sentation, and so forth, in order to get
a model to accomplish certain work.
The other is the suggestion contained
in the operational definitions that any
sequence of steps constitutes an algorithm. True, an algorithm is a series of
steps—but the steps are not arbitrary,
they must control some computational
model. A step that requires human judgment has never been considered to be
an algorithmic step. Let us correct our
computational thinking guidelines to
accurately reflect the definition of an algorithm. Otherwise, we will mis-educate
our children on this most basic idea.
Question 2: How Do We Measure
Students’ Computational Abilities?
Most teachers and education researchers have the intuition that computational thinking is a skill rather than a
particular set of applicable knowledge.
The British Computer Society CAS
description quoted earlier seems to
recognize this when discussing what
“behaviors” signal when a student
is thinking computationally.
3 But we
have no consensus on what constitutes
the skill and our current assessment
methods are unreliable indicators.
A skill is an ability acquired over time
with practice—not knowledge of facts
or information. Most recommended
approaches to assessing computational
thinking assume that the body of knowl-
edge—as outlined in Boxes 1–3—is the
key driver of the skill’s development.
Consequently, we test students’ knowl-
edge, but not their competence or their
sensibilities. Thus it is possible that a
student who scores well on tests to ex-
plain and illustrate abstraction and de-
composition can still be an incompetent
or insensitive algorithm designer. Teach-
ers sense this and wonder what they can
do. The answer is, in a nutshell, to direct-
ly test for competencies.c
The realization that mastering a do-
main’s body of knowledge need not
confer skill at performing well in the
c In 1992, Ted Sizer of Brown University started
a national movement for competency-based
assessment in schools.
31 He used the term “ex-
hibitions” for assessment events. I gave exam-
ples for engineering schools.
8 According to the
Christensen Institute, competency-based learn-
ing is a growing movement in schools.
34 In 2016,
Purdue became the first public university to
fully embrace competency-based learning in an
academic program in its Polytechnic Institute.
domain is not new. As early as 1958,
philosopher Michael Polanyi discussed
the difference between “explicit knowledge” (descriptions written down) and
“tacit knowledge” (skillful actions).
He famously said: “We know more than
we can say.” He gave many examples of
skilled performers being unable to say
how they do what they do, and of aspirants being unable to learn a skill simply by being told about it or reading a
description. Familiar examples of tacit
knowledge are riding a bike, recognizing
a face, or diagnosing an illness. Many
mental skills fall into this category too,
such programming or learning a foreign
language. Every skill is a manifestation
of tacit knowledge. People learn a skill
only by engaging with it and practicing it.
To certify skills you need a model for skill development. One of the
most famous and useful models is
the framework created by Stuart and
Hubert Dreyfus in the 1970s.
said that practitioners in any domain
progress through six stages: beginner,
advanced beginner, competent, proficient, expert, and master. A person’s
progress takes time, practice, and experience. The person moves from rule-based behaviors as a beginner to fully
embodied, intuitive, and game-chang-ing behaviors as a master. Hubert
Dreyfus gives complete descriptions
of these levels in his book on the Internet.
11 We need guidelines for different
skill levels of computational thinking
to support competency tests.
The CAS and K12CS organizations
have developed frameworks for defining computational thinking that feature
progressions of increasingly sophisticated learning objectives in various
tracks including algorithms, programming, data, hardware, communication,
and technology.d These knowledge
progressions are not the same as skill
acquisition progression in the Dreyfus
model. The CAS framework does not
discuss abilities to be acquired during
the progression. The K12CS framework
gets closer by proposing seven practicese—only three of which are directly
d CAS: https://community.computingatschool.
org.uk/resources/2324; K12CS: https://k12cs.org
e Fostering an inclusive and diverse computing
culture, collaborating, recognizing and defining computational problems, developing and
using abstractions, creating computational
artifacts, testing and refining, communicating.
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