Paper ID #5807Does it matter who teaches a core mathematics course to engineering under-graduates?Mr. Ryan Boyd Cartwright, General Electric Ryan Cartwright is a Quality Engineer with General Electric in Clearwater, FL. He holds a Master of Science in Engineering Management (2011) and a Bachelor of Science in Industrial Engineering (2011) from the University of South Florida. He is currently performing work related to the implementation of Lean Six Sigma techniques, supplier quality management, and logistics optimization in a manufacturing environment.Prof. Autar Kaw, University of South Florida Autar Kaw is a Professor
Curriculum Development. Notable is her number of years in the Academic and Educational Technology field and the experience it brings to her present position.Tonya Troka, Colorado Technical University Tonya Troka, with more than 10 years of experience working with online students, has been a leader of the adaptive learning implementation project since its initial launch in October 2012. As the University Program Director for General Education/Psychology, she works directly with the general education cur- riculum that was used to integrate the adaptive learning technology into the classroom. Troka has also provided insight into using the technology in the classroom and how success should be measured
. Her other interests include reading, photography, cooking, sewing, and various writing projects.Prof. Tonya Troka, Colorado Technical University Tonya Troka, with more than 10 years of experience working with online students, has been a leader of the adaptive learning implementation project since its initial launch in October 2012. As the University Program Director for General Education/Psychology, she works directly with the general education cur- riculum that was used to integrate the adaptive learning technology into the classroom. Troka has also provided insight into using the technology in the classroom and how success should be measured.Prof. John M. Santiago Jr., Colorado Technical University Professor John
AC 2012-4138: TEACHING PYTHAGORAS’S THEOREM USING SOFT-WAREDr. Bert Pariser, Technical Career Institutes Bert Pariser is a faculty member in the Electronic Engineering Technology and Computer Science Tech- nology departments at Technical Career Institutes. His primary responsibility is developing curriculum and teaching methodology for physics, thermodynamics, electromagnetic field theory, computers, and databases. Pariser has prepared grant proposals to the National Science Foundation, which produced the funding for a Fiber Optics Laboratory. He served as Faculty Advisor to the IEEE and Tau Alpha Pi National Honor Society. Pariser was instrumental in merging Tau Alpha Pi National Honor Society into the ASEE. In
it in various real life contexts. Through the activitydeveloped on a motion context, students can establish the relationship between position andvelocity graphs, the latter establishing the former’s behavior. The interpretation of each of thesegraphs becomes a useful tool in order to describe in words the effects in the character motionsimulation that they represent.In what follows we describe a path of four didactic activities developed in the classroom, whichallow the establishment of generalizations on the qualitative relationships of the position andvelocity graphs; but through an environment that allows a different way of reaching theserelations. It must be said that the classroom sessions allowed for plenty of motion orientedscenarios
and was categorized as FGCS or Non-FGCS. After removing individuals who did not consent and those under the age of 18, data from19,191 students and 437 instructors remained. Data for instructors and students were matchedusing R software version 4.0.3 (R core team, 2019), resulting in 17,912 survey responses fromstudents, as seen in Table 1.Table 1 Count of survey responses broken down by the gender, race, sexuality, and First-Generation CollegeStudent status of the student and instructor. Matched Social Student Instructor survey Grouping Size for MRM Marker responses
. A pre- and post-assessmentgiven with graded events enabled the faculty to classify the students into one of several groupsand make inferences as to their ability to achieve specific objectives. This ongoing work, whichis to be expanded in scope for future terms, may provide insights for identifying trends inlearning, specifically with regard to an engineering mathematics program.1. IntroductionIn some philosophical discussions, it is recognized as the Socratic Paradox, i.e. “knowing whatyou do not know,” – in this light, an individual is considered ‘better off’ knowing that and whatthey do not know, versus knowing that they do not know [1]. This brings about a number ofpedagogical questions for the classroom, some of which may lead to
