Literature Index

Displaying 1 - 10 of 3326
  • Author(s):
    Stephanie Budgett and Drusilla Rose
    Year:
    2017
    Abstract:
    Statistical information pervades everyday life in the twenty-first century. Research shows, however, that the skills needed to be able to understand and critically evaluate statistical information must be specifically taught. In 2013, an externally assessed National Certificate in Educational Achievement standard in statistical literacy was introduced for the first time in New Zealand. A small exploratory study investigated a possible teaching approach designed to enable Year- 13 students (aged 17-18) to critically evaluate media reports. Findings suggest that the learning trajectory required several key components including media reports as both a motivational and conceptual development tool. In addition, computer visualizations and procedural scaffolds appeared valuable tools for facilitating conceptual understanding of the margin of error.
  • Author(s):
    Julie Scott Jones and John E. Goldring
    Year:
    2017
    Abstract:
    The issue of poor statistical literacy amongst undergraduates in the United Kingdom is well documented. At university level, where poor statistics skills impact particularly on social science programmes, embedding is often used as a remedy. However, embedding represents a surface approach to the problem. It ignores the barriers to learning that students bring to class, which may not always be addressed solely through embedding, such as, mathematics anxiety. Instead, embedding can only work within a much deeper pedagogic model that places students at its heart, as active participants in learning. This paper examines the development of such a model within a large sociology programme, where there was an implementation of a range of pedagogic strategies to support the development of students’ statistical literacy.
  • Author(s):
    Arthur Louis Odom & Clare Valerie Bell
    Year:
    2017
    Abstract:
    This article offers a description of how empirical experiences through the use of procedural knowledge can serve as the stage for the development of hypothetical concepts using the learning cycle, an inquiry teaching and learning method with a long history in science education. The learning cycle brings a unique epistemology by way of using procedural knowledge (“knowing how”) to enhance construction of declarative knowledge (“knowing that”). The goal of the learning experience was to use the learning cycle to explore “high tech” and “low tech” approaches to concept development within the context of statistics. After experiencing both, students recognized the value of high and low tech approaches to instruction. Given that statistical literacy is essential for engaging in PK-12 education, we argue that providing experiences that help preservice teachers understand statistical concepts while modeling effective pedagogical practices will help prepare them for planning instruction and teaching statistics concepts in PK-12 classrooms. This article provides an example of how to meaningfully incorporate statistics into a nonstatistics course for preservice teachers. Empirical experiences prior to introduction of mathematical and hypothetical concepts are necessary pedagogical practice.  
  • Author(s):
    Anna Helga Jonsdottir, Audbjorg Bjornsdottir & Gunnar Stefansson
    Year:
    2017
    Abstract:
    A repeated crossover experiment comparing learning among students handing in pen-and-paper homework (PPH) with students handing in web-based homework (WBH) has been conducted. The system used in the experiments, the tutor-web, has been used to deliver homework problems to thousands of students in mathematics and statistics over several years. Since 2011, experimental changes have been made regarding how the system allocates items to students, how grading is done, and the type of feedback provided. The experiment described here was conducted annually from 2011 to 2014. Approximately, 100 students in an introductory statistics course participated each year. The main goals were to determine whether the above-mentioned changes had an impact on learning as measured by test scores in addition to comparing learning among students doing PPH with students handing in WBH.     The difference in learning between students doing WBH compared to PPH, measured by test scores, increased significantly from 2011 to 2014 with an effect size of 0.634. This is a strong indication that the changes made in the tutor web have a positive impact on learning. Using the data from 2014, a significant difference in learning between WBH and PPH for 2014 was detected with an effect size of 0.416 supporting the use of WBH as a learning tool.  
  • Author(s):
    Thomas G. Edwards , Aslı Özgün-Koca & John Barr
    Year:
    2017
    Abstract:
    Boxplots are statistical representations for organizing and displaying data that are relatively easy to create with a five-number summary. However, boxplots are not as easy to understand, interpret, or connect with other statistical representations of the same data. We worked at two different schools with 259 middle school students who constructed and interpreted boxplots. We observed that even students who were able to create boxplots had difficulty interpreting data represented in a boxplot. After sharing specific difficulties that we observed students having, we discuss ways to help students to make sense of data presented in boxplots.  
