Literature Index

Displaying 1431 - 1440 of 3326

  • Learning of quantitative research methods - university students' views, motivation, and difficulties in learning.

    Author(s):
    Murtonen, M.
    Editors:
    Lehtinen, E., & Olkinuora, E.
    Year:
    2005
    Abstract:
    The aim of this dissertation was to study the difficulties that some students of education, psychology and social science experience in their quantitative research courses at university. The problem is approached from the perspectives of anxiety studies, studies on conceptions and beliefs, orientations in learning situations and theories of conceptual change. Together, these five studies showed that students' difficulties experienced in quantitative methods, courses, research orientations and motivational factors, do constitute an interconnected web that may also have implications for content learning and to students' views of the importance of research skills for their future work.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/dissertations/05.Murtone.Dissertation.pdf

  • Learning probability through building computational models

    Author(s):
    Wilensky, U.
    Year:
    1995
    Abstract:
    While important efforts have been undertaken to advancing understanding of probability using technology, the research herein reported is distinct in its focus on model building by learners. The work draws on theories of Constructionism and Connected Mathematics. The research builds from the conjecture that both the learner's own sense making and the cognitive researchers, investigations of this sense-making are best advanced by having the learner build computational models of probabilistic phenomena. Through building these models, learners come to make sense of core concepts in probability. Through studying this model building process, and what learners do with their models, researchers can better understand the development of probabilistic learning. This report briefly describes two case studies of learners engaged in building computational models of probabilistic phenomena.

  • Learning Spurious Correlations instead of Deeper Relations

    Author(s):
    Chang, N., Koedinger, K. R., Lovett, M. C.
    Editors:
    Alterman, R., Kirsh, D.
    Year:
    2003
    Abstract:
    Effective instructional design requires navigating the tradeoff between providing helpful cues to the correct solutions and supplying hints that ultimately detract from what students learn. The present study manipulated the correlations between superficial features and the correct solutions in a set of training problems in the domain of exploratory data analysis and examined their effect on novices with no prior knowledge of statistics. Students who were trained on problems with these spurious correlations performed more poorly on posttest problems lacking these associations, making errors in the direction predicted by the misleading features. The theoretical and educational implications of the outcomes of this practice are discussed.

  • Learning Statistics at the Farmers Market? A Comparison of Academic Service Learning and Case Studies in an Introductory Statistics Course

    Author(s):
    Bridget Hiedemann and Stacey M. Jones
    Year:
    2010
    Abstract:
    We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: studentsÅ' performance on the final exam and their perceptions of the relevance of statistics for their professional development. We find no statistically significant difference between ASL and CS students with regard to final examination performance, but students who participated in the ASL project as opposed to CS were less likely to agree that "Å[they] will have no application for statistics in [their] profession[s]." The estimated relationship is both large and statistically significant (p < 0.01).
    Location:
    http://www.amstat.org/publications/jse/v18n3/hiedemann.pdf

  • Learning statistics at the PC. Program descriptions, exercises, teaching objectives for the statistics software package GSTAT: Statistik lernen am PC. Programmbeschreibungen, Uebungen und Lernziele zum Statistikprogrammpaket GSTAT

    Author(s):
    Boeker, F.
    Year:
    1989
    Abstract:
    GSTAT is a software package for the didactical support of teaching statistics to beginners having no previous knowledge in statistics or EDP. The book and the programs (which are not included but separately available on 5 1/4'' - and 3 1/2'' - disks) are giving an intuitive approach to random processes by illustrating the concepts and theorems with graphics and simulations (like the central limit theorem and the law of big numbers).

  • Learning statistics by doing statistics

    Author(s):
    Smith, G.
    Year:
    1998
    Abstract:
    To help students develop statistical reasoning, a traditional introductory statistics course was modified to incorporate a semester-long sequence of projects, with written and oral reports of the results. Student test scores improved dramatically, and students were overwhelmingly positive in their assessment of this new approach.
    Location:
    http://www.amstat.org/publications/jse/v6n3/smith.html

  • Learning Statistics By Manipulating Propositions

    Author(s):
    Broers, N. J.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Following a course in elementary statistics, students are able to demonstrate a basic knowledge of statistical concepts and ideas, but often fail to apply this knowledge to concrete problems. From research in cognitive psychology, we know that the organization of knowledge starts with the mental storage of initially isolated concepts and simple principles. A certain amount of conceptual understanding is reached when the student succeeds in forming relationships between these knowledge elements. The task faced by any teacher in statistics, is to enable the student to form such integrated knowledge networks. Research has shown that the formation of such networks is stimulated when students, confronted with a statistical problem that requires the application of their basic knowledge, actively try to explain the solution of the problem to themselves. This paper discusses a didactic method that seeks to stimulate such self explanatory activity in students.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/10_68_br.pdf

  • Learning statistics in a technological environment

    Author(s):
    Ben-Zvi, D.
    Year:
    1997

  • Learning Statistics is FUN… with the Right Mode.

    Author(s):
    Lesser, Lawrence
    Year:
    2007
    Abstract:
    Definitive comprehensive overview of modalities that can be use to making learning statistics fun, including humor, song, books, games, game shows, literary works, word games, and celebrations. Most of the strategies are research-based and/or classroom tested and the paper includes a lengthy annotated bibliography.

  • Learning statistics teaching in higher education using online and distance methods

    Author(s):
    Davies, N., Barnett, V.
    Year:
    2005
    Abstract:
    In 2000 the UK Government published a paper on Higher Education (HE) outlining, amongst other things, its desire that all new staff should receive proper training in lecturing/teaching at the induction stage of their careers and existing lecturing/teaching staff should undertake Continuing Professional Development (CPD). The general expectation was that in most universities the CPD material would be generic (non-subject-specific) and would be delievered by educational or staff development groups. As a similar time a Learning and Teaching Support Network (LTSN) comprising 24 Subject Centres was established for HE to promote good practice, and its dissemination, in teaching and learning. One of these Centres was in Mathematics, Statistics and OR (MSOR), with responsibility for Statstics and OR resting jointly with the Univeristy of Nottingham Trent University. Also at the same time the Institute for Learning and Teaching in HE (ILTHE) was set up to establish a UK professional body of teachers in HE.

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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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