ofvarious learning resources as well as their learning strategies, achievement goals andattributions). However, the focus of the current paper will be on answering the followingresearch questions: 1. How do engineering students use video tutorials in mathematics courses? 2. How frequently do engineering students use video tutorials in mathematics courses? 3. How helpful do engineering students find video tutorials in mathematics courses?The study was conducted in an advanced calculus course for engineering students at atechnical university in Germany. Important contents were differential equations and complexanalysis. In the first part of the course, basic existence and uniqueness theorems for solutionsto general ordinary differential
Page 26.410.2be due to the general character of the entrance exams. The written test, which is the main partof the entrance exam, focuses mainly on cognitive ability and logical thinking, and to a lesserdegree on mathematics and technical understanding. The study also revealed a positive,though weak, correlation between the grade point averages of our students and their universityentry score. The weakness of this correlation is in accordance with related studies, e.g. theinvestigation of the influence of the university entry score on the students’ performance inEngineering Mechanics. Thomas, Henderson, and Goldfinch3 found it impossible to reliablypredict student performance in first year Engineering Mechanics based on their overallperformance
the efficiency of collection and scoring of in-class work.DyKnow has the ability to retrieve panels from each student in a session, and this feature is idealfor collecting an electronic in-class problem. Figure 1 shows a sample in-class problem, Page 23.1330.4including student’s work, retrieved as a panel in DyKnow (student answer is in blue). In-class problem in moderator ink Student’s work in participant ink Figure 1: In-class Problem in
below. Themean rating of the helpfulness of the videos was 4.44 on a scale of 1 to 5, with 5 being the mosthelpful. Two-thirds of those who used the video recordings reported using them at least once aweek. In general, nearly all students made use of at least some of the electronic resourcesprovided to them, as shown in Figure 4. Figure 3: Reasons for student use of video recordings Page 23.720.6 Figure 4: Electronic resources used by studentsAll of the survey respondents were at least somewhat familiar with Maple prior to starting theDifferential Equations class, with a self-rated intermediate level
technical papers (published or accepted), in either journals (11), conference proceedings (33), or in magazines (1). He also actively consults with industry and is a member of ASME, SIAM and ASEE. Page 26.161.1 c American Society for Engineering Education, 2015 Advanced Undergraduate Engineering MathematicsAbstractThis paper presents the details of a course on advanced engineering mathematics taught severaltimes to undergraduate engineering students at the University of St. Thomas. Additionally, itprovides motivation for the selection of different topics and showcases related numerical
online textbook. They were given the MPE again at the end of the program. Ifthey increased their scores to meet the cut score of 22 out 33 correct, they were permitted toenroll in engineering calculus I. This study examines their responses to the surveys during thebridge program and their grades, including any correlations that exist among the variables.IntroductionAs technology advances continue to grow rapidly, there remains a need for a diverse engineeringworkforce throughout the world. Most engineering majors rely on a strong mathematicsfoundation. Specifically, being successful on college calculus courses has been crucial to earn anengineering degree [1]. However, most engineering freshmen entered college without havingnecessary
frustration.In order to test the ATCL methodology, it was implemented in a second calculus course offeredfor engineering students. To quantify its performance, the midterms and final exams’ grades andtheir pass rates were compared with those obtained in the previous year (2017) course, taughtusing traditional lectures. A detailed analysis of each of the topics evaluated in the exams is alsopresented in this work. In general, the results of the ATCL implementation are significantlybetter than those obtained from the 2017 lecture-based course.The rest of this paper is organized as follows. In Section 2, the ATCL methodology isintroduced along with all the details regarding the structure of each course session. Section 3presents our case of study, that is
and inclusionin STEM. The 14 instructors (see table 1) who participated represent a range of institutional roles(e.g., adjunct instructors, professors, and a department chair) and personal identities (e.g.,women, people of color, multilingual, first-generation college graduates). Efforts such as this PLC occupy a unique and underexplored research area supporting STEMfaculty to develop critical awareness to address inclusion and inequity. The field of STEM isparticularly unique regarding efforts to support diversity and inclusion because of the historicalmarginalization of women and people of color in particular (National Science Foundation, 2019;2020). In large part, the historical underrepresentation and marginalization of individuals is
