Literature Index

Displaying 2821 - 2830 of 3326

  • The Quanti.Com Challenge Teaching Online-Offline Courses In Quantitative Data Analysis To Quantophobic Students And Staff

    Author(s):
    Clarke, P. A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    In fulfilling their brief to build a digital university the Centre for IT in Higher Education (ITEd), presents academic modules in Digital Media to staff and students from technologically under-resourced backgrounds who have had limited opportunities for acquiring practical IT skills. In a core module on Research Data Analysis Theory and Tools, a component on Computer-based Quantitative Data Analysis develops competency in advanced software tools to assist implementation of research projects. A challenge of this course has been to build confidence in learners with limited statistical and quantitative backgrounds, who articulate anxiety and phobia about work of this nature. This paper reports on the design, implementation, assessment and outcomes of a predominantly Internet and web-based course that used the metaphor of a Big Bother/Survivor Challenge process to prepare largely 'quantophobic' post-graduate students and staff to conduct sound quantitative data analysis in research projects while reducing 'quantophobia' and building confidence. Initial results suggest that the use of the metaphor, together with other design features of the course, contributed to reduced 'quantophobia' and increased confidence with quantitative work.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/10_36_cl.pdf

  • The quantitative reasoning course at Mount Holyoke College

    Author(s):
    Cobb, G.
    Year:
    1990
    Abstract:
    In 1982, at Mount Holyoke College, a group of faculty began to plan what was eventually to find its way into the course catalog as Interdepartmental 100 -- Case Studies in Quantitative Reasoning. This paper is about the QR course, given in five sections. First, I shall describe the structure of the QR course, emphasizing its mechanics and content: then I'll turn to a closer look at some of what makes the course distinctive for some of us who have taught it. I'll end with two sections on assessment and one on exportability, in the hope not only that our blueprints can help interested others avoid reinventing a wheel or two, but equally important, that our experience can also save others from reinventing some of our more spectacular flat tires.

  • The Quest for the Constructivist Statistics Classroom: Viewing Practice Through Constructivist Theory

    Author(s):
    Miller, J. B.
    Editors:
    Norland, E. & Notz, W. I.
    Year:
    2000
    Abstract:
    The major assumption underlying this research is that all knowledge and understanding about statistics is constructed. Given that students construct their own knowledge, teaching must be designed to support knowledge construction. In this context, the global purpose addressed in this research is: "How do accomplished statistics educators support knowledge construction in their introductory statistics courses?" This global purpose is studied by attending to two more manageable questions: 1) What instructional strategies are being used in and around the statistics classroom?, and 2) What are the results of an analysis of these instructional strategies when the analysis is grounded in a constructivist perspective? "The Quest for the Constructivist Statistics Classroom" is a qualitative research study that investigated the teaching of four accomplished statistics educators (Paul Velleman at Cornell University, David Moore at Purdue University, Gudmund Iversen at Swarthmore College, and Beth Chance at California Polytechnic State University). Data collection methods included e-mail questionnaires, on-site interviews, and classroom observations of the participants. Instructional strategies employed by the participants were grouped into categories: strategies for how students come to know statistics; strategies involving technology; and, strategies for assessing student learning. For the purpose of data analysis, the following definition of constructivism was used: Constructivism is a theory of learning that allows students to develop and construct their own understanding of the material based upon their own knowledge and beliefs and experiences in concert with new knowledge presented in the classroom. During the analysis, it was decided that the instructional strategies being used in the participants' classrooms did not dichotomously support or not support constructivism, but rather supported constructivism to varying degrees. Some findings of the study included: 1) all four participants supported student construction of knowledge to some degree; 2) each of the participants employed multiple instructional strategies to involve the students in the learning process; and, 3) class size impacted the ability of the instructors to employ instructional strategies that were more supportive of knowledge construction. In addition, a series of questions intended to inspire further thought and research emerged from the study.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/dissertations/00.Miller.Dissertation.pdf

  • The Question Is Not So Much "Who Should?" but rather "Who Can?" teach statistics

    Author(s):
    Hawkins, A.
    Editors:
    Pereira-Mendoza, L.
    Year:
    1993
    Abstract:
    In 1989, the UK National Curriculum in Mathematics (Department of Education and Science, 1989) was introduced and the philosophy that statistics is a subject for all gained ground. Within the UK National curriculum, statistics (or "handling data" as it is known) now features throughout the years of compulsory education, i.e. it applies to all students between the ages of 5 and 16. For the purposes of this paper, "statistics" may be considered to be synonymous with "all the activities which pertain to data analysis", in which the role of uncertainty, or "probability", would be considered to be necessary for a full understanding of data analysis. At the 1988 ISI Round-table Conference Training teachers to teach statistics (Hawkins, 1990), the delegates concluded that there was a shortage of trained teaching personnel, both in the UK and throughout the world. Therefore, before addressing the question, "Who should teach statistics?" the 1992 Round-table delegates might have to consider the question, "Who is equipped to teach statistics?" When we attempt to answer this question, it may well be that views will emerge about the sort of person who would be most appropriately qualified (with a small "q") to teach statistics. However, if it were to be shown, for example, that only certain teachers have received any training at all in statistics, then, at least until this balance is redressed, these are presumably the teachers who "should" teach statistics.

