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Chapter 14
Visual Program Simulation Makes Sense
In this chapter, I draw on the literature presented in Parts I to III to analyze visual program simulation and
explain why it is a sensible pedagogical approach in the light of learning theory and empirical research.
Throughout this chapter, I often refer to previous chapters and sections rather than directly to the
original literature; the reader will find literature references in those chapters and sections.
I have structured this chapter as follows. Section 14.1 establishes that VPS addresses important
educational concerns. Section 14.2 considers the importance of reading code in CS1 and how VPS serves
as a vehicle for learning to read code. In Section 14.3, I discuss the form of active learning that VPS
promotes and how it fits in with the software visualization literature. Section 14.4 explains why and how
a supporting software system is useful, if not critical to the success of VPS. Finally, Section 14.5 points
out a few of the main weaknesses of visual program simulation.
14.1 The literature widely highlights the importance of program
dynamics
Several strands of computing education research have suggested in different ways that a crucial challenge
in introductory programming is learning to reason about the behavior of program code as it runs. It is
this challenge that visual program simulation seeks to address.
14.1.1 VPS targets improved mental representations of program dynamics
In the psychology of programming literature, the challenge of program dynamics is sometimes phrased in
terms of a notional machine – an abstraction of a computer as the executor of programs of a particular
kind (Section 5.4). A notional machine implements the runtime semantics of programming language
constructs and takes care of the ‘hidden’ processes that make programs work. The importance of learning
about a notional machine has gained widespread agreement and empirical support in the literature, as
Chapter 5 showed in some detail. Below, I summarize the arguments and relate them to visual program
simulation.
Mental models of a notional machine
People form mental models of the systems that they interact with. For the novice programmer, one such
system is the notional machine that the student learns to control as they learn to program (Chapter 5). A
novice’s initial mental model of a notional machine is likely to be – typically of mental models in general
– incomplete, unscientific, deficient, lacking in firm boundaries, and liable to change at any time. It may
be based on guesswork that draws on superficial program characteristics such as keywords and identifiers.
Despite these shortcomings, the learner may feel comfortable with the model and rely on it while developing
behavioral patterns for programming. Novices may also use multiple, possibly contradictory models to
deal with different situations.
By contrast, experts’ mental models are more stable and accurate, and draw on general principles
rather than superficial characteristics. Learning should facilitate the evolution of students’ models so
that they have these features. Aiding mental model formation as early as possible is important, as
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