an academic appointment in the Engineering Science and Education Department (ESED) at Clemson University. Prior to this Dr. Karen was at Oklahoma State University where she was a professor for 24 years in Chemical Engineering. She received her B.S. in chemical engineering from University of Michigan in 1985 and her M.S. in 1988 and Ph.D. in 1991 in chemical engineering both from Pennsylvania State University. Dr. Karen’s educational research emphasis includes faculty development and mentoring, graduate student development, critical thinking and communication skills, enhancing mathematical student success in Calculus (including Impact of COVID-19), and promoting women in STEM. Her technical research focuses on
• Gender.The backward-elimination method was used to generate the regression model; with DE grade asthe dependent variable, all independent variables were added to the model, and step by step,parameters were removed if they were not significant. Additionally, a logistic regression modelwith the independent variable DFW was also constructed with the backward elimination method.The significant variables that remained were used as covariates in the ANCOVA.The survey responses were reviewed by question type: (1) open ended questions were read forsimilarities, and frequent comments were highlighted, (2) the mean and distribution of Likert-scaleresponses were reviewed.ResultsThis study assessed the difference between flipped classroom design and the
, students were invited to complete a survey.The results of the pencast surveys are given in Table 1 and Figure 1 and Figure 2 below.Accesses in Table 1 was generated by counting the number of times a student clicked on aparticular pencast link through the course webpage. Table 1 shows that students thought thepencasts were easy to read, had just about the right number of steps, were easy to follow, and hadappropriate pacing.Table 1. Summary of pencast surveys and accesses Pencast # 5 6 7 8 Length 27:21 21:48 37:24 50:59 Accesses 137 139 157 138 Total Responses 63 60 63 36 Easy to Read 88% 93% 97% 81% Right Number of Steps 84% 85
12345 Confirm Student in Mentor Can Solve them Support Guide --- Apply Steps 3 4 5 in Support Guide M. Vitale, HISP Project, 6-29-17 Figure 3. Project Mentor Framework for providing mentor assistance.----------------------------------------------------------------------------------------------------------------------- Guidelines for Course-Specific Support Provided by Undergraduate Mentors 1. Mentor Preparation a. General Preparation (Need class syllabus, class textbook(s)) b. Class Specific Assistance Preparation
(9)This yields the one-dimensional wave equation ∂ 2u ( x, t ) 1 ∂ 2u ( x, t ) = 2 , (10) ∂x 2 c ∂t 2a fundamental example of a hyperbolic partial differential equation, where c = K λ is thespeed of wave propagation. The generalization of the above derivation to two and moredimensions is straightforward.ImplementationThe computer program written and implemented by the students in C# is structured in the fourmodules User Operation, Calculation, Visualization, and Error Handling. An essentialfeature is the declaration of the class Particle, because classes support inheritance
for Configuration Aerodynamics: A CaseStudy”, 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 2006.[8] D. L. Ashby, “Potential Flow Theory and Operation Guide for the Panel Code PMARC_14”,NASA/TM-209582, 1999.[9] J. McCune, H. Wachman, and E. Murman, “A Workshop on Teaching Fluid Mechanics withWorkstation Based Software”, Final Report, National Science Foundation, 1990.[10] F. Stern, T. Xing, D. Yarbrough, A. Rothmayer, G. Rajagopalan, S. P. Otta, D. Caughey, R.Bhaskaran, S. Smith, B. Hutchins, and S. Moeykens, “Hands-On CFD Educational Interface forEngineering Courses and Laboratories”, Journal of Engineering Education, 95(1), January 2006.[11] J. Cabral, “Is Generation Y Addicted to Social Media?”, The Elon Journal
courses, when context,application, and sometimes even notation can be quite different. This is often true forengineering students with respect to the Calculus sequence.In courses such as Calculus, concepts and solution methods are typically presented within amathematical context. While some students can recognize the underlying structure and themathematical construction, others have trouble identifying patterns or parallel thought structures,which makes it difficult for them to generalize the concept to a range of problem types. Forexample, students in an Introduction to Mathematical Statistics course were reported to claimthey do not know how to integrate a probability distribution over a region. The pre-requisite forthe course is Multivariable
