Literature Index

Displaying 3321 - 3326 of 3326
  • Author(s):
    Aisling M. Leavy, Ailish Hannigan, and Olivia Fitzmaurice
    Year:
    2013
    Abstract:
    Most research into prospective secondary mathematics teachers’ attitudes towards statistics indicates generally positive attitudes but a perception that statistics is difficult to learn. These perceptions of statistics as a difficult subject to learn may impact the approaches of prospective teachers to teaching statistics and in turn their students’ perceptions of statistics. This study is the qualitative component of a larger quantitative study. The quantitative study (Hannigan, Gill and Leavy 2013) investigated the conceptual knowledge of and attitudes towards statistics of a larger group of prospective secondary mathematics teachers (n=134). For the purposes of the present study, nine prospective secondary teachers, eight of whom were part of the larger study, were interviewed regarding their perceptions of learning and teaching statistics. This study extends our understandings garnered from the quantitative study by exploring the factors that contribute to the perception of statistics as being difficult to learn. The analysis makes explicit the tensions in learning statistics by highlighting the nature of thinking and reasoning unique to statistics and the somewhat ambiguous influence of language and context on perceptions of difficulty. It also provides insights into prospective teachers’ experiences and perceptions of teaching statistics and reveals that prospective teachers who perceive statistics as difficult to learn avoided teaching statistics as part of their teaching practice field placement.
  • Author(s):
    Peter K. Dunn, Michael D. Carey, Alice M. Richardson, and Christine McDonald
    Year:
    2016
    Abstract:
    Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer of challenge. This paper identifies several challenges in teaching statistics related to language. Some implications for the effective learning and teaching of statistics are raised and methods to help students overcome these linguistic challenges are suggested.
  • Author(s):
    José Alexandre Martins, Maria Manuel Nascimento, and Assumpta Estrada
    Year:
    2012
    Abstract:
    Teachers’ attitudes towards statistics can have a significant effect on their own statistical training, their teaching of statistics, and the future attitudes of their students. The influence of attitudes in teaching statistics in different contexts was previously studied in the work of Estrada et al. (2004, 2010a, 2010b) and Martins et al. (2011). This work is part of a broader study of Portuguese education teachers and statistics. In the current paper, we use a qualitative content analysis of survey responses from Portuguese first-stage in-service teachers, focusing on nine open-ended items extracted from the Escala de Actitudes hacia la Estadística de Estrada (Estrada, 2002). These responses allow us to investigate teachers’ attitudes towards statistics, and their reasons and motivations for holding these attitudes.
  • Author(s):
    Sashi Sharma
    Year:
    2016
    Abstract:
    There exists considerable and rich literature on students’ misconceptions about probability; less attention has been paid to the development of students’ probabilistic thinking in the classroom. Grounded in an analysis of the literature, this article offers a lesson sequence for developing students’ probabilistic understanding. In particular, a context familiar to teachers—exploring compound events that occur in a game of chance—is presented, and it is demonstrated how the context can be used to explore the relationship between experimental and theoretical probabilities in a classroom setting. The approach integrates both the content and the language of probability and is grounded in sociocultural theory.
  • Author(s):
    McDaniel, Scott N.; Green, Lisa B.
    Year:
    2012
    Abstract:
    Online instructional modules that combine an applet, audio-visual tutorials, and guided discovery questions were created to teach the concept of sampling variability. The modules did contribute to an increase in understanding. However, they are a supplement to, not a replacement for, traditional instruction. The researchers found, using pretests and posttests, that student understanding of sampling distributions increased. There is room for further improvement, which could be accomplished in two ways. A focus on designing for the introductory, rather than advanced, statistics student could be helpful. Also, giving students more feedback could help their performance in later modules.
  • Author(s):
    Kaplan, Jennifer J; Haudek, Kevin C.; Ha, Minsu; Rogness, Neal; Fisher, Diane G.
    Year:
    2014
    Abstract:
    Meaningful assessments that reveal student thinking are vital to the success of addressing the GAISE recommendation: use assessments to improve and evaluate student learning. Constructed-response questions, also known as open-response or short answer questions, in which students must write an answer in their own words, have been shown to better reveal students' understanding than multiple-choice questions, but they are much more time consuming to grade for classroom use or code for research purposes. This paper describes and illustrates the use of two different software packages to analyze open-response data collected from undergraduate students’ writing. The analysis and results produced by the two packages are contrasted with each other and with the results obtained from hand coding of the same data sets. The article concludes with a discussion of the advantages and limitations of the analysis options for statistics education research.

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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