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Personalized Collaboration 259
formation might be to augment documents in the system's pool with information about
people who have been actively utilizing them. Interest profiles would then emerge from
the activity patterns of the students and the overlap in the navigation of students.
Constraint-based Group Creation
Suppose that for students in the class, instructor wants to partition them into groups for
a collaborative course activity. Not only do we need to maximize the students’ individual
benefits from participating but we are also concerned with balancing the capabilities and
resources each group has available, so that everyone has roughly the same chances. These
additional constraints may be arbitrary provided they admit an efficient computational
procedure; take balancing groups on average members’ grades as an example.
Constraint-based methods have been researched in conjunction with the Semantic
Web approaches, since students’ characteristics and types of group formation constraints
can be meaningfully described by ontologies.
Students’ features are usually modeled by extending a standard ontology used to
hold additional information that is required. In (Ounnas, 2008) the FOAF (friend-of-afriend)
ontology for social relationships is enhanced by additional student’s personal, social,
and academic data (i.e. preferred learning styles) into a so-called Semantic Learner Profile
(SLP) holding a large range of information used for group formation.
Next, semantic information is put in from both sides (Figure 9-5): (1) by students
submitting their FOAF + SLP profiles, and (2) by the instructor selecting appropriate constraints
to be imposed. The framework enables instructor to specify two types of constraints:
strong that have to be met in all resulting group assignments, and weak that need
not be met necessarily at all times but the more weak constraints are met the better
the resulting assignment. In addition, priorities can be assigned to weak constraints to
facilitate generating more appropriate group formations when a perfect formation is not
possible.
The group generation process itself is done by a DLV solver, an implementation
of disjunctive logic programming, used for knowledge representation and reasoning. Instructor
specifies the constraints in DLV’s native language – Disjunctive Datalog extended
with constraints, queries and true negation (Leone, 2006). Depending on the students’ data
and instructor constraints, DLV outputs more than one grouping of the students, and the
best one considering the number of violated constraints and their priorities is selected.
Authors claim that this formation process does not leave any students unselected – socalled
orphan problem. This is achieved by the virtue of weak constraints, in such a way that
students are assigned to groups in all cases; at worst some constraints are violated producing
an imperfect solution.
With groups selected, we are interested in evaluating the quality of the selection.
Evaluations are usually done subjectively by students and the use of questionnaires
on team efficacy, peer rating, and individual satisfaction, and objectively by the demonstrated
performance. Specific methods vary. In the case of semantic group formation
framework outlined previously, authors propose various numerical metrics for evaluation
(Ounnas, 2007) such as how well the constraints are satisfied, how is the group satisfied
(depending on individual satisfaction), how well the group is formed (depending on all
goals set by the instructor), etc. More or less all proposed metrics boil down to aggregating