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Art. 06 – Vol.23 – No. 2 – 2013

Factors Facilitating a Faster Learning of Chemistry on an AR Platform


Costin Pribeanu

E-mail: pribeanu@ici.ro

National Institute for Research & Development in Informatics – ICI Bucharest

Abstract: Educational systems based on augmented reality have specific features such as: 3D visualization, animation, vocal interface for teaching and guidance, direct manipulation, and haptic feedback. This paper presents a causal model for the analysis of the contribution of these features on the faster learning of Chemistry. The model was developed by carrying on a path analysis that is based on a multilevel multiple regression. The results show the contribution of these features and enable the identification of direct and indirect effects of each factor on a faster learning.

Key words: e-learning, learning efficiency, causal relations, path analysis, augmented reality.

References

  1. Arbuckle, J. L.: AMOS 16.0 User’s Guide. Amos Development Corporation, 2007.
  2. Balog, A.; Pribeanu, C.: The Role of Perceived Enjoyment in the Students’ Acceptance of an AR Teaching Platform: A Structural Equation Modelling Approach, Studies in Informatics and Control, 19(3), 2010, pp. 319-330.
  3. Brom, C.; Preuss, M.; Klement, D.: Are Educational Computer Micro-games Engaging and Effective for Knowledge Acquisition at High-schools? A Quasi-experimental Study. Computers & Education 57, 2011, pp. 1971-1988.
  4. Cohen, P. R.; Carlson, A.; Ballesteros, L.; Amant, R. S.: Automating path analysis for building causal models from data. Proceedings of the International Workshop on Machine Learning, 1993, pp. 57-64, Sage.

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  1. Hair, J. F.; Black, W. C.; Babin, B. J.; Anderson, R. E.; Tatham, R. L.:Multivariate Data Analysis. 6th, Prentice Hall, 2006.
  2. Iordache, D. D.; Pribeanu, C.: Comparison of Quantitative and Qualitative Data from a Formative Usability Evaluation of an Augmented Reality Learning Scenario. Informatica Economica Journal, 13(3), 2009, pp. 67-74.
  3. ordache, D.D.; Pribeanu, C.; Balog, A.: Influence of Specific AR Capabilities on the Learning Effectiveness and Efficiency. Studies in Informatics and Control, 21(3), pp. 233-240, 2012.
  4. Lee, E.A.-L.; Wong K.W.; Fung C.C.: How Does Desktop Virtual Reality Enhance Learning Outcomes? A Structural Equation Modelling Approach. Computers & Education 55(4), 2010, pp. 1424-1442.
  5. Pribeanu, C.; Iordache, D.D.: Evaluating the Motivational Value of an Augmented Reality System for Learning Chemistry. Holzinger, A. (Ed.) Proceedings of USAB 2008. LNCS 5298 Springer, 2008, pp. 31-42.
  6. Pribeanu, C.:Using formative measurement models to evaluate the educational and motivational value of an AR-based application. Problems of Education in the 21st Century 50, 2012, pp. 70-79.
  7. Pribeanu, C.: Valoarea educaţională a unei aplicaţii de învăţare a chimiei – rezultate preliminare pe baza unui studiu pilot. Revista Romana de Informatică si Automatica, 22 (3), 2012, pp. 47-54.
  8. Sun, H.; Zhang, P.: Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 2006, pp. 618-645.
  9. Vos, N.; Meijden, H.; Denessen, E.: Constructing Versus Playing an Educational Game on Student Motivation and Deep Learning Strategy Used. Computers & Education 56, 2011, pp. 126-137.
  10. Wind, J.; Riege, K.; Bogen M.: Spinnstube®: A Seated Augmented Reality Display System, Proceedings of IPT-EGVE – EG/ACM Symposium, 2007, pp. 17-23.

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