Literature Index

Displaying 111 - 120 of 3326
  • Author(s):
    Blischke, W. & Ford, J. S.
    Year:
    1986
    Abstract:
    This paper discusses the features of MINITAB and the advantages of using it for teaching college statistics.
  • Author(s):
    Julie Legler, Paul Roback, Kathryn Ziegler-Graham, James Scott, Sharon Lane-Getaz, Matthew Riche
    Year:
    2010
    Abstract:
    In the May 2009 issue of The American Statistician, Brown and Kass (BK) offered thought-provoking answers to the question "What is Statistics?" which have direct implications for statistics education. For five years, St. Olaf College's Center for Interdisciplinary Research's (CIR) activities have aligned with BK in both philosophy and practice. We describe the program's motivation and design, how we recruit students and find faculty collaborators with suitable projects, and how the teams of faculty and students work together. A research skills seminar series parallels the research process and prepares students for working on teams. Inevitably, administrative issues arose which we identify and address. Landes (2009) identified significant issues related to recruiting. Our model of undergraduate education has proved to be fruitful on this front. Sending nearly 50 students to graduate school in five years from a college of fewer than 3000 speaks to the program's efficacy. Here we present a program based on authentic interdisciplinary research with undergraduates which embodies many of BK's ideas and addresses recruiting issues. Although this experience underscores the potential for new and exciting approaches to statistics education in the liberal arts environment, the model itself can be adapted by a variety of undergraduate programs. Supplemental materials are available online.
  • Author(s):
    Watson, J. M., Collis, K. F., Callingham, R. A., & Moritz, J. B.
    Year:
    1995
    Abstract:
    As in other areas of the school curriculum, the teaching, learning and assessment of higher order thinking in statistics has become an issue for educators following the appearance of recent curriculum documents in many countries. These documents have included probability and statistics across all years of schooling and have stressed the importance of higher order thinking across all areas of the mathematics curriculum. This paper reports on a pilot project which applied the theoretical framework for cognitive development devised by Biggs and Collis to a higher order task in data handling in order to provide a model of student levels of response. The model will assist teachers, curriculum planners and other researchers interested in increasing levels of performance on more complex tasks. An interview protocol based on a set of 16 data cards was developed, trialed with Grade 6 and 9 students, and adapted for group work with two classes of Grade 6 students. The levels and types of cognitive functioning associated with the outcomes achieved by students completing the task in the two contexts will be discussed, as will the implications for classroom teaching and for further research.
  • Author(s):
    Cohen, S., Tsai, F., & Chechile, R.
    Year:
    1995
    Abstract:
    Presents a simple model of generative learning that permits the definition of 4 kinds of interactions (scaffolding, investigation, reformulation, and navigation) and a system for tracing and recording how students use educational technology. This model will maintain a link between interaction and learning, thus providing one method for the assessment of a wide range of educational technology environments. Two results are presented from an evaluation of ConStatS, a program for teaching conceptual understanding of probability and statistics, in which 659 college students participated. Both results focus on a specific interaction that correlates with increased learning, and on the general patterns of interaction that characterize students who execute the interactions. (PsycLIT Database Copyright 1996 American Psychological Assn, all rights reserved)
  • Author(s):
    delMas, R. C., Garfield, J. B., & Chance, B. L.
    Year:
    1999
    Abstract:
    Researchers and educators have found that statistical ideas are often misunderstood by students and professionals. In order to develop better statistical reasoning, students need to first construct a deeper understanding of fundamental concepts. The Sampling Distributions program and ancillary instructional materials were developed to guide student exploration and discovery. The program allows students to specify and change the shape of a population, choose different sample sizes, and simulate sampling distributions by randomly drawing large numbers of samples. The program provides graphical, visual feedback that allows students to construct their own understanding of sampling distribution behavior. To capture changes in students' conceptual understanding we developed diagnostic, graphics-based test items that were administered before and after students used the program. An activity that asked students to test their predictions and confront their misconceptions was found to be more effective than one based on guided discovery. Our findings demonstrate that while software can provide the means for a rich classroom experience, computer simulations alone do not guarantee conceptual change.
