Literature Index

Displaying 3111 - 3120 of 3326

  • Using computers in teaching statistical analysis: A double-edged sword

    Author(s):
    Rubin, A.
    Year:
    1991
    Abstract:
    The ability to seek out data, organize it, and interpret it is an empowering skill, and that a person who truly understands data has a source of power to use in influencing the direction of important decisions. The goal of education in statistics and probability should be to impart this sense of power to students, Learning statistics does not mean merely mastering the fomulaic transformations that yield mean, standard deviation, and P value. A true understanding of statistics includes knowing how to use data to discover and evaluate important associations and to communicate these associations to others. It requires learning how to evaluate other people's use of data and to augment or challenge them with additional data. There are NCTM Teaching Standards (NCTM, 1991), which include a new view of pedagogy in mathematics teaching - a focus on understanding the underlying concepts of our number system rather than on memorizing addition and multiplication facts, on facility in spatial visualization rather than on learning formulas for the area of polygons, and on planning and on carrying out data analysis projects rather than on knowing the difference between mean and median. Integrated with these two major changes, researchers and practitioners are looking more to technology to support new approaches to mathematics learning, as "tools for enhancing [mathematical] discourse." (NCTM, 1991, p.52) How does the computer fit into the developing view of statistics education? At first glance, the answer seems obvious: computers free students (and teachers) from the tedious computations that are required to calculate means, standard deviations, confidence intervals, etc. They draw graphs quickly and accurately. They generate multitudes of samples in a single bound. But this list of accomplishments leaves two crucial questions unanswered: 1) Are there other more powerful ways in which computers can facilitate students' learning of statistics? 2) Are there any drawbacks to uses of computers in statistics classes? The remainder of this paper will address both of these questions.

  • Using computers in teaching statistics is it worthwhile?

    Author(s):
    Blejec, A.
    Editors:
    Brunelli, L., & Cicchitelli, G.
    Year:
    1993
    Abstract:
    Computers were invented and constructed to compute. Since statistical analyses are computing intensive it is natural that computers are widely used in statistical research and applications. Statistical applications, such as census with its problems of sorting, counting and tabulating, were among the motives for constructing the ancestors of modern computers. It was always clear that students have to be taught how to use computers, because they will use them in their later careers. With the development of modern technology, computers evolved from large mainframes to personal computers, available for individual use. Availability of personal computers changed the way of computer usage and allowed computers to be incorporated into the teaching process in various disciplines. In particular, can computers be useful in teaching of statistics? If yes, to what extent? What changes in the teaching process are needed if we want to apply computers efficiently? There are many other questions related to the usage of computers in teaching. I would like to present some of my views about computers in teaching of statistics.

  • Using computers to support students' understanding of statistical inference

    Author(s):
    Rubin, A., & Bruce, B.
    Year:
    1991
    Abstract:
    Over the past several years, we have been working on the Reasoning Under Uncertainty (RUU) project, whose goal has been to develop and test a computer-supported environment in which high school students could learn how to think in probabilistic and statistical terms. The central ideas of the project are to use the computer as a tool for data gathering, manipulation, and display, and to have students investigate questions that are meaningful to them. In contrast to the usual emphasis in statistics courses on formulas and computational procedures, RUU emphasizes reasoning about statistical problems. We believe that students should be able to engage in statistical reasoning about uncertainties that either they or society face. Such a course conforms well to the National Research Council's suggestion that "elementary statistics and probability should now be considered fundamental for all high school students" and to the new NCTM guidelines for including probability and statistics in the elementary and secondary curriculum.

  • USING CONCEPT MAPS TO ASSESS PRE-SERVICE TEACHERS' UNDERSTANDING OF CONNECTIONS BETWEEN STATISTICAL CONCEPTS

    Author(s):
    AFAMASAGA-FUATA'I, Karoline and READING, Chris
    Year:
    2007
    Abstract:
    Concept maps are powerful tools for representing understanding of a concept. After designing a teaching sequence for the statistics content of a senior secondary mathematics syllabus, pre-service teachers were asked to prepare a concept map to demonstrate their understanding of the connection between the different concepts that had been included in the sequence. The concept maps prepared by the pre-service teachers were analysed in relation to what connections were made and the quality of the connecting statements. Results showed that these pre-service teachers had very different perceptions of the connections between the basic statistical concepts. Drawing the concept maps assisted the pre-service teachers to consider the concepts at a meta-level. How the concepts maps might be used as a tool for aiding the planning of learning sequences is worthy of investigation.
    Location:
    http://www.swinburne.edu.au/lss/statistics/IASE/CD_Assessment/index.htm

  • Using Consulting for Teaching Elementary Statistics.