thecourse made any significant difference in the student’s understanding of the underlyingMathematics and Engineering principles. Results from this study, generally encouraging, werepublished last year18.Having completed Pre-Algebra in 6th grade, algebra in 7th grade, Geometry on line during 8thgrade and Algebra 2 in class during 8th grade, one of the interview subjects was in an advancedlevel Pre-Calculus in 9th grade. He is at least two years ahead in math compared to the regular9th grade cohorts. The other subject was also at a somewhat accelerated math track and hascompleted Pre-Algebra in 6th, Algebra 1 in 7th, Geometry in 8th grade and was scheduled inAlgebra 2 in 9th grade while in the robotics course. The details of the interviews conducted
, wedevelop the mathematical models of the payoff for both the firm and the university.We further give a detailed analysis on the selection of collaborator on innovationproject from the perspectives of both sides, in both non cooperative game and co-operative game settings. Under that assumption that (1) at one time a firm canonly link to one university and a university can only link to one firm, (2) a firmor a university will not enter a collaboration relation if there is limited payoffs tothemselves, the following results are obtained: (1) In general a firm will choose to collaborate with larger universities with better reputations and more relevance to their innovation project. (2) A university’s relevance to a firm’s innovation
when the class clearly needed additional work on a lesson topic.Table 2 shows the number of units and number of lessons for each of the three courses. A web-based, online, multi-media content system provide by the textbook publisher was used to assignlessons and homework. The system provides algorithmically generated questions with the abilityto score those questions automatically even when answers are mathematical expressions.Students were assigned a score for each course component and a weighted sum determined theirclass weighted average, which in-turn was the basis for the course grade. These weights areshown in Table 1. The 10% weight for class activities (CA) is due the instructor’s concern thatstudents might easily dismiss the importance
, generalizing the connection between exponential and Poisson distribution, will beused to discuss their differences and to point out nuances in the wording of someprobability problems that yield different answers when both distributions are used. Lastly,a teaching tool for explaining central limit theorem is discussed based on guessingweights of books. This guessing game proves useful to explain sampling distribution.1. IntroductionAlthough it has been argued since 1960s [1] that probability and statistics is as importantas calculus as a mathematical foundation for engineering students who have to cope withuncertainty and variability in their professional careers, majority of engineering programsin North America have one course for both probability and
increased the level of distraction aswell. Even if computers were brought to class with the purpose of taking notes, or access classmaterial, too many students were using theirs for activities not related to the lecture (e.g. surfingthe web, checking emails, instant messaging, etc.). We knew we were not alone, as many of ourcolleagues were facing the same issues, but this was of little avail. [1,2]What we didIn 2013 we received a grant from our institution to “flip the classroom” and we decided to use itfor our 4 credit course in Ordinary Differential Equations. The main reasons were 1) both of ushad been teaching the course for several semesters, and 2) the natural structure of the lecture: model of differential equation à
University of Michigan in 1985 and she received her M.S. in 1988 and her Ph.D. in 1991 in chemical engineering both from Pennsylvania State University. Dr. Karen’s educational emphasis includes: fac- ulty development critical thinking, enhancing mathematics, engineering entrepreneurship in education, communication skills, K-12 engineering education, and promoting women in engineering. Her technical work and research focuses on sustainable chemical process design, computer aided design, mixed integer nonlinear programing, and multicriteria decision making.William Bridges, Clemson University Dr. Bridges’ primary professional interests involve the statistical aspects of research projects. He has collaborated extensively with
questions tofacilitate individual reflection during the narrative writing: 1. Describe your role in this experience. 2. What are your previous experiences with and/or attitudes toward pedagogical change in STEM? 3. Describe your general experience during the implementation of the online forum (e.g. likes, dislikes, surprises, frustrations, limitations, things to improve…) 4. How has this experience changed the way the instructor does his job? Consider how the following aspects of the instructor’s job may /may not have changed: a. Instructor use of classroom time b. Preparation outside of class Page 26.1226.7
) (2) (1) (0) score: Math Compre- 2 1 12 1 2.2 hension (50%) Concept Compre- 12 3 1 2.6 hension (30%) SAGE Technique 1 6 8 1 2.4 (10%) Technical Commu- 1 13 1 1 1.9 nication (10%) Weighted average score: 2.3In the EET program at Pittsburg State University, assessment data is regularly collected forETAC/ABET accreditation using the