  • Author(s):
    Katie Makar
    Year:
    2008
    Abstract:
    Although the inquiry process is a foundational practice in statistics, it is rarely taught in school. This paper introduces a tentative model to describe primary teachers' evolving experiences in learning to teach statistical inquiry.
  • Author(s):
    Elmore, P. B., & Vasu, E. S.
    Year:
    1986
    Abstract:
    Subjects for this study were graduate students enrolled in inferential statistics classes at a midwestern university. The study was conducted to determine the importance of spatial ability, attitudes toward mathematics, mathematical background, masculinity-femininity of interest pattern, attitudes toward feminist issues, student sex, and verbal and mathematical ability as predictors of achievement in applied statistics courses for male and female students. Regression analyses were performed comparing full versus restricted models. The amount of variance in statistics achievement accounted for in the full model which included all the previously mentioned sets of predictor variables was .60. The most important predictor variable set was attitudes toward feminist issues (reduction in R-sqr = .1861). Sex-related differences were found on all variable sets except verbal and mathematical ability.
  • Author(s):
    Gandhi, B. V. R.
    Year:
    1993
    Abstract:
    This paper describes a National Science foundation (NSF) Sponsored Teacher Enhancement Program (TEP) in statistics during the years 1991 - 1994 conducted at the University of Puerto Rico. The project evolved from the belief that statistics is more meaningful to students when they plan, experiment, collect and analyze data themselves rather than when they learn a set of formulas and techniques. This idea was first incorporated locally in an NSF Sponsored Young Scholars Program in statistics during the period 1989 - 1991 in which the author worked with talented students from high schools in the region. The experience and success of the Young Scholars Program and the education department's request to expand it led to the TEP in statistics presented herein. There are three special features of this project. The first feature is to introduce the modern method of teaching statistical reasoning to students primarily through the use of examples and class projects which are interesting to students and related to current issues. A second important feature is the comprehensive nature of training in the fields of statistics, computers and research methodology. the third important characteristic is the three year follow up phase of the project which provided time to integrate the philosophy of the project into the educational system.
  • Author(s):
    Ann Ooms and Joan Garfield
    Year:
    2008
    Abstract:
    The Iterative Evaluation Model for Improving Online Educational Resources (IEM) was developed to provide a valid evaluation model to be used to improve online resources, to make them more effective and have a greater positive impact on student learning. The model focuses on the iterative evaluation of four components: (a) evaluation planning, (b) web design and content, (c) use of the educational resource, and (d) educational impact. This paper describes the IEM which was developed as part of the NSF-funded ARTIST (Assessment Resource Tools for Improving Statistical Thinking) project and used to evaluate the online resources developed by this project. The ARTIST evaluation is described in order to illustrate how the IEM may be used.
  • Author(s):
    Horvath, J. K., & Lehrer, R.
    Editors:
    Lajoie, S. P.
    Year:
    1998
    Abstract:
    Statistics is the science of modeling the world through theory-driven interpretation of data. Models of chance and uncertainty provide powerful cognitive tools that can help in understanding certain phenomena. In this chapter, we consider the development of children's models of chance and uncertainty by considering their performance along five distinct, albeit related, components of a classical model of statistics: a) the distinction between certainty and uncertainty, b) the nature of experimental trials, c) the relationship between individual outcomes (events) and patterns of outcomes (distributions), d) the structure of events (e.g., how the sample space relates to outcomes) and e) the treatment of residuals (i.e., deviations between predictions and results). After discussing these five dimensions, we summarize and interpret the model-based performance of three groups as they solved problems involving classical randomization devices such as spinners and dice. The three groups included second-graders (age 7-8), fourth/fifth-graders (age 9-11), and adults. We compare groups by considering their interpretations of each of these five components of a classical model of chance. We conclude by discussing some of the benefits of adopting a modeling stance for integrating the teaching and learning of statistics.

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