Page 52 - Special Topic Session (STS) - Volume 3

STS515 Alison L. G. et al.
been geared towards efficiency, i.e. the ability to readily solve routine
problems. This certainly rings true for the traditional undergraduate statistics
curriculum, with its emphasis on set-piece methodological solutions. The
typical undergraduate student first acquires procedural skills and knowledge,
and then tries to develop adaptive expertise through projects, capstones,
internships, or, eventually, work experience. On the other hand, Schwartz,
Bransford, & Sears note that content-free activities in critical thinking and
problem-solving (i.e. innovation) help develop the relevant skills, but these
might be insufficient for tackling larger, more complex problems. They
advocate for a balanced approach, one that supports simultaneous
development along both dimensions. Moreover, they conjecture it is not
enough to have parallel but separate content and thinking courses, but that
the two should be interlaced.
Adopting an integrated approach to developing adaptive expertise, we
look at specific ways to attain it by progressing along the innovation
dimension. Hatano (1988) lists three conditions that support the development
of adaptive vs routine expertise: a) students should continuously encounter
novel types of problems, b) they should be encouraged to seek
comprehension, and c) they should be allowed to experiment without a
pressing need for rewards. Other researchers have built upon these ideas,
especially in the context of professional education such as medicine or
engineering. Carbonell et al. (2014) provide a comprehensive review of
characteristics and learning environments for adaptive expertise, while
Mylopoulos et al. (2018) offer 12 practical recommendations for designing
curricula that support it. Alongside Hatano’s three general principles and
related suggestions, we propose three traits and attitudes that are distinctive
to Statistics; these are:
1. Inquisitiveness: We should instil into our students an intellectual curiosity
and spirit of inquiry. Statistics is the science of extracting knowledge from
data, and to do so students must be inclined to ask meaningful and
interesting questions and be willing to pursue their answers outside of
conventional beliefs and practices.
2. Statistical Thinking: We should provide our students with a general
conceptual framework through which they approach problems. We use
this familiar term to describe a way of thinking, rather than just a set of
procedures and tools. Although a formal definition is elusive, we follow
Chance’s (2002) description as “the ability to see the process as a whole
(with iteration), including ‘why,’ to understand the relationship and
meaning of variation in this process, to have the ability to explore data in
ways beyond what has been prescribed in texts.”
3. Extroversion: Statistics is by its nature an outward-facing discipline, one
that draws energy and stimulation from its interactions with other

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