Enhancing Interdisciplinary Interactions in the College of Engineering and Natural SciencesIntroduction and Project GoalsA team of faculty members in the College of Engineering and Natural Sciences at The Universityof Tulsa (TU) began work in July 2004 on a National Science Foundation (NSF)-funded Course,Curriculum, and Laboratory Improvement (CCLI) Project (Proposal # 0410653). This two-yearproject was based on the use of Interdisciplinary Lively Application Projects (ILAPs)1 as avehicle for strengthening connections among science, engineering, and mathematicsdepartments2. The concept of ILAPs originated from a consortium of 12 schools led by theUnited States Military Academy (USMA) with an NSF funded project, Project
∞ ( a ) r ( b) r ( x k )2 F1 ([ a , b],[ c], x ) ≡ ∑ k , (1) r=0 ( c) r r !where ( a ) r is the Pochhammer symbol 4, for which Γ (a + r )( a ) r = a (a + 1)(a + 2)...(a + r − 1) = , (2) Γ (a )and Γ denotes the gamma function given by the Euler Integral of the second kind 3.Hypergeometric functions are solutions to the hypergeometric differential equationz(1 − z) y ′′ + [c − (a + b + 1) z] y ′ − aby = 0 . (3)Using the Froebenius method, the complete solution to this
, at least peripherally, to provide the students some familiarity with thetechnical issues involved in the quantitative models. However, this subject (PDEs) is vast andcomplicated, and compromises have to be made in incorporating it into the undergraduate'scurriculum. A 2-semester course that deals honestly and rigorously with the subject is out of thequestion.The compromises presently employed in engineering programs at undergraduate institutions are: (1) A short treatment of PDEs that relies completely on numerical solvers; or (2) A brief tutorial that covers the basics of the separation of variables technique.Each of these is unsatisfactory. (1) is inferior to (2) because, even with the graphic capabilitiesof today's hardware
-position method) and Gauss Elimination/ Cramer’s Rule. Samples of some of themethods emphasized during the course are:Euler method ….. y(ti+1) = y(ti) + f(ti, yi)(ti +1 – ti) or new value = old value + slope x step size. where f(ti, yi) represents the slope or derivative at (ti, yi) and (ti+1 – ti) is the step size.Taylor series…. Backwards difference: f’(xi) = f(xi) – f(xi -1) h Center difference: f’(xi) = f(xi+1) – f(x-1) 2h Forward difference: f’(xi) = f’(xi+1) – f(xi) h h = step size (xi+1 – xi)Bracket methods… False position: xr = xu – f(xu)(xl
. The first involves a simple RC series circuit (modeled by afirst-order linear differential equation), and the second involves a single-degree-of-freedom Page 11.1205.2forced mass-spring-dashpot system (modeled by a second-order linear differential equation).Although these simple systems are well understood, they are new to the students, and they formthe basis of more complicated models.The RC circuit is illustrated in Figure 1. Using some basic facts from circuit theory, one canreadily derive the following differential equation to model this circuit: dQ 1 R
mechanics course which we taught during an eight month period over theinternet to Master’s level students at Boeing Aircraft Company1 . It was hoped that this wouldgive the students (future theoretical and experimental researchers in the fluids area) a soundunderstanding of (1) the derivation and limitations of the fluids equations, (2) the classicallinear and non-linear mathematical methods for solving the fluids equations, and (3) variousnumerical methods for solving the fluids equations. The mathematical challenges faced by thestudents included learning both classical mathematical techniques and numerical techniques forsolving linear and non-linear time dependent partial differential equations in various orthogonalcoordinate systems. In this
program levels. Following Cobb et. al.20, the EDE consists of three stages:preparation, experiment, and retrospective analysis.Preparations for a design experiment include the following activities:1. Identifying learning behaviors, which can indicate the contribution of the proposed learning method and research instruments to be used for measuring the outcomes.2. Determining the concepts and subjects, in which the proposed approach can lead to Page 11.779.3 efficient learning. This can possibly affect developing new frameworks that match the approach.3. Ascertaining, students’ prior knowledge and attitudes and specifying their prospective
inarchitectural design. The course contents for each direction were selected from architecturaleducation literature or recommended by the architects, as presented below:1. Arranging regular shapes to cover the plain (tessellations)Boles and Newman13 developed a curriculum which studied plain tessellations arranged bybasic geometrical shapes with focus on proportions and symmetry. Applications of Fibonaccinumbers and golden section in designing tessellations were emphasized. Frederickson14studied geometrical dissections of figures into pieces and their rearranging to form otherfigures, using two methods: examining a shape as an element of the module, and examining avertex as a connection of elements. Ranucci15 studied mathematical ideas and procedures
