pilot is continuing with a qualitativeanalysis of open-ended survey questions and an investigation into student demographics such asretention tracking. We are planning a revised course offering in the Fall 2015 semester that willallow for additional data collection from students and from university personnel regarding ourhybrid model implementation and introduction course offering. Additionally, with an eyetowards revising the course for the Fall, we are planning a Spring 2015 survey of departmentheads, introduction to engineering instructors within each of the departments, and academiccoordinators within each department.References1 Lichtenstein, G., Loshbaugh, H., Claar, B., Bailey, T., and Sheppard, S.D. (2007). Should Istay or should I go
may decide to drop the course without it appearing on theirrecords. At this week, only homework, quiz grades and attendance records were available. Alogistic regression model was created based on Spring 2011 data to predict success (i.e., receivinga grade of A, B, or C) or failure (i.e., receiving a grade of D or F) in the course. The results areshown in Table II. In this model, only quiz and homework grades were significant predictors ofsuccess. Thus attendance was removed from the model. The final model for week 2 is reported inTable III and equation (1): Page 26.304.5 Ln(p/1-p) = (4.06)*homework + (2.73)*quiz -2.23 (1
Paper ID #20525An Integrated First-Year Experience at ECST (FYrE@ECST)Dr. Gustavo B. Menezes, California State University, Los Angeles Menezes is an Associate Professor in Civil Engineering Department at CalStateLA. Since becoming part of the faculty in 2009, Menezes has focused on improving student success and has participated in sev- eral teaching workshops, including one on ”Excellence in Civil Engineering Education” and another in ”Enhancing Student Success through a Model Introduction to Engineering Course.” He is currently the PI of TUES project to revamp the sophomore-year experience at the college of engineering
Laboratory Exercises and Design Projects for First Year Engineering Students", American Society for Engineering Education Annual Conference and Exposition, 2001.2. Allam, Y., Tomasko, D.L., Trott, B., Schlosser, P., Yang, Y., Wilson, T.M., Merrill, J., "Lab-on-a-chip Design- Build Project with a Nanotechnology Component in a Freshman Engineering Course", Chemical Engineering Education, Volume 42, Number 4, 2008.3. Freuler, R.J., Hoffmann, M.J., Pavlic, T.P., Beams, J.M., Radigan, J.P., Dutta, P.K., Demel, J.T., Justen, E.D., "Experiences with a Comprehensive Freshman Hands-On Course 0 Designing, Building, and Testing Small Autonomous Robots", American Society for Engineering Education Annual Conference and Exposition, 2003.4
2006-2326: LAB-ON-A-CHIP DESIGN-BUILD PROJECT WITH ANANOTECHNOLOGY COMPONENT IN A FRESHMAN ENGINEERING COURSEYoussef Allam, Ohio State UniversityDavid Tomasko, Ohio State UniversityJohn Merrill, Ohio State UniversityBruce Trott, Ohio State UniversityPhil Schlosser, Ohio State UniversityPaul Clingan, Ohio State University Page 11.856.1© American Society for Engineering Education, 2006 Lab-on-a-chip Design-Build Project with a Nanotechnology Component in a Freshman Engineering CourseAbstractA micromanufacturing lab-on-a-chip project with a nanotechnology component was introducedto first-year engineering students as a voluntary alternative within the standard
leadingto better or worse integration and performance. Hargrove and Burge [27] proposed a six-sigmamodel for improving retention. In these models, performance, psychological parameters, and theinstitutional environment are all utilized.Because we know that many factors are important for student retention, and that performance isspecifically predictive, in this paper we analyzed the students that were “at-risk” yet still capableof graduating: the students who received a C in engineering mathematics in their first semester.By narrowing the sample to C-students, we eliminated performance as a variable and were ableto investigate other non-cognitive factors more directly. At the J. B. Speed School ofEngineering at the University of Louisville, 34% of
hardwareexperiments.Bibliography1. Besterfield-Sacre, M., Atman, C. J., Shuman, L.J., " Characteristics of freshman engineering students: Models for determining student attrition in engineering," Journal of Engineering Education, 86, 2, 1997, 139-149.2. Grose, T. K., "The 10,000 challenge," ASEE Prism, 2012, 32-35. Page 24.608.93. Johnson, M. J., Sheppard, S. D., "Students entering and exiting the engineering pipeline-identifying key decision points and trends," Frontiers in Education, 2002.4. Olds, B. M., Miller, R. L., "The effect of a first-year integrated engineering curriculum on graduation rates and student satisfaction: A longitudinal
