Adaptivity in Learning Management Systems focussing on Learning Styles

his work has been finished in December 2007.

Learning management systems (LMSs) such as WebCT, Blackboard, and Moodle are commonly and successfully used in e-education. While they focus on supporting teachers in creating and holding online courses, they typically do not consider the individual differences of learners. However, learners have different needs and characteristics such as prior knowledge, motivation, cognitive traits, and learning styles. Recently, increasing attention is paid to characteristics such as learning styles, their impact on learning, and how these individual characteristics can be supported by learning systems. These investigations are motivated by educational theories, which argue that providing courses which fit the individual characteristics of students makes learning easier for them and thus, increases their learning progress.

This thesis focuses on extending LMSs to provide adaptivity by incorporating learning styles according to the Felder-Silverman learning style model. An automated approach for identifying learning styles from the behaviour and actions of learners has been designed, implemented, and evaluated, demonstrating that the proposed approach is suitable for identifying learning styles. Based on this approach, a standalone tool for automatic detection of learning styles in LMSs has been implemented.

Furthermore, investigations have been conducted on improving the automatic detection of learning styles by using additional information from cognitive traits. The potential of working memory capacity is investigated. Results of a comprehensive literature review and two comprehensive evaluation studies show that relationships between working memory capacity and learning styles exist and that these relationships can provide additional information for the detection process of learning styles.

Moreover, a concept for extending LMSs by enabling them to automatically generate and present courses that fit the students’ learning styles has been developed, implemented, and evaluated, using Moodle as a prototype. Results show that the proposed concept for providing adaptive courses is successful in supporting students in learning.

By extending LMSs with adaptivity, a learning environment is built that supports teachers as well as learners. In such an adaptive LMS, teachers can continue using the advantages of LMSs and learners can additionally benefit from adaptive courses. This research opens ways for advanced learning systems, which are able to learn the needs and characteristics of learners, respond to them immediately, and provide learners with courses where adaptation is frequently improved and updated to the learners’ needs.


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