related to that lesson or previous unit lessons. The Unit 2 Lesson6 Class Activity handout is shown in Appendix B. If the instructor felt that a specific student wasnot working productively during class, they could lower the class activity score for that day, andstudents were warned of this. The instructor had to warn a few students, but never had to alter aclass activity score.3.5 CALC-II-2TThree changes were made to CALC-II-2T: 1) the number of semester exams was reduced byalmost 50%, and 2) the instructor switched to an online system for administering RATs and 3) ateam component was added to the RATs.The number of semester exams was reduced because the instructor saw that each day most of thestudents were fully engaged with trying to
Engineering. Dr. Callahan received her Ph.D. in Materials Science, M.S. in Metallurgy, and B.S. in Chemical Engineering from the University of Connecticut. Her educational research interests include leadership, institutional change, engineering and STEM retention, and engineering, materials science, and mathematics education.Ms. Jocelyn B. S. Cullers, Boise State University Jocelyn B. S. Cullers is a Data Analyst at the Institute for STEM & Diversity Initiatives at Boise State University. c American Society for Engineering Education, 2017 Calculus Reform – Increasing STEM Retention and Post-Requisite Course Success While Closing the Retention Gap for Women and
, even though theseare rigorous courses for science, engineering, and mathematics majors, and most of the studentsare excellent).In the late 1960s, Columbia University had three distinct calculus sequences: Calculus SequenceA, supposedly the most computational and easiest; Calculus Sequence B, more theoretical andharder (primarily for engineers and physics majors); and Calculus Sequence C, for the mostinterested and talented students. As a physics major, I was in the calculus sequence B.In spite of (or maybe because of) the comments on my mathematics work, I eventually obtainedmy Ph.D. in mathematics. After a total of over thirty years of teaching calculus, and inobservance of my fiftieth year anniversary of having taken my first calculus course
to similar problems on the exams. • I will find a small number of general principles, learn them well, and apply these principles to solve all the exam problems. • I will practice a large number of homework problems on a regular basis, and then practice will enable me to solve the exam problems. • I will read the calculus textbook and use what I learned to solve exam problems. • I will study in a group where we will teach each other to solve different types of problems.I believe the way I studied mathematics in high school will enable me to earn an A or B incollege calculus. (4-point scale: strongly agree, agree, disagree, strongly disagree)Which of the following most accurately describes how I will need to study college
included in the INST are shown in Figure 1. Fig. 1. (a) Sample questions in the INST, original version in Spanish. (b) Translation to English.Since our goal was to detect those students with the highest probability of failure in calculus, theproblems selected to construct the INST evaluated only the most basic concepts in the areaspreviously mentioned. Even more, our test was divided in 4 sections, where each one contained10 questions about basic concepts, operatory skills and word problems (applications). Thosestudents who did not obtain a satisfactory grade (less than 60 out of 100 points) in this test wereenrolled in the Math Operatory Skills Laboratory (MOSL). MOSL is our approach to
following numeric values: A+ 4.3 B+ 3.3 C+ 2.3 D+ 1.3 F 0.0 A 4.0 B 3.0 C 2.0 D 1.0 W -1.0 A- 3.7 B- 2.7 C- 1.7 D- 0.7Analyses were performed both with and without numerical W’s included in the analysis.To identify significant covariates for DE Grade, potential independent variables were investigatedusing the traditional lecture data set. The potential variables included: • ACT Math, • Prior GPA (in previous math courses), • Number of Repeats (how many times students repeated previous math classes), • First Time (whether or not this was the first time students had taken DE), and
intervention underway is flipping the Differential Equations course. Onthe other hand, since Engineering Analysis II has students both dropping out and repeating it, somecareful analysis of the current structure of the course and in-class activities warrants more attentionand reflection.Conclusions This paper reviewed two cohorts of students progressing through the mathematics sequenceat the University of Louisville J. B. Speed School of Engineering. Data verified other research andshowed that attrition is highest in and after the second semester, but it also identified somebottlenecks in later courses that cause students to repeat courses, possibly delaying their graduationdates. This analysis looked at a multidimensional dataset of student