college.Focusing on college algebra and trigonometry is especially important to the education andretention of students of color. Consistent with overall standardized test score results inWisconsin, students of color at UWM tend to score lower on the mathematics placement exam.In fall 2005, twelve students enrolled in the initial offering of this pilot course. It consisted of 11male students and 1 female student. Two of the twelve students were disadvantaged minoritystudents. Page 11.765.2Overview of Pilot CourseIn our previous paper1 we presented the conception and design of an engineering applicationscourse; we discuss the implementation of this
byinfinite planes, with information extracted from infinitely many pairs of boundary voltagepotentials, requires an understanding of infinity well beyond the intuitive.Take, for example, two digital signals, or mathematical sequences: {x(n)} = {…, x(-2), x(-1), x(0), x(1), …, x(n),… } {y(n)} = {…, y(-2), y(-1), y(0), y(1), …, y(n),… }Produce a third signal, or sequence, by discrete convolution: 3 z(n) = 5 x(k) y(n 2 k) for 1n 4{...,2 2,2 1, 0, 1, 2, 3,....} k = 23We feel that our students have difficulty grasping the meaning of minus infinity in this formulafor {z(n)}. An integral from minus infinity to plus infinity can be
about size, order or position. Their crudestuse is as identifiers such as are seen on the backs of football players. In this use, they are neveradded, subtracted or compared. Numbers used to identify houses as street addresses not onlyidentify but also provide information about the order of the houses on the street and also indicateon which side of the street houses are situated. Numbers are categorized by their algebraic ortopological (distance) properties.Kinds of numbersThe numbers, which are used to indicate the size of sets of discrete (separate and distinct)objects, are the natural or counting numbers (1, 2, 3 etc). Children learn the counting numbersand their order in the same way they learn the alphabet, by memorization. Later they
introduced to enhance thestudents’ learning and appreciation of Fourier Series and the FFT process. These two items arediscussed briefly below. Page 11.771.3III.1 Spectral Processing using a Dedicated FFT AnalyzerSeveral laboratory based projects have been used for over a decade. These labs introduceconcepts of Fourier transforms, Fourier series, Fast Fourier Transforms, spectral processing withnoise, harmonics and related topics, including frequency response measurements for mechanicaland electrical systems. These labs use dedicated FFT analyzers to address these issues as part ofa very well-scripted laboratory procedure. Using the FFT analyzer
0 1 02 3 04 05 9 9 99 Sp 200 Sp 200 00 19 19 19 19
quizzes every week. Otherapproaches were using journals, group problem solving, etc. In ECE 412 it was decided to use Page 11.1158.2quizzes. Figure 1 shows in a graphic form this process. Figure1. Learning flow1Time management is a problem that gets enhanced in our commuting campus. The majority ofour students work part-time and many full-time in order to survive. Therefore we compete fortime and involvement. One way to attract and get students involved is by giving weekly quizzes.These quizzes are straight forward and related to the material covered in class the week before.QuizzesEleven quizzes were given
Tecnologico de Monterrey and a doctoral degree in Mathematics Education from Syracuse Univer- sity, NY. Dr. Dominguez is a member of the Researchers’ National System in Mexico (SNI-1) and has been a visiting researcher at Syracuse University, at UT-Austin and at Universidad Andres Bello. She teaches undergraduate courses in Mathematics, graduate courses in Education, and is a thesis advisor on the master and doctoral programs on education at the Tecnologico de Monterrey. Her main research areas are: models and modeling, use of technology to improve learning, gender issues in STEM.Prof. Genaro Zavala, Tecnologico de Monterrey, Monterrey, Mexico, and Universidad Andres Bello, Santiago,Chile Genaro Zavala is Full Professor of
. c American Society for Engineering Education, 2017 MOSL: An Innovative Approach to a Supplementary course of Mathematics in Engineering.1. IntroductionSupplementary education has been the traditional method used by professors to help students whodo not have adequate preparation for college. According to Grubb [4], supplementary or remedialcourses are defined as a set of activities intended to meet the needs of students who do notinitially have the skills to perform well at a regular level. Currently, most universities offer thesetype of courses in a variety of formats. In Latin America, the deficiencies of most high schoolmath courses are shocking. This is especially true in developing countries, such as
