Skip to main content

Advertisement

Springer Nature Link
Log in

A context-aware mobile learning system for adapting learning content and format of presentation: design, validation and evaluation

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

Abstract

To date, the growth usage of mobile technologies and devices as well as the ubiquitous wireless communication have led to the development of new systems and applications in many fields and areas including education. This technological progress can be used to facilitate the students’ lives by creating smart and personalized solutions considering their personal and academic profiles as well as their real environments. This is because mobile technologies have the capacity to detect the contextual dimensions of learners through different sensors, and also because of the available software that have known a big improvement in the recent years. In this way, this article focuses on proposing a model called Dynamic Mobile Adaptive Learning Content and Format (D-MALCOF) that considers the learner’s knowledge level and learning styles in order to provide the suitable learning for each and every student. The paper introduces the design of our approach as well as its conception and modelling activities. In addition, we validated this model in real life learning settings by developing an Android mobile application that has been tested in the context of Moroccan higher education system. The evaluation of the model was conducted with undergraduate students, and examined the impact of D-MALCOF on the students’ knowledge level and learning achievements. The results showed that our solution has the potential to enhance the students’ knowledge level in the java programming language by comparing the tests’ results of the participants who used our mobile app with those who have learned through a traditional e-learning platform. Moreover, the students showed encouraging feedback, general satisfaction, positive perceptions and good intentions towards the application after completing their learning activities and responding to the survey.

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.
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

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

References

  • Abowd, G. D., Dey, A. K., Brown, P. J., et al. (1999). Towards a better understanding of context and context-awareness. In H.-W. Gellersen (Ed.), Handheld and ubiquitous computing (pp. 304–307). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  • Acilar A (2011) Exploring the aspects of digital divide in a developing country. In: Issues Informing Sci. Amp Inf. Technol. http://link.galegroup.com/apps/doc/A292088063/AONE?sid=googlescholar.

  • Al-Hmouz A, Shen J, Yan J, Al-Hmouz R (2010) Enhanced learner model for adaptive mobile learning. In: Proceedings of the 12th international conference on information integration and web-based applications & services. ACM, pp 783–786.

  • Almohammadi, K., Hagras, H., Yao, B., Alzahrani, A., Alghazzawi, D., & Aldabbagh, G. (2017). A type-2 fuzzy logic recommendation system for adaptive teaching. Soft Computing, 21, 965–979. https://doi.org/10.1007/s00500-015-1826-y.

    Article  Google Scholar 

  • Berge, Z. L., Muilenburg, L., & Muilenburg, L. (2013). Seamless learning: An international perspective on next-generation technology-enhanced learning. In Handbook of Mobile Learning. https://www.taylorfrancis.com.

  • Bertolami, C. N. (2001). Rationalizing the dental curriculum in light of current disease prevalence and patient demand for treatment: Form vs. content. Journal of Dental Education, 65, 725–735.

    Article  Google Scholar 

  • Bicans, J., & Grundspenkis, J. (2017). Student learning style extraction from on-campus learning context data. Procedia Comput Sci, 104, 272–278. https://doi.org/10.1016/j.procs.2017.01.135.

    Article  Google Scholar 

  • Brown PJ (1996) The stick-e document: A framework for creating context-aware applications.

  • Brown, P. J. (1998). Triggering information by context. Personal Technologies, 2, 18–27. https://doi.org/10.1007/BF01581843.

    Article  Google Scholar 

  • Brown, P. J., Bovey, J. D., & Chen, X. (1997). Context-aware applications: From the laboratory to the marketplace. IEEE Personal Communications, 4, 58–64. https://doi.org/10.1109/98.626984.

    Article  Google Scholar 

  • Bunderson, C. V., & Martinez, M. (2000). Building interactive world wide web (web) learning environments to match and support individual learning differences. Journal of Interactive Learning Research, 11, 163–195.

    Google Scholar 

  • Cavus N, Bicen H, Akcil U (2008) The opinions of information technology students on using Mobile learning.

