By Cynthia Brame, CFT Assistant Director
Statistical analysis is a critical tool in many fields, and as such, it is taught in courses across many disciplines. This teaching guide provides an overview of techniques and strategies that have been shown to improve student learning in statistics courses. It also provides links to resources that can help teachers implement these techniques and strategies.
Svetlana Tishkovskaya and Gillian Lancaster recently reviewed research on statistics education as well as web-based resources in the field (Tishkovskaya and Lancaster, 2012). They note that five principles of learning from cognitive theory, applied to statistical education by Lovett and Greenhouse, encapsulate current thought on the most effective approaches to teaching statistics (Lovett and Greenhouse, 2000). Tishkovskaya and Lancaster describe these principles in the following way:
- Students learn best what they practice and perform on their own.
- Knowledge tends to be specific to the context in which it is learned.
- Learning is more efficient when students receive real-time feedback on errors.
- Learning involves integrating new knowledge with existing knowledge.
- Learning becomes less efficient as the mental load students must carry increases.
The authors note that developing an active learning environment is essential to integrating these principles into a course. Echoing a report on promising practices in undergraduate
STEM education recently commissioned by the National Research Council (Froyd, 2008), Tishkovskaya and Lancaster suggest that inquiry-based learning and collaborative learning are effective tools for establishing an active learning environment.
There are many great resources for teaching statistics, ranging from descriptions of specific projects that have been successful at stimulating student learning to general web-based databases that offer a variety of tools. We offer a selection of those resources here.
General resources offering a variety of tools
|Types of resources available|
|Journal of Statistics Education
||Electronic peer-reviewed journal focused on the improvement of statistics education at all levels||
|CAUSEweb||An online repository of statistics education resources maintained by the Consortium for the Advancement of Undergraduate Statistics Education||
||Learning object repository maintained by the Multimedia Educational Resource for Learning and Online Teaching||
|Online Statistics Education: An Interactive Multimedia Course of Study||Online resource for learning and teaching introductory statistics developed under the lead of David Lane at Rice University||
- Vanderbilt Department of Biostatistics Datasets provides freely available datasets on topics ranging from medical (e.g., Plasma retinol/beta-carotene dataset) to economic (Boston neighborhood housing prices data) to historical (US counties and 1992 presidential election dataset). The site also has links to a variety of other statistics education resources.
- Carnegie Mellon University’s Data and Story Library (DASL) contains freely available stories and accompanying datasets on topics ranging from archeology to zoology.
- Examples of computer projects from Vanderbilt Economics professor Malcolm Getz
- Four examples of clicker questions from Vanderbilt Center for Teaching Director Derek Bruff:
- Making General Principles Come Alive in the Classroom Using an Active Case Studies Approach
- Statistics lectures on VideoLectures.NET, an award-winning free and open access educational video lectures repository.
Boyer, E.L. (1998). The Boyer Commission on Educating Undergraduates in the Research University, Reinventing Undergraduate Education: A Blueprint for America’s Research Universities. Stony Brook, NY.
Bruff, D. (2009). Teaching with Classroom Response Systems: Creating Active Learning Environments. San Francisco, CA.
Froyd, J. (2008). White paper on promising practices in undergraduate STEM education. Commissioned paper for the Evidence on Promising Practices in Undergraduate STEM Education Project, the Nation Academies Board on Science Education.
Lock, R., and Arnold, T. (1993). Datasets and stories: Introduction and guidelines. Journal of Statistics Education 1(1). www.amstat.org/publications/jse/v1n1/datasets.html
Lovett, M., and Greenhouse, J. (2000). Applying cognitive theory to statistics instruction. The American Statistician 54(3), 196-206.
Tishkovskaya, S., and Lancaster, G.A. (2012). Statistical education in the 21st centenary: a review of challenges, teaching innovations, and strategies for reform. Journal of Statistics Education