assessment methods in statistical education€¦ · · 2015-09-12assessment methods in...

Assessment Methodsin Statistical Education

An International Perspective

Edited by

Penelope Bidgood

Kingston University, UK

Neville Hunt

Coventry University, UK

Flavia Jolliffe

University of Kent, UK

A John Wiley and Sons, Ltd., Publication

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An International Perspective

Edited by

Penelope Bidgood

Kingston University, UK

Neville Hunt

Coventry University, UK

Flavia Jolliffe

University of Kent, UK

A John Wiley and Sons, Ltd., Publication

This edition first published 2010 2010 John Wiley & Sons Ltd.

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Library of Congress Cataloguing-in-Publication Data

Assessment methods in statistical education: an international perspective / edited by Penelope Bidgood,Neville Hunt, Flavia Jolliffe.

p. cm.Includes bibliographical references and index.ISBN 978-0-470-74532-8 (pbk.)1. Mathematical statistics Study and teaching Evaluation. I. Bidgood, Penelope. II. Hunt, Neville.

III. Jolliffe, F. R. (Flavia R.), 1942-QA276.18.A785 2010519.5071 dc22

2010000192

A catalogue record for this book is available from the British Library.

ISBN 978-0-470-74532-8 (P/B)

Typeset in 10/12 Times-Roman by Laserwords Private Limited, Chennai, IndiaPrinted and bound in Great Britain by TJ International Ltd, Padstow, Cornwall.

www.wiley.com

Contents

Contributors ix

Foreword xiii

Preface xv

Acknowledgements xvii

PART A SUCCESSFUL ASSESSMENT STRATEGIES 1

1 Assessment and feedback in statistics 3Neville Davies and John Marriott

2 Variety in assessment for learning statistics 21Helen MacGillivray

3 Assessing for success: An evidence-based approach thatpromotes learning in diverse, non-specialist student groups 35Rosemary Snelgar and Moira Maguire

4 Assessing statistical thinking and data presentation skillsthrough the use of a poster assignment with real-world data 47Paula Griffiths and Zoe Sheppard

5 A computer-based approach to statistics teachingand assessment in psychology 57Mike Van Duuren and Alistair Harvey

PART B ASSESSING STATISTICAL LITERACY 69

6 Assessing statistical thinking 71Flavia Jolliffe

vi CONTENTS

7 Assessing important learning outcomes in introductorytertiary statistics courses 75Joan Garfield, Robert delMas and Andrew Zieffler

8 Writing about findings: Integrating teaching and assessment 87Mike Forster and Chris J. Wild

9 Assessing students statistical literacy 103Stephanie Budgett and Maxine Pfannkuch

10 An assessment strategy to promote judgementand understanding of statistics in medical applications 123Rosie McNiece

11 Assessing statistical literacy: Take CARE 133Milo Schield

PART C ASSESSMENT USINGREAL-WORLD PROBLEMS 153

12 Relating assessment to the real world 155Penelope Bidgood

13 Staged assessment: A small-scale sample survey 163Sidney Tyrrell

14 Evaluation of design and variability concepts amongstudents of agriculture 173Mara Virginia Lopez, Mara del Carmen Fabrizioand Mara Cristina Plencovich

15 Encouraging peer learning in assessment instruments 181Ailish Hannigan

16 Inquiry-based assessment of statistical methods in psychology 189Richard Rowe, Pam McKinney and Jamie Wood

PART D INDIVIDUALISED ASSESSMENT 201

17 Individualised assessment in statistics 203Neville Hunt

18 An adaptive, automated, individualised assessment systemfor introductory statistics 211Neil Spencer

CONTENTS vii

19 Random computer-based exercises for teaching statistical skillsand concepts 223Doug Stirling

20 Assignments made in heaven? Computer-marked,individualised coursework in an introductory level statistics course 235Vanessa Simonite and Ralph Targett

21 Individualised assignments on modelling car prices using datafrom the Internet 247Houshang Mashhoudy

References 259

Index 279

Contributors

Penelope Bidgood Faculty of CISM, Kingston University, [email protected]

Stephanie Budgett Department of Statistics, The University of Auckland,New Zealand. [email protected]

Neville Davies Faculty of Education, University of Plymouth, [email protected]

Mara del Carmen Fabrizio Facultad de Agronomia, Universidad de BuenosAires, Argentina. [email protected]

Robert delMas Department of Educational Psychology, Universityof Minnesota, USA. [email protected]

Mike Forster Department of Statistics, The University of Auckland,New Zealand. [email protected]

