RETENTION OF STATISTICAL CONCEPTS IN A
PRELIMINARY RANDOMIZATION-BASED INTRODUCTORY STATISTICS CURRICULUM
NATHAN TINTLE
Dordt
College
ntintle@dordt.edu
KYLIE TOPLIFF
Hope College
kylie.topliff@hope.edu
JILL VANDERSTOEP
Hope College
vanderstoepj@hope.edu
VICKI-LYNN HOLMES
Hope College
holmesv@hope.edu
TODD SWANSON
Hope College
swansont@hope.edu
ABSTRACT
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.
Keywords: Statistics education research; Simulation;
Permutation tests; Active learning
__________________________
Statistics Education Research Journal, 11(1), 21-40, http://www.stat.auckland.ac.nz/serj
(c)
International Association for Statistical Education (IASE/ISI), May, 2012
REFERENCES
Agresti, A., & Franklin, C. A. (2007). Statistics:
The art and science of learning from data (1st edition). Upper Saddle River, NJ: Pearson.
Aliaga, M., Cuff, C., Garfield, J., Lock, R., Utts,
J., & Witmer, J. (2005). Guidelines
for assessment and instruction in statistics education (GAISE): College report.
Alexandria, VA: American Statistical Association.
[Online: http://www.amstat.org/education/gaise/ ]
Arum, R., &
Roksa, J. (2011). Academically
adrift: Limited learning on college campuses. Chicago: University of
Chicago Press.
Berenson, M.
L., Utts, J., Kinard, K. A.,
Rumsey, D. J., Jones, A., & Gaines, L. M. (2008). Assessing student
retention of essential statistical ideas: Perspectives, priorities and
possibilities. The American
Statistician, 62(1), 54-61.
Brandsma, J. A. (2000). Data collection and analysis: Examining community college students'
understanding of elementary statistics through laboratory activities (Unpublished
doctoral dissertation). North Carolina State University,
Raleigh, NC. Synopsis in Statistics
Education Research Journal Newsletter, 2(3),
September, 2001.
[Online: http://www.stat.auckland.ac.nz/~iase/serj/Newssep01.pdf ]
Bude, L. M. (2007). On the improvement of students’ conceptual
understanding in statistics education (Unpublished doctoral dissertation).
Universiteit Maastricht, The
Netherlands.
[Online: http://www.stat.auckland.ac.nz/~iase/publications/dissertations/07.Bude.Dissertation.pdf ]
Chance, B.,
Holcomb, J., Rossman, A., & Cobb, G. (2010). Assessing student learning
about statistical inference. In C. Reading (Ed.), Data and context in
statistics education: Towards an evidence-based society. Proceedings
of the Eighth International Conference on Teaching Statistics (ICOTS-8), Ljubljana, Slovenia. Voorburg,
The Netherlands: International Statistical Institute.
[Online: www.stat.auckland.ac.nz/~iase/publications/icots8/ICOTS8_5F1_CHANCE.pdf ]
Clark, J., Karuat,
G., Mathews, D., & Wimbish, J. (2007). The fundamental theorem of statistics: Classifying
student understanding of basic statistical concepts. Unpublished
manuscript.
[Online: http://www1.hollins.edu/faculty/clarkjm/stat2c.pdf ]
Cobb, G. (2007). The introductory statistics course: A Ptolemaic
curriculum? Technology
Innovations in Statistics Education, 1(1).
[Online:
http://escholarship.org/uc/item/6hb3k0nz ]
Dale, E. (1946, 1954,
1969). Audio-visual methods in teaching. New York: Dryden.
delMas, R., Garfield J., Ooms,
A., & Chance, B. (2007). Assessing students' conceptual understanding after
a first course in statistics. Statistics
Education Research Journal 6(2),
28-58.
[Online: http://www.stat.auckland.ac.nz/~iase/serj/SERJ6%282%29_delMas.pdf ]
Garner, B. E., & Garner, L.
