Abstract
Due to the large number of variables necessary to evaluate the e-learning and their imprecision or uncertainty aspects, a new approach based on neutrosophy [4] can provide a better interpretation of the assessment results.
The following classes of variables will be modelled by neutrosophic entities (sets, numbers, logics, probabilities, statistics): learner variables - including learner attitude and motivation, learning environment variables - including real, augmented or virtual environments, contextual variables - including informal, formal, or adult education, technology variables - including classic and new paradigms like mobile, and cloud/fog environments, and pedagogic variables - including methodologies, examination, and certification.
Every variable is defined under both crisp and neutrosophic views. E-learning metrics are reviewed and extended to support neutrosophic computing models using [1]. Finally, considerations on implementing an automated tool for e-assessment of e-learning under classic and neutrosophic approaches are presented.
The work uses the Smarandache's development on neutrosophy [4], neutrosophic computing models [1], e-learning metrics and assessment procedures available in literature [5] or developed by authors [2, 3].
References:
1. G. Albeanu, Neutrosophic computational models, Analele Universitatii Spiru Haret, Seria Matematica-Informatica, 2(2013).
2. G. Albeanu, e-Learning metrics, Proceedings of ICVL, 2007: http://www.cniv.ro/2007/disc2/icvl/documente/pdf/invited/invited2.pdf
3. G. Albeanu, Quality indicators and metrics for capability and maturity in e_learning, http://adlunap.ro/eLSE_publications/papers/2007/lucrare_21.pdf
4. F. Smarandache, Neutrosophy, http://www.gallup.unm.edu/~smarandache/neutrosophy.htm
5. Graham Attwell (ed.), Evaluating E-Learning: A Guide to the Evaluation of E-Learning, Evaluate Europe Handbook Series Volume 2, Leonardo da Vinci Programme, 2006. |