Maria Knobelsdorf, University of Dortmund, Germany - Didaktik der ...

est is developed by repeated experience of situational interest.
According to Mitchell [25], meaningfulness and involvement are
the two most important aspects to foster individual interest. Meaningfulness
is the perception of a learner of the content as being
valuable for her current life. Involvement is due to active engagement
in the learning process.
Based on this theoretical account, the following aspects are important
affordances for developing and analyzing experiments as
interest developing approach to teaching:
If learners feel or believe they can influence the process (e.g. the
learning process, or the process of interacting with a digital artifact)
they are more likely to be involved.
Teachers and learners often have different perceptions of what is
of most importance in the learning process. How can Outsiders be
enabled or supported to see value (=meaningfulness) in learning
computer science?
Self-efficacy (or similar: self-confidence, self-view) should be
preserved in order to prevent learners from avoiding situations
which are threatening to their self-esteem. And, vice-versa, learners
should have opportunities to succeed, because “as achievement
enhances self-image and confidence in an upward spiral
in which increased levels of achievement enhance motivation
which in turn leads to further increases in achievement” [21].
The thesis is that building bridges between function and structure
supports the perception of the value in learning structure (and
hence CS, too).
There are two issues connected to experiments: One is that such
activities are interesting due to external factors (hands on), which
are not transferred to the discipline. The other is that embedding
the disciplines’ content in engaging activities can be seen only as
one first step, and in addition to involvement also meaningfulness
needs to be supported. And here we are talking about meaningfulness
for Outsiders, who are most likely are conceptualizing CS as
related to computers and a kind of mysterious professional pattern
in interaction with such devices.
Thus the main point is to shift attention from function to structure,
while also preserving the link to the original function and context
of an Outsider as a user. When and how will computing become
meaningful and valuable for such a person?
In other words, the above described activities might be labeled as
experiments, but only if they are building a bridge between function
and structure. Most likely such a bridge can be built by starting
from everyday use experiences with digital artifacts.
Here we also see a difference to e.g. CS unplugged. Both approaches
have some commonalities: They are aiming at triggering
situational interest (of Outsiders) by engaging them in unusual
and intriguing activities, and both shift attention away from using
the PC. The difference is that CS unplugged simply removes the
distracting content and plugs out the PC, duality experiments start
with use experiences and some kind of digital artifact from an
unusual perspective that should raise curiosity in the usually
hidden structural aspect. In terms of the briefly outlined theory of
interest, CS unplugged provides a good catch facet for situational
interest, while a duality experiment aims at providing some hold
facet in order to develop individual interest by raising the impression
of personal meaningfulness and value.
From the above discussed experiments some conclusions can be
drawn, in order to highlight specific features and intended effects
of experiments in computer science education.
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Firstly, they do not aim to (immediately) change the perception of
the discipline, but to change the perception of digital artifacts, and
the perception of suitable interaction patterns with digital artifacts.
They introduce learners to the ‘internal mechanics’ (structure) of
digital artifacts so that they can reattribute the causes for usage
problems.
They should raise awareness for possible variants of the internal
mechanics, so that learners can experience and explore a ‘design
space’, in which one can understand that (within constraints)
different structure-variants can lead to (sometimes subtle but
important) differences in function.(see e.g. example in 5.2 ).
Experiments should take into account different usage approaches,
so to include different levels of use-competence and habits for
interaction with a digital artifact (see e.g. 5.1 ).
In order to support a general change, and not only a local change
in interacting with the currently analyzed digital artifact, transfer
should be included; e.g. from the actual experiment to other digital
artifacts, or other aspects of the same digital artifact (like e.g.
from ‘text alignment’ to ‘picture alignment’ in section 5.2 ).
7. CONCLUSIONS
Overall, the examples described here are based on the idea of
duality reconstruction [31]. Nevertheless empirical research is
needed to discern what in detail do students learn from such experiments.
Also empirical data could help to understand which
experiments really foster bridging structure and function and
support learners to see the dual nature of digital artifacts. And of
course, it will be interesting to conduct empirical research on the
question if learners change with regard to self-efficacy, motivation
or interest as predicted.
Structure and function are somewhat connected to Insider (-
>structure) and Outsider (->function). Providing bridges should
support the construction of learning-units that are appealing to
both groups and their different interests (and perspectives on the
topics).
And, most important, the experiment should provide opportunities
to perceive structure and function as essential. Especially using
bridges between structure and function should increase the sense
of value as it mutually supports the perspective that is not in focus
of the learner.
Questions yet to solve arise in the following aspects:
The experiments outlined above allow learners to control parts of
the experiments. The actual interaction with an artifact as part of
the experiment usually can vary and is not as strictly defined as in
‘real’ scientific experiments. More work should be done to discern
important aspects of the teaching method, including e.g. link to
learning theory and interaction of outcome and learner attributes.
The above described experiments are fulfilling more or less the
just mentioned affordances. In order to further develop the teaching
method, our discipline can learn from experiments in natural
science - both positively and negatively. For example, we know
from science education that it is possible to engage in contraproductive
trial-and error, so called ‘Hands-on, minds-off’ experiments.
It is also an open question whether the intended transfer of a
changed perception in the interaction with DA triggers a change in
perception of the computing disciplines, too. Remember e.g. the
digital caretaker, discussed in section 2.2 . Such a misconception
of the discipline should be changed – but will it?
These questions are answerable only by (empirical) research.