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222 Selected Studies on Software and Information Systems
processes, there is also a secondary, but probably even more important effect: the centralized
user model gives an opportunity to share the same user model between several adaptive
applications, where each one could take advantage of knowledge acquired by others.
8.4.1 CUMULATE
CUMULATE [16, 65] is an example of a generic user modeling server in the domain of
e-Learning (so we call it also student-modeling server). It is based on the KnowledgeTree
architecture [14]. This architecture assumes that server receives information about each
important student action from the external adaptive applications. The server processes
this information into a student model and provides student information by request to any
adaptive applications that wants to adapt to the student.
Information about student is represented in two levels:
– The Event Storage – stores events reported by external adaptive applications. An
application can store not only events, but also the student’s progress as well as any
additional, application-specific information. CUMULATE adds a timestamp to each
received event;
– Inferred User Model – the event storage is open to a variety of inference agents that
process this data and convert it into a form of attribute-value pairs that form inferred
user model.
Authors of CUMULATE created a topic based inference agent which is responsible for
topic-based knowledge modeling. CUMULATE provides a form-based authoring interface
allowing for definition of topics and their relationships to activities. Implementation of the
inference agent that exploits these relationships is then straightforward. However, it is not
clear why these relationships belong to CUMULATE and not to the agent itself, i.e., the fact
that CUMULATE, being a user modeling server, has to deal with form-based interfaces seems
like a flaw in the design.
Another point is that authors do not mention the flexibility of created user model. Does
CUMULATE allows for use of an ontology based user model?
8.4.2 MEDEA
Methodology and Tools for the Development of Intelligent Teaching and Learning Environments
(MEDEA [60]) is a complex platform in the e-Learning domain allowing for the
creation of intelligent systems offering domain-independence, extensibility and resource
reusability and interoperability. MEDEA could enhance existing tutoring systems by user
modeling and adaptivity feature, under condition that the tutoring system can be encapsulated
as a web service that accomplishes a communication protocol. MEDEA is composed
of a domain-independent kernel and set of instructional resources (see Figure 8-3).
Instructional resources are external educational systems which perform an individual,
well defined pedagogical task (e-books, simulation systems, assessment tools, etc.). Each
instructional resource has its own domain model, content authoring interface, student interface
and may have its own student model (which contain information to be passed to
MEDEA). MEDEA defines three types of instructional resources: