Literature Index

Displaying 1041 - 1050 of 3326
  • Author(s):
    Biehler, R.
    Year:
    1982
    Abstract:
    This paper critically discusses explorative data analysis (EDA) from the point of view of an empirical descriptive scientific theory. EDA deals mainly with the exploration of data by means of predominantly graphical representations, i.e. the search for striking elements and structures in data sets and for simple collective descriptions of the phenomena revealed. The proper analysis of EDA in this piece of work is intended to lay a foundation for further didactic research and development work in this field, in particular as to whether it can effectively be made available to a wider circle of people.
  • Author(s):
    Yu, C. H.
    Year:
    2001
    Abstract:
    This is a brief introducation to exploratory data analysis (EDA) and data visualization. You will come across several unfamiliar terms and graphs, but you don't have to fully understand them at this moment. The purpose of this write-up is to let you be aware that tools are available and what can be done. The philosophy and specific techniques of EDA will be introduced in further readings.
  • Author(s):
    Biehler, R.
    Editors:
    Davidson, R., & Swift, J.
    Year:
    1986
    Abstract:
    The following paper will present some tentative ideas for the discussion on how the further evolution of curricula may react to changes in statistics which have become visible by the emergence of EDA. The emergence of Exploratory Data Analysis (EDA) presents a challenge to more traditional views, attitudes and value systems of statistics, which are often also the implicit basis of curricula and teaching approaches to statistics and probability. Simple examples, ideas and techniques of EDA are sometimes considered to be a new curriculum content, because it is hoped that they may replace the rather boring teaching of techniques of descriptive statistics by more interesting examples of real data analysis, where the students may become more actively involved in processes of discovering relevant features of the systems the data refer to.
  • Author(s):
    Ben-Zvi, D. & Ben-Arush
    Editors:
    D. Frischemeier, P. Fischer, R. Hochmuth, T. Wassong and P. Bender
    Year:
    2014
    Abstract:
    Recent investigations of technology-supported learning conducted from an instrumental perspective provide a powerful framework for analyzing the process through which artifacts become conceptual tools and for characterizing the ways students come to understand and implement a tool in solving a task. In this chapter, we focus on instrumentation – the process of transforming an artifact (component/s in the tool) into an instrument that is meaningful and useful to the learners – in the context of statistics education. Our goal is to characterize children’s instrumentation in solving Exploratory Data Analysis (EDA) tasks. To illustrate this process, we bring short episodes from a case study of two fifth graders studying EDA with TinkerPlots in the 2012 Connections project. We suggest three types of instrumentation: unsystematic, systematic, and expanding. We also note that expanding instrumentation is hindered sometimes by instrumented fixation. We conclude by presenting several challenges stemming from the implementation of instrumental theory in the context of learning statistics. 
  • Author(s):
    Khurshid, A. & Sahai, J.
    Year:
    1993
    Abstract:
    For a quick and overall assessment of data sets, graphical methods are used extensively. Graphic statistics are devices for representing or summarizing numerical data or information. In this paper, some relatively new techniques, frequently referred to as methods of exploratory data analysis, are dicussed and illstrated with numerical examples.
    Location:
  • Author(s):
    JANE M. WATSON
    Year:
    2008
    Abstract:
    This study documented efforts to facilitate ideas of beginning inference in novice<br>grade 7 students. A design experiment allowed modified teaching opportunities in<br>light of observation of components of a framework adapted from that developed by<br>Pfannkuch for teaching informal inference with box plots. Box plots were replaced by<br>hat plots, a feature available with the software TinkerPlotsTM. Data in TinkerPlots<br>files were analyzed on four occasions and observed responses to tasks were<br>categorized using a hierarchical model. The observed outcomes provided evidence of<br>change in students' appreciation of beginning inference over the four sessions.<br>Suggestions for change are made for the use of the framework in association with the<br>intervention and the software to enhance understanding of beginning inference.
  • Author(s):
    Lehrer, R. &amp; Romberg, T.
    Year:
    1996
    Abstract:
    Explored 5th graders' reasoning about data modeling by conducting 2 design experiments. In Exp 1, 10 Ss assumed the role of data analysts and developed a survey, collected and coded data, and used the dynamic notations of hypermedia to compare the lifestyles of American colonists to their own. In Exp 2, 2 5th graders and their teacher developed and used a randomized distribution to reason about the likelihood of ESP. Analysis of student conversations, including their dialogue with the teacher-researcher, indicated that the construction of data was an important preamble to description and inference. Students' ideas about many elements of data modeling were related to forms of notation. Experimentation afforded a framework for teaching about inference, grounded by the creation of a randomization distribution of the students' data.
  • Author(s):
    Nancy C. Lavigne, Sara J. Salkind, Jie Yan
    Year:
    2008
    Abstract:
    We report a case study that explored how three college students mentally represented the knowledge they held of inferential statistics, how this knowledge was connected, and how it was applied in two problem solving situations. A concept map task and two problem categorization tasks were used along with interviews to gather the data. We found that the students' representations were based on incomplete statistical understanding. Although they grasped various concepts and inferential tests, the students rarely linked key concepts together or to tests nor did they accurately apply that knowledge to categorize word problems. We suggest that one reason the students had difficulty applying their knowledge is that it was not sufficiently integrated. In addition, we found that varying the instruction for the categorization task elicited different mental representations. One instruction was particularly effective in revealing students' partial understandings. This finding suggests that modifying the task format as we have done could be a useful diagnostic tool.
  • Author(s):
    Luis A. Saldanha and Patrick W. Thompson,
    Editors:
    Carmen Batanero
    Year:
    2007
    Abstract:
    Construing a collection of values of a sample statistic as a distribution is central to<br>developing a coherent understanding of statistical inference. This paper discusses key developments that<br>unfolded over three consecutive lessons in a classroom teaching experiment designed to support a group of<br>high school students in developing such a construal. Instruction began by engaging students in activities<br>that focused their attention on the variability among values of a common sample statistic. There occurred a<br>critical shift in students' attention and discourse away from individual values of the statistic and toward a<br>collection of such values as a basis for inferring the value of a population parameter. This was followed by<br>their comparisons of such collections and by the emergence and application of a rule for deciding whether<br>two such collections were similar. In the repeated application of their decision rule students structured these<br>collections as distributions. We characterize aspects of these developments in relation to students'<br>classroom engagement, and we explore evidence in students' written work that points to how instruction<br>shaped their conceptions.
  • Author(s):
    Paparistodemou, E., Noss, R., &amp; Pratt, D.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This paper focuses on 6-8 year-old children's thinking about randomness. It reports the findings of a study in which the children engaged with a game-like environment to construct for themselves spatial representations of sample space. The system was designed so that the rules governing the relationships between the selection of elements of the sample space and the outcomes of the game were available for inspection and reconstruction by the children. In response to a range of tasks, the children manipulated the sample space in ways that generated corresponding outcomes in the game. We present a case study of children's activities, which illustrates how the novel medium mediates the children's expression and understanding of chance events.

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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

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