Displaying all 22 results
- Conference Session
- Mathematics Division Technical Session 5: From Functions to Big Data–A Hands-on Challenge
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- 2020 ASEE Virtual Annual Conference Content Access
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- Jeffrey Lloyd Hieb, University of Louisville; Campbell R. Bego, University of Louisville
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- Mathematics
directed.Despite instructors’ aspirations, students who have mastered the procedural tasks for a givenexam – and even those who have gained a deep understanding of the relevant concepts – stillmake errors when working out answers to exam questions. Different types of exams handle thesemistakes differently. For the purpose of this paper, common math exam types are categorizedinto three groups: 1) essay, 2) multiple choice, and 3) computer assisted. These types varyprimarily along parameters of a) scoring entity and b) partial credit. Exam type is often selectedalong these parameters for practical reasons such as class size and grading time required (seeCherkas and Roitberg [2]).The possibility for exams to be used as formative assessments exists. One well
- Conference Session
- Mathematics Division Technical Session 5: From Functions to Big Data–A Hands-on Challenge
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- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Emre Tokgoz, Quinnipiac University; Hasan Alp Tekalp; Elif Naz Tekalp; Berrak Seren Tekalp, Quinnipiac University
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- Mathematics
evaluation when compared tothe APOS theory classification for quantitative classification. We invite other researchers to apply thetechniques that we used and introduced in this work to other empirical data sets for attaining measurableoutcomes.References[1] Asiala, M., Brown, A., DeVries, D. J., Dubinsky, E., Mathews, D., & Thomas K. (1997). A framework forresearch and curriculum development in undergraduate mathematics education. In J. Kaput, A. H. Schoenfeld,& E. Dubinsky (Eds.), Research in collegiate mathematics education II (p/. 1-32). Providence, RI: AmericanMathematical Society and Washington, DC: Mathematical Association of America. [2] Baker, B., Cooley, L., & Trigueros, M. (2000). A calculus graphing schema, Journal for
- Conference Session
- Mathematics Division Technical Session 5: From Functions to Big Data–A Hands-on Challenge
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- 2020 ASEE Virtual Annual Conference Content Access
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- Paran Rebekah Norton, Clemson University; Karen A. High, Clemson University; William Bridges, Clemson University
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- Mathematics
Paper ID #30878Towards creating motivationally supportive course structures forintroductory calculusDr. Paran Rebekah Norton, Clemson University Paran Norton is a lecturer in the School of Mathematical and Statistical Sciences at Clemson Univer- sity. She received her B.S. degree in Mathematics from the University of North Georgia in 2013, her M.S. degree in Mathematical Sciences from Clemson University in 2015, and her Ph.D. in Engineering and Science Education from Clemson University in 2020. She has taught introductory mathematics and statistics courses at Clemson University. Her primary research focuses on improving
- Conference Session
- Mathematics Division Technical Session 5: From Functions to Big Data–A Hands-on Challenge
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- 2020 ASEE Virtual Annual Conference Content Access
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- Rajendran Swamidurai, Alabama State University ; Cadavious M. Jones; Carl Pettis; Uma Kannan
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- Diversity
- Tagged Divisions
- Mathematics
from Auburn University in 2014. He is a contributor to the Australian Maths Trust, and member of the MASAMU international research group for mathematics.Dr. Carl Pettis Carl S. Pettis, Ph.D. Professor of Mathematics Department of Mathematics and Computer Science Al- abama State University Administrative role: Interim Associate Provost Office of Academic Affairs Alabama State UniversityDr. Uma Kannan Dr. Uma Kannan is Assistant Professor of Computer Information Systems in the College of Business Administration at Alabama State University, where she has taught since 2017. She received her Ph.D. degree in Cybersecurity from Auburn University in 2017. She specialized in Cybersecurity, particularly on
- Conference Session
- Mathematics Division Technical Session 2: Poster Presentations
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- 2020 ASEE Virtual Annual Conference Content Access
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- Alexander Henderson, San Jose State University ; Alexander Garcia, San Jose State University
- Tagged Divisions
- Mathematics
equations for the different parts of the bottle can thenbe determined based on these values. The straight sections of the bottle can be assumed to followthe format of the equation r=rn where r is on the x-axis and rn is the value on the x-axis itself.The curved sections follow the format for a quadratic equation (which states y=ax2+bx+c). Theconstants a, b, and c of this equation can be determined based on at least three random points offof each curve assuming that the y-axis goes straight through the middle of the bottle. Theequations for each of these curves can then be used to determine the volume of each individualsection by rotating these curves around an axis (in this case, around the h-axis). This can be doneby using the integral seen in