1.043 .307 19.717 .000 .335 .563Table 4. Results of two-way repeated measures ANOVA to check for interactionbetween gender and spatial ability. (a) (b) (c) (d) (e)Figure 3. Graph of the interaction of gender and spatial ability level on (a) MPT, (b)GPA, (c) SAT Math, (d) ACT Math and (e) ACT SCIRE.Finally, a correlation matrix is presented in Table 5 to show the extent to which each of thetest measurements correlate with each other based on the full data set n 2 3 4 5 6 1. MPT 1053
Paper ID #19737The Impact of a Flipped Math Course on Peer LearnersDr. Gianluca Guadagni, University of Virginia PhD in Mathematics University of Virginia Lecturer, Applied Mathematics, Department of Engineering and Society, School of Engineering and Ap- plied Sciences, University of Virginia.Dr. Bernard Fulgham, University of Virginia Bernard Fulgham received his PhD in Mathematics in 2002, writing his thesis in the field of non-associative algebras with advisor Kevin McCrimmon. He began teaching Applied Mathematics at the University of Virginia in August 2004 and became a full-time Lecturer in 2006
Paper ID #18552Calculus I Course Policy Changes and Impact on Various Demographic Stu-dent Group SuccessMrs. Paran Rebekah Norton, Clemson UniversityDr. Karen A. High, Clemson University Dr. Karen High is the Associate Dean for undergraduate studies in the College of Engineering, Computing and Applied Sciences at Clemson University. She also holds an academic appointment in the Engineering Science and Education department and joint appointments in the Chemical and Biomolecular Engineer- ing department as well as the Environmental Engineering and Earth Sciences department. Prior to this Dr. Karen was at Oklahoma State
) training on active learning andcollaborative methodologies in a two-day long workshop that would enable them to implement itin their classrooms. The two PD facilitators had extensive experience in active learning both inteaching engineering and mathematics courses for undergrads and in professional developmentfor university instructors. The objective of this workshop was three-fold: a) To gather data to know the instructors’ initial beliefs about teaching11. b) To introduce instructors to constructivism and active learning as a methodology that can be used in mathematics. That is, making participants aware that traditional teaching often does not foster learning and that a student-centered teaching strategy has a better chance
., and Thomas K. (1996). A framework for research and curriculum development in undergraduate mathematics education. In J. Kaput, A. H. Schoenfeld, and E. Dubinsky (Eds.), Research in collegiate mathematics education II (pp. 1-32). Providence, RI: American Mathematical Society and Washington, DC: Mathematical Association of America.2. Clark, J. M., Cordero, F., Cottrill, J., Czarnocha, B., DeVries, D. J., St. John, D., Tolias, G., and Vidakovic, D. (1997). Constructing a schema: The case of the chain rule?, Journal of Mathematical Behavior, 16, 345-364.3. Cooley, L., Trigueros M., and Baker B. (2007). Schema thematization: A theoretical framework and an example. Journal for Research in Mathematics Education, 38(4
6.5correct answer. The other 39 students selected the incorrect answer of . 5 Given the triangle in the diagram, compute the value of tan(𝛼). 5 5 A. B. 6.5 7.5 6.5 D. Cannot calculate tan(𝛼
Paper ID #19533Integrating STEM and Computer Science in Algebra: Teachers’ Computa-tional Thinking DispostionsMrs. Bailey Braaten, The Ohio State University Bailey Braaten is currently a doctoral student at the Ohio State University, where she is in her second year of the STEM education PhD program. She is a graduate research assistant on the STEM+C NSF funded project, looking at integrating computer science and engineering concepts into algebra classrooms. Bailey received her BS in mechanical engineering from Ohio Northern University and her M.Ed. in curriculum and instruction from University of Cincinnati. Her
develop the confidence in their own ability to do mathematics and to make mathematics a joyful and successful experience.Dr. Gianluca Guadagni, University of Virginia PhD in Mathematics University of Virginia Lecturer, Applied Mathematics, Department of Engineering and Society, School of Engineering and Ap- plied Sciences, University of Virginia.Stacie N. Pisano, University of Virginia, School of Engineering and Applied Science After receiving a Master of Science in Electrical Engineering from Stanford University, Stacie Pisano worked as an Electrical Engineer and Technical Manager at AT&T and Lucent Technologies Bell Labo- ratories for 16 years, designing and developing telecommunications equipment for the