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Teaching systems are further divided into mechanics of programming and learning support. Learning
support systems try to ease the process of learning to program through “basic educational supports such
as progressions of projects that gradually introduce new concepts or ways for students to connect with
and learn from each other”. Of more interest from my perspective are systems in the mechanics of
programming category, which come in three varieties. First, systems for expressing programs attempt
to make it easier for beginners to express instructions to the computer. Second, systems for structuring
programs attempt to facilitate the organization of instructions by changing the language or programming
paradigm (e.g., Pascal, Smalltalk) or by making existing programming languages or paradigms more
accessible (e.g., BlueJ). And third, systems for understanding program execution attempt to help novices
understand how such instructions are executed at runtime. It is systems in this last category that I intend
to review.
Kelleher and Pausch identify three ways in which systems try to help students to understand program
execution. A system for tracking program execution visualizes what happens in memory during a program
run. The actors-in-microworlds approach makes programming concrete by replacing a general-purpose
programming language by a mini-language whose commands have a straightforward physical explanation
in a virtual microworld. Finally, systems in the models of program execution category describe actions in
a (general-purpose) programming language through metaphors and graphics, which “help students both
to imagine the execution of their programs and perhaps more clearly understand why their programs do
not perform as expected”.
As my concern is with learning general-purpose languages – which all programming students eventually
need to learn and which represent the mainstream of CS1 education – I will not cover the actors-inmicroworlds
approach in detail. This leaves two subcategories, tracking program execution and models of
program execution, both of which involve visualizations of program execution either as abstract graphics
and text, or through metaphors. The line between these two subcategories is a vague one, and I will not
attempt to pigeonhole systems in either of them.
I make one more delimitation: I will focus on systems that are more or less generic in the sense that
they can be used to illustrate a variety of programming language constructs and corresponding runtime
phenomena. There are also many systems which attempt to tackle more specific problems by visualizing
one or a few select programming concepts. I will not attempt to cover those here. (A few examples of
such specific systems were mentioned in Section 10.3.)
To summarize the above, this chapter contains a review of generic program visualization systems in
which the execution of programs – written in a general-purpose language in a traditional non-visual way
– is visualized onscreen in a manner suitable for novice programmers so that the system can be used to
learn about program execution.
Before turning to the actual systems, let us consider the ways in which learners may use a visualization.
11.2 Engagement level may be key to the success of a visualization
“They will look at it and learn” thought many an enthusiastic programming teacher while putting together
a visualization. But it might take more than that. Petre (1995, p. 34) writes:
In considering representations for programming, the concern is formalisms, not art – precision,
not breadth of interpretation. The implicit model behind at least some of the claims that
graphical representations are superior to textual ones is that the programmer takes in a
program in the same way that a viewer takes in a painting: by standing in front of it and
soaking it in, letting the eye wander from place to place, receiving a ‘gestalt’ impression of
the whole. But one purpose of programs is to present information clearly and unambiguously.
Effective use requires purposeful perusal, not the unfettered, wandering eye of the casual art
viewer.
The representational aspects of a visualization – its constituent parts and level of abstraction – are
doubtless relevant to learning. In the lingo of the variation theory of learning (Section 7.3.2), these
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