is within the College of Engineering and NaturalSciences at The University of Tulsa, so my observations are relevant with respect to calculus forengineering students.Much has stayed the same, but the use of technology, student demographics, studentacademic/social support, the curriculum, and the way calculus is taught are some things that havechanged, comparing my calculus experiences from 1967 to those of my students in 2016. Not allthe changes appear to be for the better, and there are tradeoffs. The discussion focuses primarilyon anecdotal examples, although some statistical data are included.1. IntroductionThere are studies on the teaching of calculus at the university level that give detailed histories ofthe pedagogical changes over the
alone (Shea, Lubinski, & Benbow, 2001).Project Talent, undertaken in the US in the 1960s, involved the administration of a battery ofpsychometric tests over a one week period to a very large sample of high school students.50,000 males and 50,000 females were recruited from each of grades 9 to 12 (i.e. total n =400,000) to participate in the study and they were tracked over time (1, 5 and 11 years afterthe initial tests) to determine whether or not they pursued higher education and, if so, whatcourses they selected and the highest level of qualification they achieved. Results showed amarked difference in the verbal/spatial/mathematical ability profiles (as measured in highschool) of those who were destined to pursue a humanities social
. TheMathematical Association of America has created a subcommittee on “Curriculum Renewalacross the First Two Years” (project CRAFTY) [1]. The MAA has also published a summary ofresults from the NSF-sponsored project [2] and two reports which focus on determining themathematical needs of partner disciplines [3]. Several new directions have emerged, and themost relevant ones can be grouped into three areas: Calculus re-sequencing [4] [5], activelearning methods [2] [6] [7] [8], and applications from engineering & sciences [9] [10] [11] [12].This paper discusses a calculus redesign project that is in progress in the School of Engineeringand Applied Sciences at the University of Virginia. It will focus on the following questions: 1. How did the
preparation in mathematics, which has been shown to predict student success in engineeringschool [1, 2, 3]. It is also widely acknowledged that calculus in particular is a significant barrierfor many undergraduate engineers, because many students who do not perform well in their firstsemester of mathematics do not stay in an engineering major [4, 5]. This is a significant challengefor all engineering schools, since calculus is the basis for higher level engineering concepts, andis therefore generally taught at the beginning of engineering programs. It is important to study first year student retention in engineering programs because of thelarge number of reasons that students may leave in their first year. However, it is also important tolook
participants were enrolled. Six of the participants tried to use a technique to determine the solution to the research question by using either a numerical or an integration technique that they know. These students’ used either Integration by Parts or series expansion of the integrand to determine the numerical value of the given integral. Figure 1: Written response of RP 3 Figure 2: Written response of RP 13 Figure 3: Written response of RP 17 Figure 4: Written response of RP 12 Figure 5: Written response of RP 5 Figure 6: Written response of RP 6The rest of the participants did not try to calculate the integral and
a world where computing and computing technologies are growing at an ever-increasing rate, students need meaningfully situated opportunities to learn how to thinkcomputationally. Defined as a creative way to approach tasks or problems using concepts,practices, and perspectives from computer science, computational thinking holds promise for alllevels of education, especially K-12 classrooms [1]. Efforts to advance computational thinking ineducation include increased attention to the dispositions that people display when engaging incomputational thinking [2]. The study described in this paper extends these efforts by examiningthe impact of a summer professional development institute on teachers’ computational thinkingdispositions. As
determine which studentsare more likely to persist in engineering or leave the engineering degree program.IntroductionIn the nation, the engineering retention rate is consistently reported to be below the nationalaverage for higher education retention at around 50 percent [1] - [6]. This low retention numberis placing a growing demand on the higher education system to keep and produce more engineers[7] - [9]. There are numerous reasons students leave engineering that range from student issues toinstitutional issues, but one of the leading causes has been attributed to the coursework thatengineering students are required to take early on in their program [3], [10] - [12]. These earlycourses include a series of math courses typically made up of 2 or
