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assumed node (3), and its corresponding justification
(4). Focusing on the first rule R(a), “The student does
not know the object to be used in the following action
(put in washing powder)” is inferred by R(a). An
assumed state for this objective was already stored in
the initial model of the SM ontology (Objective
_
State
→ Specific
_
Objective
_
State → state1, with its property
acquired=false). When the rule R(a) is fired, the action
on the consequent, Add
_
SM, causes the value of the
property levelCurrentReliability of state1 to be
increased in 1.
Afterwards, the tutoring strategy decides giving a
hint about the correct object with which the student
must interact (detergent tray). This tutor’s action
involves the firing of the rule (6). “The student knows
the object to be used in the following action” is
deduced as a result. For this objective, there was not an
instance in the ontology with property acquired=true.
The action Add
_
SM in this case sets to 1 the property
levelCurrentReliability. Likewise, the ATMS is
informed of the assumed node (3) and its justification
(4). A contradiction detection rule is triggered and the
ATMS is informed with the corresponding
justification. Also, the CS is invoked and one heuristic
rule (5) establishes the cause of the contradiction as a
change in the student’s mind and the contradiction is
resolved by keeping the more recent objective state
(acquired=true).
)precondx)) plan,act(next Precond(Req
SM(KnowAdd
precondx)) plan,act(next PrecondHints(ReqGive
) action)nextHints(typeGive IF :R(c)
---
-
----
---
∧
(6)
5 CONCLUSIONS
This article has described a solution based on
ontologies to student modelling in an ITS. The
general objective has been developing a SM with the
following main characteristics: genericity,
adaptability, non-monotonic diagnosis, extensibility
and reusability. The associated non-monotonic
diagnosis method has also been presented, relying
on an ATMS, the Jena framework and a pedagogic
diagnosis module.
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