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Eugenijus Kurilovas et al.<br />

If we look separately at <strong>learning</strong> activity (LA) component of the scenarios, we’ll get the following<br />

results for LA comparative weights in LS:<br />

In general case when experts do not take into account particular learner profile: 28.3% according<br />

to trapezoidal method, or 25.2% according to triangle method for LA1 Vs 24.1% according to<br />

trapezoidal method, or 22.3% according to triangle method for LA2:<br />

⋅ f ( X ) = 0,<br />

283 0,<br />

241 ⋅ f ( X ) = 0,<br />

252 0,<br />

223<br />

aiG j<br />

a<br />

iA<br />

⋅ f ( X j ) =<br />

( )<br />

( 0,<br />

298 0,<br />

232)<br />

aiG j<br />

( )<br />

In particular case when experts take into account particular (activist) learner profile: 29.8%<br />

according to trapezoidal method, or 25.7% according to triangle method for LA1 Vs 23.2%<br />

according to trapezoidal method, or 21.8% according to triangle method for LA2:<br />

a<br />

iA<br />

⋅ f ( X j ) =<br />

( 0,<br />

257 0,<br />

218)<br />

In general case these results mean that, according trapezoidal fuzzy numbers method, LS1 meets<br />

72.7% quality in comparison with the ideal, LS2 – 68.5%, and using triangle fuzzy numbers method –<br />

LS1 – 63.8%, and LS2 – 61.0% respectively.<br />

In particular case (for activist learner) these results mean that according trapezoidal fuzzy numbers<br />

method, LS1 meets 76.4% quality in comparison with the ideal, LS2 – 69.7%, and using triangle fuzzy<br />

numbers method – LS1 – 65.6%, and LS2 – 61.8% respectively.<br />

The same tendency in ranking the alternatives is noticeable in evaluation of LA. It is understandable,<br />

because the experts have decided that LOs for each alternative should be evaluated equally, and in<br />

this case LA remains the most significant component of LS.<br />

The obtained results mean that LS1 is a better alternative in comparison with LS2 both in general case<br />

and in particular case of suitability to activist learner, and both for <strong>learning</strong> activity component and the<br />

whole <strong>learning</strong> scenario.<br />

4. Conclusion and recommendations<br />

The research results presented in the paper show that (1) complex application of principles of multiple<br />

criteria decision analysis for identification of quality evaluation criteria, (2) quality criteria classification<br />

principle, (3) fuzzy group decision making theory to obtain final evaluation measures, and (4) the<br />

original method of consecutive triple application of AHP to establish criteria weights:<br />

Are applicable in real life situations when schools have to decide on use of particular <strong>learning</strong><br />

scenarios for their education needs, and<br />

Could significantly improve the quality of expert evaluation of <strong>learning</strong> scenarios by noticeably<br />

reduce of the expert evaluation subjectivity level.<br />

Use of the method of consecutive triple application of AHP leads to establishing different weights of<br />

criteria for particular leaner groups, and respectively – to different LS alternatives evaluation results.<br />

Application of both triangle and trapezoidal fuzzy numbers shows similar alternatives’ quality<br />

evaluation results, i.e., the ranking of analysed alternatives have not changed while applying different<br />

fuzzy numbers methods. The experimental evaluation results show that proposed scientific<br />

approaches are quite objective, exact and simply to use for selecting qualitative LS alternatives for<br />

particular learner groups. On the other hand, proposed LS personalised quality evaluation approach is<br />

applicable for the aims of iTEC project in order to select LS suitable for activist learners. Therefore,<br />

these approaches have been recommended by the authors to be widely used by European policy<br />

makers, publishers, practitioners (teachers), and experts-evaluators both inside and outside iTEC<br />

project to evaluate quality and personalisation level of <strong>learning</strong> scenarios. Method of consecutive<br />

triple application of AHP presented in the paper is absolutely novel, and these new elements make<br />

the given work distinct from all the other earlier works in the area.<br />

5. Appendix 1<br />

The work presented in this paper is partially supported by the European Commission under the 7th<br />

Framework Programme – as part of the iTEC project (Grant Agreement Number 257566). The<br />

authors are solely responsible for the content of this paper. It does not represent the opinion of the<br />

388

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