Skip to main content

A new approach to personalization: integrating e-learning and m-learning

Abstract

Most personalized learning systems are designed for either personal computers (e-learning) or mobile devices (m-learning). Our research has resulted in a cloud-based adaptive learning system that incorporates mobile devices into a classroom setting. This system is fully integrated into the formative assessment process and, most importantly, coexists with the present e-learning environment. Unlike many mobile learning systems, this system provides teachers with real-time feedback about individual and group learners. Its scalable and extendable architectural framework includes the server-side pedagogical recommendation of content adaptation based on the users’ knowledge-levels and preferences. Content is also automatically adapted to the end device that is being used. This context-aware delivery allows users to switch between e-learning and m-learning, and between devices, without any loss in personalized content. Our work builds on a web-based Adaptive Learning and Assessment System (ALAS) that is built on the Knowledge Space Theory model. At present, this system is used at school computer labs and our goal was to widen this user-base by enhancing this system to support personalized learning on mobile devices. This study describes our process of developing this technology, and contains an empirical analysis of students’ performance, perceptions, and achievements when using ALAS on both personal computers and mobile devices.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  • Gay, G., Stefanone, M., Grace-Martin, M., & Hembrooke, H. (2001). The effects of wireless computing in collaborative learning environments. International Journal of Human-Computer Interaction, 13(2), 257–276.

    Article  Google Scholar 

  • Goh, T. T., & Hooper, V. (2007). To txt or not to txt: That’s the puzzle. Journal of Information Technology Education, 6, 441–453.

    Google Scholar 

  • Kinshuk, Chang, M., Graf, S., & Yang, G. (2009). Adaptivity and personalization in mobile learning. In 2009 AERA Annual Meeting, April 13-17, 2009, San Diego, CA, USA. San Diego, CA, USA.

  • Kramer, B. J. (2005). Mobile learning: The next generation of learning, fernuniversitts contributions to the 2nd year of the leonardo project mlearn2. (Tech. Rep.) ISSN 0945-0130, 5/2005, FernUniversitt in Hagen, 58084 Hagen, Germany.

  • Kukulska-Hulme, A. (2007). Mobile usability in educational context: What have we learnt? International Review of Research in Open and Distance Learning, 8(2), 1–16.

    Google Scholar 

  • Kukulska-Hulme, A., & Traxler, J. (2005). Mobile learning: A handbook for educators and trainers. London: Routledge.

    Google Scholar 

  • Laouris, Y., & Eteokleous, N. (2005). We need an educationally relevant definition of mobile learning. Proceedings of mLearn 2005.

  • Markett, C., Arnedillo Sánchez, I., Weber, S., & Tangney, B. (2006). Using short message service (SMS) to encourage interactivity. Computers & Education, 46(3), 280–293.

    Article  Google Scholar 

  • Nedungadi, P., & Raman, R. (2010). Performance improvements in schools with adaptive learning and assessment. In International Conference on Distance Learning and Education (ICDLE 2010), Puerto Rico, USA.

  • Nedungadi, P., & Raman, R. (2011). Learning-enabled computer assessment of science labs with scaffolds methodology. The Technology Interface International Journal, 11(2 Fall/Winter 2011).

  • Ohlsson, S. (1987). Some principles of intelligent tutoring. In Y. Lawler (Ed.), Artificial intelligence and education (Vol. 1, pp. 203–238). Norwood, NJ: Ablex.

    Google Scholar 

  • Quillen, I. (2011). Mobile apps for education evolving. Retrieved in Sept, 2011, from http://www.edweek.org/dd/articles/2011/02/09/02apps.h04.html.

  • Rogers, E. M. (1995). Diffusion of innovations (4th Ed.) New York: The Free Press.

  • Self, J. A. (1974). Student models in computer-aided instruction. International Journal of Man-Machine Studies, 6, 261–276.

