, 2016.[6] S. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett, and M. K. Norman, How Learning Works: Seven Research-based Principles for Smart Teaching. San Francisco, CA: Jossey- Bass, 2010.[7] L. Shulman, “Those who understand: Knowledge growth in teaching,” Educ. Res., vol. 15, no. 2, pp. 4–14, 1986.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.1347675 (DUE). Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation.
theinterview questions: • The learning achieved. • Impact on perspective relevant to and plans for the future. • Encouragement toward involvement with research. • Confirmation of abilities or areas for further development. • Confirming interests and intentions related to research, careers, and degrees. • Providing new experiences and expanding personal horizons. • Skill development. • Improving qualifications. • Increases in confidence. • Development/expansion of a relational network.During the interview in year 4, the majority of participants also stated that participation in theprogram caused them to reflect on or refine their educational goals and career plans. While thereare only 6 significant benefits
by Young andcolleagues [6] collected data on African American engineering students in a variety of co-curricular activities that the researchers classified into three categories (engineering clubs,underrepresented minority (URM) clubs, and other clubs). The study analyzed the perceiveddevelopment of communication, professionalism, lifelong learning, teamwork, and reflectivebehavior skills related to co-curricular participation. Some findings from the study include higherreported teamwork and reflective behavior related to participation in any of the three categoriesof co-curriculars, lower reported communication skills for students participating in URM clubswhen compared to peers who did not, and higher reported teamwork skills with
learning technology,students experience a tailored learning experience, specific to their learning path towards theirmastery of the given topic. Expanded research in the engineering education context can lead tomore closely aligning instructors’ teaching styles and students’ learning styles.IntroductionIt is well established that there is often conflict between the instructor’s teaching style andstudents’ learner styles in the engineering classroom [1]. The use of adaptive learning as ateaching style facilitates several learning styles, complementary to the traditional lecture style.Learning styles including sensory, intuitive, visual, auditory, inductive, deductive, active,reflective, sequential, and global [1], can all be incorporated into
are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.Reference[1] F. Chance, J. Robinson, and J. Fowler, “Supporting manufacturing with simulation: modeldesign, development, and deployment”, Proceedings of the 1996 Winter Simulation Conference,December 8-11, 1996, San Diego, CA.[2] Imai, M., Kaizen-The key to Japan's competitive success, New York, N.Y., Random House,1986.[3] S. Barraza, M. R. González, F. Gabriel.. “Bringing Kaizen to the classroom: lessons learned inan Operations Management course”. Total Quality Management & Business Excellence. 26. 1-15,2015.10.1080/14783363.2015.1068594.[4] W. D. Kelton, R. P. Sadowski, and D. T. Sturrock, Simulation with Arena, 6e, McGraw Hill,2013.[5
emotions such as sympathy,empathy, and sensitivity, and views persons as relational and interdependent.The study of care has permeated other areas of knowledge, including education. Noddings [2]described the attributes of the teacher as a carer. In such a role, she proposes teachers should beattentive to the needs of students, responding always in such a way that the caring relation ismaintained. She emphasizes additional attributes of caring teachers: the ability to listen, theability to empathize with the student, and the ability to reflect upon the actions to be taken in caseof need. Finally, caring teachers should also promote a caring environment, encouraging theirstudents to read and respond to their peer’s feelings. Gholami and Tirri [3
supported with resources and the use offacilities at the Salem VA Medical Center, Salem, VA. The views expressed in this abstract arethose of the authors, and do not necessarily reflect the official policies of the Uniformed ServicesUniversity of the Health Sciences, the U.S. Departments of the Army, Navy, Air Force, Defense,U.S. Department of Veterans Affairs, nor the United States Government.References[1] D. L. Waszak and A. M. Holmes, "The Unique Health Needs of Post-9/11 U.S. Veterans," Workplace Health & Safety, vol. 65, no. 9, pp. 430-444, 2017, doi: 10.1177/2165079916682524.[2] M. Olenick, M. Flowers, and V. J. Diaz, "US veterans and their unique issues: enhancing health care professional awareness," (in eng), Adv
activities we will increasestudents mindset in the three C’s as compared to a control group. The assessment includescuriosity scale pre & post survey and three reflection assignments.MethodsParticipants - This research project was approved by Vanderbilt’s IRB # 191344. Participants inthis research were broken into two major groups, intervention and control. The interventiongroup are students who enrolled in the new introductory chemical engineering module. Thecontrol group are students who enrolled in the historical model of the chemical engineeringsection. Table 1 below, summarizes the number of students in the control and interventiongroups.Table 1. Enrollment data for Control and Intervention Modules Control