  • The Reasoning Behind Informal Statistical Inference

    Author(s):
    Katie Makar; Arthur Bakker; Dani Ben-Zvi
    Year:
    2011
    Abstract:
    nformal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical literature on inference to argue that in order for students to generate informal statistical inferences, there are a number of interrelated key elements that are needed to support their informal inferential reasoning. In particular, we claim that ISI is nurtured by statistical knowledge, knowledge about the problem context, and useful norms and habits developed over time, and is supported by an inquiry-based environment (tasks, tools, scaffolds). We adopt Peirce's and Dewey's view that inquiry is a sense-making process driven by doubt and belief, leading to inferences and explanations. To illustrate the roles that these elements play in supporting students to generate informal statistical inferences, we provide an analysis of three sixth-graders' (aged 12) informal inferential reasoning - the reasoning processes leading to their informal statistical inferences.
    Location:
    http://www.informaworld.com/smpp/title~db=all~content=g932654185

  • The reasoning behind informal statistical inference

    Author(s):
    Makar, K., Bakker, A., & Ben-Zvi, D.
    Year:
    2011
    Abstract:
    Informal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical literature on inference to argue that in order for students to generate informal statistical inferences, there are a number of interrelated key elements that are needed to support their informal inferential reasoning (IIR). In particular, we claim that ISI is nurtured by statistical knowledge, knowledge about the problem context, and useful norms and habits developed over time, and supported by an inquiry-based environment (tasks, tools, scaffolds). We adopt Peirce’s view that inquiry is a sense-making process driven by doubt and belief, leading to inferences and explanations. To illustrate the roles that these elements play in supporting students to generate informal statistical inferences, we provide an analysis of three sixth graders’ (aged 12) informal inferential reasoning—the reasoning processes leading to their informal statistical inferences.

  • The relationship between attitudes toward statistics, math self-concept, test anxiety, and graduate students' achievement in an introductory statistics course

    Author(s):
    Perney, J., & Ravid, R.
    Abstract:
    The major objective of this study was to determine the relationship between academic performance in a statistics course and the students' attitude toward statistics, math self-concept, and attitudes toward tests. A secondary objective was to determine what relationships exist between students' attitudes toward statistics, math self-concept, and attitudes toward tests. A third objective was to investigate the relationship between students' mathematics background and attitude toward statistics, math self-concept, and attitude toward tests.

  • The relationship between college student learning styles and assessment methods in elementary statistics

    Author(s):
    Miller, G. F.
    Editors:
    Smith, P. J.
    Year:
    2001
    Abstract:
    This study investigated the relationship between students' learning styles and their choice of grade weightings for and performance on three classroom assessment instruments. The student learning styles were measured using McCarthy's Learning Type Measure. Students chose weightings for a take home examination, a research project, and an in-class examination. The sample for the study consisted of 44 students in two sections of an elementary statistics course at an urban community college. Using analysis of variance of the weighting and performance data when organized by student learning type, the investigator found no significant relationship between learning types and the grade weightings and no significant relationship between learning types and performance on the assessment instruments. There was a significant positive correlation between the score for Type 2 learners and the performance on the in-class examination and also a significant positive correlation between the score for Type 4 learners and the weight assigned for the project. Both of these correlations validate the characteristics of these learning types as they are conceptualized by McCarthy, Kolb, and others. Type 4 learners are generally risk takers and learn by perceiving through concrete experience and processing through active experimentation. Type 2 learners generally perceive information through reflective observation data, the investigator conducted clinical interviews to ascertain differences between students' rationales for their grade weightings and how student learning styles may have affected students' performances on the various assessment instruments.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/dissertations/dissertations.php

  • The relationship between computer science aptitude and success in statistical methods courses utilizing statistical computer packages: Zusammenhang zwischen Begabung fuer Informatik und Begabung fuer Statistik

    Author(s):
    Stephens, L. J.
    Year:
    1984
    Abstract:
    Previous research has shown that a significant correlation exists between mathematical competency and computer science aptitude and achievement. These results were reported by Wileman, Stephens and Konvalina (1982). In Addi Tion, Stephens (1982) reported a significant correlation between mathematical competency and achievement in statistical methodology. This paper reports on research carried out to determine if this relationship is transitive. That is, since achievement in statistical methods is significantly correlated to mathematical competency, and mathematical competency is significantly correlated to computer science aptitude, is it true that achievement in statistical methodology is significantly correlated with computer science aptitude. (orig.)

  • The relationship between mathematical and statistical anxiety and statistical attitudes

    Author(s):
    Loch, S. Y.
    Year:
    2000
    Abstract:
    The research literature on affect and statistics consists of many studies that examined the relationships between mathematics and statistical anxiety and achievement, and between statistical attitudes and achievement. However, what has not been looked at is the relationship between mathematics and statistical anxiety and statistical attitudes. This is an important relationship to examine because it may help explain the relationship in these two bodies of research. Therefore, the current study was designed to investigate the relationship between students' pre-course mathematical and statistical anxiety and their post-course statistical attitudes. In addition, gender differences and graduate/undergraduate status differences will be examined to see how they figure into this relationship.

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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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