104 | JADE
ED DE QUINCEY ET AL
Running the simple K-means algorithm on this set of data revealed
the two most prominent classes of students with the following
centroids (average values of the attributes considered):
Full Data
(66 students)
Cluster 0
(40 students)
Cluster 1
(26 students)
programmeID
P11361
P11361
P03657
CW Mark
48%
34%
70%
Attendance
61%
55%
70%
Total File Views
40
24
64
Tutorial Views
24
15
37
Lecture Views
13
6
22
CW Spec. Views
2
1
3
Attribute
Table 2: Returned clusters from the K-means algorithm
The above two descriptors of the two classes show a clear
distinction between the performance of students within each
cluster (according to their coursework mark). The better performing
students in Cluster 1 (i.e. those who have achieved a 70% average
mark) attended the lectures and tutorials more regularly and
accessed all types of material on the CMS intranet more frequently
than the students in Cluster 0.
Of greater interest however are “the exceptions” to the above
inferences. The figure below shows the distribution of student
marks compared to their degree programme (represented by the
“P” code on the x-axis). Each point, representing a student, has
been assigned a colour that relates to one of the 2 clusters detailed
in Table 2 above.