E. (2001). Retention of concepts and skills in traditional
and reformed applied calculus. Mathematics
Education Research Journal, 13(3), 165-184.
Holcomb, J., Chance, B., Rossman,
A., Tietjen, E., & Cobb, G. (2010). Introducing concepts of statistical inference via randomization
tests. In C. Reading (Ed.), Data and context in statistics education:
Towards an evidence-based society. Proceedings of the Eighth
International Conference on Teaching Statistics (ICOTS-8), Ljubljana, Slovenia. Voorburg,
The Netherlands: International Statistical Institute.
[Online: http://www.stat.auckland.ac.nz/~iase/publications/icots8/ICOTS8_8D1_HOLCOMB.pdf ]
Hulsizer, M. R., & Woolf, L. M.
(2009). A guide to teaching statistics: Innovations
and best practices. Chichester, UK: Wiley-Blackwell.
Kvam, P. H. (2000). The effect
of active learning methods on student retention in engineering statistics.
The American Statistician, 54(2),
136-140.
Lockwood, C.
A., Ng, P., & Pinto, J. (2007). An interpretive business statistics course
encompassing diverse teaching and learning styles. Academy of Educational Leadership Journal,11(1),
11-23.
Lovett, M.,
Meyer, O., & Thille, C. (2008). The open learning initiative: Measuring the
effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media
in Education.
[Online: https://oli.web.cmu.edu/openlearning/publications/71 ]
Malone, C., Gabrosek, J., Curtiss, P., & Race, M. (2010). Resequencing topics in an introductory applied statistics course. The American Statistician, 64(1), 52-58.
Parr, W. C.,
& Smith, M. A. (1998).
Developing case-based business statistics courses. The American Statistician, 52(4), 330-337.
Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement,
design, and analysis: An integrated approach. Hillsdale, NJ: Erlbaum.
Richardson, A. (2008, December).
Retention of knowledge between statistics courses: Results of a pilot study.
Paper presented at the Third Annual
Applied Statistics Education and Research Collaboration Conference.
Newcastle, Australia.
[Online: www.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow074264.pdf ]
Rossman, A., Chance, B., Cobb, G., & Holcomb, J. (2008). NSF/CCLI/DUE-0633349.
Concepts of statistical inference: A
randomization-based curriculum.
[Online: http://statweb.calpoly.edu/csi ]
Scheaffer, R. (1997). Discussion to New pedagogy and
new content: The case of statistics. International
Statistics Review, 65(2), 156-158.
[Online: http://www.stat.auckland.ac.nz/~iase/publications/isr/97.Moore.pdf ]
Stangl, D., Banks, D., House, L., & Reiter, J. (2006). Progressive mastery teaching: Does it
increase learning and retention? Yes and No. In A. Rossman
& B. Chance (Eds.), Working
cooperatively in statistics education: Proceedings of the Seventh International
Conference on Teaching Statistics, Salvador, Brazil. [CDROM].
Voorburg, The Netherlands:
International Statistical Institute.
[Online: http://www.stat.auckland.ac.nz/~iase/publications/17/C315.pdf ]
Tintle, N. L., Chance, B., Cobb, G., Rossman, A.,
Roy, S., Swanson, T., & VanderStoep, J. (2011). Introduction to statistical investigations. Unpublished manuscript.
[Online: http://math.hope.edu/isi ]
Tintle, N. L., VanderStoep,
J., Holmes, V. L., Quisenberry, B., & Swanson, T.
(2011). Development and assessment of a preliminary randomization based
introductory statistics curriculum. Journal of Statistics Education, 19(1).
[Online: www.amstat.org/publications/jse/v19n1/tintle.pdf ]
Tintle, N. L., VanderStoep, J., & Swanson, T.
(2009). An active introduction to
statistical inference (Preliminary edition).
NATHAN TINTLE
Mathematics, Statistics and Computer Science Department
498 4th Ave. NE
Dordt College
Sioux Center, IA 51250