to construct two representations—a less and a more abstract one than a given one, rather than to categorize, compare, or explain some aspect of a given object while referring to abstraction. Here are several instructors’ answers to the question: “What specific abstraction skills does Pattern 5 measure?” which reflect this explanation [italics were added by the authors]:

˲ Ability to invent new abstractions at different levels of abstraction.

˲Ability to “do” abstraction—to throw away detail, while keeping the essential structure. Also, ability to “do” refinement—to add detail.

˲ Ability to CREATE (rather than just assess), from an external knowledge source, different levels of abstraction.

˲ The ability to maneuver between abstraction levels, as needed, by add-ing/removing details. Also, to identify the entities that comprise an abstraction level, and, constructively, detailing or abstracting them in order to achieve the desired level of abstraction.

˲ … the ability to devise abstractions at various levels.

Conclusion

The question: “Are abstraction skills
assessable?” is part of the discussion
on teaching soft CS concepts. The ex-
perts’ responses, which reflect a con-
structionism perspective, indicate that
they value students’ active engagement
and creativity in the learning process.
One of the instructors suggested that
Pattern 5 “… seems too open ended to
me for a test question—although great
for a studio-style discussion.”
Based on the experts’ answer, this
Viewpoint has focused on distinguish-
ing between the creative construction
of abstractions as against the passive as-
sessment of abstraction level. Another
particularly crucial aspect in abstraction
is that of purpose and the consideration
of fit-for-purpose in activities such as
modeling and problem solving. This in-
cludes both the creation of new abstract
formalisms and languages to express
abstractions—as is created by research-
ers and language designers, and the use
of these to create specific abstractions
for specific applications. We believe that
abstraction purpose needs to be more
directly and explicitly considered and
discussed by instructors, students, and
practitioners when reflecting on their
use of abstraction. In this spirit, one of
the experts who responded to the sur-
vey suggested to highlight “[…] what the
system needs to achieve and what are
the criteria for better solution. It will im-
prove the quality of the answers and will
provide wider range of good answers.”
Another one added: “I would recom-
mend to couple it with a specific pur-
pose to guide what kind of aspects that
can be left out or needs to be included.”
We see this Viewpoint as a starting
point of the discussion of this topic
and invite the ACM community to join
and contribute to the discussion. Such
a discussion is especially relevant to-
day when a variety of teaching methods
and frameworks are available for stu-
dents who seek the highest return for
their effort investment.

References

1. Bennedsen, J.B. and Caspersen, M.E. Abstraction ability as an indicator of success for learning object-oriented programming? SIGCSE Bulletin inroads 38, 2

(2006), 39–43.

2. Davis, R., Maher, C., and Noddings, N. Introduction: Constructivist views on the teaching and learning of mathematics. In R. Davis, C. Maher, and N. Noddings, Eds. Constructivist Views on the Teaching and Learning of Mathematics. National Council of Teachers of Mathematics, Reston, VA, 1990, 7–18.

3. Denning, P.J., et al. Computing as a discipline.

Commun. ACM 32, 1 (Jan. 1989), 9–23.

4. Kramer, J. Is abstraction the key to computing?

Commun. ACM 50, 4 (Apr. 2007), 37–42.

5. Papert, S. and Harel, I. Constructionism. Ablex Publishing Corporation, 1991.

 

Orit Hazzan ( [e-mail]) is a Professor of the Faculty of Education in Science and Technology at Technion–Israel Institute of Technology.

Jeff Kramer ( [e-mail]) is Professor of Distributed Computing in the Department of Computing at Imperial College London.

We would like to thank the experts who answered our survey, invested the time, and shared their thoughts with us.

Copyright held by authors.

of scores and disagreement exhibited with respect to almost all patterns concerning their suitability to assess abstraction skills. For example, patterns 1–3 received all scores. These three patterns provided the students with a set of object representations and asked the students to categorize them according to the student’s choice of abstraction. In other words, in these patterns, students were not asked to create/con-struct an object while considering abstraction.

Agreement: As mentioned, Pattern 5 has the highest average score with the lowest distribution. In this pattern and in another one (Pattern 4, which also was assessed high), the students were asked to create some system representations by referring to abstraction. We may conclude that instructors tend to agree that constructing abstraction is a suitable way to assess abstraction skills. One of our experts explains: “This is a nice concrete type of pattern question. Looks at abstraction generation skill but in a much more constrained setting.”

Psychological Explanation

Theoretically, we explain the experts’
ranking by the theory of construction-
ism, inspired by the constructivist
theory. According to constructivism,
learners construct mental models in
order to understand the world around
them. Constructivism advocates stu-
dent-centered, discovery learning.
One outcome of the constructivism
framework is that one has to be active-
ly involved with the material he or she
learns. 2 Accordingly, “The simplest
definition of constructionism evokes
the idea of learning by making … . 5
The scores presented in Table 2 in-
dicate that the experts, who answered
our questionnaire, saw the potential
contribution of Pattern 5 to students’
understanding. From a construction-
ism perspective, this can be explained
by the fact that Pattern 5 asks students
Table 2. CS and SE instructors’ assessment of the survey patterns.

Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8 Pattern 9 Pattern 10

Average 6.26316 6 6.464286 7.285714 7.535714 6.518519 6.785714 6.666667 4.789474 6.1923077 SD 2.90291 3.05505 2.545616 2.565543 2.008909 2.517177 2.424522 2.557042 2.323287 3.2163223 N 19 28 28 28 28 28 28 27 19 27 # of scores 10 10 10 8 7 8 9 9 8 9