Implementing Hands-on Laboratory Exercises and Design Projects for First Year Engineering Students", American Society for Engineering Education Annual Conference and Exposition, 2001.2. Allam, Y., Tomasko, D.L., Trott, B., Schlosser, P., Yang, Y., Wilson, T.M., Merrill, J., "Lab-on-a-chip Design- Build Project with a Nanotechnology Component in a Freshman Engineering Course", Chemical Engineering Education, Volume 42, Number 4, 2008.3. Freuler, R.J., Hoffmann, M.J., Pavlic, T.P., Beams, J.M., Radigan, J.P., Dutta, P.K., Demel, J.T., Justen, E.D., "Experiences with a Comprehensive Freshman Hands-On Course 0 Designing, Building, and Testing Small Autonomous Robots", American Society for Engineering Education Annual Conference
education to improve academicachievement and encourage learning [5]. Research suggests that tutoring has many positiveoutcomes affecting both tutors and tutees, including [6][7]: a. Improve students’ performance b. Improve the learning for both tutors and tutored students c. Improve overall performance in large mixed ability classes d. Help disadvantaged students academically and give them a sense of belonging to the school e. Help students develop a more positive attitude toward hard courses f. Increase social enhancementAside from its academic and social effect, Keerthana in [8] explained that "peer tutoring canprovide more than twice as much achievement than computer aided instructions and three timesmore than reducing class
AC 2010-1358: IMPLEMENTATION OF AN EARLY WARNING SYSTEM INENGINEERING: A PARTNERSHIP WITH ACADEMIC ADVISORS ANDINSTRUCTORS ACROSS THE CAMPUSMary Goodwin, Iowa State UniversityAmy Brandau, Iowa State UniversityDeb DeWall, Iowa State UniversityBing Du, Iowa State University Page 15.675.1© American Society for Engineering Education, 2010 Implementation of an Early Warning System in Engineering: A Partnership with Academic Advisors and Instructors across the CampusAbstractRetention of engineering students has become a major concern for universities across thecountry. At Iowa State University the college of engineering loses about 10
learning.The DYP ProgramAn innovative, best practices approach, called the “Design Your Process for Becoming a ‘World-Class’ Engineering Student” (DYP) program, has been developed by Raymond B. Landis14 toincrease the quality of the educational experience of first-year engineering undergraduatestudents. Typically, approaches to increase the nature and quality of undergraduate educationexperience are focused on instructional and/or curricular changes. The DYP program is differentin that it focuses on what the students can do themselves to become self-regulated students andtherefore are not only more likely to graduate with an engineering degree but also with a higherquality, i.e. with a higher GPA. Self-regulated learning (SRL) is the process that a
of general rules to minimize accidents caused by ignorance. B. Train the students on the four most commonly used machines in the shop. C. Create training manuals that promoted the safe use of machines above all else. D. Require the students to physically demonstrate the proper use of machine tools to the SAs. When choosing the content that would be included in the Mandatory Shop Training wetook input from students from previous years, instructors, Student Assistants (SAs) and the shopstaff. We used these comments to help us ensure that our training would make the Synthesis,Analysis and Optimization and Evaluation steps in the design phase more safe and injury-free.Based on the feedback, we developed a one hour hands-on
Freeman, Northeastern University Susan Freeman, is a member of Northeastern University’s Gateway Team, a group of teaching faculty expressly devoted to the first-year Engineering Program at Northeastern University. The focus of this team is on providing a consistent, comprehensive, and constructive educational experience that endorses the student-centered, professional and practice-oriented mission of Northeastern University.Dr. B. Kris Jaeger, Northeastern University Beverly Kris Jaeger, PhD is on the full-time faculty in the Department of Mechanical and Industrial Engineering at Northeastern University teaching Simulation Modeling and Analysis, Facilities Planning, and Human-Machine Systems. She has also been an
% 47% FED101 Spring 2020 ENGR101 Spring 2020 ENGR101 Fall 2019 Week 1-8 Week 9-15 Figure 1: Attendance comparison pre and post COVID for FED101 and ENGR101 (a) (b)Figure 2: Overall assignment/homework submission rate for FED101 for (a) Fall 2019 and (b) Spring 2020Figure 2 shows the impact of COVID on the submission rate of assignments for FED101. As seenin Figure 2(a), the general trend of submission for Fall 2019 is downwards, but picks up slightlyin the end due to final project and final report submission. In Figure 2(b) depicting Spring 2020,the
Texas, ArlingtonProf. Stephen P Mattingly, University of Texas, ArlingtonZiaur Rahman, The University of Texas at Arlington Ziaur Rahman received his Bachelor of Science (B. Sc.) degree in Civil Engineering from Bangladesh University of Engineering & Technology, Dhaka, in June 2007. After completing his Bachelor degree, he started his graduate studies in Civil Engineering at The University of Texas at Arlington in August 2008. He completed his Masters of Engineering (M. Eng.) degree under the supervision of Dr. Siamak Ardekani. He continued his graduate studies as a Ph. D. student under the supervision of Dr. Stephen Mattingly in Fall 2010. The author’s research interests include Incident Management, Operations and
