Literature Index

Displaying 2441 - 2450 of 3326
  • Author(s):
    Brower, G. D.
    Year:
    1992
    Abstract:
    This paper reports three experiments with the curriculum of Sophomore Economics and Business statistics. Their purpose was to improve students' performance in the course and to offer them more useful tools for later courses and for employment Evaluations are based on my own perceptions and on anonymous written comments by students.
    Location:
  • Editors:
    Ben-Zvi, D., & Garfield, J.
    Year:
    2005
  • Author(s):
    Garfield, J. B., & Gal, I.
    Year:
    1999
    Abstract:
    Statistical reasoning may be defined as the way people reason with statistical ideas and make sense of statistical information. This involves making interpretations based on sets of data, representations of data, or statistical summaries of data. Much of statistical reasoning combines ideas about data and chance, which leads to making inferences and interpreting statistical results. Underlying this reasoning is a conceptual understanding of important ideas, such as distribution, center, spread, association, uncertainty, randomness, and sampling. This chapter begins by distinguishing reasoning from mathematical reasoning, and then outlines goals for students studying statistics. Challenges in assessing statistical reasoning are described and information is provided on a unique paper and pencil instrument, the Statistical Reasoning Assessment. The final section suggests ways teachers may help students develop sound statistical reasoning skills.
  • Author(s):
    Ben-Zvi, D., & Makar, K.
    Year:
    2013
  • Author(s):
    Ben-Zvi, D., & Burrill, G.
    Editors:
    G. Kaiser
  • Author(s):
    Rodrigues Miguel, M. I.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    In this article, we describe an experience with the teaching and learning of the Poisson´s Model, directed to university students. We used the Anthropological Theory of the Didactic and the principles of the Mathematical Modeling Process for the elaboration of the teaching sequence, showing that it is possible to build the model without resorting to the Binomial limit, making use of the notions of calculus. In the development of the learning activities, the computer was used as a didactic tool. The analysis of results was based on the Theory of the Semiotic Functions and proved that the students learned many significant elements considered in the teaching. On the other hand, the study allowed for the identification of difficulties throughout the process: for example, the interpretation of expressions such as "at least" and "at most", the representation used, the analysis of the results of hypothesis tests and the manipulation of the software.
  • Author(s):
    Reich, R. M., & Arvanitis, L. G.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    To enhance student comprehension of basic sampling concepts, Arvanities and Reich have developed a Forest Sampling Simulator (FOSS) for microcomputer (Arvanities and Reich, 1989). It has been well-documented by numerous studies that computer simulation fosters understanding of complex systems by permitting students to manipulate individual parts and observe the effects of their action on the rest of the model (Heerman, 1988). This system is described in Section 2 below. In section 3 its possible augmentation by an expert system is described, which would automatically generate the most appropriate sampling strategy for a particular situation, given the appropriate input information.
  • Author(s):
    Oosthuizen, J. H.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    Criticism of, exposure to, and justification of the educational task of universities is escalating. The era of the untouchable ivory tower has gone forever. Universities are experiencing an increasing loss of status. The reason for this is that functionality is used as criterion for efficiency end effectiveness. The value and meaning of universities for society, in terms of the provision of manpower, contribution to the national economy, planning, and the solving of problems, are of prime importance. In addition, the democratisation process requires increasingly more claims on and participation in the management and control of universities by all interested parties, whether parents or students or donors or the community (as ratepayers) or the professions. Whatever the case may be, the searchlight is directed more and more at the lecturing function of universities. Present-day universities are no longer "elite" universities, but mass universities. Because of this, as well as the ever-increasing cost of equipment and facilities, the claims of the ratepayer are growing, and therefore he or she looks more and more critically at the effectiveness of universities, which according to him or her - inadmissably oversimplified - is measured in pass and fail figures. In its great and unique task, namely the provision of high-level manpower, only one guarantee for success exists for the educational task of universities: to strive for excellence at all levels; and only one successful reality: a healthy balance between the timeless - striving for intellectual and academic progress and the contemporary - meeting the demand of relevance. For the lecturer this means the optimum allocation of his or her time to teaching, research, and rendering of professional service, and to build and develop these tasks on excellence.
  • Author(s):
    Greer, B., & Mukhopadhyay, S.
    Editors:
    Jones, G. A.
    Year:
    2005
    Abstract:
    We deal with the conceptual development of probability as part of mathematics that grew historically in intimate relationship with its applications. As well, we consider the role of probability in contemporary society. We use these analyses to present the arguments for the importance of education for understanding probabilistic thinking as a tool for understanding the physical and social worlds. Lastly, we consider the challenges facing this endeavor, and offer suggestions for meeting these challenges.
  • Author(s):
    de Lange, J.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    A series of examples from experiences taken from experiments that have taken place in The Netherlands and the USA were discussed. This work has been carried out by the Research Group on Mathematics Education of Utrecht University, with the USA work a collaboration with the National Center for Research in Mathematical Sciences Education of the University of Wisconsin in Madison. Materials were developed on the subject of Data Visualisation. This subject treats skills such as drawing basic plots and calculating basic numerical summaries of data, but it also treats questions such as the following as being of at least equal importance. What graphical representation is best for this set of data? What do these data tell me? How can I communicate the message through a picture? This approach has implications for testing and evaluation as well as teaching.

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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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