An Effective Cloud-Based Simulator Facilitating Learning Analytics on Mobile Devices

  • Vincent Tam
  • Alex Yi
  • Di Xu
  • Edmund Y. Lam
Part of the Lecture Notes in Educational Technology book series (LNET)


Learning analytics is targeted to better understand each learner’s interests and unique characteristics in learning so as to build a personalized learning environment. However, many learning analytics techniques may require relatively intensive computation, thus inappropriate for any mobile application. In this paper, we propose an interactive and personalized e-learning system named the COMPAD+ simulator facilitated by an intelligent learning analytics algorithm running on the cloud server to quickly estimate the learner’s areas of interests on the simulation results as based on his/her initial inputs and then flexibly generate the simulation details as appropriate. To protect the data privacy of each individual learner, the personal data is stored in the learner profile under each password-protected account on the cloud server with all the intermediate simulation data to be erased after each learning task. Up to our understanding, this work represents the first attempt to successfully develop a flexible, interest-based, and platform-independent simulator directly run on any mobile device facilitated by an efficient learning analytics algorithm working on the cloud server. To demonstrate its feasibility, a prototype of our cloud-based COMPAD+ e-learning system is built and carefully evaluated on various mobile devices. Clearly, there are many promising directions in terms of both pedagogical and technological impacts to extend and enhance our interest-based COMPAD+ simulation platform for future e-learning systems.



The authors are deeply grateful to Dr. Daniel Churchill, Dr. Kinshuk, and Professor Yi Shang for their fruitful discussions and valuable inputs on learning analytics and mobile learning.


  1. Affenzeller, M., Winkler, S., Wagner, S., & Beham, A. (2009). Genetic algorithms and genetic programming: Modern concepts and practical applications (pp. 1–23). Boca Raton: Chapman & Hall/CRC.Google Scholar
  2. Chen, C.-M., Peng, C.-J., & Shiue, J.-Y. (2008). Ontology-based concept map for planning personalized learning path. In Proceedings of the IEEE Cybernetics and Intelligent Systems, Chengdu, November 2008, pp. 1337–1342.Google Scholar
  3. Chuang, H. M., & Shen, C. C. (2008). A study on the relationship among learning path, learning style, and e-learning performance. In Proceedings of the 7th International Conference on Machine Learning and Cybernetics, Kunming, July 2008, pp. 2481–2486.Google Scholar
  4. Fernández, A., Peralta, D., Herrera, F., & Benítez, J. (2012). An overview of e-learning in cloud computing. Workshop on LTEC, 2012, 35–46.Google Scholar
  5. Fung, S., Tam, V., & Lam, E. Y. (2010). Improving an interactive simulator for computer systems with learning objects. In: Proceedings of the IEEE 2nd International Conference on Education Technology and Computer (ICETC 2010) (Vol. 3, pp. 16–20). Shanghai, China: IEEE Computer Society Press, June 22–24 2010.Google Scholar
  6. Hong, C., Chen, C., & Chang, M. (2005). Personalized learning path generation approach for web-based learning. In 4th WSEAS International Conference on E-ACTIVITIES, Miami, Florida, USA, November 2005, pp. 62–68.Google Scholar
  7. Masud, M. A. H., & Huang, X. (2012). An e-learning system architecture based on cloud computing. World Academy of Science, Engineering and Technology 2012, vol. 62, pp. 74–78.Google Scholar
  8. Miller, G. H. (2004). Microcomputer engineering (3rd ed.) Upper Saddle River, N.J.: Pearson Education/Prentice Hall.Google Scholar
  9. Mitchell, M. (1998). An introduction to genetic algorithms (pp. 2–26). Cambridge: MIT Press.Google Scholar
  10. Pocatilu, P., Alecu, F., & Vetrici, M. (2010). Measuring the efficiency of cloud computing for e-learning systems. WSEAS Transactions on Computers, 9, 42–51. ISSN: 1109-2750.Google Scholar
  11. Tam, V., Lam, E. Y., & Fung, S. (2012). Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization. In Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012) (pp. 592–596). Rome, Italy: The IEEE Computer Society Press, July 4–6 2012.Google Scholar
  12. Tam, V., Yi, A., & Lam, E. Y. (2013). Building an interactive simulator on a cloud computing platform to enhance students’ understanding of computer systems. In Conference Proceedings of the 13th IEEE International Conference on Advanced Learning Technologies (ICALT 2013) (pp. 154–155). Beijing, China: The IEEE Computer Society Press, July 15–18 2013.Google Scholar
  13. Tocci, R. J., & Ambrosio, F. J. (2003). Microprocessors and microcomputers: hardware and software (6th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  14. Tsang, E. (1993) Foundations of constraint satisfaction (pp. 305–310). London: Academic Press.Google Scholar
  15. Velicanu, A., Lungu, I., Diaconita, V., & Nisioiu, C. (2013). Cloud e-learning. In Conference proceedings ofeLearning and Software for Education(eLSE), Issue 02, pp. 380–385.Google Scholar
  16. Windows Azure Development Team. (2015). Microsoft azure: cloud computing platform and services. Retrieved March 31, 2015, from
  17. Wong, L., & Looi, C. (2009). Adaptable learning pathway generation with ant colony optimization. Educational Technology and Society, 12(3), 309–326.Google Scholar
  18. Xu, D., Wang, Z., Chen, K., & Huang, W. (2012). Personalized Learning Path Recommender Based on User Profile Using Social Tags. In 2012 Fifth International Symposium on Computational Intelligence and Design (ISCID) (Vol. 1, pp.511–514). Hangzhou, October 2012.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.The University of Hong KongHong Kong SARPeople’s Republic of China

Personalised recommendations