- Type C Type E Options description posing Derivative Type A - Type F information: (rate of change) Function Type B Type D - (magnitude)For each of the seven Types of items, there is an extra feature that allows a new rating accordingto three types of contexts where the information has to be stated. It could be the real environmentof Motion Context (MC) that has been studied in class through SimCalc, or it could be anotherreal magnitude involved in Other Context (OC), or it
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
vertical asymptote.b) Local maximum, local minimum and inflection points of f(x).c) Intervals where f (x) is increasing and decreasing.d) Intervals where f (x) is convex and concave.e) Please draw the graph of f ( x) = xx+1 by using the information you have in parts (a), (b), (c), and (d) if they are applicable. During the interviews, participants were initially asked to explain their answers briefly toall the parts (a)-(e) of the question and change the written information if it appears to beincorrect. If they made a mistake in one of the parts (a)-(d), participants were asked toanswer particular conceptual questions. If the graph was sketched in part (e) with no orpartial responses to the parts (a)-(d), these participants were asked to
and one requiring a written explanation). An example of a problem in thecategory of basic mathematics (numeracy) is the following (problem 1): “10% of the boys and10% of the girls at school play soccer. How many percent of all students in the school playsoccer? A) 5%, B) 10%, C) 15%, D) 20%, E) Cannot answer.” A problem from scientificmathematics (calculations with scientific notation and units) is (problem 2) “Complete thecalculation: s = vt = 3.0 ⋅ 108 m/s ⋅ 2.0 ⋅ 10-5 s = ”.The same mathematics test was used as pre- and post-test. The pre-test was administered inclass during the first week of first semester, before the physics course had started, and thepost-test was administered in the second week of second semester, which was the
Paper ID #12661STEM Majors’ Cognitive Calculus Ability to Sketch a Function GraphDr. Emre Tokgoz, Quinnipiac University Emre Tokgoz is currently an Assistant Professor of Industrial Engineering at Quinnipiac University. He completed a Ph.D. in Mathematics and a Ph.D. in Industrial and Systems Engineering at the University of Oklahoma. His pedagogical research interest includes technology and calculus education of STEM majors. He worked on an IRB approved pedagogical study to observe undergraduate and graduate mathe- matics and engineering students’ calculus and technology knowledge in 2011. His other research interests
Paper ID #12098Improving Performance in College Algebra Using TechnologyMrs. Judith A Komar, CEC/CTU Judy Komar is Vice President of Educational Technology at Career Education Corporation (CEC), a global provider of post-secondary education programs and services. She is responsible for providing innovative technology solutions for CEC students, developing content for more than 500 new courses annually and facilitating and integrating educational technologies for more than 45 CEC campuses. She also facilitates program development, academic requests, and institutional growth, as well as the continuous improvement of the
Paper ID #14208An Elective Mathematics Readiness Initiative for STEM StudentsDr. Janet Callahan, Boise State University Janet Callahan is the Founding Associate Dean for the College of Engineering at Boise State University and a Professor in the Materials Science and Engineering Department. Dr. Callahan received her Ph.D. in Materials Science, her M.S. in Metallurgy and her B.S. in Chemical Engineering from the University of Connecticut. Her educational research interests include freshmen engineering programs, math success, K-12 STEM curriculum and accreditation, and retention and recruitment of STEM majors.Ms. Judith A
, mechanical, aerospace, andchemical engineering involve the study of interconnected dynamic systems modeled bydifferential or difference equations, such as feedback control systems. The traditional frameworkfor the analysis and design of such systems is based on the transfer function, which modelssingle-input single-output (SISO) linear time-invariant (LTI) systems. It can be defined by takingthe Laplace transform of a differential equation (in continuous time) or the z-transform of adifference equation (in discrete time).In the continuous LTI case, the differential equation may be written the form ua(D) = yb(D),where u ∈ C∞ is the input signal, y ∈ C∞ is the output signal, a, b ∈ R[x] are real polynomials withb = 0, and D is the differential
students into a Calculus 1 course who would otherwise have started their firstsemester in a Pre-Calculus or College Algebra course. This is a significantly higher success ratethan has been found in other Pre-Calculus courses including other courses with an Emporium Page 26.1692.3model intervention. One possible reason for this is that the students are self selecting as moremathematically adept than their peers. Of those students, 16 enrolled in a Calculus 1 class in theFall semester and 10 of them successfully completed Calculus 1 with an A, B, or C. While this isan improvement in the passing rate over general Calculus 1 students, it is not