    Author(s):
    Truran, J. & Arnold, A.
    Editors:
    Goodall, G.
    Year:
    2002
    Abstract:
    Consulting in statistics is usually deferred until at least near the end of a first degree, but this article shows how some aspects can be effectively taught to students in upper secondary or early tertiary courses in a way which reinforces their learning of standard basic concepts. We suggest that the existence of a real client adds a degree of realism not available in other ways, and emphasizes to students the importance of blending statistical calculations with meaningful communication.

  • Using cooperative learning in a large introductory statistics class

    Author(s):
    Magel, R. C.
    Year:
    1998
    Abstract:
    This article discusses one active learning technique, cooperative learning, that can be used in large classes. This technique requires that students be divided into learning teams. A method for quickly dividing a large class of students into learning teams is presented. Two examples of cooperative learning exercises used in an introductory statistics class are given. These serve as illustrations of the type of cooperative learning exercises that can be assigned in a large class. In particular, these exercises were used in a class of 195 students. Preliminary findings by the instructor of the advantages of using cooperative learning exercises are discussed.
    Location:
    http://www.amstat.org/publications/jse/v6n3/magel.html

  • Using cooperative learning to teach statistics

    Author(s):
    Jones, L. V.
    Year:
    1991
    Abstract:
    Mosteller noted the paucity of studies on "how to improve collegiate or university teaching in a behavioral way," and offered examples of techniques that he had found effective in the classroom. Stimulated by Mosteller's suggestions and by research results on "cooperative learning", I adopted new procedures for teaching an introductory undergraduate course in psychological statistics and compared results with those from the course that I had taught by more conventional methods in prior years. A Letter to the Editor in The American Statistician provides a summary of findings. That letter is reproduced (with the permission of the publisher) on the facing page. The present report provides a more complete description of the methods employed, with the intent that it may facilitate their use by others.

  • Using data comparison to support on a focus on distribution: Examining preservice teachers' understandings of distribution when engaged in statistical inquiry.

    Author(s):
    Leavy, A.
    Editors:
    Gal, I., & Short, T.
    Year:
    2006
    Abstract:
    This exploratory study, a one group pretest-posttest design, investigated the development of elementary preservice teachers' understandings of distribution as expressed in the measures and representations used to compare data distributions. During a semester-long mathematics methods course, participants worked in small groups on two statistical inquiry projects requiring the collection, representation, analysis and reporting of data with the ultimate goal of comparing distributions of data. Many participants shifted from reporting descriptive exclusively to the combined use of graphical representations and descriptive statistics which supported a focus on distributional shape and coordinated variability and center. Others gained skills and understandings related to statistical measures and representations yet failed to utilize these when comparing distributions. Gaps and misconceptions in statistical understanding are discussed. Recommendations for supporting the development of conceptual understanding relating to distribution are outlined.
    Location:
    http://www.stat.auckland.ac.nz/~iase/serj/SERJ5(2)_Leavy.pdf

  • Using data to enhance the understanding of functions: Data analysis in the precalculus curriculum.

    Author(s):
    Flanagan-Hyde, P., & Lieb. J.
    Editors:
    Burrill, G. F.
    Year:
    2006
    Abstract:
    This article examines two essential questions in making a good model from data - describing the pattern of variation and explaining the proportion of the variation by using functions.

  • Using data, student sexperiences and collaboration in developing probabilistic reasoning at the introductory tertiary level.

    Author(s):
    Mcgillivray, H. L.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    In the focus over the past decade on data-driven, realistic approaches to building statistical literacy and data analysis curriculum, the explicit development of probability reasoning beyond coins and dice has received less attention. There are two aspects of probability at the introductory tertiary level: for use in introductory data analysis; and as foundation for further study in statistical modelling and applications, and increasingly in areas in information technology, engineering, finance, health and others. This paper advocates a minimalist objective-oriented approach in the former, and a constructivist, collaborative and data-linked approach in the latter. The latter is the main focus here, with strategies to help students unpack, analyse and extend what they have brought with them to tertiary study, enabling them to consciously develop coherent probabilistic understanding and linking with real investigations and processes.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/6B4_MACG.pdf

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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