Literature Index

Displaying 971 - 980 of 3326
  • Author(s):
    FRANCIS, Glenda, KOKONIS, Sue, and LIPSON, Kay
    Year:
    2007
    Abstract:
    A new simulation has been developed to facilitate developmental learning of statistical inference. This simulation has been designed in the light of current multimedia design principals and cognitive theory. While many simulations have been developed to help students understand a variety of statistical concepts, evaluations of what these simulations actually achieve have been relatively scarce. This paper presents a model for the evaluation of simulations. In particular, the paper discusses the way in which the development of the students' conceptual understanding has been assessed. Some preliminary results from the evaluation of this specific project are presented.
  • Author(s):
    Periasamy, J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    This paper is a culmination of the study carried out after using project work as an intervention to enhance the learning of statistics as a service subject. It discusses how a project encompassing real-world problems directly relevant to the learners chosen career-path helps in motivating and sustaining the students' quest for learning statistics. The sample group comprised of learners studying towards the Diploma in Extraction Metallurgy and the project work was centred on the main-stream course Mineral Processing. This project was based on actual experiments conducted by learners in their Mineral Processing course so that learners could see the relevance of applied statistics to main-stream courses. The learners' performance was tracked throughout this study.
  • Author(s):
    Nicolas Christou
    Year:
    2008
    Abstract:
    In this paper we present an application of statistics using real stock market data. Most, if not all, students have some familiarity with the stock market (or at least they have heard about it) and therefore can understand the problem easily. It is the real data analysis that students find interesting. Here we explore the building of efficient portfolios through optimization using examples of two and three stocks, and how covariance and correlation can help the investor to diversify his or her risk. We discuss why diversification works, but also the problems that arise in portfolio management. Stock market data can be incorporated at any level of statistics, from lower division, to upper division, to graduate courses of Mathematics and Statistics. From our experience, students find this topic very interesting and often they want to enroll in other courses related to this area.
  • Author(s):
    Wallman, K. K.
    Year:
    1993
    Abstract:
    All sectors of society must have a basic knowledge of statistically sound concepts in order to make optimal use of research data and statistically significant information. The encouragement of statistical thinking can be facilitated through the federal statistical system, schools and universities and the media. Lastly, professional statisticians must strive to elucidate the whole statistical process whenever an appropriate oppourtunity arises.
  • Author(s):
    Anderson, J. A. & Sungur, E. A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In this paper we will present ways in which we have improved our introductory statistics courses by making connections with our community. We focus on three primary approaches: course structure, course content, and bringing in outside contacts and experiences. Changes to course structure include things like course projects and assignments that form an explicit part of the course workload for students. Our discussion of course content will consist of examples we use to illustrate learning objectives through a community connection. We then discuss how we incorporate consulting and other outside contacts to improve our courses. We also discuss student feedback and reactions.
  • Author(s):
    Batanero, C., Godini, J. D., Vallecillos, A., Green, D. R., & Holmes, P.
    Year:
    1994
    Abstract:
    This paper presents a survey of the reported research about students' errors, difficulties and conceptions concerning elementary statistical concepts. Information related to the learning processes is essential to curricular design in this branch of mathematics. In particular, the identification of errors and difficulties which students display is needed in order to organize statistical training programmes and to prepare didactical situations which allow the students to overcome their cognitive obstacles. This paper does not attempt to report on probability concepts, an area which has received much attention, but concentrates on other statistical concepts, which have received little attention hitherto. (orig.)
  • Author(s):
    Begg, A.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    Statistics education has become a significant part of the school mathematics curriculum. Now that it is well established, it is timely to look at some aspects of it to ensure high quality results which involves research in statistics education, as many of us are not aware of the work that is being done. The intention of this paper is to initiate discussion, to establish a research agenda and to use this framework as a means of relating present and desired research activity.
  • Author(s):
    Herrick, M. L., & Gold, K.
    Year:
    1994
    Abstract:
    In the course of research conducted with Michael Harwell of the University of Pittsburgh, we have developed a series of five instruments intended to explore several aspects of statistics textbook selection. It should be noted that many of the categories of questions in these instruments were inspired by the previously named articles, and our debt to their groundwork is extensive. However, choosing a statistics text for social science students is not as straightforward a task as choosing a text for students in their major field of study, and therefore warrants a specialized series of instruments. The instruments we have developed range from a general survey for instructors and students who are currently using a statistics textbook in a course, to particular instruments are designed so that data obtained from their administration could be useful to broad-range researchers, to departments trying to choose a textbook, or to writers and publishers of new statistics texts. The five instruments are reproduced in whole in the appendix. These are: 1) a student survey for currently-used textbooks, 2) an instructor survey for currently-used textbooks, 3) an instructor survey of what an ideal statistics textbook would be like, 4) an expert evaluation instrument that may be used on any statistics textbook, and 5) an instrument covering relevant objective information about any statistics textbook.
  • Author(s):
    Jane M. Watson, Rosemary A. Callingham and Julie M. Donne
    Year:
    2008
    Abstract:
    This report considers the pedagogical content knowledge (PCK) of 42 teachers selected to be part of a professional learning program in statistics. As part of a profile measuring many aspects of teacher confidence, beliefs, teaching practice, assessment practice, and background, PCK is addressed through responses to student survey items and how the items could be used in the classroom. Rasch analysis is used to obtain a measure of teacher ability in relation to PCK. Based on measured ability, three hierarchical clusters of teacher ability are identified, and the characteristics of each described in terms of the items likely to be achieved. These are exemplified with kidmaps of individual teachers' performances from each of the three clusters.
  • Author(s):
    Arkes, H. R., & Harness, A. R.
    Year:
    1983
    Abstract:
    Psychologists interested in such diverse areas as scientific reasoning, attribution theory, depression, and judgment have central to their theories the ability of people to judge the degree of covariation between two variables. We performed seven experiments to help determine what heuristics people use in estimating the contingency between two dichotomous variables. Assume that the two variables are Factor 1 and Factor 2, each of which may be present or absent. In Experiment 1 we hypothesized that people assess contingency solely based on the number of instances in which both Factor 1 and Factor 2 are present. By manipulating column and row tables of a 2x2 matrix, we were able to place various values in this "present-present" cell, also called Cell A. If subjects do base their contingency estimate on Cell A, we would expect a monotonic relation between Cell A frequency and the contingency estimate. This test of the Cell A heuristic led us to conclude that it could not represent a complete explanation of contingency estimation. Although Experiment 2 resulted in a rejection of one possible explanation of the results of Experiment 1, Experiment 2 and 3 together provided us with an essential finding: Very low cell frequencies are greatly overestimated. In Experiment 4 participants in a contingency estimation task involving no memory demands used rather complex heuristics in judging contingency. When the memory demands were increased in Experiment 5, the comparatively simple Cell A heuristic emerged as the modal strategy. Two factors, the use of simple heuristics by most subjects and the overestimation of small cell frequencies, combined to explain the results of Experiments 2 and 3. In Experiment 6 we showed that in a contingency estimation task, salience can augment the impact of one type of data but not another. In Experiment 7 we learned that the versus at the end of the data stream, can influence the final estimate. From this group of experiments we concluded that the "framing" of the task affects the contingency estimate; a number of factors that bear no logical relation to the contingency between two factors nevertheless influence one's perception of the contingency. Finally, we related our findings to a variety of analogous findings in the research areas of memory, attribution theory, clinical judgment, and depression.

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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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