Student profile ergonomically adapted to e-learning. a data clustering and statistical analysis based survey

Authors:
Liana Stanca;Ramona Lacurezeanu;Vasile Paul Bresfelean;Ioana Pop
Affiliations:
Business Information Systems Department, Babes-Bolyai University, Cluj-Napoca, Romania;Business Information Systems Department, Babes-Bolyai University, Cluj-Napoca, Romania;Business Information Systems Department, Babes-Bolyai University, Cluj-Napoca, Romania;University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
Venue:
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
Year:
2011

Citing 6
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
iTeach: ergonomic evaluation using avatars in immersive environments

UAHCI'07 Proceedings of the 4th international conference on Universal access in human computer interaction: coping with diversity
Effectiveness of multimedia systems in children's education

EHAWC'07 Proceedings of the 2007 international conference on Ergonomics and health aspects of work with computers
Generating adaptive route instructions using hierarchical reinforcement learning

SC'10 Proceedings of the 7th international conference on Spatial cognition
Towards an ergonomics of knowledge systems: improving the design of technology enhanced learning

EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present work we carried out a study over a 4 years period in order to develop a student profile that matches computer-assisted learning. In our opinion, much of the teaching-learning effort will be reduced if the forms of education that fit each individual can be correctly identified. The ergonomics of teaching / learning comprises the correct identification of the student profile so as to connect with the right method and tools for learning. In the process of student profile identification we used the statistic analysis, association rules, and the data mining clustering techniques based on the K-means algorithm.