Skip to main content
Springer Nature Link
Log in

Architectural Support for Context-Aware Mobile Learning Applications

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Context-aware mobile learning applications provide learning materials to suit the needs of individual learners. Despite several applications developed, there is a lack of architectural support for developing these applications. This has resulted in a number of challenges; lack of standardization, poor quality of developed applications, and reliability. Motivated by this shortcoming, a reference architecture was designed using requirements gathered from twenty-four context-aware mobile learning applications. The reference architecture provides architectural support for context capturing and processing. Evaluation of the reference architecture was conducted, and the results indicate that it solves the motivated problem and also consolidates necessary requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bass, L., Clements, P., & Kazman, R. (2003). Software architecture in practice Addison-Wesley Professional.

  • Berri, J., Benlamri, R., & Atif, Y. (2006). Ontology-based framework for context-aware mobile learning. Paper presented at the Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, 1307-1310.

  • Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2).

  • Cloutier, R., Muller, G., Verma, D., Nilchiani, R., Hole, E., & Bone, M. (2010). The concept of reference architectures. Systems Engineering, 13(1), 14–27.

    Google Scholar 

  • de Witt, C., & Gloerfeld, C. (2018). Mobile learning and higher education. The digital turn in higher education (pp. 61-79) Springer.

  • Economides, A. A. (2008). Context-aware mobile learning. Paper presented at the World Summit on Knowledge Society, 213-220.

  • Ennouamani, S., & Mahani, Z. (2019). A comparative study of the learner model in adaptive mobile learning systems. Paper presented at the Proceedings of the 2nd International Conference on Networking, Information Systems & Security, 1-11.

  • Finnegan, D. J., & Currie, W. L. (2010). A multi-layered approach to CRM implementation: An integration perspective. European Management Journal, 28(2), 153–167.

    Article  Google Scholar 

  • Guinea, S., Kecskemeti, G., Marconi, A., & Wetzstein, B. (2011). Multi-layered monitoring and adaptation. Paper presented at the International Conference on Service-Oriented Computing, 359-373.

  • Harris, J. D., Quatman, C. E., Manring, M. M., Siston, R. A., & Flanigan, D. C. (2014). How to write a systematic review. The American Journal of Sports Medicine, 42(11), 2761–2768.

    Article  Google Scholar 

  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1–26.

    Google Scholar 

  • Kruchten, P., Obbink, H., & Stafford, J. (2006). The past, present, and future for software architecture. IEEE Software, 23(2), 22–30.

    Article  Google Scholar 

  • Kumar, B. A., & Sharma, B. (2019). Context aware mobile learning application development: A systematic literature review. Education and Information Technologies, , 1-19.

  • Kumar, B. A., Sharma, B., & Nakagawa, E. Y. (2020). Context aware mobile learning: A systematic mapping study. Education and Information Technologies, , 1-20.

  • Madhubala, R., & Akila, A. (2017). Context aware and adaptive mobile learning: A survey. Advances in Computational Sciences and Technology, 10(5), 1355–1370.

    Google Scholar 

  • Nakagawa, E. Y., Oquendo, F., & Becker, M. (2012). Ramodel: A reference model for reference architectures. Paper presented at the 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, 297-301.

  • Nakagawa, E. Y., Oquendo, F., & Maldonado, J. C. (2014). Reference architectures. Software Architecture, 1, 55–82.

    Google Scholar 

  • Papadakis, S., Kalogiannakis, M., Sifaki, E., & Vidakis, N. (2018a). Access Moodle Using Smart Mobile Phones. A Case Study in a Greek University. In A. Brooks, E. Brooks, N. Vidakis (Eds), Interactivity, Game Creation, Design, Learning, and Innovation. ArtsIT 2017, DLI 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 229, (pp. 376-385), Switzerland, Cham: Springer.

  • Papadakis, St., Kalogiannakis, M., Sifaki, E., & Vidakis, N. (2018b). Evaluating Moodle use via Smart Mobile Phones. A case study in a Greek University, EAI (European Alliance for Innovation) Endorsed Transactions on Creative Technologies, 5(16), e1, 1-12.

  • Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2018). Educational apps from the android Google play for Greek preschoolers: A systematic review. Computers & Education, 116(2018), 139–160.

    Article  Google Scholar 

  • Papadakis, S., & Kalogiannakis, M. (2017). Mobile educational applications for children. What educators and parents need to know. International Journal of Mobile Learning and Organisation, 11(3), 256–277.

    Article  Google Scholar 

  • Razavian, M., Paech, B., & Tang, A. (2019). Empirical research for software architecture decision making: An analysis. Journal of Systems and Software, 149, 360–381.

    Article  Google Scholar 

  • Sharples, M., Arnedillo-Sánchez, I., Milrad, M., & Vavoula, G. (2009). Mobile learning. Technology-enhanced learning (pp. 233-249) Springer.

  • Shaw, M., & Clements, P. (2006). The golden age of software architecture. IEEE Software, 23(2), 31–39.

    Article  Google Scholar 

  • Sievi-Korte, O., Beecham, S., & Richardson, I. (2019). Challenges and recommended practices for software architecting in global software development. Information and Software Technology, 106, 234–253.

    Article  Google Scholar 

  • Sittón-Candanedo, I., Alonso, R. S., Corchado, J. M., Rodríguez-González, S., & Casado-Vara, R. (2019). A review of edge computing reference architectures and a new global edge proposal. Future Generation Computer Systems, 99, 278–294.

    Article  Google Scholar 

  • Surahman, E., & Alfindasari, D. (2017). Developing adaptive mobile learning with the principle of coherence mayer on biology subjects of high school to support the open and distance education. Paper presented at the 3rd International Conference on Education and Training (ICET 2017), 184-190.

  • Traxler, J. (2005). Defining mobile learning. Paper presented at the IADIS International Conference Mobile Learning, 261-266.

  • Van Vliet, H., & Tang, A. (2016). Decision making in software architecture. Journal of Systems and Software, 117, 638–644.

    Article  Google Scholar 

  • Weyrich, M., & Ebert, C. (2015). Reference architectures for the internet of things. IEEE Software, 33(1), 112–116.

    Article  Google Scholar 

  • Wu, W., Wu, Y. J., Chen, C., Kao, H., Lin, C., & Huang, S. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817–827.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, Fiji National University, Suva, Fiji

    Bimal Aklesh Kumar

  2. School of Mathematical and Computing Science, University of the South Pacific, Suva, Fiji

    Bibhya Sharma

  3. Institute of Mathematics and Computational Sciences, University of São Paulo, São Paulo, Brazil

    Elisa Yumi Nakagawa

Authors
  1. Bimal Aklesh Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Bibhya Sharma
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Elisa Yumi Nakagawa
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Bimal Aklesh Kumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

  1. 1.

    Zhang, B., Yin, C., David, B., Chalon, R., & Xiong, Z. (2016). A context-aware mobile system for work-based learning. Computer Applications in Engineering Education, 24(2), 263-276.

  2. 2.

    Yau, J. Y. K., & Hristova, Z. (2017). Evaluation of an Extendable Context-Aware “Learning Java” App with Personalized User Profiling. Technology, knowledge and learning, 1-16.

  3. 3.

    Yao, C. B. (2017). Constructing a user-friendly and smart ubiquitous personalized learning environment by using a context-aware mechanism. IEEE Transactions on Learning Technologies, 10(1), 104-114.

  4. 4.

    Sevkli, A. Z., Motiwalla, L., & Abdulkarem, H. F. (2017). The design and implementation of a context-aware mobile hadith learning system. International Journal of Mobile Learning and Organisation, 11(4), 295-313.

  5. 5.

    Nuzhat, S., Shaikh, T., & Ismail, S. (2018, March). A Context Aware Prototype Application for University Students and Lecturers. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 155-160). IEEE.

  6. 6.

    Nguyen, V. A., Pham, V. C., & Ho, S. D. (2010, November). A context-aware mobile learning adaptive system for supporting foreigner learning English. In Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010 IEEE RIVF International Conference on (pp. 1-6). IEEE.

  7. 7.