that fallunder 1-3 of the learning outcome categories. Figure 3 presents the number of students who havehighlighted each of the learning outcome categories, from 2017 to 2019. Since one student coulddescribe up to three learning outcomes of the same category, the counts do not reflect the totalnumber of mentions per category.Combining all data from 2017 to 2019 (Fig. 3), the responses were categorized according to theiralignment to the five key learning objectives of the course (Appendix I): Reactor Physics Theory(11 of 29 students), Nuclear Fuel Life Cycle (9 of 29), Reactor Technology (12 of 29), NuclearSafety (8 of 29), and finally the Connection between the Nuclear Sector and Society/Public (15 of29). The societal aspect of nuclear
engineering, who are particularlyvulnerable to dropping out of engineering careers.Career commitment reflects students’ intention to work in the field of engineering. Measures ofstudents’ self-reported commitment to career have primarily been used by others as outcomevariables [10], [11]. In our analysis, we model the possibility that commitment to an engineeringcareer may serve as a motivator to obtain the knowledge and credential often necessary forstudents to obtain their occupational goals. Because these are early career students, we expectthem to have relatively low commitment to the field of engineering in this baseline data, butmodeling their expressions of commitment throughout their undergraduate education may helpus better understand their
reach statisticalsignificance, and curiously they show the opposite of what appears to be the objective truth; thecohort that used the continuous applications believed they understood less than the students thatused the discrete applications (Figure 3). This may reflect the Dunner-Kruger paradox thatexplains the cognitive bias which occurs when low-ability people lack the framework to assesstheir abilities accurately, and high-ability people overestimate the abilities of others [12],[13].Figure 3: Comparison of the students’ self-assessment of their subject mastery before theycompleted the objectively-scored portion. It is noticeably below the objective scores, andsurprisingly show a generally opposite trend from their actual understanding in
]. According to recent studies, the MM-GTresearch approach has become useful to develop and test theory in the fields of education[8], [9]. In this study, we plan to develop theoretical models of difficulty at a course level,following best practices of MM-GT application to provide insights for course curriculumdevelopment and teaching reflection in the field of engineering education.2. Research Design and Current Data CollectionIn this study, we plan to use an exploratory sequential design based on MM-GT to developand test theoretical models in four phases (see Figure 1). This paper presents the results ofthe first phase, which consisted of a grounded theory approach to identify the factorsassociated to what students perceive as easy courses and
55 70 57 45*Hours reflect time spent during the academic year. Much research is done over the summer withoutfunding or salary.Being a part of a large R1 university offers many resources such as additional training, software, andpotential grants, though most are housed at the main campus. Some training classes are brought to thebranch campus and even less are offered remotely (i.e. via conference call). Lab space and equipmentfor research is extremely limited, and any lab equipment is more for teaching purposes and notappropriate for research. Grants that are appropriate and practical for the teaching professor are verysmall and would not begin to cover a summer salary. While larger grants are possible through the
keep records of old exams and students study from them. If the professoruses old questions, they should expect that those questions are available.” (Stack Exchange,2019). By reusing the same test questions every time a course is taught, instructors may not be gradingan exam that is actually reflective of whether a student has learned the material. In this case, it is alsoarguable whether or not the student committed an act of academic dishonesty. Instructors must beconscious that their exams have, in all likelihood, been shared by one student or another. It is essentialthat new and original tests are created each time the course is offered in order to determinewhat level of mastery their current students have actually achieved.In many college
scores match those expected scores,i.e. the degree of the platform effectiveness. Moreover, except for one data point of Introductionto EE, Q4, Intro to EE always scores lowest and Digital II always scores best for all 16 questions.Indeed, using an overall score from -1 to 1 representative of all questions as in the equation 16 𝑆̅ = ∑ 𝑆(𝑛)𝑆𝑒 (𝑛) /16 𝑛=1the surveys from Intro to EE, Digital I, and Digital II gave scores of 0.24, 0.50, and 0.69respectively. One explanation is that the results reflect the growth pattern of the learning curve ofFPGA platform. As students become more knowledgeable about digital logic design