  • Chang, C.-Y., Lai, C.-L., & Hwang, G.-J. (2018). Trends and research issues of mobile learning studies in nursing education: A review of academic publications from 1971 to 2016. Computers in Education, 116, 28–48. https://doi.org/10.1016/j.compedu.2017.09.001.

    Article  Google Scholar 

  • Chow, J. Y. (2013). Nonlinear learning underpinning pedagogy: Evidence, challenges, and implications. Quest, 65, 469–484. https://doi.org/10.1080/00336297.2013.807746.

    Article  Google Scholar 

  • Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40, 4715–4729. https://doi.org/10.1016/j.eswa.2013.02.007.

    Article  Google Scholar 

  • Crompton, H. (2013). A historical overview of mobile learning: Toward learner-centered education. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning. Florence: Routledge.

    Google Scholar 

  • Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers in Education, 123, 53–64. https://doi.org/10.1016/j.compedu.2018.04.007.

    Article  Google Scholar 

  • Davies N, Mitchell K, Cheverst K, Blair G (1998) Developing a context sensitive tourist guide. In: 1st workshop on human computer interaction with Mobile devices, GIST technical report G98-1.

  • de Witt, C., & Gloerfeld, C. (2018). Mobile learning and higher education. In D. Kergel, B. Heidkamp, P. K. Telléus, et al. (Eds.), The digital turn in higher education: International perspectives on learning and teaching in a changing world (pp. 61–79). Wiesbaden: Springer Fachmedien.

    Chapter  Google Scholar 

  • Dey A (1998) Context-aware computing: The CyberDesk Project. Spring Symp Intell Environ 51–54.

  • Dey AK, Abowd GD, Wood A (1998) CyberDesk: A framework for providing self-integrating context-aware services. Knowledge-Based Systems 11:3–13. https://doi.org/10.1016/S0950-7051(98)00053-7.

  • Dey AK, Salber D, Futakawa M, Abowd GD (1999) An architecture to support context-aware applications. Georgia Institute of Technology.

  • El Aissaoui, O., El Alami El Madani, Y., Oughdir, L., & El Allioui, Y. (2019). A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies, 24, 1943–1959. https://doi.org/10.1007/s10639-018-9820-5.

    Article  Google Scholar 

  • El Guabassi, I., Bousalem, Z., Al Achhab, M., et al. (2018). Personalized adaptive content system for context-aware ubiquitous learning. Procedia Comput Sci, 127, 444–453. https://doi.org/10.1016/j.procs.2018.01.142.

    Article  Google Scholar 

  • Ennouamani S, Mahani Z (2017) An overview of adaptive e-learning systems. In: 2017 eighth international conference on intelligent computing and information systems (ICICIS). Pp 342–347.

  • Ennouamani S, Mahani Z (2019) A comparative study of the learner model in adaptive Mobile learning systems. In: Proceedings of the 2Nd international conference on networking, information systems & security. ACM, pp 19:1–19:11.

  • Ennouamani S, Akharraz L, Mahani Z (2019) Integrating ICT in education: An adaptive learning system based on users’ context in Mobile environments. In: Big Data and Smart Digital Environment. Springer International Publishing, pp 15–19.

  • Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–668.

    Google Scholar 

  • Richard M. Felder, Barbara A. Soloman (2005) Index of Learning Styles Questionnaire. https://www.webtools.ncsu.edu/learningstyles/. Accessed 2 Jun 2019.

  • Fickas S, Kortuem G, Segall Z (1997) Software organization for dynamic and adaptable wearable systems. In: Digest of Papers. First International Symposium on Wearable Computers. pp 56–63.

  • Franklin D, Flaschsbart J (1998) All gadget and no representation makes Jack a dull environment. Spring Symp Intell Environ 155–160.

  • Garcia A, Garcia E, De-Marcos L, et al (2011) A system for adaptation of educational contents to learners and their Mobile device. In: 2011 IEEE 11th international conference on advanced learning technologies. Pp 484–485.