what we have experienced.Keywords: statistics, undergraduate, technology, online classroomIntroductionWe have become a data-driven society [1]. In any discipline, digitalization has made theknowledge and understanding of statistics necessary [2]. The University of HoustonMathematics department realized the need of a statistical course that can accommodate severalmajors but still have the prerequisite of calculus. Previously, there was a course called“Statistics” that had a prerequisite of “Probability.” In 2009 the math department at theUniversity of Houston (UH) changed the prerequisite to only requiring Calculus 2. The namechanged to Statistics for the Sciences and then became a “service course” for students that werein other disciplines
ESCC team in mechanical engineering (ME) had already designed an effectivecore engineering curriculum almost a decade before this time. It had to make changes accordingto this new focus. The effort in the present paper is to discuss the role of mathematics forimplementation of such a T-shaped curriculum.ME students learn a significant amount of applied mathematics to succeed functionally. How canthe presentation style of conventional mathematical topics be improved so that students becomebetter learners, and also retain mathematical thoughts for life? This is the research focus now.We present an archived multiple choice (MC) examination question to begin discussion.Fig.1 Student performance assessment example from a Dynamics final
readyfor college-level mathematics, rather than for calculus placement. The highest level ofAssessment and Learning in Knowledge Spaces (ALEKS) is pre-calculus. The MAA MaplesoftPlacement Testing Suite offers both Calculus Readiness and Calculus Concept Readiness Tests,but no distinction between Calculus I and Calculus II or Multivariable Calculus. In addition,both Texas A&M and the New Jersey Institute of Technology use math placement tests, butthese tests are focused on determining proficiency in pre-calculus because they are onlyinterested in evaluating readiness for Calculus I. See [1], [2], [3].Hsu and Bressoud [4] reported on placement policies and strategies across a variety ofinstitutions. As a PhD granting institution with below
Engineering Education, 2019 Mechanical Engineering Organized Around Mathematical SophisticationThis paper describes a work in progress. It is applying a proven, NSF funded problem-solvingapproach to a new and important demographic of underrepresented minority students. Those thataspire to become engineering majors, but are not calculus ready. The work will determine if itincreases success for that population. The intervention, called the Conservation and AccountingPrinciples or CAP, is applicable to all Engineering Science (ES) [1]. The CAP unifies theapproach to ES problems and has Algebraic, Trigonometric and Calculus formulations. The CAPallows a student to solve real world (Authentic) problems in
mathematics skills from 1-NotVery True to 5-Very True. These questions were developed using a study that was originally done at TheOhio State University but were adapted to fit the requirements for this project (Harper, Baker, &Grzybowski, 2013). The two key questions posed in the survey are these:• How important is it for students from the University of Toronto undergraduate engineering program to be able to competently apply mathematics concepts from each of these areas listed?• How competent (i.e., what level of competence to you perceive) is the average student from the University of Toronto undergraduate engineering program in the following areas?The survey was administered through the Dean’s office to all faculty; an introductory
and I have worked in the following lines of work: 1. teacher training and teaching managers, 2. education in mathematics , science and technology (engineering), 3. the evaluation of / for the / and as learning, 4. the design, revision and / or adaptation of didactic or instructional materials, and 5. pedagogical advice in research and innovation in the classroom (docents practices). Currently, I am a consultant and my topics of interest are the research in the classroom, particularly the study of teaching practices as generators of networks and learning commu- nities, the relationships between science, technology, society and culture, and the evaluation of programs and educational policies. I believe that my
details. The paper introduces thisconcept using (1) Examples, such as a Galton Board and flipping coins, (2) Visualizing basicconcepts and some key concepts, (3) Real-life, experience-based examples such as heightdistribution, (4) A puzzle involving a multiple-choice exam, and (5) An in-class experiment ofrolling a die. It should be noted that this paper is a work in progress. In addition, this method ofteaching is meant to be supplemental and not to replace existing textbooks or other teaching andlearning methodologies. The work in this paper has been presented to 21 students in a Probabilityand Statistics classroom setting. Following the presentation, it has been assessed and receivedvery positive feedback. This visual, intuitive, and