July 2007 Volume 10 Number 3 - Educational Technology & Society

preference MAE varies against the number of experiments. The values oscillate between 0.5 and 1 and this proves
again that the algorithms can infer learners’ preferences accurately.
(a) (b)
(c)
Figure 9. Comparison of preference feedbacks. (a) Direct preference feedbacks
(b) Preference MAE varies against the number of learning objects studied.
(c) Preference MAE varies against the number of experiments.
Experiment Result 2 –Analysis the variation of helpfulness feedbacks with different weights
This experiment observes the variation of preference feedbacks for the learners with the same set of weight
proportions as the first experiment. Learners send feedbacks according to how they feel on the content of learning
objects recommended for them, helpful or not. As mentioned, five levels have been defined for the values of
feedback. The results are shown in Figure 10.
In Figure 10(a), it’s clear that the helpfulness feedbacks of three different weight proportions are between 3.5 and 4.
As shown in Figure 10(b) the helpfulness MAE is close to 0.7 with increasing number of learning objects studied.
And in Figure 10(c), it can be seen that, with more learning objects studied, the variation of content MAE becomes
milder. This indicates that the system can accurately predict learners’ preference and learning objects’ suitability
when it becomes more stable. The so-call cold-start problem can be observed here that erroneous predictions reduced
when there are enough feedbacks from learners in the system. The experiment also shows that this problem can be
reduced or eliminated even faster in the proposed model when learning objects are also ranked according to the
average of other learners’ feedbacks.
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