RETENTION OF STATISTICAL CONCEPTS IN A PRELIMINARY RANDOMIZATION-BASED INTRODUCTORY STATISTICS CURRICULUM

Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76) to a cohort of students who used the consensus curriculum (n = 79). Overall, students taking the randomization-based curriculum showed higher conceptual retention in areas emphasized in the curriculum, with no significant decrease in conceptual retention in other areas. This study provides additional support for the use of randomization-methods in teaching introductory statistics courses. First published May 2012 at Statistics Education Research Journal: Archives

[1]  Lynn E. Garner,et al.  Retention of Concepts and Skills in Traditional and Reformed Applied Calculus. , 2001 .

[2]  Julie M. Clark,et al.  The “ Fundamental Theorem " of Statistics : Classifying Student Understanding of Basic Statistical Concepts , 2007 .

[3]  Marsha C. Lovett,et al.  The Open Learning Initiative: Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning. , 2008 .

[4]  George W Cobb,et al.  The Introductory Statistics Course: A Ptolemaic Curriculum? , 2007 .

[5]  Beth Chance,et al.  ASSESSING STUDENTS’ CONCEPTUAL UNDERSTANDING AFTER A FIRST COURSE IN STATISTICS , 2007 .

[6]  R. Wilcox.  Comparing Two Groups , 2022, Introduction to Robust Estimation and Hypothesis Testing.

[7]  Richard Arum,et al.  Academically Adrift: Limited Learning on College Campuses , 2011 .

[8]  F. Paas,et al.  On the improvement of students' conceptual understanding in statistics education , 2003 .

[9]  Michael R. Hulsizer,et al.  A Guide to Teaching Statistics: Innovations and Best Practices , 2008 .

[10]  Elazar J. Pedhazur,et al.  Measurement, Design, and Analysis: An Integrated Approach , 1994 .

[11]  Allan J. Rossman,et al.  ASSESSING STUDENT LEARNING ABOUT STATISTICAL INFERENCE , 2010 .

[12]  William C. Parr,et al.  Developing Case-Based Business Statistics Courses , 1998 .

[13]  P. Kvam.  The Effect of Active Learning Methods on Student Retention in Engineering Statistics , 2000 .

[14]  E. Dale.  Audio-visual methods in teaching , 1946 .

[15]  John Gabrosek,et al.  Resequencing Topics in an Introductory Applied Statistics Course , 2010 .

[16]  W. M. McClain.  The fundamental theorem , 2009 .

[17]  Steven R. Conn,et al.  Academically Adrift: Limited Learning on College Campuses , 2012 .

[18]  Alan Agresti,et al.  Statistics: The Art and Science of Learning from Data , 2005 .

[19]  Nathan L. Tintle.  Introduction to Statistical Investigations , 2014 .

[20]  James V. Pinto,et al.  An interpretive business statistics course encompassing diverse teaching and learning styles: Working paper series--05-04 , 2005 .

[21]  Beth Chance,et al.  INTRODUCING CONCEPTS OF STATISTICAL INFERENCE VIA RANDOMIZATION TESTS , 2010 .

[22]  Todd Swanson,et al.  Development and Assessment of a Preliminary Randomization-Based Introductory Statistics Curriculum , 2011 .

[23]  D. S. Moore,et al.  New Pedagogy and New Content: The Case of Statistics , 1997 .

[24]  Alice Richardson.  Retention of knowledge between statistics courses: results of a pilot study , 2009 .

[25]  M. Sundberg,et al.  Assessing student learning. , 2002, Cell biology education.

[26]  Mark L. Berenson,et al.  Assessing Student Retention of Essential Statistical Ideas , 2008 .