  • Georgsen M (2013) Changing education through ICT in developing countries. Aalborg Universitetsforlag.

  • Hanbidge AS, Sanderson N, Tin T (2015) Using Mobile technology to enhance undergraduate student digital information literacy skills: A Canadian case study. IAFOR J Educ.

  • Hirsch B, Ng JWP (2011) Education beyond the cloud: Anytime-anywhere learning in a smart campus environment. In: 2011 international conference for internet technology and secured transactions. Pp 718–723.

  • Hull R, Neaves P, Bedford-roberts J (1997) Towards situated computing. In: In Proceedings of The First International Symposium on Wearable Computers. pp 146–153.

  • Jagušt, T., & Botički, I. (2019). Mobile learning system for enabling collaborative and adaptive pedagogies with modular digital learning contents. J Comput Educ., 6, 335–362. https://doi.org/10.1007/s40692-019-00139-3.

    Article  Google Scholar 

  • Kalolo, J. F. (2019). Digital revolution and its impact on education systems in developing countries. Education and Information Technologies, 24, 345–358. https://doi.org/10.1007/s10639-018-9778-3.

    Article  Google Scholar 

  • Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769–776. https://doi.org/10.1016/j.chb.2016.06.014.

    Article  Google Scholar 

  • Keengwe, J., & Bhargava, M. (2014). Mobile learning and integration of mobile technologies in education. Education and Information Technologies, 19, 737–746. https://doi.org/10.1007/s10639-013-9250-3.

    Article  Google Scholar 

  • Khaddage F, Cosío JH (2014) Trends and barriers on the fusion of Mobile apps in higher education where to next and how? Association for the Advancement of computing in education (AACE), pp 903–909.

  • Khaddage, F., Christensen, R., Lai, W., et al. (2015). A model driven framework to address challenges in a mobile learning environment. Education and Information Technologies, 20, 625–640. https://doi.org/10.1007/s10639-015-9400-x.

    Article  Google Scholar 

  • Li, M., Ogata, H., Hou, B., et al. (2013). Context-aware and personalization method in ubiquitous learning system. Journal of Educational Technology & Society, 16(3), 362–373.

    Google Scholar 

  • Lima, R. H. P., El-Bishouty, M. M., & Graf, S. (2013). A framework for automatic identification and visualization of Mobile device functionalities and usage. In A. Holzinger & G. Pasi (Eds.), Human-computer interaction and knowledge discovery in complex, unstructured, Big Data (pp. 148–159). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  • Lowenthal, J. N. (2010). Using Mobile learning: Determinates impacting behavioral intention. American Journal of Distance Education, 24, 195–206. https://doi.org/10.1080/08923647.2010.519947.

    Article  Google Scholar 

  • Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers in Education, 49, 581–596. https://doi.org/10.1016/j.compedu.2005.10.011.

    Article  Google Scholar 

  • Nedungadi, P., & Raman, R. (2012). A new approach to personalization: Integrating e-learning and m-learning. Educational Technology Research and Development, 60, 659–678. https://doi.org/10.1007/s11423-012-9250-9.

    Article  Google Scholar 

  • O’Malley C, Vavoula G, Glew J, et al (2005) Guidelines for learning/teaching/tutoring in a mobile environment.

  • Pascoe J (1998) Adding generic contextual capabilities to wearable computers. In: 2nd international symposium on wearable computers. Ieee Computer Soc, Pittsburgh, Pa, pp 92–99.

  • Reategui, E., Boff, E., & Campbell, J. A. (2008). Personalization in an interactive learning environment through a virtual character. Computers in Education, 51, 530–544. https://doi.org/10.1016/j.compedu.2007.05.018.

    Article  Google Scholar 

  • Rodden T, Chervest K, Davies N, dix a (1998) Exploiting Context in HCI Design for Mobile Systems. In: in Workshop on Human Computer Interaction with Mobile Devices.

  • Ryan N, Pascoe J, Morse D (1997) Enhanced Reality Fieldwork: the Context Aware Archaeological Assistant. Comput Appl Archaeol 8.

