course advisors are not available, our exercise
assistant can recognize learning progress and
provide appropriate feedback and support. This
significantly improves the learning situation for
students working remotely in a virtual environment,
which is common at universities for distance
education. Besides this automatic support, the
exercise assistant can verify intermediate and
complete solutions of an exercise.
We also presented an approach to formally
model exercises in a manner processable by the
exercise assistant. For that purpose the exercise
author can define possible activities and sequences
using a graph structure. Description logic is used to
define conditions for the verification of these
activities. The exercise author is also able to define a
feedback strategy by adding feedback attributes to
the graph.
Especially for courses with many participants,
our experience shows that teaching staff can benefit
from utilizing the exercise assistant. While the
teaching method of tutors personally and
individually supporting students is certainly one of
the most effective for knowledge transfer, it is not
feasible for courses of sufficient size. In such
scenarios, the exercise assistant can e.g. be used to
offer all students a basic guided tutoring support not
only wherever and whenever they want, but also at
the speed that best suits their own learning style and
their own abilities.
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