,speed, time and landing site. Students learn about renewable energy by using an experimental kitthat help them to study the effect of wind speed and light intensity on electrical production. Robotic arm assisting welding at CNM Robotic arm laser engravement Robotic arm plotting engine design Students work on the bridge experiment Students work on tTime of flight Experiment A nine-questions anonymous survey was distributed to the students to reflect on the success andeffectiveness of the course and identify areas for improvement. The results of the survey are Proceedings of the 2020 ASEE Gulf-Southwest Annual Conference University of New
data. The incorporation of direct and indirect tools wasnecessary to better assess the development of the students' communication skills as well as groupinterpersonal skills [3] [4]. The direct assessment was used in evaluating measurable tasks suchas meeting deadlines, establishing goals, and meeting objectives. At the same time, the indirectassessment was more suitable in assessing students' ability to work productively with others,their leadership skills, and communication skills [6]. Finally, a set of rubrics was developed todescribe the student’s performance level and summarize the assessment’s results. The rubricswere generated and organized to directly measure and reflect the students’ mastery of eachoutcome using a variety of
, inductive and deductive codes from thetranscripts were generated through an open coding technique. Second, after the codes wererefined, axial codes were generated, and the transcripts were re-coded. To ensure reliability andvalidity, the lead author created research memos as reflective writing tools throughout theprocess. After each iteration of coding and writing memos, all authors discussed the codes toensure the reliability and validity of the coding scheme. From the data, we selected two participants, Parker and Jordan, to be the foci of thispaper. Parker and Jordan were chosen because they elicited their experiences at the intersectionof several marginalized identities in greater detail than any other participant. In the
statements, authorsand reviewers do not discuss how they have critically reflected on their own identities and couldinfluence how research is conducted and reviewed, which could perpetuate systemic racism.Positionality statements are one approach towards transparent communications and disrupting powerdynamics in research contexts (Secules et al., 2021).Outside of the EER community, there are initiatives and resources that could be leveraged to promotediversity and inclusive practices within the academic communications ecosystem, for example the JointStatement of Principles by the Coalition for Diversity and Inclusion in Scholarly Communication (C4DISC).C4DISC promotes diversity and inclusive practices within the scholarly communications ecosystem
. FindingsHow does a combined lab kit and neuroscience curriculum differentially relate to STEMmotivation between diverse school systems? Results from the pre-test show that students already have high science motivations, valueof science, and learning motivations. As these tests were conducted near the end of the schoolyear, this result may reflect work of the science teachers who self-selected into the study that hadalready helped boost these beliefs and attitudes in their students throughout the year. Since eachof these were already high, a ceiling effect was observed, as very little change could be achievedfrom the lab kit and curriculum. For this inventory and all those used in this study, we use thetraditional procedure of reporting each
Texture Aesthetic Have to Do With It? “I definitely am conscious of my appearance at all times in academic settings. I guess one example of probably is that I was under a lot of stress. I think it was during my second year and my hair was thinning and it was not looking very healthy. People would continuously ask me if I was tired and making comments so I felt like it was a direct reflection. I pretty much have had short hair now for the past two years to avoid [comments] just because my appearance is important [in academia].” —Niela13 Strategies to Thrive: Black Women’s
tutoring spaces often reflect the demographics of the department oruniversity at large. Tutors also bring their own identities and biases into these spacesthat can serve to enhance or diminish the self-efficacy and sense of belonging ofattendees. If these factors are not explicitly addressed by training or intentionalhiring, administrators should almost expect that they are sending their students intoa non-inclusive learning environment. 7While our office recognizes all of these limitations of tutoring, we aim to provide amore inclusive tutoring space within which attendees from our target groups (womenand underrepresented minority students) can seek academic
