Mobile Learning Recommender System Based on Learning Styles

Saryar, Shivam and Kolekar, Sucheta and Pai, Radhika M and Pai, Manohara M.M. (2019) Mobile Learning Recommender System Based on Learning Styles. In: International Conference on Soft Computing and Signal Processing, 22/06/2018, Malla Reddy College of Engineering and Technology.

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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.Thismobile 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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: M-Learning · FSLSM · Recommendation system · LS
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 09 Mar 2019 04:30
Last Modified: 09 Mar 2019 04:30
URI: http://eprints.manipal.edu/id/eprint/153414

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