Joan Garfield Department of Educational Psychology, Universityof Minnesota, USA. [email protected]

Paula Griffiths Department of Human Sciences, Loughborough University,UK. [email protected]

Ailish Hannigan Department of Mathematics and Statistics, Universityof Limerick, Ireland. [email protected]

Alistair Harvey Department of Psychology, University of Winchester, [email protected]

Neville Hunt Department of Mathematics, Statistics and Engineering Science,Coventry University, UK. [email protected]

Flavia Jolliffe Institute of Mathematics, Statistics and Actuarial Science,University of Kent, UK. [email protected]

Mara Virginia Lopez Facultad de Agronomia, Universidad de Buenos Aires,Argentina. [email protected]

x CONTRIBUTORS

Helen MacGillivray Mathematical Sciences, Faculty of Science andTechnology, Queensland University of Technology, [email protected]

Moira Maguire Department of Nursing, Midwifery and Health Studies,Dundalk Institute of Technology, Ireland. [email protected]

John Marriott School of Computing and Mathematics, Nottingham TrentUniversity, UK. [email protected]

Houshang Mashhoudy Department of Mathematics, Statistics and EngineeringScience, Coventry University, UK. [email protected]

Pam McKinney CILASS, Information Commons, [email protected]

Rosie McNiece Faculty of CISM, Kingston University, [email protected]

Maxine Pfannkuch Department of Statistics, The University of Auckland,New Zealand. [email protected]

Mara Cristina Plencovich Facultad de Agronomia, Universidad de BuenosAires, Argentina. [email protected]

Richard Rowe Department of Psychology, University of Sheffield, [email protected]

Milo Schield Department of Business Administration, Augsburg College, [email protected]

Zoe Sheppard Department of Human Sciences, Loughborough University, [email protected]

Vanessa Simonite School of Technology, Oxford Brookes University, [email protected]

Rosemary Snelgar Department of Psychology, University of Westminster, [email protected]

Neil Spencer Business School, University of Hertfordshire, [email protected]

Doug Stirling Institute of Fundamental Sciences, Massey University,New Zealand. [email protected]

Ralph Targett School of Technology, Oxford Brookes University, [email protected]

CONTRIBUTORS xi

Sidney Tyrrell Department of Mathematics, Statistics and Engineering Science,Coventry University, UK. [email protected]

Mike Van Duuren Department of Psychology, University of Winchester, [email protected]

Chris J. Wild Department of Statistics, The University of Auckland,New Zealand. [email protected]

Jamie Wood CILASS, Information Commons, [email protected]

Andrew Zieffler Department of Educational Psychology, Universityof Minnesota, USA. [email protected]

Foreword

In education, assessment is amongst the most useful things that we do forourselves and our students. It is also amongst the most harmful things we do thebest and the worst.

It is useful for our students when it enables them to see what they do notunderstand and gives them insight and motivation to improve. It is useful for usas teachers when it helps us see where our teaching can be improved, when itgives us insight into the way our students are learning and when we can see therewards of a job well done. It is useful for administrators when it helps them seewhich sort of structures work best for learning and which sort of people makegood teachers, and ways in which they can improve the overall learning process.

It is harmful when it is seen as an end in itself. It is harmful to students whenit makes the goal getting a paper qualification rather than gaining competence.It is harmful when it distorts the learning process and encourages learning andteaching for the test. Assessment is harmful when its contents do not match upwith what is important to learn. To quote a phrase I first heard from ProfessorHugh Burkhardt of the Shell Centre for Mathematical Education in Nottingham,what you test is what you get WYTIWYG. It is harmful when it is seenmerely as a hurdle and when it promotes fear of failure, so encouraging strategiesof getting high scores (particularly passing an examination) at the expense ofimproving teaching and learning.

The position is made more difficult by the fact that many students studyingstatistics are not doing so out of choice. They may have to take a basic statisticscourse because it is an integral part of their main discipline and they are notnecessarily convinced of its usefulness. They may see it as an imposition, notan interesting learning experience to be applied in their profession. This makesit all the more likely that they will do the minimum necessary to get a piece ofpaper saying they have qualified.

All of the above may appear to say: formative assessment good, summativeassessment bad. But it is not as easy as this. It is possible to develop goodmethods of summative assessment. This is only done by maintaining the focusthat all assessment is subservient to the overall aims of improving teaching andlearning and improving the statistical abilities of all our students.

assessment methods in statistical education€¦ · · 2015-09-12assessment methods in...

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