Feedback services for stepwise exercises

Advanced learning environments such as intelligent tutoring systems for algebra,
logic, programming, physics, etc. let a student practice with stepwise exercises,
and support a student solving such exercises by providing feedback. These environments
usually provide various types of feedback, for example about the
correctness of a step, common errors, hints about how to proceed, or complete
worked-out solutions. Calculating feedback is generally delegated to a dedicated
expert knowledge module, also known as a domain reasoner. Existing
architectural descriptions of learning environments do not precisely specify the
interaction between this module and the rest of the learning system. We propose
a design based on the stateless client-server architecture that clearly decouples
the expert knowledge module from the learning environment. We describe a
set of feedback services that support the inner (interactions within an exercise)
and outer (over a collection of exercises) loops of a learning system, and that
provide meta-information about a class of exercises, such as solving quadratic
equations, or performing Gaussian elimination. The feedback services do not
depend on a particular domain and are based on the various feedback types
described in the literature.
The paper analyzes which domain-specic knowledge about an exercise class
is needed for implementing the feedback services. Based on this analysis, we
developed a framework for implementing domain reasoners that oers generic
functionality such as rewriting, simplifying, and comparing terms. We have implemented
several domain reasoners in this framework, both for external learning
environments and for simple prototypes. The proposed design is evaluated
with these implementations, and we re
ect on our experience with developing
domain reasoners.