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of color, the layout of objects and variables, and all other aspects of the visualization are strictly under
the control of the system; UUhistle uses them in an attempt to bring about a particular interpretation of
the visualization and enhance role-expressiveness (see above).
If the purpose of UUhistle were different, the complete lack of support for secondary notation could
be a problem. However, since it is intended to be used by novices with a shaky (albeit improving)
understanding of the content of the visualization, this lack might even be a blessing. Someone with a
poor understanding of the concepts involved is not likely to make efficient use of secondary notation and
might use it to produce representations that are misleading; Green et al. (2006) conjecture that novices
may benefit from a system that minimizes secondary notation. There is also a real danger that learners
would confuse any facilities for using secondary notation with the actual simulation steps. Given the
further consideration that secondary notation tends to increase viscosity – already a somewhat problematic
dimension for UUhistle (see below) – I conclude that UUhistle is probably better in this respect as it is.
Hidden dependencies
In a VPS exercise in UUhistle, not much happens without the direct involvement of the user, and most of
what happens happens right where the user’s attention is expected to be: at the mouse cursor and in the
program code panel. Simulation exercises themselves do not, then, suffer from major hidden dependencies.
There is one exception: built-in functions may have non-obvious side effects when applied. For instance,
executing the print function causes output to appear in the console below. We are planning to highlight
side effects visually in future versions of UUhistle.
Some hidden dependencies are intrinsic to Python and programming. 8 UUhistle cannot eliminate these
dependencies (and must not, since the user is expected to learn to live with them), but can assist the
learner with them. For instance, changing an object’s state through a reference impacts on every other
part of the program in which that object is used through any reference. As shown in Figure 13.4, UUhistle
tries to draw learners’ attention to this kind of hidden dependency, which is known to be problematic for
many novices.
Hard mental operations
“Hard mental operations” are operations that are not intrinsically difficult aspects of what is being
expressed, but are instead made difficult by the notation used. Further, they have the property that even
though a single occurrence of the operation is easy to understand, a combination of even two or three
such operations greatly complicates things. When faced with such combinations, users often experience
cognitive overload and resort to strategies such as making notes on paper and tracing visualizations with
their fingers (Green and Petre, 1996).
I am not aware of UUhistle’s visualization introducing any hard mental operations in this sense.
Progressive evaluation
In a VPS exercise, UUhistle not only progressively assesses the user’s actions at each step but provides
automatic and instantaneous feedback on any mistakes the user makes. The user does not have to wait
until the end of the program is reached to find out if their solution works.
Giving feedback only at the end of a simulation exercise would not be a user-friendly option and would
punish the learner excessively for mistakes. Constant progressive evaluation has obvious benefits. However,
from a learning point of view, there is also a potential drawback: instant feedback given at each simulation
step may encourage mindless trial-and-error strategies. We have considered various midway alternatives
between these two extremes, but no alternative feedback-giving strategies are currently implemented in
UUhistle.
The availability of constant automatic feedback also significantly impacts on several of the remaining
cognitive dimensions.
8 Python as a program-authoring language is an entirely different artifact to which the cognitive dimensions can be applied.
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