robust web-based tools to repeatedly measure theirexplicit and implicit attitudes toward self, math, engineering, and careers. Our objectives were:(a) Measure the implicit biases of freshman engineering men and women regarding STEM.(b) Determine whether engineering students and professionals are implicitly self-associated with engineering.(c) Determine whether project-based learning increases freshman students’ self-association with engineering.MethodsWe employed the Implicit Association Test (IAT) 20 and a recent variant, the Brief ImplicitAssociation Test (BIAT) 21, to measure association strengths between concepts (e.g., math andlanguages) and evaluations (e.g., good or bad) or attributes (e.g., male or female). In the IAT,participants
statistics.Acknowledgment The authors would like to acknowledge the Henry M. Rowan College of Engineering forits support, in particular the Experiential Engineering Education (ExEEd) Department. Theauthor would also like to acknowledge funding provided by the U.S. Department of EducationGraduate Assistance in Areas of National Need (GAANN) Grant Number P200A180055.References[1] National Academy of Engineering, Grand Challenges for Engineering. Nat. Academy of Eng.Washington, DC. 2016.[2] B. Bloom, “Learning for Mastery Instruction and Curriculum. Regional EducationLaboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1.,” Eval.Comment, vol. 1, no. 2, pp. 1–12, 1968.[3] J. Moore and J. Ranalli, “A Mastery Learning Approach to
Hodge 2006 Yes 0 1 0 Hodge 2007 No impact 0 1 0 Hodge 2008 No impact 0 1 0 Hodge 2009 Yes 0 1 0 Vasko 2012 Yes 1 0 0 Tsang 2013 n/ac 2 1 0 a Freshman retention not reported, but retention at other times was higher for the LLC. b Study was published in 2001 about the 2000
influence of role models on major and career decisions [4]. The researchdiscussed in this paper focused on three subscales of the LAESE survey, (1) Engineering Self-Efficacy 1, (2) Engineering Self-Efficacy 2, and (3) Engineering Career Expectations. EngineeringSelf-Efficacy 1 is a measure of the student’s perception of their ability to earn an A or B in physics,math, and engineering courses and succeed in their engineering curriculum without sacrificingoutside interests. Engineering Self-Efficacy 2 is a measure of the student’s perception of theirability to complete engineering requirements such as their science and math coursework, as wellas their general ability to complete any engineering major [5].Researchers from the University of Michigan
to together develop a sharedunderstanding of and solution for an ill-structured problem.4 Teachers are redefined as coacheshelping students work toward a set of possible open-ended solutions, and students take someownership of their own learning through reflection. Typically, students learn about team skills inaddition to the course content. Engeström5 identified three stages characteristic of collaborativelearning. In his view, for learning to be truly collaborative, students must (a) work towards ashared problem definition, (b) cooperate to solve the problem, and (c) then engage in reflectivecommunication, reconceptualizing the process. Similarly, Johnson et al.6 argue that there arefive basic elements critical for cooperative work to be
engineering problems using project-‐specific math, engineering, and science concepts. (a, e) 2. Analyze, interpret and make decisions about quantitative data using basic concepts of descriptive statistics (mean, median, standard deviation, normal distributions, and mode) and m easurement, including issues in: (b) a. precision and accuracy; b. sample and population; c. error and uncertainty. 3. Solve an open-‐ended design problem by: (c, e) a. transforming an open-‐ended design problem into an answerable one; b. breaking down a complex design problem into sub-‐problems; c. determining assumptions involved in
Paper ID #25912Using LEGO Mindstorms and MATLAB in Curriculum Design of ActiveLearning Activities for a First-year Engineering Computing CourseDr. Shelley Lorimer, Grant MacEwan University Shelley Lorimer is an Associate Professor in Engineering (BSEN) Transfer Program at MacEwan Univer- sity. She is an instructor in the introductory engineering courses as well. The BSEN program at MacEwan has grown from forty students since in started almost fifteen years ago, to the current 216 students. The majority of the students in the program transfer to second year engineering at the University of Alberta. Shelley is a graduate of
into groups. In Proceedings of Frontiers in Education Conference. Tempe, AZ, USA.4. Oakley, B., Felder, R. M., Brent, R., & Elhajj, I. (2004). Turning student groups into effective teams. Journal of Student Centered Learning, 2(1), 9-34.5. Marra, R.M., Rodgers, K.A., Shen, D., Bogue, B. (2009). Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engineering Education, 98, 27-38.6. Hutchison, M. A., Follman, D. K., Sumpter, M., Bodner, G. M. (2006). Factors influencing the self- efficacy beliefs of first-year engineering students. Journal of Engineering Education, 95, 39-47.7. Okudan, G.E., Horner, D., Bogue, B., & Devon, D. (2002). An