    Article  Google Scholar 

  • Sharpe, T. (2006). ‘Unpacking’ Scaffolding: Identifying Discourse and Multimodal Strategies that Support Learning. Language and Education, 211–231

  • Sharma, S. K., & Kitchens, F. L. (2004). Web services architecture for m-learning. Electronic Journal on e-Learning, 2(1), 203–216.

    Google Scholar 

  • Traxler, J. (2005). Defining mobile learning. In: IADIS International Conference Mobile Learning 2005, Qawra, Malta

  • Waycott, J., & Kukulska-Hulme, A. (2003). Student experiences with PDAs for reading course materials. Personal and Ubiquitous Computing, 7(1), 30–43.

    Article  Google Scholar 

  • Yang, S. J. H., Shao, N. W. Y., & Sue, A. Y. S. (2004). Personalized metadata mechanism applied to adaptive mobile learning. In Proceedings of the 2nd IEEE international Workshop on Wireless and Mobile Technologies in Education (WMTE’04).

  • Zurita, G., & Nussbaum, M. (2004). Computer supported collaborative learning using wirelessly interconnected hand-held computers. Computers & Education, 42(3), 289–314.

    Article  Google Scholar 

Download references

Acknowledgments

This project derives direction, motivation and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This work is funded by Amrita Vishwa Vidyapeetham. The authors would like to thank the reviewers whose comments have led to considerable improvement in the quality of the paper. The authors would like to acknowledge the contributions of Mark McGregor for his help with the statistical analysis, Rathish Gangadharan for help with the pilot study in schools, the faculty at Amrita and the teachers at the pilot schools whose feedback and guidance were invaluable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prema Nedungadi.

Appendix A: student survey questionnaire

Appendix A: student survey questionnaire

Survey options: strongly disagree (1); disagree (2); neither agree nor disagree (3); agree (4); strongly agree (5)

1. I find it easy to use the mobile device for learning.
2. Learning using the mobile device is fun.
3. Based on this experience, I will spend more time learning on the mobile device.
4. It is easy to find the assessment function on the mobile device.
5. Tests delivered on the mobile device were easy to understand.
6. I would recommend mobile learning as a method of study to my friends.
7. It is easy to communicate and get feedback from the teacher with mobile learning.
8. Mobile learning is more useful and helpful when it includes learning videos.
9. I will be happy to receive my marks through the mobile device.
10. I feel comfortable receiving personalised and immediate assessment feedback through text messaging.
11. Learning on the mobile is easier than learning on the computer.
12. Mobile learning gives me greater control over my learning.
13. Mobile learning can improve my group work projects with classmates.
14. I like to experiment with new ways of learning, like using mobile devices.
15. My school supports the use of mobiles for learning.
16. My teacher supports the use of mobiles for learning.
17. I have the resources necessary to use the mobile for learning.
18. I have the knowledge necessary to use the mobile for learning.
19. Students need support while learning with a mobile device.
20. I will use my mobile for learning if my teachers use them.
21. I will use my mobile for learning if my friends use them.
22. Mobile devices can be used for learning more often than in the classroom, science lab, and computer lab.
23. I prefer the bigger screen of the computer to the small screen of the mobile device.
24. It was difficult to find the hint button on the mobile device.
25. I will use mobile for learning whether or not my friends use them.
Do you prefer Learning lessons on computers, or on mobiles?
 Better on computers.
 Better on mobiles.
 Both are the equally good.
Did it take longer to complete the session using a mobile, or using a PC?
 Longer to complete the session using a mobile.
 Longer to complete the session using a PC.
 Both took the same time.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Nedungadi, P., Raman, R. A new approach to personalization: integrating e-learning and m-learning. Education Tech Research Dev 60, 659–678 (2012). https://doi.org/10.1007/s11423-012-9250-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-012-9250-9

Keywords

  • Adaptive assessment
  • Mobile learning
  • Personalized learning
  • Adaptive learning
  • Mobile device
  • Mobility
  • Intelligent tutoring
  • m-Learning
  • e-Learning