and revising by pen-and-paper. Some ofthe facets of cognitive writing theory are visible—thatis, they are easily tracked through visible outcomesmanifested through writing (e.g., composition orrevision), while some of the categories in the initialmodel might be invisible (such as planning orconsidering needs of the audience.) In using
mission, would you approve it? Why or why not? 2. If you were planning this trajectory, would you be worried about the lifetime of the spacecraft? Why or why not? What if the trajectory had the same altitude around Earth? 3. If the goal of the fly-by was to fly in-between Saturn’s rings, would you have the spacecraft perform this fly-by? Why or why not?The questions were evaluated on two dimensions: “correct answer” (yes/no recommendation)and “correct reasoning.” The “correct answer” was marked as no answer, correct, or incorrect. Ifthe student provided the correct “correct answer”, the “correct reasoning” was evaluated ascorrect or incorrect. The three questions spanned the semester and increased in difficulty intandem to the
linkages between knowing, learning, and analytics, andthat knowing is not an objectively defined or agreed-upon term [14].Since the main thrust of this paper is about designing navigating and planning instructional andassessment activities, the knowledge-learning-instruction (KLI) framework [12] developed out ofthe knowledge tracing literature [13] serves as a productive starting point. The KLI frameworkdifferentiates between observable events (instructional events and assessment events) andunobservable events (learning events). Therefore, moves by an instructor (instructional events)aimed at producing learning (learning events) set up moments during which the learnerdemonstrates knowing (assessment events). The learning that occurs as a result of
; a review ofTable 1. Schedule for class and laboratory. Week Class Lecture/Lab Section 1 1 1 Syllabus, Review of Mechanics 2 2 Circuits / Ohms law 3 Data acquisition / Signals and sampling 3 4 Planning a Monitoring program / Uncertainty / Accuracy 5 Strain Sensors / Vibrating wire gages 4 6 Foil Gages, theory and installation Section 2 7 Foil Gages, selection and voltage 5 8 Fiber optics / Load cells 9 Piezometers / Linear deformation
Provost for Research and Gradu- ate Studies. A Professor of Software Engineering, Dr. Acharya joined Robert Morris University in Spring 2005 after serving 15 years in the Software Industry. His teaching involvement and research interest are in the area of Software Engineering education, Software Verification & Validation, Software Security, Data Mining, Neural Networks, and Enterprise Resource Planning. He also has interest in Learning Objectives based Education Material Design and Development. Dr. Acharya is a co-author of ”Discrete Mathematics Applications for Information Systems Professionals” and ”Case Studies in Software Verification & Val- idation”. He is a member of Nepal Engineering Association and is
% Caucasian 96% 100% African American 4% 0% Male 62% 79% Female 38% 21% GPA range 3.0-4.0 2.6-4.0 GPA mean 3.6 3.6 Recipients included all levels of undergraduate STEM majors, mostly engineering,with the majority including sophomores, juniors, and seniors, with only 15 percent freshmen(1 to 30 credits) (Table 2). Sixteen per cent of participants planned to attend graduate schoolin the first-year survey (with another 27 per cent indicating graduate school or employment).Thirty
betaking the course. In general, since this knowledge did not come from specific users, insightsapplied to the entire group of students, or the potential variation in the group. Their focus wasinsights in two areas: student preparedness for learning and factors that could affect interest andmotivation. The team sought a general understanding such that they could plan content andactivities that were appropriate and engaging for the entire class.This technique may have stemmed from deep knowledge of prior students that has grown into acomposite image over time, and the assumption that future students will fit into this composite.For example, the instructor’s insights came from having taught the same class several times inthe past. However, since
. Meetingslasted 1–2 hours and featured discussion of the course to be redesigned, negotiation of coursecontent, planning assessments and pedagogy, and collaborative decision-making and artifactbuilding. While not every team member participated in each meeting, at least three teammembers participated in all meetings. We focused on meetings during the month preceding andthe month and a half after the beginning of the semester due the heavier focus on planning anddesign of the course (later meetings tended to discuss logistics of implementation and feedbackon planned activities). In total, we analyzed 15 meeting transcripts from 17.6 hours of audio, plusdetailed notes from an additional 6 meetings that were not audio-recorded. Interviews,reflections, design
“First you draw out your plans, then you collect your supplies, then you build his body, then you code it on this. You can keep going around again if you have enough time, so you could maybe attach an arm here and make it move like this.” Mina (pseudonym), Middle School studentThese excerpts were captured in video-recorded interviews of elementary and middle schoolstudents who participated in girls’ engineering afterschool programs in Seattle and Washington,DC. Researchers used artifact elicitation interviews to assess girls’ understanding of the cyclicalnature of the engineering design process. In the first interview excerpt, Samara offered a succinctexplanation. She explained the engineering design process as a cycle, saying
linked to goal-setting, self-regulation,and success in engineering programs [2], [6], [10]–[13]. In this paper, domain-general(Connectedness, Value), domain-specific (Perceptions of the Future, Present on Future, Futureon Present), and context-specific constructs (Perceived Instrumentality) were considered. Ingeneral, Value, often termed valence, is the “anticipated subjective value”[14] (p. 567) of futuregoals for a person; thus students may place a higher value or hold one goal in higher regard thananother goal. The second domain-general FTP construct, Connectedness, is “general feeling ofconnectedness to and planfulness about the future” [15] (p. 116). Perceived Instrumentality (PI)[15]–[17] is a context-specific variation of connectedness
