Linda Sheffield, Regents Professor Emerita of Mathematics Education at Northern Kentucky University, is a co-author of Math Innovations, a middle grades mathematics series as well as the Javits-funded Project M 3: Mentoring Mathematical Minds and the NSF Project M 2: Mentoring Young Mathematicians, two series of units for elementary and primary students. She is also co-author of the Project M³ units, an author for the NCTM Navigations Series in grades 3-5, and senior author of Math Matters: Understanding the Math You Teach, Grades K-6. is an associate professor of mathematics education at Boston University, with expertise in mathematical discourse. For further information regarding her research projects and curriculum, please visit and Suzanne H Chapin #How to use wolfram mathematica for m3 math challenge professionalIn addition, as a consultant she provides professional development for teachers and administrators in school districts throughout the United States and presents annually at national and international conferences including invited keynote presentations. Gavin has written over 100 articles and book chapters on gifted mathematics education, is a member of the writing team for the National Council of Teachers of Mathematics Navigations series and has co-authored a series of creative problem solving books. Rosenbaum Leadership in Mathematics Award from the Association of Teachers of Mathematics in Connecticut (ATOMIC). Gavin’s awards include the 2006 Early Leader Award from NAGC, the 2012 Distinguished Researcher Award from the University of Connecticut, and the 2015 Robert A. She is also a co-author on the middle school mathematics textbook series, Math Innovations. This Project has received the NAGC Curriculum Division award for three consecutive years. Results again show statistically significant achievement gains for project students over the comparison group of students. Gavin is also the Director and Senior Author of the National Science Foundation Project M2, Mentoring Young Mathematicians, curriculum units for students in Kindergarten through Grade 2. Gavin and her colleagues also received the 2009 Research Paper of the Year award from Gifted Child Quarterly, the leading United States research journal in gifted education, for an article that reported the Project M3 research results. Department of Education research grant have won the National Association for Gifted Education (NAGC) Curriculum Division Award for six consecutive years. Research results show statistically significant mathematical achievement gains for the students in the projects over a comparison group of like-ability students. She is the Director and Senior Author of two multi-year curriculum research projects that involve the development of advanced mathematics units for mathematically talented students in Grades K-6. Katherine Gavin has over 30 years of experience in education as a mathematics teacher, math district coordinator, elementary assistant principal, and associate professor at the Neag Center for Gifted Education and Talent Development at the University of Connecticut. Transpose each side of the equation to get: This is not the usual linear algebra form of Ax = b. ''' calculate dy by 4-point center differencing using array slices \frac = v3\) # set last element by backwards differenceĭyf = (y - y)/(x - x) Y = np.sin(x) 0.1 * np.random.random(size=x.shape) Plt.plot(x,dy_analytical,label= 'analytical derivative') ''' the centered formula is the most accurate formula here ''' Print( ' Centered difference took %f seconds' % (time.time() - tc1)) Print( ' Backward difference took %f seconds' % (time.time() - tb1)) # set first element by forward difference dyb = (y - y)/(x - x) '''and now a backwards difference''' tb1 = time.time() Print( ' Forward difference took %f seconds' % (time.time() - tf1)) # set last element by backwards difference dyf = (y - y)/(x - x) ''' lets use a forward difference method: that works up until the last point, where there is not a forward difference to use. they are surprisingly fast even up to 10000 points in the vector. ''' These are the brainless way to calculate numerical derivatives.
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