Title :
Identifying Learning Styles in Learning Management Systems by Using Indications from Students´ Behaviour
Author :
Graf, Sabine ; Shuk, Kin ; Liu, Tzu-Chien
Author_Institution :
Grad. Inst. of Learning & Instruction, Nat. Central Univ., Chungli
Abstract :
Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.
Keywords :
computer aided instruction; educational courses; human factors; Felder-Silverman learning style model; e-learning management system; individual learning style identification; online course; student behaviour; Collaboration; Computer aided instruction; Conference management; Contracts; Councils; Electronic learning; Feedback; Information systems; Performance evaluation; Technology management; learning management systems; learning styles; student modelling;
Conference_Titel :
Advanced Learning Technologies, 2008. ICALT '08. Eighth IEEE International Conference on
Conference_Location :
Santander, Cantabria
Print_ISBN :
978-0-7695-3167-0
DOI :
10.1109/ICALT.2008.84