design teams and professional engineering societies, has been shown topromote engineering identity development, graduate school intentions, and plans to pursueengineering careers after graduation.In this work we posit that it is not simply differences in SES that separate highly involved,successful students in engineering from their less involved, less successful counterparts. Insteadwe postulate that such differences inform students’ socialization into engineering and, as a result,their patterns of co-curricular participation. Weidman defines socialization as “the process bywhich individuals acquire the knowledge, skills, and dispositions that make them more or lesseffective members of their society” [5]. In this study, we hypothesize that an
design appropriate simple robotic systems to accomplish a task in a manner that is effective and safe. 3. Students can distinguish between open-loop and feedback control for velocity and position of a single joint and can implement feedback for single-joint position control. 4. Students are able to select appropriate sensors, and make use of digital and analog sensors (including visible-light cameras) to obtain and utilize information in a robotic system.The course is structured in a way that a seemingly impossible final deliverable, a functioningrobot, is broken down into a planned and sequenced set of minor deliverables that eventuallyculminate in the final creation. There is little in the way of independent
spring2017, continuing through summer planning months and through the first course offering duringthe fall 2017 semester. This analysis is timely as the events have recently occurred and thedetails of each negotiation and adaptation are not yet obscured by the broad brush strokes ofinstitutional record.The bulk of data shared in this paper include auto-ethnographic observations and recollectionsbased on the lived experiences of the course instructor and coordinator, the assessment director,and supporting administrators and researchers (Ellis, Adams, & Bochner, 2011). Institutionalartifacts constitute a secondary source of data; they include presentation slides, emails and otherwritten communications, curricular flowcharts and other digital files
?” Student Immigration into and within Engineering. Journal of Engineering Education, 2008. 97(2): p. 191-205.24. Trenor, J.M., et al., The relations of ethnicity to female engineering students' educational experiences and college and career plans in an ethnically diverse learning environment. Journal of engineering education, 2008. 97(4): p. 449-465.25. Brainard, S.G. and L. Carlin, A six‐year longitudinal study of undergraduate women in engineering and science. Journal of Engineering Education, 1998. 87(4): p. 369-375.26. Bell, A.E., et al., Stereotype threat and women's performance in engineering. Journal of Engineering Education, 2003. 92(4): p. 307-312.27. Foor, C.E., S.E. Walden, and D.A. Trytten, “I wish that
faculty member secured a funded teaching fellowship to enhanceexisting and grow new group-based, project-driven modules in the Bachelor of ElectricalEngineering program. He had worked with his college’s Head of Learning Development to createhis fellowship proposal. The awarding of this fellowship was aligned with Walker and Laurence’s(2005) recommendation to support the activities of organizing, planning meetings, researching andpublicizing issues, and educating stakeholders about “appropriate actions to take” (p. 268). Itencouraged the fellow to take such a role.During the teaching fellowship a group of seven (five staff members, one Fulbright scholar, andthe Head of Learning Development) met once a month to discuss issues regarding
purposes of this paper,we work from the perspective that learning is at the core of institutions of higher education.As we began our efforts to systemically advance innovations in teaching across campus, we(teaching and learning center staff) learned from preliminary interactions that faculty werestruggling to make sense of what we meant by various educational terms. Additionally, mostfaculty had no pedagogical training and little to no understanding of cognition and how to1 In an earlier paper the theoretical perspectives that inform our work is described in greater detail [6].2 We did take into account external factors in the design and planning of the interventions, but that was a secondlevel consideration and will not be addressed in this
commonly practiced whencovering the LSM topics puts greater emphasis on content coverage than inculcating the aboveskills in students [10]. This means that despite our best intentions, there is a misalignmentbetween the way LSM topics are covered and how KI modules are planned, resulting in studentsnot being adequately prepared to make the most of the KI activities. Thus, there is a need totailor the way in which the LSM content is delivered to ensure that not only is the contentcoverage adequate and timely but also that the students are being better trained in the higherlevel skills of learning. For this purpose, the authors of this paper have developed a new activelearning model to be used for content delivery during the LSMs.Active learning is
of the lesson from what was previously planned(e.g. developing a new example problem on the fly to address a student’s question). As signifiedby the feedback loop in Figure 1, the instructor’s response may also involve initiating additionalinstances of formative assessment. This three-stage model of formative assessment may repeatmultiple times throughout a class session, with frequent interaction between students andinstructors. Formative Assessment Student Instructor Initiation by Instructor Response Response Figure 1. Three-stage conceptual model of formative assessmentObservation