Literature Index

Displaying 2311 - 2320 of 3326
  • Author(s):
    Monteiro, C., & Ainley, J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    The official inclusion of the teaching of graphing in school curricula has motivated increasing research and innovative pedagogical strategies such as the use of media graphs in school contexts. However, only a few studies have investigated knowledge about graphing among those who will teach this curricular content. We discuss aspects of the interpretation of media graphs among primary school student teachers from Brazil and England. We focus on data which came from questionnaires which gives evidence of elements of "Critical Sense," which involves the mobilisation of several kinds of knowledge and experiences, in the interpretation of statistical graphs.
  • Author(s):
    Meletiou-Mavrotheris, M. & Lee, C.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The findings reported in this article came from a study which took place in an introductory college-level statistics course and which adopted a nontraditional approach to statistics instruction that had variation as its central tenet. The conjecture driving the study was that poor understanding of statistical concepts might be the result of instructional neglect of variation and that instruction which puts emphasis on building student intuitions about variation and its relevance to statistics should also lead to improved comprehension of other statistical concepts. The results of the study point to a number of critical junctures and obstacles to the conceptual evolution of variation. The article discusses one of those critical junctures and obstacles, the understanding of histograms.
  • Author(s):
    Mellissinos, J. Ford, J. E., & McLeod, D. B.
    Year:
    1997
    Abstract:
    In the past decade, various countries have produced national mathematics eudcation reform documents that recommend that students learn to produce, explore and interpert disctributions of data meaningfully. The purpose of this study is to expand on what has been learned about student notions of the average as a represeentation of a distribution. For this paper we report interview data with Jim, a middle school student. We followed the investigation of Mokros and Russell (1995) and their framework for understanding how children develop an understanding of the concept of mean. Jim did not seem to fall clearly into any one of the strategy types identified by Mokros and Russell. His problem solving strategies for finding the mean seem to vary, but at the same time he maintains a consistent interest in making sense of the results of his computations. Jim did not appear to make sense of the mean as a statistical measure of a distribution.
  • Author(s):
    Belli, G. M.
    Editors:
    Brunelli, L., & Cicchitelli, G.
    Year:
    1993
    Abstract:
    What is it that makes the difference for students? What is their perspective on what benefited or hindered their learning in a first statistics class? Additionally, can students' reasons be categorized based on different student characteristics or learning styles? To answer these questions, the present investigation is a qualitative follow-up to a quantitative analysis that was aimed at determining relationships among learning styles, academic programs, background variables and attitude toward statistics.
  • Author(s):
    Li, K. Y., & Shen, S. M.
    Year:
    1992
    Abstract:
    This article classifies and discusses some of the common technical and conceptual mistakes found in the entries to student statistical project competitions in Hong-Kong.
  • Author(s):
    Samantha C. Bates Prins
    Year:
    2009
    Abstract:
    This paper provides an example of how student-centered instruction can be used in a theoretical statistics class. The author taught a two-semester undergraduate probability and mathematical statistics sequence using primarily teacher-centered instruction in the first semester and primarily student-centered instruction in the second semester. A subset of the students in the teacher-centered course also took the student-centered course. Student feedback suggests that the student-centered approach, while more difficult for both student and instructor, is beneficial when compared to the teacher-centered approach. The specific method of implementation will need to vary with class size and level of student preparation but the author's example presents a starting point for those interested in moving away from a traditional teaching approach in theoretical statistics classes.
  • Author(s):
    Roberts, H. V.
    Editors:
    Gordon, F., & Gordon, S.
    Year:
    1992
    Abstract:
    The present paper offers some ideas for student projects that can be used in the introductory statistics class.
  • Author(s):
    Konold, C., Pollatsek, A., Well, A., & Gagnon, A.
    Year:
    1997
    Abstract:
    In describing the work of the nineteenth-century statistician Quetelet, Porter (1986)<br>suggested that his major contribution was in:<br><br>...persuading some illustrious successors of the advantage that could be gained in certain cases by turning attention away from the concrete causes of individual phenomena and concentrating instead on the statistical information presented by the larger whole (pg. 55).<br><br>This observation describes the essence of a statistical perspective - attending to features of aggregates as opposed to features of individuals. In attending to where a collection of values is entered and how those values are distributed, statistics deals for the most part with features belonging not to any of the individual elements, but to the aggregate which they comprise. While statistical assertions such as "50% of marriages in the U.S. result in divorce" or "the life expectancy of women born in the U.S. is 78.3 years" might be used to make individual forecasts, they are more typically interpreted as group tendencies or propensities. In this article, we raise the possibility that some of the difficulty people have in formulating and interpreting statistical arguments results from their not having adopted such a perspective, and that they make sense of statistics by interpreting them using more familiar, but inappropriate, comparison schemes.
  • Author(s):
    Ana Elisa Castro Sotos, Stijn Vanhoof, Wim Van den Noortgate and Patrick Onghena
    Year:
    2007
    Abstract:
    A solid understanding of inferential statistics is of major importance for designing and interpreting empirical results in any<br>scientific discipline. However, students are prone to many misconceptions regarding this topic. This article structurally summarizes<br>and describes these misconceptions by presenting a systematic review of publications that provide empirical evidence of them. This<br>group of publications was found to be dispersed over a wide range of specialized journals and proceedings, and the methodology<br>used in the empirical studies was very diverse. Three research needs rise from this review: (1) further empirical studies that identify<br>the sources and possible solutions for misconceptions in order to complement the abundant theoretical and statistical discussion<br>about them; (2) new insights into effective research designs and methodologies to perform this type of research; and (3) structured<br>and systematic summaries of findings like the one presented here, concerning misconceptions in other areas of statistics, that might<br>be of interest both for educational researchers and teachers of statistics.<br>&copy; 2007 Elsevier Ltd. All rights reserved.
  • Author(s):
    Gordon, S.
    Editors:
    Biddulph, F. &amp; Carr, K.
    Year:
    1997
    Abstract:
    In this study I look at university students' orientations to learning statistics--how they feel about learning it, their conceptions of the subject matter and their approaches to learning it. I use complementary methods of anlysis to understand the relationships among students' appraisals and attainments on assessments. The findings reveal underlying dimensions of the variables and present dramatically different profiles of students' experiences. Students' learning is linked to a complex web of personal, social and contextual factors.

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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