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UNDERGRADUATE PROGRAMS AND THE FUTURE OF ACADEMIC STATISTICS
STATISTICS AND MATHEMATICS: TENSION AND COOPERATION
STATISTICS AMONG THE LIBERAL ARTS: ASA Presidential Address
STATISTICS EDUCATION FIN DE SI`ECLE
MULTIMEDIA FOR TEACHING STATISTICS: PROMISES AND PITFALLS
NEW PEDAGOGY AND NEW CONTENT: THE CASE OF STATISTICS
BAYES FOR BEGINNERS? SOME REASONS TO HESITATE
BAYES FOR BEGINNERS? SOME PEDAGOGICAL QUESTIONS
MATHEMATICS, STATISTICS, AND TEACHING
MAA FOCUS 15 (1995) Number 2, 5-8.
Remarks on receiving the MAA's national award for college or university
teaching. This is the closest I have come to the fashionable ``statement
of teaching philosophy.''
The American Statistician 49 (1995), 250-260.
Higher education faces an environment of financial constraints, changing
customer demands, and loss of public confidence. Technological advances
may at last bring widespread change to college teaching. The movement for
education reform also urges widespread change. What will be the state of
statistics teaching at the university level at the end of the century?
This article attempts to imagine plausible futures as stimuli to discussion.
It takes the form of provocations by the first author with responses from
the others on three themes: the impact of technology, the reform of teaching,
and challenges to the internal culture of higher education.
Multimedia presentation combines text, sound, still images, full-motion
video, animation, and computer graphics in a single computer-based system.
The user interacts with the system via keyboard and mouse. Such systems
have considerable promise for teaching. In particular, they encourage constant
active participation by the learner and can adapt to individual differences
in preparation, pace, and learning style. This paper examines the pedagogical
(as opposed to technical) issues in using multimedia technology for teaching
statistics. It points to weaknesses as well as advantages, and attempts
to make clear what is known and what remains to be understood. It argues
that human teachers will remain essential to support the social aspects
of learning (especially motivation) and for assessing learning.
International Statistical Review, 65 (1997), 123-165
Statistical education now takes place in a new social context. In addition,
it is influenced by a movement to reform the teaching of the mathematical
sciences in general. At the same time, the changing nature of our discipline
demands revised content for introductory instruction, and technology strongly
influences both what we teach and how we teach. The case for substantial
change in statistics instruction is built on strong synergies between content,
pedagogy, and technology. Statisticians who teach beginners should become
more familiar with research on teaching and learning and with changes in
educational technology. The spirit of contemporary introductions to statistics
should be very different from the traditional emphasis on lectures and
on probability and inference.
The American Statistician, 51 (1997), 254-261 and 272-274
Is it reasonable to teach the ideas and methods of Bayesian inference
in a first statistics course for general students? This paper argues that
it is at best premature to do so. Surveys of the statistical methods actually
in use in several fields suggest that Bayesian techniques are little used.
A reading of Bayesian texts and research suggests that Bayesians have not
yet agreed on standard approaches to standard problem settings. Finally,
an emphasis on Bayesian inference might well impede the trend toward experience
with real data and a better balance between data analysis, data production,
and inference in first statistics courses.
Advances in Statistical Decision Theory, Birkhäuser, Boston, 1997, 3-17
Dedicated to Shanti S. Gupta, chair of the Purdue Statistics
Department during most of my career. Ought we to base beginning
instruction in statistics for general students on the Bayesian approach
to inference? In the long run, this question will be settled by
progress (or lack of progress) in persuading users of statistical
methods to choose Bayesian methods. This paper, a companion to the one
listed above, is primarily concerned with the pedagogical challenges
posed by Bayesian reasoning. It argues, based on research in psychology
and education and a comparison of Bayesian and standard reasoning, that
Bayesian inference is harder to convey to beginners than the already
hard reasoning of standard inference.
American Mathematical Monthly, 104 (1997), 801-823
How does statistical thinking differ from mathematical thinking? What
is the role of mathematics in statistics? If you purge statistics of its
mathematical content, what intellectual substance remains? In what follows,
we offer some answers to these questions and relate them to a series of
examples that provide an overview of current statistical practice. Along
the way, and especially towards the end, we point to some implications
for the teaching of statistics.
American Statistical Association 1998 Presidential Address
Journal of the American Statistical Association, 1998,
pp. 1253-1259
The liberal arts are usually understood to be general and flexible modes of reasoning. By this definition, statistics qualifies as a liberal art, and it is important to the health of the discipline that it be recognized as such. The ``philosophical'' tradition of the liberal arts that is now dominant has alternated with an ``oratorical'' tradition that also gives insight, as do ideas of ``evolutionary psychology.'' This paper considers how understanding statistics as a liberal art influences our appreciation of the discipline and especially our teaching of beginners.
American Mathematical Monthly, 2000, pp. 615-630
The gradual distancing of statistics from mathematics, due mainly to the fact that statistics has changed much more rapidly than mathematics, carries risks for both disciplines. Statistics risks dissipating back into the many fields from which it coalesced or being swallowed by broader information technology. Mathematics risks following academic philosophy into irrelevant profundity. In fact, statistics has cultural strengths that might greatly assist mathematics, while mathematics has organizational strengths that can provide shelter for academic statistics. Moreover, mathematics and statistics are natural intellectual allies in the debate over the relative roles of thought and automation. Increased cooperation between professional societies could benefit both fields. We offer some specific suggestions for such cooperation.
The American Statistician, 2001, pp. 1-6
This paper is the keynote address from a symposium ``Opportunities in Undergraduate Education'' sponsored by the American Statistical Association at the 2000 Joint Statistical Meetings. The paper offers background information relevant to planning undergraduate programs in statistics and some opinions based on this information.
The American Statistician, 2005, pp1-3
This paper introduces a special section on preparing graduate students to teach. It provides background and raises issues to keep in mind when reading the papers that follow, which describe the teacher preparation programs of several leading graduate departments.
Purdue University Department of Statistics