  • Sampson D, Karagiannidis C (2002) Personalised learning: Educational, technological and standardisation perspective. Interact Educ Multimed IEM 24-39–39.

  • Saryar, S., Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2019). Mobile learning recommender system based on learning styles. In J. Wang, G. R. M. Reddy, V. K. Prasad, & V. S. Reddy (Eds.), Soft computing and signal processing (pp. 299–312). Singapore: Springer.

    Chapter  Google Scholar 

  • Schilit B, Adams N, Want R (1994) Context-aware computing applications. In: 1994 First workshop on Mobile computing systems and applications. IEEE, pp 85–90.

  • V. J. Shute DZ Rivera (2012) Adaptive Educational Systems.

  • Soloway E (1994) Learner-Centered Design: The Challenge for HCI in the 21st Century. In: Interactions , no. April (1994): 36–48. CUMINCAD.

  • Su, J.-M., Tseng, S.-S., Lin, H.-Y., & Chen, C.-H. (2011). A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments. User Modeling and User-Adapted Interaction, 21, 5–49. https://doi.org/10.1007/s11257-010-9094-0.

    Article  Google Scholar 

  • Teixeira, A., Garcia-Cabot, A., Garcia-Lopez, E., et al. (2016). A new competence-based approach for personalizing MOOCs in a Mobile collaborative and networked environment. RIED: revista iberoamericana de educación a distancia, 19, 143–160. https://doi.org/10.5944/ried.19.1.14578.

    Article  Google Scholar 

  • Terras, M. M., & Ramsay, J. (2012). The five central psychological challenges facing effective mobile learning. British Journal of Educational Technology, 43, 820–832. https://doi.org/10.1111/j.1467-8535.2012.01362.x.

    Article  Google Scholar 

  • Traxler J (2005) DEFINING MOBILE LEARNING. IADIS international conference Mobile learning 6.

  • Tsatsou, D., Vretos, N., & Daras, P. (2019). Adaptive game-based learning in multi-agent educational settings. J Comput Educ, 6, 215–239. https://doi.org/10.1007/s40692-018-0118-9.

    Article  Google Scholar 

  • Tseng, J. C. R., Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers in Education, 51, 776–786. https://doi.org/10.1016/j.compedu.2007.08.002.

    Article  Google Scholar 

  • Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27, 118–130. https://doi.org/10.1016/j.chb.2010.07.038.

    Article  Google Scholar 

  • Vavoula, G., & Sharples, M. (2009). Meeting the challenges in evaluating Mobile learning: A 3-level evaluation framework. Int J Mob Blended Learn IJMBL, 1, 54–75. https://doi.org/10.4018/jmbl.2009040104.

    Article  Google Scholar 

  • Wang, L., Rau, P.-L. P., & Salvendy, G. (2011). A cross-culture study on older adults’ information technology acceptance. International Journal of Mobile Communications, 9, 421–440. https://doi.org/10.1504/IJMC.2011.042452.

    Article  Google Scholar 

  • Ward, A., Jones, A., & Hopper, A. (1997). A new location technique for the active office. IEEE Personal Communications, 4, 42–47. https://doi.org/10.1109/98.626982.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National School of Applied Sciences, Ibn Zohr University, Agadir City, Morocco

    Soukaina Ennouamani

  2. School of Technology, Ibn Zohr University, Agadir City, Morocco

    Zouhir Mahani

  3. Faculty of Sciences, Ibn Zohr University, Agadir City, Morocco

    Laila Akharraz

Authors
  1. Soukaina Ennouamani
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zouhir Mahani
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Laila Akharraz
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Soukaina Ennouamani.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ennouamani, S., Mahani, Z. & Akharraz, L. A context-aware mobile learning system for adapting learning content and format of presentation: design, validation and evaluation. Educ Inf Technol 25, 3919–3955 (2020). https://doi.org/10.1007/s10639-020-10149-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10149-9

Keywords