results in lower performance averages by students compared to PSVT:R problems forthis population. It is expected, based on Reusch et al.’s results [22], that there is a higherdifficulty level on the MCT problems used. This, in turn, may have been reflected by themoderate increase in EDA when we consider dividing this value by the time expended on theMCT problem-set as can be estimated from the two Figure 3 graphs (e.g., 0.05microSiemens/minute for MCT). The preliminary results of this pilot study corroborate thesefindings by suggesting a higher normalized arousal (or mean range-corrected EDA/timed event)found in these types of problems compared to PSVT:R (0.13 microSiemens/minute) and Staticsproblems (0.01 microSiemens/minute) (Figure 3).Parallel
measured in the cognitivedomain, attitudes most often are a reflection of one’s value system and, as such, outcomes relatedto attitude should be measured in the affective domain. Duczyminski [15] points out that,regardless of topic, affective outcomes are often closely related to deeper levels of thinking.Students engaged in a subject who recognize its value, can exhibit a change of attitude, andultimately achieve a consistent behavior. A number of academics have recognized the need tosupplement cognitive learning with the attainment of affective outcomes to promote deeperlearning and have incorporated specific learning strategies to accomplish this [16],[17],[18].Bielefeldt [18], for example, used project based learning and project based service
Foundation under DRL GrantNumbers 1615019 and 1614496. Any opinions, findings, conclusions, orrecommendations presented are those of the authors and do not necessarily reflect theviews of the National Science Foundation.References[1] "Tapping America's potential: The education for innovation initiative," Business Roundtable, Washington, D.C.2005, Available: http://www.tap2015.org/about/TAP_report2.pdf.[2] "An American imperative: Transforming the recruitment, retention, and renewal of our nation’s mathematics and science teaching workforce.," Business Roundtable, Washington, D.C.2007, Available: http://www.bhef.com/solutions/anamericanimperative.asp.[3] Rising Above the Gathering Storm: Energizing and Employing America
, the shared Redshirt model consists of seven mainprogrammatic elements that are designed to improve the engagement and rates of retention andgraduation of students underrepresented in engineering and computer science. These elementsare “intrusive” academic advising and support services; an intensive first-year academiccurriculum; community-building; programming to develop career awareness and identification;mentoring by an engineering or computer science faculty member; financial support, includingthe NSF S-STEM scholarships; and second-year academic support. There is flexibility acrossinstitutions in how these core components are implemented, reflecting distinctions in theadministrative structure, resources, and student populations at each
consumer is, however, unharmed bythe product’s color despite not being happy about it.Value systems are influenced by many factors, including upbringing, geographic location,historic time, life experiences, reflective thought, education, knowledge, and even prejudices.What might have been considered safe in the 1950s is not considered safe today and what is nowconsidered safe may not be regarded as safe in the 2030s. To drive this point home, the courseincludes a historic review of various changes in the safety of food [7], consumer products [8],and automobiles [9]. D. Product-Safety ConceptsWhen either establishing or assessing the safety of a product, the engineer must know what theproduct is intended to do. Strangely enough, this is not
first-year students (n=353) just beyond the mid-point of their first-year.The Workload Measurement Survey (WMS) was administered weekly, and was distributed byemail to groups of 20 first-year students from each program throughout the first semesters inYears 1 (2016) and 2 (2017) of our study. These twenty students were selected at random fromeach of our 8 engineering programs each week; surveys were distributed at the end of the weekfor a twelve-week fall semester in order to encourage reflection and responses based on thatparticular week of study. In 2016, the survey received a response rate of 26.87% with acompletion rate of 77.88%; in 2017, the response was 46.27% and presented a completion rate of77.87%. This survey explored the perceived
Science Foundation (NSF) (PRIME #1544259). Anyopinions, findings, and conclusions or recommendations expressed in this material are those of theauthors and do not necessarily reflect the views of NSF.The authors would like to thank FutureLearn for providing the data and the many reviewers whomade this a much stronger paper.8. REFERENCES[1] R. F. Kizilcec and C. Brooks, “Diverse big data and randomized field experiments in MOOCs,” in Handbook of Learning Analytics, 1st ed., C. Lang, G. Siemens, A. Wise, and D. Gasevic, Eds. Society for Learning Analytics Research (SoLAR), 2017, pp. 211–222.[2] R. F. Kizilcec, C. Piech, and E. Schneider, “Deconstructing disengagement: analyzing learner subpopulations in massive open online
