Literature Index

Displaying 121 - 130 of 3326
  • Author(s):
    Doerr, H. M., English, L. D.
    Year:
    2003
    Abstract:
    A modeling approach to the teaching and learning of mathematics shifts the focus of the learning activity from finding a solution to a particular problem to creating a system of relationsips that is generalizable and reusable. In this article, we discuss the nature of a sequence of tasks that can be used to elicit the development of such systems by middle school students. We report the results of our reserach with these tasks at two levels. First, we present a detailed analysis of the mathematical reasoning development of one small group of students across the sequence of tasks. Second, we provide a macrolevel analysis of the diversity of thinking patterns identified on two of the problem tasks where we incorporate data from multiple groups of students. Student reasoning about the relationships between and among quantites and their application in related situations is discussed. The results suggest that students were able to create generalizable and reusable systems or models for selecting, ranking, and weighting data. Furthermore, the extent of variations in the approaches that students took suggests that there are multiple paths for the development of ideas about ranking data for decision making.
  • Author(s):
    Petruccelli, J. D., & Nandram, B.
    Year:
    1993
    Abstract:
    We describe an NSF-funded project to develop a new curriculum for introductory statistics for engineering, science and management students. The goals of the curriculum are to get students to think critically about data, and to demonstrate the role of statistics in scientific investigation. The curriculum features a number of one-week modules each keyed to project and laboratory experience. The modular structure offers flexibility in course design and gives students the ability to tailor the course to individual needs. The learning environment is problem-driven and alternative modes of delivery are emphasized.
  • Author(s):
    Easterday, K., & Smith, T.
    Year:
    1992
    Abstract:
    Proposes an alternative means of approximating the value of complex integrals, the Monte Carlo procedure. Incorporating a discrete approach and probability, an approximation is obtained from the ratio of computer-generated points falling under the curve to the number of points generated in a predetermined rectangle. (MDH)
  • Author(s):
    Danielle N. Dupuis, Amanuel Medhanie, Michael Harwell, Brandon LeBeau, Debra Monson, and Thomas R. Pos
    Year:
    2012
    Abstract:
    In this study we examined the effects of prior mathematics achievement and completion of a commercially developed, National Science Foundation-funded, or University of Chicago School Mathematics Project high school mathematics curriculum on achievement in students’ first college statistics course. Specifically, we examined the relationship between students’ high school mathematics achievement and high school mathematics curriculum on the difficulty level of students’ first college statistics course, and on the grade earned in that course. In general, students with greater prior mathematics achievement took more difficult statistics courses and earned higher grades in those courses. The high school mathematics curriculum a student completed was unrelated to statistics grades and course-taking.
  • Author(s):
    Chromiak, W., Hoefler, J., Rossman, A., & Tesman, B.
    Editors:
    Gordon, F., & Gordon, S.
    Year:
    1992
    Abstract:
    The authors of this paper represent a variety of disciplines and levels of experience in teaching statistics.
  • Author(s):
    Bulmer, M.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Some students in a service statistics course struggle with the material because they focus too much on the mathematical details and miss the broader issues and relevance to their degree program. It has proved useful for them to have the lecturer narrate a story, which gives a broad overview of the area while simultaneously drawing a rough concept map as an illustration. Of course this is very time consuming and impractical for large classes. We are currently developing and trialing a computer-based version of this setting, creating an interactive concept map with a narrative that students can follow as needed.
  • Author(s):
    Hawkins, A. S.
    Editors:
    Davidson, R., & Swift, J.
    Year:
    1986
    Abstract:
    In the United Kingdom, the Statistics Prize is one of many school competitions catering to a wide range of disciplines and types of pupils. It therefore vies for interest in the schools and also among potential sponsors. This paper discusses the benefits, as well as the problems and misconceptions that have occurred since the establishment of the competition.
  • Author(s):
    Keeler, C., & Steinhorst, K.
    Year:
    2001
    Abstract:
    The probability unit in a first statistics course is difficult to teach because there is not much time, the concepts and mechanics are difficult, and the students do not see the relevance of learning it. Research by Cosmides and Tooby (1996) supports our findings that instructors should avoid fractions and decimals and capitalize on students' affinity for counting things. In addition, we avoid the use of normal tables at the beginning of our discussion of continuous random variables by using uniform and triangular distributions. These ideas may be used in traditionally structured classes or in group-based and activity-based classes.
  • Author(s):
    Linn, S.
    Year:
    2004
    Abstract:
    Courses in clinical epidemiology usually include acquainting students with a single 2X2 table. All diagnostic test characteristics are explained using this table. This pedagogic approach may be misleading. A new didactic approach is hereby proposed, using two tables, each with specific analogous notations (uppercase and lowercase) and derived equations. This approach makes it easier to discuss the use of Bayes' Theorem and the two stages of analyses, i.e., using sensitivity to calculate predictive values. Two different types of false negative rates and false positive rates are discussed.
  • Author(s):
    Biehler, R.
    Year:
    1984
    Abstract:
    This chapter deals with Exploratory Data Analysis (EDA) and is based on a detailed theoretical analysis of the latter.

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