    Morales, R., Igler, B., Böhm, S., & Chitchaipoka, P. (2015). Context-aware mobile language learning. Procedia Computer Science, 56, 82-87.

  8. 8.

    Martin, S., Sancristobal, E., Gil, R., Castro, M., & Peire, J. (2008). Mobility through location-based services at university. International Journal of Interactive Mobile Technologies (iJIM), 2(3), 34-40.

  9. 9.

    Martín, E., & Carro, R. M. (2009). Supporting the development of mobile adaptive learning environments: a case study. IEEE Transactions on Learning Technologies, 2(1), 23-36.

  10. 10.

    Hwang, G. J., Yang, T. C., Tsai, C. C., & Yang, S. J. (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 53(2), 402-413.

  11. 11.

    Hsu, C. K., & Hwang, G. J. (2014). A context-aware ubiquitous learning approach for providing instant learning support in personal computer assembly activities. Interactive Learning Environments, 22(6), 687-703.

  12. 12.

    Hashemi, H. S. F., Orooji, F., & Taghiyareh, F. (2012, November). A context-aware mobile learning model for web-based learning environments. In Telecommunications (IST), 2012 Sixth International Symposium on (pp. 924-928). IEEE.

  13. 13.

    Gómez, S., Zervas, P., Sampson, D. G., & Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University-Computer and Information Sciences, 26(1), 47-61.

  14. 14.

    Gómez, S., Zervas, P., Sampson, D. G., & Fabregat, R. (2013, July). Supporting context-aware adaptive and personalized mobile learning delivery: evaluation results from the use of UoLm player. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on (pp. 354-358). IEEE

  15. 15.

    Gómez, S., Huerva, D., Mejía, C., Baldiris, S., & Fabregat, R. (2009, June). Designing context-aware adaptive units of learning based on IMS-LD standard. In EAEEIE Annual Conference, 2009 (pp. 1-6). IEEE.

  16. 16.

    Curum, B., Gumbheer, C. P., Khedo, K. K., & Cunairun, R. (2017, July). A content-adaptation system for personalized m-learning. In Next Generation Computing Applications (NextComp), 2017 1st International Conference on (pp. 121-128). IEEE.

  17. 17.

    Curum, B., Chellapermal, N., & Khedo, K. K. (2016, November). A Context-Aware Mobile Learning System Using Dynamic Content Adaptation for Personalized Learning. In International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (pp. 305-313). Springer, Cham.

  18. 18.

    Chen, C. M., & Li, Y. L. (2010). Personalised context-aware ubiquitous learning system for supporting effective English vocabulary learning. Interactive Learning Environments, 18(4), 341-364.

  19. 19.

    Chen, C. C., & Lin, P. H. (2016). Development and evaluation of a context-aware ubiquitous learning environment for astronomy education. Interactive Learning Environments, 24(3), 644-661.

  20. 20.

    Benlamri, R., & Zhang, X. (2014). Context-aware recommender for mobile learners. Human-centric Computing and Information Sciences, 4(1), 12.

  21. 21.

    Baccari, S., & Neji, M. (2016, December). Design for a context-aware and collaborative mobile learning system. In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on (pp. 1-6). IEEE.

  22. 22.

    Aziz, Z., Anumba, C. J., & Peña-Mora, F. (2010). Using context-aware wireless technologies to support teaching and learning in built environment. International Journal of Construction Education and Research, 6(1), 18-29.

  23. 23.

    Al-Mekhlafi, K., Hu, X., & Zheng, Z. (2009, June). An approach to context-aware mobile Chinese language learning for foreign students. In Mobile Business, 2009. ICMB 2009. Eighth International Conference on (pp. 340-346). IEEE.

  24. 24.

    Abdulkarem, H. F., & Şevkli, A. Z. (2014, October). A Context-aware Mobile Application for Cultural Learning. In Proceedings of the 3rd International Conference on Context-Aware Systems and Applications (pp. 37-41). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, .A., Sharma, B. & Nakagawa, E.Y. Architectural Support for Context-Aware Mobile Learning Applications. Educ Inf Technol 27, 3723–3741 (2022). https://doi.org/10.1007/s10639-021-10771-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-021-10771-1

Keywords