labelled, several additional scripts extract features and create a dataframe so thedata can be processed using machine learning. In addition to the command-line logs, we are storingthe chat sessions generated by student-instructor interactions, and the web interface data generatedby students submitting solutions to the app. The latter two data sets have not yet been used formachine learning but we are working to integrate them.ResultsWe chose a basic 50% threshold of completion as a target label because our goal is to demonstratethat command-line logs are feasible for use in our machine learning auto detection system. Inpractice, a parallel system of classifiers, each detecting a different threshold or a multi-class systemwould be used. Since we
to teachers’ research projects as well as the topics defined by Alabama Course of Study(ALCOS), and also aligned with the Next Generation Science Standards (NGSS).Curriculum module implementation. Teachers implemented project-based curricular modulesthey developed during the Computing and Robots for All (CR4ALL) summer camp during the5th week of the RET program. The CR4ALL summer camp included 16 students at 6th-7th gradelevel (Figure 1). Teachers had the opportunity to model hands-on, inquiry-based teachingstrategies through the project-based RET curricular modules. Faculty mentors also provideddemonstrations using their expertise on robotics and AI concepts to the students and teachers aspart of the summer camp. As a culminating summer
d) Use Microsoft word PowerPoint and MATLAB to present technical data e) Use MATLAB to solve equations, represent data, and as a tool for engineering design f) Acquire, plot and analyze data from an experiment g) Recreate and explain the engineering design processThe general structure of the course is shown in Fig. OSU-2. The course consists of threemodules—algebra (context of circuits and chemical mixing), sinusoids (context of Snell’s Lawand a clock reaction), and calculus (context of a calculus based car)—which each focus on ageneral mathematical topic critical for engineering. Several weeks at the beginning and end ofcourse are reserved for introduction and wrap-up. Each module covers two topics on math thatengineers use every
pieces to be attachedto the drone to accomplish a purpose. While all of these approaches worked for the after-schoolenvironment, student learning was limited because of short exposure to the curriculum, theduration of time between sessions (typically a week), and inconsistent student attendance for theduration of the 10 weeks. Table 1: Selected findings based on Version 1 of the EngEx Drone Curriculum Positive Results Recommendations for Improvement Significant positive change in student Time allocated per week for the program is not affect and better understanding of enough for the students to get in-depth engineering understanding of a topic
m values of S2/D are close to 1. Within the whole class, S2/D isgenerally a little less than 1, which indicates that the perceived achievement is a little less thanthe actual achievement. When we look at the upper 50% and lower 50% students separately, weobserve clear divergence in S2/D. It appears that on campus students had general betterestimation on their achievements than on-line students, and the higher 50% of the class hadbetter estimation on their learning achievement than the lower 50% of the class. In particular, weobserve that in on-line section, the lower 50% students had much higher perceived achievementthan actual achievement.Lab 2. Frequency domain reconstruction – number of projects, interpolation methods (x-ray CT, MRI
course. Over this year, there have been more than30 student-led projects. Different topics in materials science were covered by those projects,ranging from physical materials phenomena, through processing and application of differentmaterials, to solving concrete technical problems. Students reported their research results andconclusions in research reports and presented them in the class. Some examples of the projectpresented are:• “Properties and Applications of Common Adhesive Tapes” – where students carried out tensile testing, shear testing and adhesion testing of different types of adhesive tapes. Based on the results of testing, students suggested the best applications for different types of adhesive tapes, Figure 1.• “Sodium Acetate
integrated career resources.Leveraging evidence-based practices from S-STEM programs, the grant team identified nine keycomponents for the first year of the SSP: 1. Cohorting the SSP students 2. Additional Engineering Course Meetings 3. Supplemental Instruction Sessions with Peer Mentors 4. Faculty Mentors 5. Professional Development Lunches 6. Social Activities 7. Invited Guest Speakers 8. Career Fair 9. Industry TourCohorting the SSP studentsAt Louisiana Tech University, all first-year engineering students are required to take the samefirst-year engineering course sequence, irrespective of their chosen engineering discipline. Thesecourses are blocked with a series of mathematics courses. To foster community, connect
