NLP-based error analysis and dynamic motivation techniques in mobile learning

Abstract

Mobile learning uncovers new dimensions of learning and personal growth. Mobile phones have completely dominated our lives from communication and entertainment to socializing and learning. In view of providing more individualized learning through mobile phones, several intelligent techniques should be incorporated in mobile-assisted learning systems. As such, this paper presents an effective analysis of students’ errors during the assessment process in mobile learning using Natural Language Processing (NLP) techniques. The error analysis can reason between grammatical, syntax and careless errors using the Levenshtein distance. Moreover, it describes dynamic methods for motivating students in order to improve their learning experience. As such, students can receive motivation in case of making errors, cognitive inconsistencies, etc. Dynamic motivation is enriched with the delivery of badges as a means to further enhance knowledge acquisition. As a testbed for our research, a mobile language learning application for tutoring the English language has been designed, fully developed and evaluated. Concluding, this paper presents real examples of operation of the presented system and the evaluation results show the acceptance of the NLP-based error analysis and the dynamic motivation techniques by students.