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Data
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Fig. 7.6 Data of the graduate courses with grades: event 1 (course access) … event 10 (forum
participation)
• Exercise practice and course achievement were also found to be independent
events for some datasets. This could be due to the profile of some students,
which included a background in information and telecommunications technol-
ogy as well as knowledge about the course content. For those students, partic-
ipating in those event activities would be irrelevant.
In a second stage, we applied ICA after reducing the data to five components by
PCA [
26
]. We applied PCA to group the events of the web activity into learning
dimensions taking into account Felder's framework [
24
]. PCA reduced 10 web
event activities to five components. Table
7.4
contains the first six sorted contri-
butions of the web activities of the ICA mixture matrix for the five estimated
sources. Each source was associated with one learning dimension from Table
7.3
,
analyzing the weight of the web activities and considering the principal evaluation
methodologies employed by teachers for graduate courses with grades. -Dimension
1 was not detected and Dimension 5 was detected twice (5 and 5')-. The meth-
odologies assigned grades focusing on: achievement, individual student partici-
pation, or group work. The implicit teaching styles of the evaluation methodologies
encouraged specific learning styles of the students, as we explain below.
The learning dimension 1 (sensory-intuitive) corresponding to ''perception''
was not detected in the ICA mixing matrix. This could be due to the fact that the
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