second-year students. In2017, 50% of students who started in Civil or Environmental Engineering (CEE) changed majorsor left the institution in their first two years [1]. A similar trend exists nationally, with 40%-60%of engineering students in generally dropping out or changing major [2]. Recent reports internally[3] and from professional associations [4]–[6] have highlighted the need for holistic engineers, i.e.,engineers who can integrate cognitive, affective, and interpersonal skills and apply themeffectively in problem-solving and design. Supporting this is the development of an entrepreneurialmindset (EM) in the context of engineering. Engineering students who have an entrepreneurialmindset curiously explore and challenge existing solutions
Paper ID #32854Virtual International Collaboration for Community College STEM ProgramsProf. Karen Wosczyna-Birch, CT College of Technology Dr. Karen Wosczyna-Birch is the Executive Director and Principal Investigator of the Regional Center for Next Generation Manufacturing, an National Science Foundation Center of Excellence. She is the State Director for the College of Technology, a seamless pathway in technology and engineering from all 12 public community colleges to 10 public and private universities. Dr. Wosczyna-Birch has expertise with both the recruitment and persistence of under represented populations, especially
Consulting Group, initiated the bank’s non-credit service product management organization and profit center profitability programs and was instrumental in the breakthrough EDI/EFT payment system imple- mented by General Motors. Dr. Ferguson is a graduate of Notre Dame, Stanford and Purdue Universities, a special edition editor of the Journal of Engineering Entrepreneurship and a member of Tau Beta Pi.Dr. Misty L. Loughry, Rollins College Misty L. Loughry, Ph.D. is a Professor of Management at Rollins College in the Crummer Graduate School of Business.Dr. David Jonathan Woehr, University of North Carolina Charlotte David J. Woehr is currently Professor and Chair of the Department of Management at The University of North
identity, PBL, innovative learning-centered pedagogies, assessment of student learning, engineering de- sign, capstone design, etc. She also conducts research in cardiovascular fluid mechanics and sustainable energy technologies. She holds a BS and MS in Engineering Mechanics and a PhD in Biomedical Engi- neering from Virginia Tech. c American Society for Engineering Education, 2017 Methods and Preliminary Findings for Developing and Assessing Engineering Students’ Cognitive Flexibility in the Domain of Sustainable DesignExecutive SummaryEngineering problem-solving requires knowledge from multiple domains (i.e., technical,environmental, economic, and social) in
non-profit organizations nationwide. He researchers STEM pathways and retention of K-12 students, undergraduates, and early career professionals, as well as en- trepreneurial mindset.Lisa Olcese OlceseDr. Samantha Ruth Brunhaver, Arizona State University, Polytechnic campus Samantha Brunhaver is an Assistant Professor of Engineering in the Fulton Schools of Engineering Poly- technic School. Dr. Brunhaver recently joined Arizona State after completing her M.S. and Ph.D. in Mechanical Engineering at Stanford University. She also has a B.S. in Mechanical Engineering from Northeastern University. Dr. Brunhaver’s research examines the career decision-making and professional identity formation of engineering students
, such as relative to others in their peergroup or in the field. Consider one student’s diagram:Figure 1: This student’s deep expertises included Linux, technical problem-solving, and “going through airports (transport).” The shallow expertises included cycling, compilers, digital circuits, and signal processing.The student commented that s/he was keeping the order of deep expertises increasing down thevertical axis, to represent expertise as a distribution with more general knowledge up towards thetop of the vertical bar and more esoteric knowledge down at the bottom, where “you’re like0.001%” of the experts at this level (see bottom right of Figure 1). S/he placed “Russia” outsidethe T diagram
. This paper focuses on the preliminary development of the groundedtheory model.IntroductionThere are limitations in the current understanding of leadership that necessitate further study ofhow the concept is defined and developed in civil engineering and construction (CEC). In theCEC literature, leadership focuses on who a leader is and their skills or actions, which maps tothe trait and behavior periods, respectively, in leadership studies [1]. These periods weredominant in the first half of the 20th century in leadership studies but still dictate the conversationin CEC education and practice. These leader-centric paradigms focus on leader development,training charismatic technical experts for supervisor roles, instead of leadership
reviews that is available to any peer-assessment researcher.References [1] Topping, Keith J. "Peer assessment." Theory into practice 48, no. 1 (2009): 20-27. [2] Tinapple, David, Loren Olson, and John Sadauskas. "CritViz: Web-based software supporting peer critique in large creative classrooms." Bulletin of the IEEE Technical Committee on Learning Technology 15, no. 1 (2013): 29. [3] Palanski, M., D. Babik, and E. Ford. "Mobius SLIP: Anonymous, peer-reviewed student writing." OBTC 2014 at Vanderbilt University (2014). [4] Hart-Davidson, William, Michael McLeod, Christopher Klerkx, and Michael Wojcik. "A method for measuring helpfulness in online peer review." In Proceedings of the 28th ACM international
