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Personalized Collaboration 261
cluster. Afterwards, for each cluster new center is calculated as the average of values of its
member students. Given the new centers, students are re-assigned to the now closest clusters
accordingly, etc. This process is repeated until the assignment into groups converges
and no more students change their group during the final iteration. The method was evaluated
on a set of 52 learners, and for the presented experimental data it outputs a rather
good assignment with quality of 94 out of 101 possible.
Gogoulou further improves the group formation by using a genetic algorithm that for
the given dataset produces an assignment having the quality of 96. These methods seem
nearly perfect in respect to the proposed quality measures, and thus naturally posit a question
of utility and appropriateness of their measures given that no breakthrough outputs in
collaboration were achieved. A genetic algorithm is an optimization method that starts with
a set (population) of randomly selected solutions and tries to modify (mutate) individual
solutions and/or combine (crossover) different solutions to produce another possibly better
(according to a pre-specified fitness function) set of solutions. Iteratively producing better
sets of solutions it converges to the optimal solution. By having multiple solutions simultaneously
the method is robust against falling into local optima. Besides devising the algorithm
itself, which is rather straightforward, the input parameters such as mutation and
crossover probabilities, number of generations, and population size with which the algorithms
perform effectively are important. In this case though, authors do not mention the
values of these important parameters that produced their results, and thus it is not clear if
they can be recreated. Either way, authors demonstrated this to be a viable method for
group formation, slightly outperforming the basic clustering method.
The use of statistical and/or optimization methods is not seldom, and other methods
have been proposed such as repeated hill-climbing optimization with weighed constraints
(Cavanaugh, 2004), Fuzzy C-Means clustering (Christodoulopoulos, 2007) and an interesting
Ant Colony Optimization method (Graf, 2006).
Statistical methods represent students’ characteristics in a coarse-grained way. Therefore
statistical group formation methods that produce one-time assignments suffer from
similar deficiencies as constraint-based methods, namely they simply generate an assignment
of students into groups and evaluate its quality by how many rules are satisfied (or
broken), all under the assumption that the proposed method might somehow improve the
probability of successful collaboration; never receiving any feedback whether it really did.
In the next section, we examine methods that generate group assignments repeatedly, each
time hopefully better than the previous one driven by the feedback on the previous assignment.
Repeated Group Creation
Repeated group formation assumes that groups are created dynamically or need to be
created for several successive occasions so that payoffs for individual students can end up
more balanced compared to a single allocation methods described previously. In other
words, possibly disadvantaged students in one round of collaboration might get a more
favorable assignment in following runs.
In Opportunistic Group Formation (OGF) framework groups are formed dynamically at
appropriate situations with the help of personal agents that negotiate and manage collaborative
learning activities the students can engage in (Inaba, 2000). Agents in OGF sup-