. Seymour and N. Hewitt, Talking About Leaving: Why Undergraduates Leave the Sciences. Boulder, Colorado:Westview Press, 1997.2. U.S. Department of Education, “A Test of Leadership: Charting the Future of U.S. Higher Education,”Washington, DC, 2006.3. B. R. Butler, “Introducing Freshmen to Engineering: A Model Course,” Engineering Education, vol. 69, pp. 739-742, 1979.4. E. Soulsby, “University Learning Skills: A First Year Experience Orientation Course for Engineers,” presented at29th ASEE/IEEE: Frontiers in Education Conference, San Juan, Puerto Rico, 1999.5. F. E. Weber, R. M. Bennett, J. H. Forrester, P. G. Klukken, J. R. Parsons, C. D. Pionke, W. Schleter, J. E. Seat,and J. L. Yoder, “The ENGAGE Program: Results from Renovating the First Year
other external electronic components to design morecomplex projects. For example, students can connect Aksense to a microphone to detectthe noise level and set a buzzer if excessive level of noise is detected. Figures 1(b)-(c)show the plots of the received data using Excel.We should note that for many students finding a clear project idea was initially a bitdaunting. Many did not know how to find a good project. Therefore, over the first severalweeks the instructor had to dedicate a fair amount of time explaining different practicalscenarios and how Aksense can be utilized for various measurements. As the semesterpassed by, it appeared that most students became more comfortable with findinginteresting applications for Aksense.5. Experiment
, along with existing self-assessments of technical and communicationsskills. Additional observations of team engagement, or a lack of it, during in-class activities,beginning early in the semester, could also be compared to student feedback about teamperformance in their project status and reflective updates, which begin with Weeks 3 and 4. References [1] M. H. M. S. A. Hakanen, "Trust in Building High-Performing Teams - Conceptual Approach," Electronic Journal of Business Ethics and Organization Studies, vol. 20, no. 2, pp. 43-53, 2015.[2] C. L. F. Larson, Team Work. What must go right/What can go wrong, Thousand Oaks, CA: Sage Publications, 1989.[3] N. a. B.-L. M. Van Tyne, "Ethics for the "Me
Challenges for Engineering project were graded.The first three assignments, to introduce their topic, complete the annotated bibliography, andprepare the summary of their topic, were worth 5% of their final grade per assignment. The finalpresentation was worth 20% of their final grade for the course. Therefore, all four components ofthe project were worth a total of 35% of their final grade for the course. The average grade for allgroups on the overall project was a B. From the signed consent forms, it was noted that theaverage grade for the students that completed the survey was a B+, while the average grade forthe students that chose not to complete the survey was a C.Updates for Future ClassesIn general, the feedback about this project was quite
in theELLC, remain in good standing with the university, and continue to make academic progresstowards a degree program in the Bobby B. Lyle School of Engineering at Southern MethodistUniversity (SMU).The program directors worked closely with the engineering recruiting office to identify eligibleadmitted students with an interest in pursuing a major in engineering or computer science.Candidates were recruited based on past academic achievement, leadership potential,curricular/extracurricular experiences, demonstrated financial need, and diversity.IDEAL scholars participated in three main co-curricular experiences aimed at buildingcommunity and increasing their leadership skills: block scheduling, academic advising and aweekly seminar. IDEAL
students feel that theyneed to make a decision quickly or that they have a lot of time to decide? Do students understandthe that there may be some unintended wasted credit hours by switching their major, which iswhy it may be important to make an informed decision within the first year? Additional researchinto these areas may help faculty tailor the first-year curriculum to provide the most benefit tostudents and improve retention efforts.References1. Argrow, B. M., Louie, B., Knight, D. W., Canney, N. E., Brown, S., Blanford, A. J., Gibson, C. L., & Kenney, E. D. (2012). Introduction to Engineering: Preparing First-Year Students for an Informed Major Choice. American Society for Engineering Education Annual Conference. San
% considered themselves “B” students (one person did not provide a clear answer). Incomparison, the high school GPA for the COT for 2014-2015 academic year was similar with anaverage GPA being 3.58/4.0.ParentsEighteen parents participated in the survey. One person from each household was asked torespond, and mothers represented 83.33% of the group. When asked for their education level, thedistribution was as follows:Table 2Self-reported distribution of educational levels among parents of the PI studentsEducational level Percentage of parents (N=168), %High School Diploma 11.1Some college