that meetevery three weeks during the academic year. Thus far, there have been over 140 participants infour cohorts. Participants are selected from a wide variety of STEM fields, including traditionalsciences, agricultural disciplines, and technical fields that are characteristic of land-grantuniversities. Altogether, more than half of the faculty participants are women, and roughly halfof the participants are among traditionally underrepresented populations in higher education (i.e.,in association with gender, age, race, and ethnicity) [1]. In addition to NSF-funded participants,there have also been 25 non-STEM academic participants in the program. These participantswere financially supported through the Office of the Provost to extend
concentration innanotechnology. Each course contributes to an undergraduate educational program innanotechnology built on nanoscience fundamentals. More details on the entire program aregiven elsewhere [2]. Since details on the freshman nanotechnology seminar are available [2, 3],this paper discusses newly developed courses offered in 2012. Unlike the freshmannanotechnology seminar that meets 1 hour per week, each of these new courses are 3 creditcourses that can be used as technical electives.Nanotechnology in chemical applicationsNanotechnology in Chemical Applications is a new elective course developed for this NSF NUEsponsored program. The main goal of the course is to introduce fundamental concepts fromcolloid and interface science that will
whenand how to use technical tools; and deciding when to override mathematically calculated answers[14]. These four main sections encompass a total of 15 different codes that we use to identifystudents' emerging judgment.Current WorkIn our first semester of the project, we collected data to address RQs 1 and 3 by assigning a newOEMP to a junior-level fluid mechanics course. This OEMP is a pipe system optimizationproblem. Students need to design a system that can deliver water from a lake to a greenhousewhile minimizing cost and avoiding specified red zones. Students need to use their knowledge ofpipe flow to choose a type of pipe while considering size, roughness and cost. This courserepresents a class level and course topic that we have never
astandardized root mean square residual (SRMR) of .06; less than 0.8 suggests a good fit [14].These indicate that our model, despite being complex, is overall a good fit.Results and discussionOverall, we found that students expressed a strong intention to persist to degree (M = 6.46; SD =1.67, all constructs on scale 1-7), not surprising given the percent of seniors in the sample. Theyalso expressed generally positive intentions to persist in engineering careers followinggraduation (M = 5.80; SD = 1.23). Students reported high design self-efficacy, (M = 5.64; SD =1.00) and engineering identity (M = 5.55; SD = 1.17). In terms of the framing agency constructs,students reported very high shared consequentiality (M = 6.22; SD = 0.96); high
Figure 2: General design of the assessment experiments(1) students with no choice who use the same assigned simulated system for all three ISBL as-signments; (2) students with no choice who are given a different simulated system for each ISBLassignment; (3) students who can choose their preferred simulated system at the beginning but can-not change their choice for future assignments; and, (4) students who can choose at the beginningand switch between the three simulated systems for subsequent assignments.We used the following instruments/methods for data collection: • Demographics survey: Collects data about the subject’s age, gender, race, grade point aver- age (GPA), grade in a prerequisite course, major, semester standing, work
size 10. (c) A clique of size 3. (d) A star with 5 leaves. (e) A star with 10 leaves. (f) A star with 3 leaves.Figure 1. Six examples of cliques and stars. The cliques presented in (a), (b), and (c) represent 5, 10, and3 nodes that are all adjacent to each other. A clique of size 3 is also referred to as a triangle. The starspresented in (d), (e), and (f) represent graphs where all leaves are adjacent to a center (depicted in blue).The number of leaves are 5, 10, and 3.Network GenerationFor generating our network 𝐺(𝑉, 𝐸), we focus on a specific part of the questions in the survey: thecourses graduate students have taken from each department during their tenure at the university. The setof nodes of the
traditional end-of-semester course project approach. (5) The instructors were enthusiastic about seeing students thoroughly engaged andworking diligently on a problem. Many students reported enjoying the process of exploring anddiscovering that went with the EFFECTs problems. (6) A review of student work shows that students generated individual ideas and uniquesolutions, which re-emphasized the value of the EFFECTs approach for both the instructors andstudents.In addition to the positive outcomes, the instructors and students identified several challengesand difficulties. (1) As a practical matter, EFFECTs are time-consuming, requiring at least one classsession, but usually several sessions. It would probably not be practical to
