Literature Index

Displaying 2981 - 2990 of 3326
  • Author(s):
    Ben-Zvi, D. & Arcavi, A.
    Editors:
    Phillips, B.
    Year:
    1997
    Abstract:
    We shall describe episodes of middle school students working on Exploratory Data<br>Analysis (EDA) developed within an innovative curriculum. We outline the program and<br>its rationale, analyze the design of the tasks, present extracts from students' activities and speculate about their learning processes. Finally, from our observations, we propose a new construct -- learning arena, which is suggested as a curriculum design principle, which may also facilitate research.
  • Author(s):
    Ben-Zvi, D., & Arcavi, A.
    Editors:
    L. Pereira-Mendoza, L. S. Kea, T. W. Kee & W. K. Wong
    Year:
    1998
  • Author(s):
    Joliffe, F.
    Editors:
    Batanero, C., &amp; Joliffe, F.
    Year:
    2003
    Abstract:
    Definitions of research in statistical education are discussed. A system of keywords for categorising statistical education research is outlined. Proposals for a Web-based survey of statistical education researchers to collect details of their research activities in statistical education, and the design of a database to store these details are described.
  • Author(s):
    Ben-Zvi, D., Makar, K., & Bakker, A.
    Editors:
    K. Makar
    Year:
    2009
    Abstract:
    The aim of our current collaboration has been to explore concepts for an emerging framework designed to better understand the relationship between learners’ inferential reasoning in statistics and their processes of argumentation embedded mostly in context. The concepts we will present grow out of our reading of philosophy, the literature in different fields on argumentation and inference, and our experiences over several years with a wide range of students and adults involved in drawing statistical inferences. This preparatory paper is theoretical in its stance, but in our SRTL-6 presentation we will show excerpts in statistical classrooms drawn from projects by the authors at different school levels to illustrate the concepts in our emerging framework. 
  • Author(s):
    Djordje Kadijevich, Vlasta Kokol-Voljc and Zsolt Lavicza
    Year:
    2008
    Abstract:
    Earlier studies on sampling distribution, its founding concepts, misconceptions about sampling distributions, and the use of simulation highlighted that (1) learning of statistics requires an understanding of multifaceted issues and relations among them; (2) learning may be examined in terms of task, technique, theory, and learner's profile, each of which is influenced by instructional context; and (3) learning environments should be designed to stimulate flexible travelling along the network of these issues. Considering these emerging findings we attempt to outline a possible instructional design to teach sampling distribution with technology. Suggestions for training teachers in statistics education are included.
  • Author(s):
    Nellen, S., &amp; Lovett, M. C.
    Year:
    2004
    Abstract:
    Learning to make good choices in a probabilistic environmentrequires that the Decision Maker resolves the tension betweenexploration (learning about all available options) andexploitation (consistently choosing the best option in order tomaximize rewards). We present a mathematical learningmodel that makes selections in a repeated-choice probabilistictask based on the expected payoff associated with each optionand the information gain that will result from choosing thatoption. This model can be used to analyze the relative impactof exploration and exploitation over time and under differentconditions. It predicts the aggregated and individual learningtrajectories of participants in various versions of the tasksufficiently well to support our basic argument: Informationgain is a valid and rational criterion underlying humandecision making. Future modeling work will be addressingthe exact nature of the interaction between exploration andexploitation.
  • Author(s):
    Shuyten, G., Dekeyser, H., &amp; Goeminne, K.
    Year:
    1999
    Abstract:
    This study focuses on the feasibility of implementing independent learning in a traditional university and the feasibility of providing this independent learning by means of an electronic interactive learning environment. Three experimental variables were designed: learning environment, delivery, and support. This created five different learning conditions to which subjects were assigned at random.
  • Author(s):
    Sharon Gunn and Roslyn Steel
    Year:
    2008
    Abstract:
    Early attempts to define statistical thinking revolved around discussions of the need for data,<br>understanding the nature of variability and how statisticians go about solving statistical<br>problems. More recently, Wild and Pfannkuch (1999) have proposed a framework in which they<br>identify five types of thinking they perceive as being fundamental to thinking statistically. We<br>understand these types of thinking as a mapping onto the statistical problem solving cycle (real<br>world problem, statistical problem, statistical solution, real world solution) and as providing us<br>with a beginning definition for statistical thinking. Wild and Pfannkuch (2004), in their paper on<br>understanding statistical thinking discussed the historical development of statistical thinking,<br>emerging from this discussion was a broader view of statistical thinking, namely, statistical<br>thinking is a way of making sense of the world, a particular world view.<br>We believe understanding statistical thinking as a world view may provide additional insights<br>into how we, as educators, can recognise, develop and assess statistical thinking within our<br>students. We explore the links between these constructs and what we observed when we<br>introduced a new teaching and learning strategy into a statistics design and analysis subject.
  • Author(s):
    Pfannkuch, M. &amp; Wild, C.
    Editors:
    Ben-Zvi, D. &amp; Garfield, J.
    Year:
    2004
    Abstract:
    There has been an increasingly strong call from practicing statisticians for statistical education to focus more on statistical thinking (e.g., Bailar, 1988; Snee, 1993; Moore, 1998). They maintain that the traditional approach of teaching, which has focused on the development of skills, has failed to produce an ability to think statistically: "Typically people learn methods, but not how to apply them or how to interpret the results" (Mallows, 1998, p. 2).<br>Solutions offered for changing this situation include employing a greater variety of learning methods at undergraduate level and compelling students to experience statistical thinking by dealing with real-world problems and issues. A major obstacle, as Bailar (1988) points out, is teacher inexperience. We believe this is greatly compounded by the lack of an articulated, coherent body of knowledge on statistical thinking that limits the pedagogical effectiveness even of teachers who are experienced statisticians. Mallows (1998) based his 1997 Fisher Memorial lecture on the need for effort to be put into developing a theory for understanding how to think about applied statistics, since the enunciation of these principles would be useful for teaching.<br>This chapter focuses on thinking in statistics rather than probability. Although statistics as a discipline uses mathematics and probability, as Moore (1992b) states, probability is a field of mathematics, whereas statistics is not. Statistics did not originate within mathematics. It is a unified logic of empirical science that has largely developed as a new discipline since the beginning of the 20th century. We will follow the origins of statistical thinking through to an explication of what we currently understand to be statistical thinking from the writings of statisticians and statistics educationists.
  • Author(s):
    Hogg, R. V., et al
    Editors:
    Gordon, F., &amp; Gordon, S.
    Year:
    1992
    Abstract:
    The present paper represents a summary of the results of a workshop on statistical education.

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