Literature Index

Displaying 3221 - 3230 of 3326
  • Author(s):
    Crawford, E. & Bowman, A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The learning and teaching support network is a programme funded to promote good practice in teaching and learning in UK higher education. Subject networks have been established in twenty-four different areas, including one for mathematics, statistics and operational research. Among all the different activities of this network, the web, of course, offers a rich source of primary material and a convenient means of dissemination. The web provides a vast collection of material on every subject known to man, including statistics. The aim of the ltsn msor web site is to offer a convenient and filtered gateway to a wide variety of teaching and learning material. This paper describes some of the resources available in statistics in particular. Some of the organisational aspects of setting up the website are also mentioned.
  • Author(s):
    Icaza, G., Bravo, C., Guiñez, S., & Muñoz, J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    We constructed a web site to support service statistic courses at the University of Talca (http://dta.utalca.cl/estadistica/). The web site was developed around two fundamental ideas: object learning and concept maps. The statistical content was structured based on object learning organized around the scientific method. The object learning is imbedded in concept maps which highlight the structure and connections in statistics. Each concept map links complementary information in various formats. Students have positively evaluated the web page. This work was founded by the Education Ministry of Chile, MECESUP TAL0103 project: "Diversification of strategies for teaching and learning in basic sciences" (Diversificación de las estrategias de enseñanza-aprendizaje en las Ciencias Básicas).
  • Author(s):
    O'Connell, A. A., Ataya, R. L., & Zhao, J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Use of the internet to support instruction in general and the use of statistical software in particular provides instructors and students with an opportunity to improve learning while maintaining effective use of limited classroom time. We have developed a Web site (http://power.education.uconn.edu/) that encompasses instruction in power analysis issues and teaches students and others how to use the nQuery Advisor© software to establish sample size for research designs ranging from the simple to the complex. The evaluation results of our Power Project Web site and materials are promising, and the purpose of this paper is to share our approach and materials with other instructors of statistics and research design.
  • Author(s):
    Connor, D., Davies, N. & Payne, B.
    Editors:
    Goodall, G.
    Year:
    2002
    Abstract:
    Pupils in England and Wales are increasingly being asked to undertake investigative-type work, be it the new compulsory projects in data handling for GCSE Mathematics (age 14-16) (see Browne 2002) or the Key Skills topic application of number. This article shows how teachers can generate realistic project scenarios using real data and produce indicative model solutions from the same data. The projects range from simple presentational problems for data,through hypothesis testing to complex modelling scenarios.
  • Author(s):
    Wisenbaker, J. M., & Douzenis, C.
    Year:
    2000
    Abstract:
    The purpose of the project that formed the basis of the work reported herein was to provide students a series of web-based statistical readings designed to illustrate common statistical concepts via "real-life" educational research situations.
  • Author(s):
    Helman, D.
    Editors:
    Goodall, G.
    Year:
    2004
    Abstract:
    A lottery coincidence provides the background for discussing the assessment of probabilities and the definition of events.
  • Author(s):
    Lann, A. & Falk, R.
    Editors:
    Lee, C. & Satterlee, A.
    Year:
    2003
    Abstract:
    The most prominent characteristic of people's dealings with variability (that we are aware of to date) is their tendency to eliminate, or underestimate the dispersion of the data (e.g., Kareev, Arnon &amp; Horowitz-Zeliger, 2002), that is, the differences among individual observations and among means of samples from a population (Tversky &amp; Kahneman, 1971). One typically focuses on the average, and forgets about the individual differences in the material.<br><br>Shaughnessy and Pfannkuch (2002) report that when asked to analyze a set of data, many students just calculated a mean or a median. They claim that past teaching and textbooks concentrated heavily on such measures and neglected variation. Shaughnessy and Pfannkuch maintain, however, that variability is important. It exists in all processes. Understanding of variation is the central element of any definition of statistical thinking. They quote David Moore's slogan "variation matters" (p. 255).<br><br>In the history of statistics, the tendency to eliminate human variability was represented (in the first half of the 19th century) by Quetelet, who focused on regularities. According to Gigerenzer, et al. (1989), Quetelet understood variation within species as something akin to measurement- or replication-error: The average expressed the "essence" of humankind. "Variations from the average man were accidental - matters of chance - in the same sense that measurement errors were" (p. 142). Quetelet's conception of variation was diametrically different from Darwin's, who focused on variability itself and regarded variations from the mean as the crucial materials of evolution by natural selection.<br><br>One example of the tendency to ignore variability is obtained when assignment of probabilities (or weights) to a set of possible outcomes is called for. People often tend to distribute these probabilities equally over the available options (Falk, 1992; Lann &amp; Falk, 2002; Pollatsek, Lima &amp; Well, 1981; Zabell, 1988), employing what we call the uniformity heuristic. Equi-probability, or zero variability among the probabilities, is the simplest and easiest choice to fall back on.
  • Author(s):
    Liu, Y.
    Year:
    2004
    Abstract:
    This study investigates students' statistical learning in a high school classroom setting. Using a theoretical framework derived from interpretive and sociolinguistic ethnography, the study explores the meaning of "statistical understanding" that developed in a statistics class over the course of a school year. Seventeen students enrolled in an AP statistics course participated in the study. Data were collected through participant observation, videotaping of classroom sessions, field notes, and interviews. Initial analysis identified a series of tensions that highlight the shared beliefs of "what counts as statistical understanding". In the paper, I will present and discuss the emergence of these tensions in relation to the classroom interaction and students' learning.
  • Author(s):
    Russell, S. J. &amp; Mokros, J.
    Year:
    1996
    Abstract:
    Investigates students' understanding of the idea of average. Relationship between data and the average of the data; Confusion of the term with mode and median.
  • Author(s):
    Meletiou, M., Confrey, J., Lee, C., &amp; Fouladi, R.
    Abstract:
    This paper compares the learning experiences of students from a technology based introductory statistics course with that of a group of students with non-technology based instruction.

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