- Conference Session
- Mathematics Division Technical Session 3: Diversity in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- John Kerrigan, Rutgers University; Lydia Prendergast, Rutgers University; Jillian A.S. Mellen, Rutgers University; Geraldine L. Cochran; Antonio D. Silva
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- Diversity
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- Mathematics
class review/Q&A online Station #1 Station #2 online quiz quiz Three-station 10 min 10 min 40 min 40 min 40 min 10 min class review/Q&A online Station #1 Station #2 Station #3 online quiz (workshop) quizFigure 3. Class timeline (150 minutes)Learning Assistant Classroom SupportAn important part of the rotating station design was the availability of an undergraduate LearningAssistant (LA) provided by the University. Undergraduate students who qualify to become anLA have earned an A or B+ in the course they are an LA for, successfully
- Conference Session
- Mathematics Division Technical Session 1: Best Practices in Engineering Math Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- JaCoya Thompson, Northwestern University ; Sally P.W. Wu, Northwestern University; Jacob Mills, Evanston Township High School
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- Mathematics
, Ben. 2015.A Data Science Course for Undergraduates: Thinking with Data. The American Statistician, vol. 69, no. 4, pp. 334–342.[4.] Ben-Zvi, D. (2000). Toward Understanding the Role of Technological Tools in Statistical Learning. Mathematical Thinking and Learning, 2, 127-155.[5.] Chance, B. L., Ben-Zvi, D., Garfield, J., & Medina, E. (2007). The role of technology in improving student learning. Technology Innovations in Statistics Education,1 (1).[6.] Datta, Soma, and Veneela Nagabandi. 2017.Integrating Data Science and R Programming at an Early Stage. 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI), doi:10.1109/iscmi.2017.8279587.[7.] DiSessa, A.A. (2018
- Conference Session
- Mathematics Division Technical Session 1: Best Practices in Engineering Math Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- Charles Lam, California State University, Bakersfield; Melissa Danforth, California State University, Bakersfield; Ronald Hughes, California State University, Bakersfield
- Tagged Divisions
- Mathematics
assessments," Teacher Education and Special Education, vol. 29, pp. 261-274, 2006.[4] Q. Hang and K. Rabren, "An Examination of Co-Teaching: Perspectives and Efficacy Indicators," Remedial and Special Education, vol. 30, no. 5, pp. 259-268, 2009.[5] T. Moorehead and K. Grillo, "Celebrating the Reality of Inclusive STEM Education: Co-Teaching in Science and Mathematics," Teaching Exceptional Children, vol. 45, no. 4, pp. 50-57, 2013.[6] J. D. Orlander, M. Gupta, B. G. Fincke, M. E. Manning and W. Hershman, "Co‐ teaching: a faculty development strategy," Medical Education, vol. 34, no. 4, pp. 257- 265, 2000.[7] C. Rasmussen and J. Ellis, "Who is Switching out of Calculus and Why?," in Proceedings of the 37th Conference of
- Conference Session
- Mathematics Division Technical Session 3: Diversity in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- Nicholas A. Baine, Grand Valley State University
- Tagged Divisions
- Mathematics
(MATLAB),rearrange topics, and slow down delivery. The result is a course that many students rave about asthey are taking calculus and physics, and best yet, their average course GPA shows a half-to-fullletter grade improvement, which bodes well for retention.References[1] N. Klingbeil, K. High, M. Keller, I. White, B. Brummel, J. Daily, R. Cheville and J. Wolk, "The Wright State Model for Engineering Mathematics Education: Highlights from a CCLI Phase 3 Initiative," in 2012 ASEE Annual Conference & Exposition, San Antonio, TX, 2012.[2] N. Klingbeil, "The Wright State Model for Engineering Mathematics Education," [Online]. Available: https://engineering-computer-science.wright.edu/research/the-wright-state-model-for- engineering
- Conference Session
- Mathematics Division Technical Session 4: Assessing Success in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- Daniel Raviv, Florida Atlantic University; Daniel Ryan Barb, Florida Atlantic University
- Tagged Divisions
- Mathematics
lower levels, it encounters rows with more pegs, and theprobability of the marble ending up at (or near) the center column becomes higher compared tothe extreme left and right columns, as shown in Figure 7. Figure 7: Possible paths for the first few rows of the Galton Board If we count the number of possibilities for getting to a specific point, we get a chart asshown in Figure 8. This theoretically infinitely long chart is known as Pascal’s Triangle, and isdiscussed in Appendix B. Figure 8: The number of possibilities for each row of pegs Note that each number is the sum of the two numbers above it. For example, (referring tofigure 9) 4+6=10. This means that if there are 4 possible paths for a
Calculus Readiness and Retention Rates for Home-schooled Students in a Four-year Engineering Program
- Conference Session
- Mathematics Division Technical Session 4: Assessing Success in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- Danielle Marie Fredette, Cedarville University
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- Diversity
- Tagged Divisions
- Mathematics