student learning outcomes, we will compare pre- intervention vs.post- intervention student learning outcomes. Because students and Faculty are nested inclassrooms, and because this study employs repeated measures, multilevel modeling will be usedto control for non-independence. The general MLM, following Raudenbush & Bryk [40],equations for these analyses are listed below:Level 1: Level 2: Mixed Model:As these equations indicate, a Level-1 (L1) linear regression will be used to predict learningoutcomes (Ylearning: e.g., students’ responses to the item, “How much do you think you havelearned in this course?”) from student-level (L1) predictors (X). Such L1 predictors will includereports of the classroom environment
enough time outside of work to prepare for exams, do homework, work on projects, and sleep. I only get approximately 4 hours per night.Figure 2 shows a word cloud generated from working students’ comments from the fall 2018survey. Table 1. Key findings from surveys of ETCS students. Year 1 Year 2 Survey question (n = 168) (n = 136) enrolled in more than 12 credits 90% 88% currently employed 59% 68% commuter student 60% 66% applied for financial aid
as well as exploring new questions aswe perform the necessary data analysis. To explore RQ1 and RQ2, Scheidt [14] used nationaldata from the initial deployment of the survey in 2017-2018 and found that engineering studentsNCA profiles fall into four discernable clusters. At the time, the data set included 2339undergraduates at 17 different institutions. The clusters include: • Cluster 1: The Typical Cluster (n = 832). Members of this cluster had factor means that were all similar to the overall sample mean. • Cluster 2: High Positive NCA Factors but with a Fixed Mindset (n = 500). The members in Cluster 2 were generally high in many of the factors, with many statistically different from all other clusters
is to teach ageneration of future engineers the impact that algae can have on solving humanitarian issuesaround the world.1. Introduction1.1 Project Goals/PartnershipAlgae Grows the Future is a project focused on advancing a generation of engineers dedicated toimproving the world for all people. Through the use of science and engineering, this projecthopes to spark students’ interest in engineering by redefining how discovery, learning, andinnovation is approached in the classroom. The goals of the project are to ensure the highestquality of STEM education in order to improve learning and comprehension of engineeringconcepts. Additionally, the designed curriculum takes a multidisciplinary approach to teachingengineering in order to show how
industry. He has also focused on collaborative and innovative educational research. Abdelrahman is passionate about outreach activities for popularizing engineering research and education. His activities in that arena included NSF funded sites for research experience for undergraduates and research experience for teachers. He has published his re- search results in more than 90 papers in refereed journals and conference proceedings and 30+ technical reports.Prof. Patrick L. Mills, Texas A&M University, Kingsville Patrick Mills is the Frank H. Dotterweich Chair and Professor in the Department of Chemical and Nat- ural Gas Engineering at Texas A&M University, Kingsville. He is also President of Catalytic Reaction
. Therecruitment survey was used to gather general background and demographic information fromfaculty, establish a baseline understanding of their knowledge and perspectives of diversity,equity, and inclusion at their respective universities, and gauge their willingness to participate ina focus group on this topic. To facilitate purposive sampling for focus groups, Likert-scale itemswere created that prompted faculty to indicate their level of agreement (1 - strongly disagree; 7 -strongly agree) with 11 statements. These statements were adapted from those developed bySecules and colleagues [9], and example adaptations for two of the items are shown in Table 1.Table 1: Example translations to develop recruitment survey prompts Sample Topic
developed in order to assess the barriers students faced intransferring knowledge. The particular problem that students were asked to solve is detailed inFig.1. This problem is technically classified as a rigid body equilibrium problem and is commonto engineering statics courses taught across a range of disciplines. Importantly for this study, theproblem requires the successful transfer of mathematical skills such as integration to solvecompletely - both the area of the plate and the location of its centroid must be determined via theuse of integration. The framework of knowledge transfer developed by Belenky & Nokes [28,29]was used as a guide to the problem solving process of the participants as it agreed with theauthors own conceptions of the
generally in agreement,however. Because of the multiple statistical tests run, Bonferroni’s correction was considered(Bland & Altman, 1995; Perneger, 1998). Practical significance was assessed using Cohen’s deffect size.4. ResultsThe participants in this study were junior and senior-level mechanical engineering students at alarge research university in the southeastern U.S. The average weekly reflection participationrates ranged from 79% to 85% depending on the semester, with a total overall rate of 83% ofenrolled students.4.1 Weekly Reflections: Content Analysis ResultsWeek 1: The top response categories for the week 1 planning question about how to supportone’s in-class problem solving are shown in Table 4 and were as follows: 1) study or pre