Mobile learning recommender system based on learning styles

Shivam Saryar, Sucheta V. Kolekar, Radhika M. Pai, M. M. Manohara Pai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the Internet era, more and more learners now have the option of using multimedia to engage in a learning environment, for example, videos, text, pictures. They also prefer more control over their learning sessions, i.e., being able to choose which topics, which mode of multimedia, as that is one thing which classroom learning cannot provide. Classroom learning does not give the freedom of choosing a pace, a learning style, or a suitable medium for learning. Moreover, the existing teaching methods do not encourage from exploring other possible means of learning which could turn out to be more helpful. Also, classroom learning or learning over the Internet, most learners are still not well aware of their learning styles. In this paper, an approach is proposed to develop a mobile learning (M-learning) Android application which implements a learning style (LS) model as per Felder-Silverman learning style model (FSLSM) and recommendation component (RC) model. LS model is used to identify the learning behavior and characteristics of each learner. According to the identified learning style as well as the user’s other in-app activities, it uses a recommendation system to recommend relevant course material to the user which he/she might find useful. This gives the learner a greater insight into his/her own learning pattern and becomes self-aware about what mode of learning suits them more or what might be more useful to them. This mobile learning application provides seamless availability of course material to the learners on the go. As opposed to the e-learning platforms, this approach has been implemented as a mobile application, which allows learners to access course material whenever and wherever they want.

Original languageEnglish
Title of host publicationSoft Computing and Signal Processing - Proceedings of ICSCSP 2018
EditorsV. Kamakshi Prasad, G. Ram Mohana Reddy, Jiacun Wang, V. Sivakumar Reddy
PublisherSpringer Verlag
Pages299-312
Number of pages14
ISBN (Print)9789811335990
DOIs
Publication statusPublished - 01-01-2019
EventInternational Conference on Soft Computing and Signal Processing, ICSCSP 2018 - Hyderabad, India
Duration: 22-06-201823-06-2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume900
ISSN (Print)2194-5357

Conference

ConferenceInternational Conference on Soft Computing and Signal Processing, ICSCSP 2018
CountryIndia
CityHyderabad
Period22-06-1823-06-18

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Recommender systems
Internet
Application programs
Teaching
Availability

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Saryar, S., Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2019). Mobile learning recommender system based on learning styles. In V. K. Prasad, G. R. M. Reddy, J. Wang, & V. S. Reddy (Eds.), Soft Computing and Signal Processing - Proceedings of ICSCSP 2018 (pp. 299-312). (Advances in Intelligent Systems and Computing; Vol. 900). Springer Verlag. https://doi.org/10.1007/978-981-13-3600-3_29
Saryar, Shivam ; Kolekar, Sucheta V. ; Pai, Radhika M. ; Manohara Pai, M. M. / Mobile learning recommender system based on learning styles. Soft Computing and Signal Processing - Proceedings of ICSCSP 2018. editor / V. Kamakshi Prasad ; G. Ram Mohana Reddy ; Jiacun Wang ; V. Sivakumar Reddy. Springer Verlag, 2019. pp. 299-312 (Advances in Intelligent Systems and Computing).
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abstract = "In the Internet era, more and more learners now have the option of using multimedia to engage in a learning environment, for example, videos, text, pictures. They also prefer more control over their learning sessions, i.e., being able to choose which topics, which mode of multimedia, as that is one thing which classroom learning cannot provide. Classroom learning does not give the freedom of choosing a pace, a learning style, or a suitable medium for learning. Moreover, the existing teaching methods do not encourage from exploring other possible means of learning which could turn out to be more helpful. Also, classroom learning or learning over the Internet, most learners are still not well aware of their learning styles. In this paper, an approach is proposed to develop a mobile learning (M-learning) Android application which implements a learning style (LS) model as per Felder-Silverman learning style model (FSLSM) and recommendation component (RC) model. LS model is used to identify the learning behavior and characteristics of each learner. According to the identified learning style as well as the user’s other in-app activities, it uses a recommendation system to recommend relevant course material to the user which he/she might find useful. This gives the learner a greater insight into his/her own learning pattern and becomes self-aware about what mode of learning suits them more or what might be more useful to them. This mobile learning application provides seamless availability of course material to the learners on the go. As opposed to the e-learning platforms, this approach has been implemented as a mobile application, which allows learners to access course material whenever and wherever they want.",
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Saryar, S, Kolekar, SV, Pai, RM & Manohara Pai, MM 2019, Mobile learning recommender system based on learning styles. in VK Prasad, GRM Reddy, J Wang & VS Reddy (eds), Soft Computing and Signal Processing - Proceedings of ICSCSP 2018. Advances in Intelligent Systems and Computing, vol. 900, Springer Verlag, pp. 299-312, International Conference on Soft Computing and Signal Processing, ICSCSP 2018, Hyderabad, India, 22-06-18. https://doi.org/10.1007/978-981-13-3600-3_29

Mobile learning recommender system based on learning styles. / Saryar, Shivam; Kolekar, Sucheta V.; Pai, Radhika M.; Manohara Pai, M. M.

Soft Computing and Signal Processing - Proceedings of ICSCSP 2018. ed. / V. Kamakshi Prasad; G. Ram Mohana Reddy; Jiacun Wang; V. Sivakumar Reddy. Springer Verlag, 2019. p. 299-312 (Advances in Intelligent Systems and Computing; Vol. 900).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Saryar S, Kolekar SV, Pai RM, Manohara Pai MM. Mobile learning recommender system based on learning styles. In Prasad VK, Reddy GRM, Wang J, Reddy VS, editors, Soft Computing and Signal Processing - Proceedings of ICSCSP 2018. Springer Verlag. 2019. p. 299-312. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-13-3600-3_29