Literature Index

Displaying 1251 - 1260 of 3326

  • Improving open-ended problem solving: Lessons learned in industry

    Author(s):
    Zayac, S. A.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    What's needed to excite interest in mathematics? We've found a common approach useful both in industry and the schools. Focussing on open-ended problems reveals both the practical impact of quantitative thinking and the role advanced numerical methods play in improving the quality of decisions. Using examples drawn from industry, a framework will be provided to introduce statistical thought into asking questions and getting answers.

  • Improving Self-efficacy in Statistics: Role of Self-explanation & Feedback

    Author(s):
    Simin Hall and Eric Vance
    Year:
    2010
    Abstract:
    Novice problem solvers often fail to recognize structural similarities between problems they know and a new problem because they are more concerned with the surface features rather than the structural features of the problem. The surface features are the story line of the problem whereas the structural features involve the relationships between objects in the problem. We used an online technology to investigate whether students' self-explanations and reception of feedback influenced recognition of similarities between surface features and structural features of statistical problems. On average students in our experimental group gave 12 comments in the form of self-explanation and peer feedback. Students in this Feedback group showed statistically significantly higher problem scores over the No-Feedback group; however, the mean self-efficacy scores were lower for both groups after the problem solving experiment. The incongruence in problem scores with self-efficacy scores was attributed to students' over-rating of their abilities prior to actually performing the tasks. This process of calibration was identified as an explanation for the statistically significant positive correlation between problem solving scores and post self efficacy scores for the Feedback group (p<.01).
    Location:
    http://www.amstat.org/publications/jse/v18n3/hall.pdf

  • Improving statistical education through the experience of reflective practice.

    Author(s):
    Porter, A. L.
    Editors:
    Hedberg, J., & Griffiths, D.
    Year:
    2001
    Abstract:
    This thesis is the recount of a study that began with the aim of unpacking the statistical expertise of the teacher and author, with the intent of improving statistics teaching and learning. In the process of doing this, the researcher examined the expertise of other experts through a case study of a statistics professor, concept mapping of ideas of statistics professionals and through an examination of statistical literature. As the researcher and teacher moved to a position of accepting that statistics is a study of variation, she discovered a failure by authors of many introductory textbooks to appropriately acknowledge variation as a (the?) fundamental statistical concept.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/dissertations/01.Porter.Dissertation.pdf

  • Improving Statistical Reasoning By Using The Right Representational Format

    Author(s):
    Sedlmeier, P.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    New results in research on judgment under uncertainty show a way of how to improve the teaching of statistical reasoning. The implications of this research are that (i) successful learning needs doing, and (ii) that the format in which information is represented plays a decisive role. Statistical problems are, for instance, solved much better if the relevant pieces of information are presented as frequencies rather than probabilities. It also helps a lot if random processes can be observed rather than only read about. A computer program is presented that incorporates these implications from psychological research. The software accompanies an elementary text book on probability theory to be used in high school.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/3f3_sedl.pdf

  • Improving Statistical Reasoning: Theoretical Models and Practical Implications

    Author(s):
    Sedlmeier, P.
    Year:
    1999
    Abstract:
    Statistical literacy, the art of drawing reasonable inferences from an abundance of numbers provided daily by the media, is indispensable for an educated citizenship, as are reading and writing. Unlike reading and writing, however, sound statistical reasoning is rarely taught, and if it has been taught, it was with little success. This book presents and discusses new empirical and theoretical results about the topic of eveyday statistical reasoning, that is, how people think and act on probabilistic information. It focuses on how porcesses of statistical reasoning work in detail and how training programs can exploit natural cognitive capabilities to improve statistical reasoning. (From preface)

  • Improving statistics education through simulations: the case of the sampling distribution

    Author(s):
    Earley, M. A.
    Year:
    2001
    Abstract:
    This paper presents a summary of action research I am doing investigating statistics students' understandings of the sampling distribution of the mean. With 4 sections of an introductory Statistics in Education course (n=98 students), I have implemented and evaluated a computer simulation activity (delMas, Garfield, & Chance, 1999) to show students the sampling distribution "in action," as recommended by many authors and statistics professors. Assessments after the activity point to clear deficiencies in student understanding, such as not understanding how sample size affects the shape and variability of sampling distributions. With the addition of the computer simulation, students' understandings are still incomplete. This is most evident as I move forward in class and introduce inferential statistics. My results discuss some of the deficiencies I have identified in student understandings. Also included is a discussion of how the action research cycle will continue to work on these deficiencies.

  • Improving student learning: Using computer generated data sets to individualise student assignments

    Author(s):
    Glencross, M. J.
    Year:
    1990
    Abstract:
    This paper describes a recent and on-going development intended to enhance student learning by making use of individualised computerised assignments for a class of first year students at the University of Witwatersrand, Johannesburg.

  • Improving students' attitudes towards statistics

    Author(s):
    Phillips, B.
    Editors:
    Hawkins, A.
    Year:
    1990
    Abstract:
    This paper looks at the question of students' attitudes towards statistics and suggests ways of improving their feelings about this subject.
    Location:
    RISE

  • Improving the quality of teaching statistics: Techniques from research on classroom learning and assessment

    Author(s):
    Busk, P. L.
    Abstract:
    Quality of TQM have become busswords of the 90s. They follow you everywhere, at work, at school, and even into your classroom. Applying TQM to the teaching of statistics means that we need to know how our students learn in order to affect the quality of our teaching. The time and energy that an instructor puts into preparation and teaching og a course will be wasted and the teacher will be ineffective, if he or she does not moticate and direct student learning. Boroto and Zahn (1989) claim that "quality improvement iscritical for all levels of statistics education if we are to avoid withering and dying as a discipline" (p. 71).

  • Improving the Teaching of Applied Statistics: Putting the Data Back Into Data Analysis

    Author(s):
    Singer, J. D., & Willett, J. B.
    Year:
    1990
    Abstract:
    Artificial data sets are often used to demonstrate statistical methods in applied statistics courses and textbooks. We believe that this practice removes much of the intrinsic interest in learning to do good data analysis and contributes to the myth that statistics is dry and dull, In this article, we argue that artificial data sets should be eliminated from the curriculum and that they should be replaced with real data sets. Real data supplemented by suitable background material enable students to acquire analytic skills in an authentic research context and enable instructors to demonstrate how statistical analysis is used to model real data into applied statistics curricula, we identify seven characteristics that make data sets particularly good for instructional use and present an annotated bibliography of more than 100 primary and secondary data sources.

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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