Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics

Elias Gounopoulos, Stavros Valsamidis, Ioannis Kazanidis, Sotirios Kontogiannis

2017

Abstract

People’ s educational needs and requirements change. At the same time, educational technologies and tools also evolve. Therefore, contemporary educational methods are obliged to adapt to both. E-learning is the mode of learning which serves the former while exploits the latter. As e-learning capabilities are moving into the third decade of their implementation (Kulik et al., 1990), the necessity of thorough assessment is imminent. Moreover, the adoption to e-learning of assessment features which were successfully used by e-commerce is also a challenging issue. In this study, a novel approach is presented and put to test. The approach tries to utilize applicable features of e-commerce technology to e-learning in an effort to measure usage, user trends and knowledge affiliations. To the extent, some already tested indexes and metrics are used for the quantification of qualitative features of e-learning. These indexes and metrics contribute to the assessment of both educational content exposed by the educators and content usage by the learners. In this paper the identified features are classified. Finally, an experimental case scenario that took place in a Greek university e-learning platform is presented. From the revealed results there is evidence that these corresponding to features variables can be used for the measurement of reach, richness and information density of an e-learning platform system.

References

  1. Evans, P. and Wurster T., 1997. Strategy and the New Economics of Information. Harvard Business Review (September-October 1997).
  2. Evans, P. and Wurster T., 1999. Getting Real About Virtual Commerce. Harvard Business Review (November-December 1999).
  3. Feng, M. and Heffernan, N., 2006. Informing teachers live about student learning: Reporting in the assistment system,” Technol., Instruction, Cognition, Learn. J., vol. 3, pp. 1-8.
  4. Gibbs J. and Rice M., (2003). Evaluating student behavior on instructional Web sites using web server logs,” in Proc. Ninth Sloan-C Int. Conf. Online Learn., Orlando, FL, 2003, pp. 1-3.
  5. Gounopoulos, E., Kontogiannis, S., Kazanidis, I., Valsamidis, S., (2016), A framework for the evaluation of multilayer web based learning, 20th Panhellenic Conference on Informatics (PCI 2016) in Patra, Greece, 10 - 12, 2016.
  6. GUNet, 2016. Open eClass - Course Management System. Retrieved August 12, 2016 from http://eclass.gunet.gr/.
  7. Hwang, G. J., Tsai, P. S., Tsai, C. C., and Tseng, J. C. R. 2008. A novel approach for assisting teachers in analyzing student web-searching behaviors. Computers and Education, 51(2), 926-938.
  8. Ingram, A., 1999. Using web server logs in evaluating instructional web sites, J. Educ. Technol. Syst., vol. 28, no. 2, pp. 137-157.
  9. Internet World Stats, 2015, Internet Users in the World. Distribution by World Regions - 2015, Available at http://www.internetworldstats.com/stats.htm.
  10. Jin, H., Wu, T., Liu, Z., and Yan, J., 2009. Application of visual data mining in higher-education evaluation system. In Proceedings of ETCS 2009. (pp. 101-104). Wuhan, Hubei, China: IEEE Computer Society Press.
  11. Kulik, C.; Kulik, A. and Bangert-Drowns, L., 1990. Effectiveness of Mastery Learning Programs: A MetaAnalysis. Review of Educational Research. 60: 265- 299. doi:10.3102/00346543060002265.
  12. Laudon, K.C and Traver, C.G., 2014. E-commerce 10th edition, Addison Wesley, NY.
  13. Lee, M., 2010. Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, vol. 54, pp. 506-516.
  14. Pahl, C., and Donnellan, C., 2003. Data mining technology for the evaluation of web-based teaching and learning systems. In Proceedings of E-Learn 2002. (pp. 1-7). Montreal, Canada, October: AACE Press.
  15. Lingyte M, Valsamidis S, Mitsinis N, Polychronidou P., 2012. E-commerce behaviour of Lithuanian and Greek women. Intellectual Economics; 1(9): 85-98.
  16. Romero, C. and Ventura, S., 2010. Educational data mining: a review of the state of the art. Trans. Sys. Man Cyber Part C, vol. 40, no. 6, pp. 601-618, Nov. 2010.
  17. Shapiro, C. and Varian, H., 1997. Information Rules. A Strategic Guide to the Network Economy. Cambridge, MA: Harvard Business School Press 1999.
  18. Valsamidis, S., Kontogiannis, S., Kazanidis, I., Karakos, A., 2010A. Homogeneity and Enrichment: Two Metrics for Web Applications Assessment, 14th Panhellenic Conference on Informatics (PCI) 2010, 10-12 September, Tripolis.
  19. Valsamidis, S., Kazanidis, I., Kontogiannis, S., Karakos, A., 2010B. Course ranking and automated suggestions through web mining, ICALT August 2010.
  20. Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., Karakos, A., 2012A. A clustering methodology of web log data for Learning Management Systems, Journal of Educational Technology and Society, Vol. 15, Issue 2, pp. 154-167, http://www.ifets.info/.
  21. Valsamidis, S., Kazanidis, I., Kontogiannis, S., Karakos, A., 2012B. Measures for Usage Assessment in ELearning, 4th International Conference on Education and New Learning Technologies, 2-4 July, 2012, Barcelona, Spain.
  22. Zinn, C. and Scheuer, O., 2006. Getting to know your students in distance learning contexts,” in Proc. 1st Eur. Conf. Tehcnol. Enhanced Learn., pp. 437-451.
  23. Zoubek, L. and Burda, M., 2009. Visualization of differences in data measuring mathematical skills. In Proceedings of EDM 2009. (pp. 315-324). Cordoba, Spain.
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Paper Citation


in Harvard Style

Gounopoulos E., Valsamidis S., Kazanidis I. and Kontogiannis S. (2017). Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E, ISBN 978-989-758-239-4, pages 649-655. DOI: 10.5220/0006399606490655


in Bibtex Style

@conference{a2e17,
author={Elias Gounopoulos and Stavros Valsamidis and Ioannis Kazanidis and Sotirios Kontogiannis},
title={Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E,},
year={2017},
pages={649-655},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006399606490655},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E,
TI - Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics
SN - 978-989-758-239-4
AU - Gounopoulos E.
AU - Valsamidis S.
AU - Kazanidis I.
AU - Kontogiannis S.
PY - 2017
SP - 649
EP - 655
DO - 10.5220/0006399606490655