research guides specifically dedicated to chemical pricing.The presence or absence of certain sources on a research guide is often governed by perceivedpatron needs and what resources are licensed by the library. If a librarian is not familiar with theneed (or has no requests), they are not likely to include such information. Furthermore, librariesmay have policies that restrict the types and organization of research guides, leaving littlediscretion for a librarian to create chemical pricing research guide or even a chemical pricing tab.On the other hand, the presence or absence of chemical pricing guides may just be a reflection ofthe resources needed and used at a given institution.Many of the sources mentioned by Maizell8 and Reichardt15 remain
GPA. In the follow-up interviews, the students consistently praised SITE for: Working in teams Working with students of different backgrounds Exposure to other fields Meeting faculty on a close basis Working on projects with real applications Integrating material learned in courses to solving complex problems Opportunity to think about careers in industry Good for the resume At this early stage in their educational careers, SITE represented one of the first times that many of these students were able to engage in and reflect upon these important aspects of STEM training. The following highlights some markers of positive impact on students: 22% of students
participation equivalent to asingle 3 or 4-credit course. Building upon this credit structure, some academic units have establishedcredit-use policies that incentivize multiple semesters of participation in VIP [5]. However, whethercurricular incentives yield higher persistence has not been examined.The VIP model has been adopted by twenty-six colleges and universities, and at the Georgia Institute ofTechnology (Georgia Tech), additional departments continue to adopt and refine curricular policiesregarding the program. This expansion demands reflection on how policies affect student persistence inthe VIP program, and how other factors may contribute. We hypothesize that different Georgia Techcredit-use policies affect student persistence in different
categories reflected, and grew out of the previous presentation rubric, but with specificpoints now guiding student preparation, peer assessment, and instructor assessment, equally.The Content area was reworded to address the points from the Target rubric, so that the studentswere given the expectation that their critical thinking process needed to be demonstrated duringtheir oral presentation as well as during the writing. The other points addressed technical aspectsof the presentation including: organization, visuals/slides, timing, speaking, and nonverbalcommunication. The full IOP Rubric is given in Appendix B.The student poll of rubric effectiveness (see appendix A) indicates that 83% of respondentsAgreed or Strongly Agreed that they found the
, which in turnprovides control signals to the motors controlling the launching mechanism. For objectdetection, the two obvious choices are ultrasonic sensors, and infrared sensors. Ultrasonicsensors can provide very accurate ranging information, and since they use sound instead of light,they are not adversely affected by direct sunlight [10]. Accurate ultrasonic sensors do tend to bemore expensive than infrared sensors, however.Infrared sensors do have the capability of providing accurate ranging information, but they runinto problems when used in direct sunlight [10]. Additionally, since light reflects differently offdifferent surfaces and different colors, the range reading can differ between two objects whichare the same distance away from
biomedical engineering not seen from the classroom, allowing me to become more aware of the possibilities I may want to pursue in the future.”While most students focus on the positive benefits of the course, few comment on anyapprehension or anticipated challenges. A written assignment completed before clinic rotationsincludes a student reflection on “Fears and Concerns”. Table 5 lists the most common responsesfrom the engineering students. The course syllabus and handbook include topics addressingthese issues and may have influenced the students’ responses.Table 5 - Common answers to “Fears and Concerns” Questions Common Answers • Overwhelmed by
of learning and engagement in a makerspace environment.Our analysis revealed the instrument had acceptable levels of reliability (above .65 and below.95), whichwe maintain makes the instrument suitable for assessing student perceptions and engagement inmakerspaces. Further, the acceptable reliability indicates students are answering the items consistentlywhich further reflects alignment of the items to our four constructs of interest.We were able to provide an additional level of assurance that our instrument is aligned with ourassessment goals through our analysis of the students’ responses in conjunction with their individualcharacteristics. The only association with student individual characteristics we found to be predictive ofthe survey
