April 2012 Volume 15 Number 2 - Educational Technology & Society

IMD122 33 71 7 45 0,535 0,901 0,718 32,32
IMD21 11 25 8 24 0,560 0,680 0,620 14,88
IMD134 25 27 4 27 0,074 0,852 0,463 12,50
IMD15 11 24 7 20 0,542 0,708 0,625 12,50
IMD49 14 23 5 21 0,391 0,783 0,587 12,33
IMD67 18 23 4 22 0,217 0,826 0,522 11,48
We considered the evaluation of the courses primarily by the UPCS which is a quantitative metric. It is a quantitative
metric because it just counts the number of instances. Courses with a high number of UPCS are quite popular among
the students.
Since there are courses with the same UPCS (or with values very close to each other), we wanted to refine the
situation and add an absolutely qualitative metric, named Quality index, which combines properly Enrichment and
Interest. The final score derives as the product of UPCS with the Quality index.
Clustering results
The next step according to the proposed methodology involves the clustering of the data. In this phase we performed
two distinct clusterings. The first one clusters the courses based on the proposed metrics described in the previous
step. The second one clusters the students according to the courses they have in common. Thus, the proposed
methodology provides the instructor with insight not only into the courses, but also into the students.
Course clustering
The clusters of the courses allow us to better assess the information contained in the proposed metrics. The clustering
was performed using the open source data mining tool Weka (Weka, 2009). The metrics and indices described in the
previous step were used with the SimpleKmeans for clustering platform courses. The properties of SimpleKmeans
were Euclidean distance with 2 clusters, since our goal was to separate the 39 courses into high activity and low
activity ones. The produced results show that 9 (23%) of the courses had high activity and 30 (77%) of them low
activity.
Visualization of the results using UPCS is shown in Figure 2. Grey points in the left indicate high activity courses
whereas points in black show low activity courses. It is evident that the information contained in the aforementioned
metrics contains important information about the courses and can distinguish them in distinct groups.
Figure 2. Cluster visualization using UPCS
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