case has been offered, but more specificsurvey data and/or qualitative study would be necessary to draw a firm conclusion as to whyhomeschoolers have such relatively high retention rates in undergraduate engineeringprograms.References [1] T. D. Snyder, C. de Brey, and S. A. Dillow, “Digest of education statistics 2014, nces 2016-006.” National Center for Education Statistics, 2016. [2] B. D. Ray, “Research facts on homeschooling,” 2019. [Online]. Available: https://www.nheri.org/research-facts-on-homeschooling/ [3] A. Hirsh, “The changing landscape of homeschooling in the united states.” Center on Reinventing Public Education, 2019. [4] M. F. Cogan, “Exploring academic outcomes of homeschooled students.” Journal of College
- Conference Session
- Mathematics Division Technical Session 1: Best Practices in Engineering Math Education
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Guenter Bischof, Joanneum University of Applied Sciences; Maximilian Brauchart, Joanneum University of Applied Sciences; Patrick Jenni, Joanneum University of Applied Sciences; Jeremias Pirker, Joanneum University of Applied Sciences; Julian Sachslehner, Joanneum University of Applied Sciences; Christian J. Steinmann, Joanneum University of Applied Sciences; Tobias Markus Zörweg, Joanneum University of Applied Sciences
- Tagged Divisions
- Mathematics
computer program for the numerical simulation and visualization ofdynamic vibration absorbers. Only minimum requirements were defined in the projectassignments and no limits were placed on the students’ creativity or on the amount of timethey should invest in order to complete the projects. This kind of creative freedom paired withthe competition between the teams led to one acceptable and two very presentable results. Allthe figures presented in this paper are based on the programs written by those latter twoteams, hereinafter referred to as “group A” and “group B”.Equations of motion of dynamic vibration absorbersDynamic vibration absorbers are widely used passive vibration control devices. They can berealized as a comparatively lightweight
- Conference Session
- Mathematics Division Technical Session 3: Diversity in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Shuvra Das, University of Detroit Mercy; Kirstie A. Plantenberg, University of Detroit Mercy
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- Diversity
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- Mathematics
25 23 20 18 15 13 9 10 7 6 4 5 4 5 1 0 A B C D F Figure 3: Grades in ENGR1234 Other Math classes taken with ENGR1234 50
- Conference Session
- Mathematics Division Technical Session 3: Diversity in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Lee Singleton, Whatcom Community College; Eric Davishahl, Whatcom Community College; Todd Haskell, Western Washington University
- Tagged Divisions
- Mathematics
smaller. Knowing this, C has to be the biggest because of how far the region is from its axis of rotation. With C it is rotating around y=4, which will create the most overlap, which will make it smaller. A is the next smallest, rotating around x=2. That will make a cup 3 shape. The largest is B, rotating around y=-5. This will create the biggest range and will cover the most of the graph. Since A's shape involves a larger area when turned around x = 2, it gives the largest 2 volume. Since C has a larger radius than B, it has the larger volume making B the smallest volume. The size of the hole in the washer determines the area since the
- Conference Session
- Mathematics Division Technical Session 2: Poster Presentations
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Daniel Raviv, Florida Atlantic University
- Tagged Topics
- Diversity
- Tagged Divisions
- Mathematics
littleattention to connecting the concept to reality. The paper focuses on two sets of examples: 1. Examples that are unrelated to time. These include (a) discontinuity in space, forexample water levels at different sides of the locks in Panama Canal, sharp change in elevationof sidewalks (known as curbs), length of unused paper towel or toilet paper, change in brightnesslevel from light to shadow and between intensity level of pixels in a digital image, (b) numericaldisplays, such as an abrupt change in the numerical display of an elevator’s floor, change indigital display of radio frequencies, (c) switch-based devices such as light switches, (d) audiofrequencies, such as audio frequencies of piano keys, and (e) cartoon-based and non
- Conference Session
- Mathematics Division Technical Session 2: Poster Presentations
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Anibal Sosa, Universidad Icesi, Colombia; Norha M. Villegas, Universidad Icesi, Colombia; Stephanie Celis Gallego, Universidad Icesi, Colombia; Diego Antonio Bohórquez, Universidad Icesi, Colombia
- Tagged Divisions
- Mathematics
able to: • Identify when an operation is closed (or which sets are not closed under an operation). An operation (*) is closed if given two elements a,b, of that set, the result of operating them, a*b, belongs to the set. Through given examples of certain sets in 𝑅𝑅 2 or in 𝑅𝑅 3 , in which the sum or the product for a scalar is not closed, it is sought that the student discover, among other things, why the bounded sets cannot be subspaces, and why zero has to be an element of every subspace. • Identify linearly independent sets (in 𝑅𝑅 2 and 𝑅𝑅 3 ), that is, those non-zero unitary sets of vectors, or those with two vectors that belong to non-parallel
- Conference Session
- Mathematics Division Technical Session 1: Best Practices in Engineering Math Education
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Nathaniel Rossi, Arizona State University; Adam R. Carberry, Arizona State University; Scott Adamson, Chandler-Gilbert Community College
