ASSESSMENT METHODS IN STATISTICAL EDUCATION
ASSESSMENT METHODS IN STATISTICAL EDUCATION
AN INTERNATIONAL PERSPECTIVE
Assessment Methods in Statistical Education: An International Perspective provides a modern, international perspective on assessing students of statistics in higher education. It is a collection of contributions written by some of the leading figures in statistical education from around the world, drawing on their personal teaching experience and educational research. The book reflects the wide variety of disciplines, such as business, psychology and the health sciences, which include statistics teaching and assessment. The authors acknowledge the increasingly important role of technology in assessment, whether it be using the internet for accessing information and data sources or using software to construct and manage individualised or online assessments.
Key Features:
* Presents successful assessment strategies, striking a balance between formative and summative assessment, individual and group work, take-away assignments and supervised tests.
* Assesses statistical thinking by questioning students’ ability to interpret and communicate the results of their analysis.
* Relates assessment to the real world by basing it on real data in an appropriate context.
* Provides a range of individualised assessment methods, including those that deter plagiarism and collusion by providing each student with a unique problem to solve or dataset to analyse.
This book is essential reading for anyone involved in teaching statistics at tertiary level or interested in statistical education research.
Cuprins
Contributors.
Foreword.
Preface.
Acknowledgements.
PART A: SUCCESSFUL ASSESSMENT STRATEGIES.
1 Assessment and feedback in statistics (Neville Davies and
John Marriott).
2 Variety in assessment for learning statistics (Helen
Mac Gillivray).
3 Assessing for success: An evidence-based approach that
promotes learning in diverse, non-specialist student groups
(Rosemary Snelgar and Moira Maguire).
4 Assessing statistical thinking and data presentation skills
through the use of a poster assignment with real-world data
(Paula Griffiths and Zoe Sheppard).
5 A computer-based approach to statistics teaching and
assessment in psychology (Mike Van Duuren and Alistair
Harvey).
PART B: ASSESSING STATISTICAL LITERACY.
6 Assessing statistical thinking (Flavia Jolliffe).
7 Assessing important learning outcomes in introductory tertiary
statistics courses (Joan Garfield, Robert del Mas and Andrew
Zieffler).
8 Writing about findings: Integrating teaching and assessment
(Mike Forster and Chris J. Wild).
9 Assessing students’ statistical literacy (Stephanie
Budgett and Maxine Pfannkuch).
10 An assessment strategy to promote judgement and understanding
of statistics in medical applications (Rosie Mc Niece).
11 Assessing statistical literacy: Take CARE (Milo
Schield).
PART C: ASSESSMENT USING REAL-WORLD PROBLEMS.
12 Relating assessment to the real world (Penelope
Bidgood).
13 Staged assessment: A small-scale sample survey (Sidney
Tyrrell).
14 Evaluation of design and variability concepts among students
of agriculture (Maria Virginia Lopez, Mar´ia del
Carmen Fabrizio and Mar´ia Cristina Plencovich).
15 Encouraging peer learning in assessment instruments
(Ailish Hannigan).
16 Inquiry-based assessment of statistical methods in psychology
(Richard Rowe, Pam Mc Kinney and Jamie Wood).
PART D: INDIVIDUALISED ASSESSMENT.
17 Individualised assessment in statistics (Neville
Hunt).
18 An adaptive, automated, individualised assessment system for
introductory statistics (Neil Spencer).
19 Random computer-based exercises for teaching statistical
skills and concepts (Doug Stirling).
20 Assignments made in heaven? Computer-marked, individualised
coursework in an introductory level statistics course (Vanessa
Simonite and Ralph Targett).
21 Individualised assignments on modelling car prices using data
from the Internet (Houshang Mashhoudy).
References.
Index.
Despre autor
PENELOPE BIDGOOD, Kingston University, UK
NEVILLE HUNT, Coventry University, UK
FLAVIA JOLLIFFE, University of Kent, UK
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