Paper ID #12520Precision Low-Cost Robotics for Math Education Work In ProgressDr. Ravi T. Shankar, Florida Atlantic University Ravi Shankar has a PhD in Electrical and Computer Engineering from the University of Wisconsin, Madi- son, WI, and an MBA from Florida Atlantic University, Boca Raton, FL. He is currently a senior professor with the Computer and Electrical Engineering and Computer Science department at Florida Atlantic Uni- versity. His current research interests are on K-12 education, engineering learning theories, and education data mining. He has been well funded by the high tech industry over the years. He
engineering education.Dr. Rafael Ernesto Bourguet-Diaz, Tecnologico de Monterrey BSIE minor in electronics (1983), MSEE (1994), and PhD AI (2003). Assistant Professor at Tecnologico de Monterrey, Department of Industrial and Systems Engineering. Research interest on: (a) knowledge re-utilization in corporate using System Dynamics and Systems methodologies, and (b) on hybrid envi- ronments for learning and teaching Mathematics and Systems Thinking. Page 26.302.1 c American Society for Engineering Education, 2015 Building Bridges between Mathematics and Engineering:Identifying
, 2004.[4] www.wolfram.com.[5] Wylie, C. R. and Barrett, L. C., Advanced Engineering Mathematics, 6th Edition, McGraw-Hill, New York, NY, 1995.[6] Kreyszig, Advanced Engineering Mathematics, 10th Edition, Wiley, Hoboken, NJ, 2011.[7] Kreyszig, Advanced Engineering Mathematics, 3th Edition, Wiley, Hoboken, NJ, 1972.[8] Klingbeil, N. W., and Bourne, A., “A national model for engineering mathematics education: Longitudinal impact at Wright State University,” 120th ASEE Annual Conference and Exposition, June 23-26, 2013.[9] Sun, C., Dusseay, R., Cleary, D., Sukumaran, B., and Gabauer, D., “Open-ended projects for graduate school- bound undergraduate students in civil engineering,” ASEE Annual Conference and Exposition, p 7647-7656
). Page 26.355.8Figure 2, using the same time axis, shows the university-wide pass rate in Calculus I, (number ofA, B, C grades divided by total 10th day enrollment.) The results show a clear correlationbetween the implementation of Coherent Calculus across multiple sections (beginning in Spring2014) and improved pass rates in the course. Calc I Pass Rate 80.0% 75.0% 70.0% 65.0% 60.0% 55.0% 50.0% Spring 2008 Spring 2009 Spring 2010
recommended not to use any devices or aids, like calculators,computers, or formula collections which they are not allowed to use during exams. Apart fromthe three interventions: everyday examples, the joint construction of definitions, and motivating Page 26.401.3application examples, the lectures are given in traditional fashion. The examination itself iscentrally designed and administered to all first-year students. The course literature consists of aSwedish book, Analys i en variable, by Persson and B¨oiers and Calculus: A complete course byAdams and Essex.Aim of the studyThe overarching aim of the study is to scaffold engineering students
-Mendívil, E. (2014). How can Augmented Reality favor the learning of Calculus? In H. R. Arabnia, A. Bahrami, L. Deligiannidis, & G. Jandieri (Eds.), Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (pp. 443–447). Las Vegas, Nevada, USA: CSREA Press. 3. Carvalho de Alencar, C. V., & Lemos, B. M. (2014). Possibilities of Augmented Reality use in mathematics aiming at a meaningful learning. Creative Education, 5(9), 690–700. 4. Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory Augmented Reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7
Excellent 'A' 2 77 – 89 Good 'B' 3 64 – 76 Satisfactory 'C' 4 51 – 63 Sufficient 'D' 5 0 – 50 Insufficient Failing grade 'E/F'The correlations between Engineering Mathematics grades and the final grades in EngineeringMechanics and other mathematically-oriented courses are illustrated in Figures 9 to 11. Thesedata were obtained from more than ten classes of the four-year degree program.Figure 9: Engineering Mathematics grades versus Engineering Mechanics grades for therespective semestersThe highest correlation coefficient was obtained for the case when both
ApplicationsAbstract In this paper an example of a method to present a basic numerical analysis method’s such as the Secant Method, Bisection method and the Regula Falsi Method is described in the way it is used in sustainable energy application. A solar panel is examined and students are provided with its P-V characteristic curve. The arbitrary function f(x), that was the target of finding the root for in a numerical analysis textbook, is no longer a function without any significance (Fig. 3). It becomes a derivative of the P-V characteristic curve which has a root that corresponds to the maximum power point for efficient power extraction of the solar panel. This can be applied to wind energy, fuel cells and so on.Introduction The need for