THE THREE DIMENSIONS OF
MULTIMEDIA TEACHING OF
STATISTICS
Nathaniel Derby, Wolfgang Härdle, and Bernd Rönz
Institute for Statistics and Econometrics
Economics Department
Humboldt University of Berlin
Spandauer Straße 1
10178 Berlin
Germany
THE NEED FOR MORE ADAPTIVE
TEACHING METHODS
Most students find statistics difficult and uninspiring:
• Conflicts with pre-conceived ideas about chance and
data analysis
• Increasingly complex statistical data structures
• Complex mathematical formulas
• Instruction still dependent on blackboard-based
lectures and paper-and-pencil homework
THE THREE DIMENSIONS OF
TEACHING STATISTICS
1
The Third
Dimension:
Jumping between
frames of the
sequence (context)
2
3
4
3.3
3.1
3.2
The Second Dimension:
Examples
and more information
(deepening)
The First Dimension: The sequence of the
lesson (storyline)
Map these three dimensions onto the computer.
WHY A NET-BASED SOLUTION?
• Web browser the only connection between different
platforms
• Easy access to real data sets
• allows for the use of web-based tools
GOALS
For the teacher:
1. Move from classroom examples to more elaborate
statistical data analysis
2. Handle more data and examples in the instruction
3. Emphasize more statistical critical thinking
4. Demonstrate the assumptions connected with
various statistical methods and models
5. Concentrate more on the students’ difficulties
GOALS
For the student (three dimensions):
1. Review the lectures at his/her own pace
2. Perform examples with several data sets and view
the results
3. Discover relations between the course subjects
THE PRESENT SITUATION
Transparencies:
• An outline of the lectures in catch phrases
• Important formulas
• Graphic representations
• Examples -- few computer-based, presented by
lecturer
THE PRESENT SITUATION
Teaching material for the students:
•
•
•
•
•
Outline of the lectures
Selected textbook references
Collection of the formulas
Printed versions of transparencies
Prepared exercises with solutions
Limited computer use, not stressed by the lecturer
THE PROPOSAL: MM*STAT
• Hypertext functionality throughout all pages
• More information
• Examples (Fully explained, enhanced, interactive,
programmable)
• Multiple choice questions at the end of paragraphs
• Glossary
• Introductory page with general explanations
• Help system
• Video sequences
• Sound
THE PROPOSAL: MM*STAT
Ideas for future development:
• Question-answer page
• Page for notes
• Page for the timing of the lecture
• Links to other subject areas for more information
Table of Contents:
List of subtopics:
Lecture unit:
More information:
Lecture unit revisited:
Fully explained example:
Enhanced Example:
Interactive Example:
Table of Contents Revisited:
Computing Example:
Video:
REFERENCES
Härdle, W., Klinke, S., and Marron, J. (1999). Connected Teaching
of Statistics, recently submitted to the Journal of Statistical
Planning and Inference.
Müller, M. (1998). Computer-Assisted Statistics Teaching in
Network Environment. COMPSTAT 1998 Proceedings in
Computational Statistics (ed. Payne,
R., Green, P.), 77 - 88.
Heidelberg: Physica-Verlag
Müller, M. (1998). Teaching Statistics with XploRe. Math&Stats
Newsletter, 21 - 24. Glasgow: CTI Statistics
REFERENCES
Redfern, E.J., Bedford, S.E. (1994). Teaching and Learning through
Technology: The Development of Software for Teaching
Statistics to Non-Specialist Students. COMPSTAT 1994
Proceedings in Computational Statistics (ed. Dutter, R.,
Grossmann, W.), 408 - 414. Heidelberg: Physica-Verlag
Sonderforschungsbereich staff and projects
Talbot, M. (1998). Statistics Training and the Internet. In:
COMPSTAT 1998 Proceedings in Computational Statistics
(ed. Payne, R., Green, P.), 461 - 466. Heidelberg: PhysicaVerlag
REFERENCES
Tufte, Edward, The Visual Display of Quantitative Information,
Graphics Press, New Haven, 1983
West, R.W. (1997). Statistical Applications for the World Wide Web.
In: Bulletin of the International Statistical Institute, 51st Session
Proceedings Book 2, 7 - 10, Turkey