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- Diversity
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- Mathematics
1. State name, occupation, course subject, level of students, and active learning methods utilized. How familiar are you with Peter Liljedahl’s research? 2. Describe what it was like using active learning methods in your classroom for the first time. a. What aspects of the methods were either effective or ineffective at achieving the learning outcomes for the lesson. b. How did the students respond to the methods? 3. What strategies have you used for developing classroom problems? a. [Ask this question only if the respondent notes they have used textbook problems] Do you have any recommendations or best practices in converting these types of
- Conference Session
- Mathematics Division Technical Session 3: Diversity in Mathematics Education
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Kathleen Marie Fick, Methodist University; Denise H. Bauer, Methodist University
- Tagged Divisions
- Mathematics
Mathematics Majors Homework Quiz – Other Types of Equations NAME: __________________________________ MAT 1050: College Algebra Score: ___________ a. Find a problem from the homework that 1. Solve the following: would be solved using the same process. 𝟒 𝟐 𝒙+𝟏 −𝟓 𝒙+𝟏 = −𝟒 b. Without solving, what mathematical cues caused you to choose that particular problem from the
- Conference Session
- Mathematics Division Technical Session 4: Assessing Success in Mathematics Education
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Johannah L. Crandall, Washington State University; Kristin Lesseig, Washington State University
- Tagged Divisions
- Mathematics
the past fiveyears?Which engineering disciplines are being pursued by students who take your classes?What programming languages do you personally use for your work or research?What programming languages do you use in your classes, either as a requirement or as a demonstration?Which, if any, mathematical modeling software do you personally use for your work or research?Which, if any, mathematical modeling software do you use in your classes, either as a requirement or as ademonstration?What languages and software do you feel are most crucial for engineering students' industrypreparedness?Other thoughts about mathematical and computational tool learning for (engineering) students?Appendix B: Student Survey InstrumentWhat is your academic major
- Conference Session
- Mathematics Division Technical Session 2: Poster Presentations
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- MiKyla Jean Harjamaki, Playful Learning Lab; Annmarie Thomas, University of St. Thomas; Krista Schumacher, University of St. Thomas; Abby Bensen, University of St. Thomas; Emma Michelle Monson, University of St. Thomas
- Tagged Divisions
- Mathematics
the followingstandards.CCSS.MATH.CONTENT.6.SP.B.5 [6]: Summarize numerical data sets in relation to theircontext, such as by: ● CCSS.MATH.CONTENT.6.SP.B.5.A: Reporting the number of observations. ● CCSS.MATH.CONTENT.6.SP.B.5.B: Describing the nature of the attribute under investigation, including how it was measured and its units of measurement. ● CCSS.MATH.CONTENT.6.SP.B.5.D: Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.ISTE Empowered Learner [7] ● 1c: Students use technology to seek feedback that informs and improves their practice and to demonstrate their learning in a variety of ways.ISTE Computational Thinker
- Conference Session
- Mathematics Division Technical Session 4: Assessing Success in Mathematics Education
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- 2020 ASEE Virtual Annual Conference Content Access
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- Rebecca Ann George, University of Houston; Weihua Fan, University of Houston; Daijiazi Tang, University of Houston
- Tagged Topics
- Diversity
- Tagged Divisions
- Mathematics
during which the surveys were administered.MeasuresThe survey consists of (a) section of demographic information and (b) section of questions onself-beliefs in success (academic self-efficacy and subjective values), academic engagement(efforts and persistence), learning climate, and achievement emotions (enjoyment, anxiety,hopeless, shame, and anger before, during, and after class). In (a) section, the demographicitems measure students’ gender (male= 0, female =1), age, race, major, academic year, andself-reported GPA. The (b) section includes 98 Likert-scaled items from 1 (strongly disagree)to 5 (strongly agree) and from 1 (not at all true of me) to 7 (very true of me). All Likert-scaled items were adapted from existing research [9]. Some
- Conference Session
- Mathematics Division Technical Session 2: Poster Presentations
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- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Yosi Shibberu, Rose-Hulman Institute of Technology
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- Diversity
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- Mathematics
Paper ID #29911Mathematics Content of an Undergraduate Course on Deep LearningProf. Yosi Shibberu, Rose-Hulman Institute of Technology Dr. Yosi Shibberu is professor of mathematics at Rose-Hulman Institute of Technology. He has taught undergraduate courses on data mining, machine learning, bioinformatics and computational biology. Dr. Shibberu spent a year at Jimma University, Ethiopia, as a Fulbright Scholar and formerly held the en- dowed chair for innovation in science, engineering and mathematics education at Rose-Hulman Institute of Technology. c American Society for Engineering Education, 2020