Literature Index

Displaying 1541 - 1550 of 3326
  • Author(s):
    Shaughnessy, J. M., Ciancetta, M. & Canada, D.
    Year:
    2003
    Abstract:
    This paper summarizes the thinking of 84 middle school mathematics students' about variability in three stochastics tasks that involve repeated trial. Differences in students' acknowledgement of variability were found, depending on whether the task was from a sampling environment, or a probability environment. Students' tended to neglect variability in the probability environment. We conjecture that the way that probability is normally introduced to students is part of the cause of this phenomenon.
  • Author(s):
    McNeill, K., & Krajcik, J.
    Editors:
    Lovett, M. C., & Shah, P.
    Year:
    2007
  • Author(s):
    Ayşe Yolcu
    Year:
    2014
    Abstract:
    This study examined the role of gender and grade level on middle school students’ statistical literacy. The study was conducted in the spring semester of the 2012-2013 academic year with 598 middle-school students (grades 6–8) from three public schools in Turkey. The data were collected using the Statistical Literacy Test, developed based on Watson’s (1997) statistical literacy framework. Two-way ANOVA results revealed no significant grade level differences although female students performed significantly better than male students. The spiral curriculum in middle school mathematics may explain the lack of differences between grades. The higher performance of female students may be related to the linguistic aspects of statistical literacy, in contrast to the situation in school mathematics.
  • Author(s):
    Falk, R.
    Abstract:
    Descriptive statistics offer us several averages for a given set of variable numbers. Most elementary courses introduce the mean (i.e., arithmetic mean), the median and the mode.<br>On average, which is supposed to characterize a given distribution of values, is never identical with all the values (except for the trivial case). Each possible suggestion of an average involves some inaccuracy. The answer to the question "what is the best representation of the numbers?" depends on what is meant by "best representation". One could interpret this to mean that the average incurs the least possible "cost" in terms of differences between the average and the actual values. Each definition of the "cost" could be minimized by an appropriate average. Asking students to pay (symbolically) the costs of the errors incurred through use of different averages might introduce the averages via the idea of the least combined error.<br><br>The following procedure, which may be represented as a game in the classroom, has helped my students on both secondary school and college level.
  • Author(s):
    Romeu, J. L., &amp; Gascon, V. G.
    Year:
    1999
    Abstract:
    Laboratory, workshop, and cooperative learning approaches are some pedagogical methods that raise student interest and involvement in their course work. The present article describes an experiment in applying such methods to teaching a general statistics course to non-mathematics majors, and its statistical assessment. A voluntary, one-hour weekly lab was offered to the general statistics course students. It was developed using computers, e-mail, and Minitab, in conjunction with learning groups, and with the utilization of a Lab Assistant. The results of such experience was then assessed through several instruments, including a student survey that collected their reactions, comments, and suggestions for improvements. Then, a preliminary statistical analysis of some of the course data collected, comparing grade results of students who attended the workshop with those who did not, is presented. Finally, some general conclusions regarding this workshop's effectiveness, its recruitment and retention efforts and directions for future work, are also discussed.
  • Author(s):
    Butler, A., Rothery, P. &amp; Roy, D.
    Editors:
    Goodal, G.
    Year:
    2003
    Abstract:
    Describes a set of Minitab macros that perform randomization and bootstrap versions of basic statistical techniques. Content of the macros; Use of the macros for teaching; Example.
  • Author(s):
    Wilkerson, M., &amp; Olson, J.R.
    Year:
    1997
  • Author(s):
    Jun, L. &amp; Pereira-Mendoza, L.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The study investigated the probabilistic misconceptions of Chinese students, and whether selected misconceptions could be overcome through a focused teaching intervention. A questionnaire was given to a 567 Chinese students from grades 6, 8 and 12 and two streams (advanced and ordinary). In addition 64 of the students were interviewed. Fourteen groups of misconceptions were identified. The SOLO taxonomy was used in this study to describe students' hierarchical understanding levels on the concept of probability. It was found that, generally there was no improvement in developmental level from grades 6 and 8, the two grades without any formal probability training. Grade 12 students have a better understanding than the younger students. The results of the activity-based short-term teaching programme with grade 8 students show that even a short intervention can help students overcome some of their misconceptions.
  • Author(s):
    Shaughnessy, J. M.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., &amp; Constable, G. M.
    Year:
    1983
    Abstract:
    In this paper, we will discuss the types of misconceptions that arise from students' responses to the five problems. Then we will consider the implications of these misconceptions for the teaching of probability and statistics, and suggest some approaches to probability that may be useful for confronting the misconceptions that our students possess.
  • Author(s):
    Shaughnessy, J. M.
    Year:
    1977
    Abstract:
    This article discusses the misconceptions of probability based on an experiment with college students.

Pages

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

register