Literature Index

Displaying 481 - 490 of 3326

  • Beyond testing and grading: Alternative approaches to assessing student learning

    Author(s):
    Garfield, J. B.
    Abstract:
    This report covers the topic of assessment. It provides a broad overview of the definition of assessment and the different types of assessments (portfolio, authentic, performance), and it then discusses issues such as what the purposes of assessment are, what should be assessed, how assessment should proceed, and what the implications of assessment are for instructors.

  • Beyond Testing and Grading: Using Assessment to Improve Student Learning

    Author(s):
    Garfield, J. B.
    Year:
    1994
    Abstract:
    Changes in educational assessment are currently being called for, both within the fields of measurement and evaluation as well as in disciplines such as statistics. Traditional forms of assessment of statistical knowledge provide a method for assigning numerical scores to determine letter grades but rarely reveal information about how students actually understand and can reason with statistical ideas or apply their knowledge to solving statistical problems. As statistics instruction at the college level begins to change in response to calls for reform (e.g., Cobb 1992), there is an even greater need for appropriate assessment methods and materials to measure students' understanding of probability and statistics and their ability to achieve more relevant goals, such as being able to explore data and to think critically using statistical reasoning. This paper summarizes current trends in educational assessment and relates these to the assessment of student outcomes in a statistics course. A framework is presented for categorizing and developing appropriate assessment instruments and procedures.
    Location:
    http://www.amstat.org/publications/jse/v2n1/garfield.html

  • Bibliography In Statistics To Enhance Research

    Author(s):
    Del Angel, F. C.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The document presented here, called "Análisis Multidimensional de Datos" in Spanish ("Multidimensional Data Analysis") is geared toward engineering students in Mexico, Latin America, and Spain. It shows all the theory of data analysis, starting with a biographical sketch of its historical development and how data are organized. It deals with the theory of factorial analysis and scalograms, beginning with establishing information management. A large variety of applications with actual data are presented throughout the text, and a set of programs is furnished that can be implemented easily in a personal computer. The related software is listed at the end.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/10_64_an.pdf

  • Biostatistics teaching in the new genome era.

    Author(s):
    Soler, J. P., & Pereira, A.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    The objective of this article is to introduce a multidisciplinary biostatistics course program, targeting professionals and academics involved in researching genetics and genomics. The program is designed to adapt statistical concepts, uses and language to this field, which is at the forefront of knowledge. The idea underlying this initiative arose through a research project conducted by physicians and statisticians of the University of São Paulo, Brazil, whose purpose was to identify genetic determinants associated with cardiovascular risk factors in the Brazilian population.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/4C2_SOLE.pdf

  • Blind Students’ Learning of Probability Through the Use of a Tactile Model

    Author(s):
    Aida Carvalho Vita, and Verônica Yumi Kataoka
    Year:
    2014
    Abstract:
    The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence ‘Jefferson’s Random Walk’, in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D (using the toys) to represent the possible ways in which Jefferson can visits his five friends and the expected frequencies of visits. The analysis of students’ answers was based on the SOLO taxonomy, and developed from initial prestructural responses to final responses that were classified at the relational level. The study suggests adaptations of materials and teaching methods for helping blind students to learn about probability.
    Location:
    http://iase-web.org/documents/SERJ/SERJ13(2)_Vita.pdf

  • Bluebells and bias, stitchwort and statistics

    Author(s):
    du Feu, C.
    Editors:
    Goodall, G.
    Year:
    2005
    Abstract:
    Infants are too young to engage in real, useful statistical work. This activity allowed comparisons between distributions of two species of flowers in three different habitats.<br>How old must children be before they can learn about statistics? I had agreed to lead a group of six-year-old around a woodland nature reserve. The prime aim of the visit was for it to be used as a stimulus for written work in connection with the Key Stage 1 writing test (England, age 6-7) where children are expected to recount sequences of events. I wished to ensure that the visit was used to give them some understanding of at least some aspects of the ecology of the wood - clearly an opportunity for statistical work. I was assured by their teacher that they could record using the 5-bar-gate tallying system. This is a basic statistical skill but is sufficient to be able to examine differences in habitat preferences between plant species.

  • Book review: Stochastics in the interplay of intuition and mathematics: Buchbesprechung: Stochastik im Wechselspiel von Intuition und Mathematik

    Author(s):
    Sill, H. D.
    Year:
    1993
    Abstract:
    Rezension von Borovcnik: Stochastik im Wechselspiel von Intuition und Mathematik, 1992. Kurze Inhaltsangabe der einzelnen Kapitel.

  • Bootstrapping student understanding of what is going on in econometrics

    Author(s):
    Kennedy, P. E.
    Year:
    2001
    Abstract:
    Econometrics is an intellectual game played by rules based on the sampling<br>distribution concept. Most students in econometrics classes are uncomfortable<br>because they do not know these rules and so do not understand what is going<br>on in econometrics. This article contains some explanations for this phenomenon<br>and suggestions for how this problem can be addressed. Instructors are encouraged<br>to use explain-how-to-bootstrap exercises to promote student understanding<br>of the rules of the game.
    Location:
    http://www.indiana.edu/~econed/pdffiles/spring01/kennedy.pdf

  • Bootstrapping students' understanding of statistical concepts.

    Author(s):
    Hesterberg, T.
    Editors:
    Burrill, G. F.
    Year:
    2006
    Abstract:
    This article shows K-12 teachers how the bootstrap and permutations tests can be used to make abstract concepts such as sampling distributions and standard error concrete. Some background information on the bootstrap is provided before a demonstration on the mechanics of the bootstrap, including the calculation of confidence intervals and standard errors is provided. Finally a comparison of bootstrap confidence intervals with classical intervals is presented.

  • Brain-based mechanisms underlying complex causal thinking

    Author(s):
    Fugelsang, J. A. &amp; Dunbar, K. N
    Year:
    2005
    Abstract:
    We use functional magnetic resonance imaging (fMRI) and behavioral analyses to study the neural roots of biases in causal reasoning.<br>Fourteen participants were given a task requiring them to interpret data relative to plausible and implausible causal theories. Encountering<br>covariation-based data during the evaluation of a plausible theory as opposed to an implausible theory selectively recruited neural tissue in the<br>prefrontal and occipital cortices. In addition, the plausibility of a causal theory modulated the recruitment of distinct neural tissue depending<br>on the extent to which the data were consistent versus inconsistent with the theory provided. Specifically, evaluation of data consistent with<br>a plausible causal theory recruited neural tissue in the parahippocampal gyrus, whereas evaluating data inconsistent with a plausible theory<br>recruited neural tissue in the anterior cingulate, left dorsolateral prefrontal cortex, and precuneus. We suggest that these findings provide a<br>neural instantiation of the mechanisms by which working hypotheses and evidence are integrated in the brain.
    Location:
    http://www.dartmouth.edu/~kndunbar/pubpdfs/FDneuropsych2005.pdf

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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