Department Head who sees this as the top priority.The traditional approach to measuring diversity in engineering involves counting racial andethnic minorities and women, while measuring gains in representation as reflected by thenumbers. We believe that this traditional approach needs to consider other important aspects ofdiversity, in addition to the traditional approaches, to maximize the inclusiveness within thefield. Decades of educational policy and practice have under-considered the existence of groupssuch as LGBTQ, poor, and disabled, thereby perpetuating exclusionary social patterns (Riley etal., 2014). Our multi-pronged approach to increasing diversity and inclusion begins withexpanding the fundamental definition of diversity to include
for all students in CBEE?Ultimately, we aspire to both transform the activities systems in CBEE and to serve as a modelfor others in engineering education as we move towards an inclusive and creative engineeringprofession for the 21st Century.AcknowledgementsThe authors are grateful for the enthusiasm and participation in our work from so many membersof our CBEE School community – students, staff, and faculty. We also acknowledge the supportprovided by the National Science Foundation through grant EEC 1519467. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation.References1. Engeström, Y. (2001). Expansive
rate of scholars (losses due to GPA, only) will also be assessed for evidence ofsuccessful interventions.AcknowledgmentThis work is supported by the National Science Foundation Award under Grant No. 1153250.Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the authors and do not necessarily reflect the views of the National Science Foundation.References [1] Geisinger, Brandi N. and Raman, D. R., “Why They Leave: Understanding Student Attrition from Engineering Majors,” International Journal of Engineering Education (1993): 29 (4), 914–925. [2] Chen, Xianglei and Soldner, Matthew, “STEM Attrition: College Students’ Paths Into and Out of STEM Fields,” National Center For Education
the sophomoreyear may work better for students once they understand, from the year-long counseling sessions,the need to catch up with their cohort. Unfortunately, participation in the summer bridge has notincreased significantly to date.As we reflect on the overall assessment plan, we realize that while some Program elements havethorough assessments, we need to disaggregate the data even more so that we better understandthe various cause and effect relationships.Initial ConclusionsWhile there are some promising initial results in terms of 1st to 3rd semester retention rates, it isclear that participation in the Program elements that help students catch up academically hasbeen low. Since implementation, we made several changes to the Program
college levelduring the 2014-15 academic year. The number of student-hours of instruction delivered at thefour-year level was double that delivered by community colleges and may reflect a greater abilityto apply the technology or the need for greater depth of instruction at the four-year level.The gender data shows that females are a distinct minority in microcontroller classes and that theclass is composed mainly of students of Caucasian ancestry. Students of Hispanic andAsian/Pacific Islander ancestry make up a higher percentage at the four-year level than in two-year community college microcontroller classes.Interest in professional development workshops similar to those offered through the projectseems to remain high. Registrations are
even further.AcknowledgementsThis material was supported by the National Science Foundation’s Research Experience forUndergraduate Education (REU) Program (Award no. 1263293). Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author and do notnecessarily reflect the views of the National Science Foundation.Bibliography[1] https://www.nsf.gov/pubs/2013/nsf13542/nsf13542.pdf[2] Brownell, J.E., and Swaner, L.E.. Five High-Impact Practices: Research on Learning, Outcomes, Completion, and Quality; Chapter 4: "Undergraduate Research." Washington, DC: Association of American Colleges and Universities, 2010.[3] Crowe, M., and Brakke, D. "Assessing the Impact of Undergraduate-Research
skills. Students also learn to use Excel/Matlab for data analyses, plotting andstatistical methods.3.9 Ethics, Social and Environmental InjusticesOne of highlights of the project is the inclusion of progressive humanities and qualitative socialsciences. Students in teams are required to watch movies and documentaries that reflect onsocial/environmental injustices, breach of ethics along with gender biases in STEM fields, andsocial prejudices. Students also participate in debates that focus on public policy and arerequired to study the discussions and decisions of the Whitehouse Office of Science andTechnology Policy.3.10 WebsiteA website has been created for the Algae Grows the Future project to promote outreach andmake the project’s resource
improvement.AcknowledgementThis material is supported by the National Science Foundation under DUE Grant Numbers 1501952and 1501938. Any opinions, findings, conclusions, or recommendations presented are those of theauthors and do not necessarily reflect the views of the National Science Foundation.References[1] Wang, J., Fang, A. & Johnson, M., (2008). Enhancing and assessing life long learning skills through capstone projects. ASEE Annual Conference and Exposition, Conference Proceedings. Pittsburgh, PA, 2008-324.[2] Shuman, L.J., Besterfield-Sacre, M. & Mcgourty, J., (2005). The abet "professional skills" — can they be taught? Can they be assessed? Journal of Engineering Education, 94 (1), 41-55.[3] Earnest, J., (2005). Abet
or agreed that they had the chance toupdate the writing instructional materials for their courses during this workshop. This feelingwas reflected in Figure 2(a) which shows the response to the statement “I had the chance toupdate the writing instructional materials for my courses during today’s sessions”. “I learned how to generate dimensions for my rubric” “I had the chance to update the materials for my own course” Engineering Engineering English English (a) Generating rubric dimensions (b) Updating
80 63 14 13 2015 206 154 24 63 2016* 94 86 20 19*In 2016, we recruited one math teacher who was suitably matched to a research project, but he failed to completethe program.Table 2 shows the diversity of the applicant pool demographics reflecting the diversity of theteachers in the Houston region.Table 2. Demographics of the RET applicant pool (2014-2016).Ethnicity % Gender %Asian 13% Female 64
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
and flagged to generate a listing of internally consistent, discretecategories (open coding), followed by fractured and reassembled (axial coding) of categories bymaking connections between categories and subcategories to reflect emerging themes andpatterns. Categories were integrated to form grounded theory (selective coding), to clarifyconcepts and to allow for interview interpretations, conclusions and taxonomy development.Frequency distribution of the coded and categorized data were obtained using a computerizedqualitative analytical tool, Hyperrresearch® version 3.5.2. The intent of this intensive qualitativeanalysis was to identify patterns, make comparisons, and contrast one transcript of data withanother during our taxonomy and CPPI