Personalised instructional feedback in a mobile-assisted language learning application using fuzzy reasoning Online publication date: Thu, 30-Jun-2022
by Konstantina Chrysafiadi; Christos Troussas; Maria Virvou
International Journal of Learning Technology (IJLT), Vol. 17, No. 1, 2022
Abstract: This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalisation to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a database of categories of errors and misconceptions which have been reported as common in the related literature. The system is also responsible for conducting model-based error diagnosis using machine learning techniques and identifying errors such as knowledge transfer, spelling or verb mistakes. In conjunction with error diagnosis, the system employs fuzzy logic to automatically model these misconceptions and errors and then provide personalised feedback to students based on their personal learning needs. The system has been fully evaluated, using the CIAO! framework and t-test. The evaluation results are positive and encouraging regarding the educational effectiveness.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Learning Technology (IJLT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook