ARTICLE #6 | 103
DATA MINING FOR LEARNING ANALYTICS
including all 66 students who were originally enrolled on the
module. The K-means algorithm attempts to find k clusters in a set
of observations/samples. Once the algorithm is run and clustering is
completed each sample is assigned to the cluster with the nearest
centroid (cluster centre). The centroid of a cluster is one that best
represents the cluster. The centroid’s attributes are computed by
finding the means of the attribute values of the cluster’s members.
Results
For the programming component of the module there were a total
of 2,622 views of related materials (mean=39.7 views per student).
The following table shows the breakdown of views for the different
material types.
Resource Type
Number
of Files
Total
Views
Avg. Views per
Student (n=66)
Tutorial Instructions
11
1559
23.6
Lecture Slides
231
825
12.5
Coursework Specification
1
127
1.9
Table 1: Breakdown of views per resource type
In order to determine clusters of student behaviour on the module,
the following features were then considered:
• the student’s degree programme (a code comprised of “P”
followed by a set of numbers)
• their coursework mark for the programming component of the
module
• their physical attendance percentage in lectures and tutorials
• the number of times they have viewed module related
programming materials such as lecture slides, tutorial
instructions and the coursework (CW) specification.