Literature Index

Displaying 441 - 450 of 3326

  • Assessment of performance in introductory linear models courses

    Author(s):
    Jennings, E., & Ward, J. H.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    We have been teaching a General Linear Models approach to problem formulation and analysis to university students and applied researchers for over 25 years. The students and researchers have come from a wide variety of application areas such as education and all of the social sciences, biology, management, and operations research. The objectives of the course range from the very clearly defined to the very unclear. The range of objectives and the heterogeneity of backgrounds create difficulties in the assessment of performance. Among these difficulties are defining the objectives of the instruction, developing instruments to evaluate student performance, and methods of awarding grades.

  • Assessment of quantitative reasoning to enhance educational quality

    Author(s):
    Sundre, D. L.
    Year:
    2003
    Abstract:
    The 2003 AERA theme: Accountability for Educational Quality: Shared Responsibility provides a solid foundation for research on the means by which quality assessment can improce both instructional delivery and student learning. A counterpoint to the prevalent accountability focus is intentionally sought to promote participation by both faculty and students toward assessment methos that can improce teaching and learning. This paper provides a progress report on an effort to enhance assessment of quantitative reasoning with an eye toward greater student engagement and assessment results that might better inform pedagogy.

  • Assessment of statistical understanding in Australian schools

    Author(s):
    Watson, J. M., Collis, K. F., & Moritz, J. B.
    Year:
    1993
    Abstract:
    This paper will describe the initial phases of a three-year project whose purpose is to devise assessment instruments and follow the implementation of the Chance and Data content of A National Statement on Mathematics for Australian Schools. A discussion of the rationale used in devising items will be followed by descriptions of the four types of instruments developed. Finally, the results of pilot trials carried out in recent months will be presented.

  • Assessment of Students' Understanding of Variation

    Author(s):
    Jane M. Watson & Ben A. Kelly
    Year:
    2007
    Abstract:
    Several tasks used in research studies are presented with assessment rubrics and examples of the development of student understanding. The tasks focus on students' appreciation of variation in several contexts and illustrate the need to discuss variation in the classroom and to ask students specifically about it during assessment.
    Location:
    http://www.blackwell-synergy.com/doi/full/10.1111/j.1467-9639.2007.00295.x

  • Assessment of the pedagogical utilization of the statistics online computational resource in introductory probability courses: A quasi-experiment.

    Author(s):
    Dinov, I., & Sanchez, J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Java-applets have proved to be effective in different settings to demonstrate statistical concepts. The NSF-funded Statistics Online Computational Resource (SOCR) provides a number of interactive tools for enhancing instruction in various undergraduate and graduate courses in probability and statistics. In this paper, we present the results of a quasi-experiment aimed at assessing the effect of moderate use of SOCR on three outcome measures: course scores, student satisfaction and choice of technology to complete the final examination. One section of an upper division "Introduction to probability" class, the treatment group, was conducted with the instructor using the probability applets and materials as assessment tools in homework. Another section, the control group, of the same class was conducted exactly the same way, but not using SOCR. We compared the three outcome measures in the two groups and discuss ways to improve the quasi-experiment. Ideas are given on how to incorporate the applets in various probability courses.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/C312.pdf

  • Assessment of the understanding of statistical concepts

    Author(s):
    Jolliffe, F. R.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    Much has been written about methods of teaching statistics and about how to assess students' knowledge of statistics, but almost nothing on the extent to which assessment procedures measure whether students understand statistical concepts, or whether they understand what is involved in the application of statistical techniques. There has in fact been very little research on the development of instruments designed specifically to measure statistical understanding. There is, however, work in related areas which has some bearing on the assessment of understanding of statistical concepts. In reviewing this work this paper discusses the extent to which understanding is covered by some classification schemes which have been developed for use in mathematics and looks at ways in which attitude scales investigate understanding. Some alternatives to traditional methods of examining brought about by changes in the method of teaching are also considered.

  • Assessment on a budget: Using traditional methods imaginatively

    Author(s):
    Wild, C., Triggs, C., & Pfannkuch, M.
    Editors:
    Gal, I., & Garfield, J. B.
    Year:
    1997
    Abstract:
    Because many of the authentic assessment methods described in this book tend to be very demanding of teacher-time, there is still an important place for finding ways to employ inexpensive, traditoinal methods more creatively in an attempt to come closer to achieveing the same goals that those who advocate authentic assessment methods are targeting. The basic idea is to identify the elements of statistical thinking that we want to foster and then find ways of testing these elements with objective assessment methods (in particular, multiple choice). The chapter explores the extent to which objective testing can approximate the results of authentic assessment techniques and the extent to which it falls short. We provide guidelines for the writing of objective test items, together with examples.

  • Assessment: New Ways of Pupil Evaluation Using Real Data.

    Author(s):
    Holmes, P.
    Editors:
    Goodall, G.
    Year:
    2002
    Abstract:
    Typically, external assessment of school statistics concentrates on lower--level skills. This article discusses how use of the real data of CensusAtSchool makes it possible to devise questions and activities that assess deeper levels of understanding, as described in BloomÅfs Taxonomy of Cognitive Learning.

  • Assessments, change, and exploratory data analysis.

    Author(s):
    Dossey, J. A.
    Editors:
    Burrill, G. F.
    Year:
    2006
    Abstract:
    This article examines some techniques that teachers and other individuals interested in school-related data may find helpful and informative in examining tabular displays of data that are frequently found in local newspapers and school publications. Such techniques include looking for tabular patterns, median polish, and examining trifold percents in a table of cross-categorized values.

  • Assignments and Assessment

    Author(s):
    Sharpe, N. R.
    Year:
    1997
    Abstract:
    We cannot discuss assessment of students without also discussing student assignments. Why? The content of a course depends on the customized curriculum of each individual instructor, department, or even, in some cases, the institution. Therefore, here I am going to focus on what we might choose to assess, how we might choose to assess our students, and how we can design our assignments to assist us in our assessment strategies. Finally, I will present an example from my applided multiple regression course.

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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