  • Author(s):
    Julian Stander & Luciana Dalla Valle
    Year:
    2017
    Abstract:
    We discuss the learning goals, content, and delivery of a University of Plymouth intensive module delivered over four weeks entitled MATH1608PP Understanding Big Data from Social Networks, aimed at introducing students to a broad range of techniques used in modern Data Science. This module made use of R, accessed through RStudio, and some popular R packages. After describing initial examples used to fire student enthusiasm, we explain our approach to teaching data visualization using the ggplot2 package. We discuss other module topics, including basic statistical inference, data manipulation with dplyr and tidyr, data bases and SQL, social media sentiment analysis, Likert-type data, reproducible research using RMarkdown, dimension reduction and clustering, and parallel R. We present four lesson outlines and describe the module assessment. We mention some of the problems encountered when teaching the module, and present student feedback and our plans for next year.
  • Author(s):
    Khalil, K.I., & Konold, C.
    Year:
    2002
    Abstract:
    In this experiment, we investigate the correspondence between how graph-readers visually inspect a graph to answer a comparison question about two groups and the justifications they offer. We recorded how people visually inspected graphs using a device that restricted how much data they could see at any given time. Students offered a variety of justifications for why two groups differed (e.g., slices, cut-points, modal clumps), and these appear to correspond to how they visually parsed the data.
  • Author(s):
    Wiggins, G.
    Year:
    2001
    Abstract:
    The result of students' endless exposure to typical tests is a profound lack of understanding about what mathematics is:"Perhaps the greatest difficulty in the whole area of mathematics concerns students' misapprehension of what isactually at stake when they are posed a problem. . . . [S]tudents are nearly always searching for [how] to follow the algorithm. . . . Seeing mathematics as a way of understanding the world . . . is a rare occurrence."9 Surely this has more to do with enculturation via the demands of school, than with some innate limitation.10
  • Author(s):
    Trickett, S., Trafton, J. G., Saner, L., & Schunn, C.
    Editors:
    Lovett, M. C., & Shah, P.
    Year:
    2007
  • Author(s):
    Saldanha, L.
    Editors:
    Thompson, P.
    Year:
    2004
    Abstract:
    This study explores the reasoning that emerged among eight high school juniors and seniors as they participated in a classroom teaching experiment addressing stochastic conceptions of sampling and statistical inference. Toward this end, instructional activities engaged students in embedding sampling and inference within the foundational notion of sampling distributions - patterns of dispersion that one conceives as emerging in a collection of a sample statistic's values that accumulate from re-sampling.<br>The study details students' engagement and emergent understandings in the context of<br>instructional activities designed to support them. Analyses highlight these components: the design of instructional activities, classroom conversations and interactions that emerged from students' engagement in activities, students' ideas and understandings that emerged in the process, and the design team's interpretations of students' understandings. Moreover, analyses highlight the synergistic interplay between these components that drove the unfolding of the teaching experiment over the course of 17 lessons in cycles of design, engagement, and interpretation. These cycles gave rise to an emergent instructional trajectory that unfolded in four interrelated phases of instructional engagements:<br>Phase 1: Orientation to statistical prediction and distributional reasoning;<br>Phase 2: Move to conceptualize probabilistic situations and statistical unusualness;<br>Phase 3: Move to conceptualize variability and distribution;<br>Phase 4: Move to quantify variability and extend distribution.<br>Analyses reveal that students experienced significant difficulties in conceiving the distribution of sample statistics and point to possible reasons for them. Their difficulties centered on composing and coordinating imagined objects with actions into a hierarchical structure in re-sampling scenarios that involve: a population of items, selecting items from the population to accumulate a sample, recording the value of a sample statistic of interest, repeating this process to accumulate a collection of data values, structuring such collections and conceiving patterns within and across them in ways that support making statistical inferences.

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