societies, managers of large federal facilities• Goals of ASEE Advocacy – Conducting outreach to Congress to support funding and sound policy for engineering research and education – Supporting ASEE Councils to enhance advocacy goals of deans and other constituencies – Engaging the Administration and federal agency officials to inform future programs and create new opportunities – Elevating the role of ASEE within the Washington, DC-based scientific, STEM, and higher education advocacy communities and ensuring community advocacy reflects ASEE prioritiesAdvocacy: What’s the Point?Why Advocate?• Advocacy: The process by which ordinary citizens make their interests known to Congress• You can help Members of Congress make informed decisions on
ofpedagogy.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.1628976. Any opinions, findings, conclusions or recommendations expressed in this material arethose of the authors and do not necessarily reflect the views of the National Science Foundation.References[1] S. Freeman, S. L. Eddy, M. McDonough, M K. Smith, N. Okoroafor, H. Jordt, and M.P. Wenderoth, “Active learning increases student performance in science, engineering, and mathematics,” in Proceedings of the National Academy of Sciences, (111,23), 2014. pp. 8410-8415.[2] D.H. Jonassen, J. Strobel, and C. Lee, “Everyday Problem Solving in Engineering: Lessons for Engineering Educators,” Journal of Engineering Education, vol
assess both the potentialcommercialization of the teams’ innovations from an entrepreneurial perspective and thetechnical feasibility of the design from an academic and engineering perspective.Table 1: Scoring criteria for innovation and potential commercialization of the resultingpresentations during the hackathon event. Criteria Description Health Impact The proposed concept represents a viable solution to a real problem, the / Clinical Need problem the group is trying to solve is clear and the need is well-defined. Innovative The solution is unique and reflects a creative and innovative approach, addresses the identified need and the benefits of it are clear. Usability and Provides a solution with ideas
transformation of engineering education.Dr. Ryan C. Campbell, Texas Tech University Having completed his Ph.D. through the University of Washington’s interdisciplinary Individual Ph.D. Program (see bit.ly/uwiphd), Ryan is now a Postdoctoral Research Associate at Texas Tech University. He currently facilitates an interdisciplinary project entitled ”Developing Reflective Engineers through Artful Methods.” His scholarly interests include both teaching and research in engineering education, art in engineering, social justice in engineering, care ethics in engineering, humanitarian engineering, engineering ethics, and computer modeling of electric power and renewable energy systems.Dr. Roman Taraban, Texas Tech University
vectors and graphed parameters. Further work will include analyzing studentsurvey data to explain student perceptions and to determine how student comprehension andlearning compares between remote instruction vs. in-person.AcknowledgementsWe acknowledge the support from National Science Foundation (NSF) through grants DUE1821439 and 1821638. Any opinions, findings, and conclusions or recommendations expressedare those of the authors and do not necessarily reflect the views of the NSF.References[1] J. D. Bransford and A. L. Brown, How People Learn: Body, Mind, Experience and School. Washington, D.C.: Commission on Behavioral and Social Science and Education, National Research Council, 2000.[2] J. Engelbrecht, C. Bergsten, and O. Kågesten
devise a strategy for future unplanned contingencies.In this manuscript, we reflect on the challenges faced, processes adopted after the remotetransition, and lessons learned for two core courses in the Department of Biomedical Engineeringat the University of Arkansas: Biomedical Instrumentation and Biomolecular Engineering. TheBiomedical Instrumentation course is a sophomore-level, core course that encompasses bothfundamentals of electric circuits as well as relevant physiological topics, with an overarchinggoal of teaching the basics behind modern measurement instrumentation in the context ofbiological systems. The Biomolecular Engineering course is a junior level, core course thatbegins with an introduction to the tools and techniques of
students to foster flawed habits such aslittle reflection on the scenario in a broad manner and subsequent lack of dynamic behavior todetermine and obtain the necessary information [14]. The ramifications of this are evinced whenstudents encounter problems in a realistic context and are unable to solve them since they were notpresented in clean and rational problems the way they tend to in courses [15] [16]. It is ideal toavoid this and encourage cognizance and successful application of the engineering problemframing process as it also insinuates engineering students and practicing engineers comprehendthe scope they are within and solving the problem(s) at hand [7].3. Research Methods3.1 Design and Deployment of Modules 1 and 2 [10]The primary
Digital Conversion sixteen analog inputs eight analog inputs Figure 4: Input/Output in the S12 and ATmega32 197 The comparisons for memory and input/output detailed in Figures 3 and 4 reflect the capabilities of the specific processors used on the Dragon-12 and EasyAVR development boards. In each case, many other members of the processor family exist, with varying amounts of memory and input/output capabilities, so either processor family is likely to include a family member that meets the