). Engineering practitioners spend more time gatheringinformation, considering alternatives, and perhaps most importantly, designing. The result ofthese differences in activity patterns are reflected in the overall quality of the design.Additionally, research in problem solving has shown that even through practice, engineeringstudents often struggle with the transfer of learned information to new situations (Venters &McNair, 2010).Consequently, research has shown that engineering graduates are ill-prepared for the workplaceand the complex open-ended problems that are typical of engineering design (Collins, 2008;Education et al., 2005). The problems engineering students solve in school are thought to requirethe same fundamental concepts that
in similar projects are highly appreciated and welcomed.References1. G.T. Heydt, and V. Vittal, Feeding our profession, IEEE Power & Energy Magazine, vol. 1(1), 2003, pp 38-45.2. Energy Utility Consultants Inc. Proceedings of January 23-24, 2007 seminar, Solutions to an Aging Workforce.3. U. S. Department of Labor, Bureau of Labor Statistics, http://www.bls.gov.4. W. Reder, Managing an Aging Technical Workforce, EnergyBiz., May/June 2005.5. G. Gross, G.T. Heydt, P. Sauer P. and V. Vittal, Some reflections on the status and trends in power engineeringeducation, IERE Workshop: The next generation of power engineers and researchers, Montreal, Quebec, Canada, 10Oct. 2003.6. K. C. Judson, Restructuring Engineering Education: Why, How And When
were completed byeveryone in the group. During both years, the results were kept confidential. However, theinstructors intervened as necessary when significant differences and problems were observed.The discussion on these results is presented in the next section.4. Results and Survey DiscussionFirst, the results of the numerical peer evaluations are presented when the instructor assignedteams. As each team leader led a presentation, several disagreements and conflicts within thegroups were shared with the instructors, and these results were reflected in the numerical peerevaluation. Figure 2 shows the results of the numerical surveys provided to the students duringthe Fall 2016 semester when teams were assigned based on individual academic
material is based upon work supported by the National Science Foundation under Grant No.1154146. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.Bibliography1. Rossetti, Manuel, Kim LaScola Needy, Ed Clausen, Carol Gattis, and Micah Hale. "Enrichment Activities in Support of a Student Integrated Intern Research Experience." American Society of Engineering Education (2014): 1-7. Web. 1 Aug. 2017.” American Society of Engineering Education (2014): 1-7. Web. 1 Aug. 2017.2. Rossetti, Manuel, Kim LaScola Needy, Ed Clausen, Carol Gattis, and Micah Hale. "On the Development of a Student Integrated
improvecourses by bettering integrating the training and laboratories, applying inquiry based learningmethods such as flipped classrooms and more judicious selection of topics. The managementteam is also working at better defining the course requirements for the student cohorts to betteraccommodate different levels of expertise in biology, mathematics and data science.AcknowledgementsThis material is based upon work supported by the National Science Foundation under GrantNumber DGE-1545463. Any opinions, findings, and conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.Bibliography[ASPB, 2013] American Society of Plant Biologists, Unleashing a Decade
laboratory environments.Acknowledgement This research is funded by the National Science Foundation NSF NRI #1527148. Anyopinions, findings, or conclusions found in this paper are those of the authors and do notnecessarily reflect the views of the sponsors.References1. National Robotics Initiative 2.0: Ubiquitous Collaborative Robots (NRI-2.0) (nsf17518) | NSF - National Science Foundation.2. Tucker C, Kumara S. An Automated Object-Task Mining Model for Providing Students with Real Time Performance Feedback. In: ; 2015:26.178.1-26.178.13.3. Hu Q, Bezawada S, Gray A, Tucker C, Brick T. Exploring the Link Between Task Complexity and Students’ Affective States During Engineering Laboratory Activities. In: ASME 2016
, 2011) argue that while there are three main affordances — proximity,privacy, and permission — that support interactions in a space, finding the right balance amongthem is crucial because “a lopsided distribution is more likely to inhibit than promote beneficialinteractions” (Fayard and Weeks, 2011, p.110). In particular, Fayard and Weeks (2011) stressthat people always interpret what are the appropriate behaviors in a space (e.g., in a librarypeople tend to be silent or speak in a low voice) and that these interpretations often reflect anorganization’s culture.The role of culture is also highlighted in research on makerspaces, especially through the senseof community makerspaces promote and nurture: “Participants often refer to the space as
