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260 Selected Studies on Software and Information Systems
previous simpler metrics by the use of mean or standard deviation, and without providing
any relevant empirical results on these measures the utility of computing these numerical
measures remains largely unanswered.
In addition, constraint-based group formation assumes that instructor knows exactly
what constraints are good for making the groups collaborate effectively. However, knowing
what exactly makes collaboration effective is still unclear and currently is the focus
of intensive research. Therefore, methods that attempt to optimize further decisions based
on previous possibly imperfect choices seem to be a more suitable alternative.
Figure 9-5. Semantic group formation framework (Ounnas, 2008).
Statistical Group Creation
Statistical methods compared to the constraint-based ones do not dwell into detailed constraint
or rule selections but rather process students in a more data-driven way. Students’
features are usually represented by a multidimensional vector-space model of attributes;
i.e. a student is represented by an n-dimensional vector where the value in i-th dimension
corresponds to the student’s value for i-th attribute.
Having a vector-based representation in place, we can easily compare two different
students on the basis of differences in respective dimensions of their vector representations.
(Gogoulou, 2007) propose a group creation method that groups students with similar
personality features. Learner’s personality and performance attributes are represented
by a n-dimensional vector whose values are from a five-level Likert scale, the difference
between two students is the sum of differences on respective dimensions of their vectors,
and group quality is evaluated with respect to attributes as the difference of the lowest and
highest value for members of the group. Total group quality is the sum of group quality
across all attributes, and the groups’ assignment quality is the sum of group qualities
across all the groups. Next, the proposed group formation method is analogous to the k-
means clustering and assigns students into M groups of K students as follows: As a first
step, M centers (points in the n-dimensional space) of clusters are chosen at random. Then,
for each center, K closest (by Euclidean distance) students are assigned to the appropriate