Literature Index

Displaying 1341 - 1350 of 3326
  • Author(s):
    Nasoetion, A. H.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    This paper elucidates the way Data Analysis was introduced through Mathematics in the schools of Indonesia prior to 1994, and what approach could be used in the 1994 curriculum to make Indonesian Middle School students of the turn-of-the-century quantitatively literate.
  • Author(s):
    Morin, A.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    In France, data analysis is a part of the curriculum of primary schools (students younger than 11 years old). In primary schools, one teacher teaches all the subjects in the curriculum. In secondary schools (students from 11 to 18 years old) the style of education is very different and there is a different teacher for each subject. However, data analysis is still part of the curriculum in mathematics, economics, geography, biology, etc. with each teacher developing his/her own methods for data analysis. We will emphasize this point later. This paper offers a definition of data analysis, states goals for teachers and describes what is appropriate for the schools.
  • Author(s):
    Starkings, S. A.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    The aim of this paper is to draw attention to the main areas where data analysis is currently being taught, namely: (i) mathematics containing a data analysis element; (ii) specific courses in statistics; (iii) data analysis taught specifically in other subject areas and (iv) courses which make an inherent assumption of statistical knowledge. Furthermore, the paper and presentation are designed to promote discussion on appropriate teaching methods of data analysis within our educational establishments.
  • Author(s):
    Beth Chance & Allan Rossman
    Year:
    2008
    Abstract:
    Math majors, and other mathematically inclined students, have typically been introduced to statistics through courses in probability and mathematical statistics. We worry that such a course sequence presents mathematical aspects of statistics without emphasizing applications and the larger reasoning process of statistical investigations. In this webinar we describe and discuss a data-centered course that we have developed for mathematically inclined undergraduates.
  • Author(s):
    Delia North and Jackie Scheiber
    Year:
    2008
    Abstract:
    A new school curriculum, with substantial statistics content at all levels, is currently being phased in throughout South Africa. This paper focuses on a government roll-out plan that aims to upgrade the knowledge of in-service teachers in order to empower them to successfully engage with the statistics content of the new school syllabus.
  • Author(s):
    Ernest, P.
    Year:
    1984
    Abstract:
    All too often mathematics is considered to be the study of certainties: certain truth and certain falsity. We need to overcome this misleading impression and to show that mathematics can describe the uncertainties of everyday life. Much of our daily life is unpredictable and uncertain. A branch of mathematics, probability theory, can help us cope with this aspect of life. However, if the theory of probability is taught formally, then students may not make the connection between the theory and its usefulness in daily life. The following introduction helps ensure that this connection is made.
  • Author(s):
    Ben-Zvi, D., & Garfield J.
    Year:
    2008
    Abstract:
    Increasing attention has been given over the last decade by the statistics, mathematics and science education communities to the development of statistical literacy and numeracy skills of all citizens and the enhancement of statistics education at all levels. This paper introduces the emerging discipline of statistics education and considersits role in the development of these important skills.The paper begins with information on the growing importance of statistics in today’s society, schools and colleges, summarizes unique challenges students face as they learn statistics, and makes a case for the importance of collaboration between mathematicians and statisticians in preparing teachers to teach students how to understand and reason about data. We discussthe differences and interrelations between statistics and mathematics, recognizing that mathematics is the discipline that has traditionally included instruction in statistics. We conclude with an argument that statistics should be viewed as a bridge between mathematics and science and should be taught in both disciplines.
  • Author(s):
    Tauber, L. & Sánchez, V.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In this work, we describe the elements of meaning related to normal distribution, which appear in a data analysis course based on the use of computers. The course was directed to students in their first year of university studies. We study the elements implemented in a teaching unit for the normal distribution in which computers were introduced as a didactic tool. We pay special attention to the specific meaning conveyed by the use of computers as well as to the meaning attributed by the students throughout the teaching sequence.
  • Author(s):
    Silva Coutinho, C. D. Q.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This paper proposes a first approach with random situations by using a modeling process within the model of Bernoulli's Urn. This way of learning is accessible to 14-15 years old pupils. The software Cabri-géomètre II is used as an empirical computation environment for simulation of the game of "Franc-Carreau", principal activity proposed to pupils in our didactical engineering.
  • Author(s):
    Jones, G. A. and the authors
    Editors:
    Jones, G. A.
    Year:
    2005
    Abstract:
    This chapter outlines the challenges in teaching and learning probability and states the mission of this book. This book aims to review and analyze the research literature on the teaching and learning of probability in the K-12 curriculum.

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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