Technological Evaluation and Optimization of E-Learning Systems Components - E-Infrastructures and Technologies for Lifelong Learning - page 170

Table 8. Conversion of linguistic variables and
QWS symbols into non-fuzzy values E

obtained by adding all the criteria together with
their weights. It is valid from the point of view
of the optimization theory, and a special theorem
exists for this case.
Therefore, we have here the experts' additive
utility function:

Linguistic variables and
QWS symbols

Non-fuzzy values E

Excellent (E or *) 0.850

Good (#) 0.675

Fair (+) 0.500

m m

Poor (|) 0.325

∑ 1 ∑ =

1

f f(X) ( ) = a f X

i

( ), a i 1, a i >0. (3)

i

Bad (0) 0.150

i = i =

where f i (X j ) is the rating (non-fuzzy value E) (see
Table 8) of the criterion i for each of the examined
alternatives (we examine three alternatives here)
X j : X 1 - ATutor (2009), X 2 - Ilias (2009), and X 3
- Moodle (2009).
Here i denotes the order numbers of the VLE
quality evaluation criteria presented in Table 6.
The first four of these criteria are the general
'internal quality' VLE quality criteria, and the
other four represent the VLE adaptation 'quality
in use' criteria (see Table 6).
The higher the value of the utility function (3)
is, the better a VLE meets the particular learner
needs.

For example, for the most simple case, where
all the VLE evaluation criteria are of equal
importance, the experts could consider equal
'normalized' weights a i = 0.125 according to the
normalization requirement

m

∑ =
1

a i 1, a i >0. (2)

i =

for the VLEs quality evaluation criteria i = {1,
…, 8} (see Table 6).
A possible decision could be to transform
a multi-criteria task into a one-criterion task

Table 9. Summary of the VLEs technological evaluation

Technological evaluation criteria ATutor Ilias Moodle

General criteria ratings
Architecture and implementation 0.500 0.325 0.850

Interoperability 0.675 0.675 0.500

Internationalization and localization 0.325 0.500 0.675

Accessibility 0.850 0.325 0.500

Interim rating 2.350 1.825 2.525

Adaptation criteria ratings
Adaptability

0.325 0.500 0.675

Personalization 0.675 0.675 0.500

Extensibility 0.675 0.850 0.850

Adaptivity 0.325 0.150 0.325

Interim rating 2.000 2.175 2.350

Total evaluation rating 4.350 4.000 4.875

Total rating f(X) (weights = 0.125) 0.5437 0.5000 0.6093