women and URM, but Pell-eligible students are not as wellserved.6.0 AcknowledgmentsThe authors gratefully acknowledge the support of the National Science Foundation throughGrant No DUE-1347830, and the ongoing support of the Dean of Arts & Sciences and the Officeof the Provost.ReferencesAllexsaht-Snider, M. and Hart, L.E. (2001). Mathematics for All: How do we get there? Theory IntoPractice, 40(2) 93-101.Ames, C. (1992). Classrooms: Goals, structures and student motivation. Journal of EducationalPsychology, 84, 261-271.Bandura, A. (1977). Self-efficacy: Toward a Unifying Theory of Behavioral Change. PsychologicalReview 84 (2), 191-215.Bloom, B. S. (1994). "Reflections on the development and use of the taxonomy". In Rehage, Kenneth J
. Grant funded career navigation efforts continue to be institutionalized within the university structure. Career navigation focused initiatives are also undergoing an evaluation to better understand how these efforts support the project’s overall objectives and project goal. Acknowledgements Support for this research was provided by the National Science Foundation ADVANCE Institutional Transformation program under Award No. 1209115. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. References1. “RIT_EFFORT_Career_Life_Survey.pdf” NSF ADVANCE RIT (2009, October). Web
the earliest ages standthe best chance of continuing on career paths that will bring them greater economic prosperity.By increasing the opportunities for a greater and more diverse population of students to haveaccessibility to these subjects, the greater the number of curious, scientifically literate studentswill be prepared to learn and pursue engineering careers.AcknowledgmentsThis material is based upon work supported by the National Science Foundation (under GrantNo. 1647405) and National Grid. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the views of thefunding partners.References[1] J. P. Holdren, M. Cora, and S. Suresh. Federal STEM
lessons we learned throughout the process aswell. First was the use of a kick-off event. We learned quickly that it was easier to get studentsto attend a kick-off event where they could learn about the competition, find teammates, and signup, than it was to ask students to directly sign up. We also recognize that it is important to havea solid timeline before the competition begins. That timeline should reflect both the dates thecompetitors will need to submit their deliverables, and also the dates of the workshops and otherevents. In planning this competition our original schedule, given out at the kickoff, set theworkshops for a specific week, and the actual date was given out a week or so before hand. Thislead to confusion and scheduling
little trouble with Word and PowerPoint, but many students struggledinitially with Excel and Mathematica.The students were generally familiar with PowerPoint (PP) which was reflected through theirassignment results using this software. The first PP task was assigned using a set templateprovided to the students. The most common source of grade penalty was failure to use/follow theassigned template. This might seem a minor issue, but the students were admonished that thevalue of following the explicit instructions should not be underestimated in the success inEngineering College or any other career path.Most students had previous experience using Word. However, it is essential to note that moststudents were already accustomed to using Word for the
turn can be used to identify asolution. Engineering educators tend to treat “society” as a distinctly separate silo fromengineering itself. This is not to say that society isn’t discussed within the engineeringclassroom, but it is often framed as a linear progression -- something is engineered, then it hasan impact on society. This is reflected in the 2016-2017 ABET outcome H: “the broad educationnecessary to understand the impact of engineering solutions in a global, economic,environmental, and societal context.” The very language of this statement indicates societalcontext is seen as relevant, but distinctly separate, from engineering solutions.Similarly, students’ lived experiences are typically contained in a separate silo. Students
primary goals of ourworkshops. Confidence and motivation promote community building, a significant focus area ofThe Carpentries.The final survey instrument included 26 questions. Figure 1 provides a select few questions fromthe survey. The entire survey, data set, and code used to prepare this paper can be found on ourGitHub repository at https://github.com/kariljordan/ASEE. The statements below reflect ways in which completing a Carpentry workshop may have impacted you. Please indicate your level of agreement with the statements ● I have been motivated to seek more knowledge about the tools I learned at the workshop. ● I have made my analyses
found useful to reflect upon before attempting to adapt/developany materials for the new format. For example: 1) What types of content and learning outcomes should the students be responsible for outside of the classroom versus in the classroom? How and when will that content be delivered? 2) Should the entire class period be devoted to active learning or would the students benefit from starting with a brief (e.g. 10-15 minute) lecture first to review important or challenging concepts, prior to transitioning to activities for the remainder of the class time? 3) Will the students be tested on (e.g. online or in-class quizzes) or otherwise held accountable for pre-class content, prior to starting the in-class