Sabine Graf

An evaluation of open source e-learning platforms stressing adaptation issues

This paper presents an evaluation of open source e-learning platforms. The main focus is on adaptation issues. The result of the evaluation shows that the platform Moodle outperforms all other platforms and also obtained the best rating in the adaptation category.

In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

Abstract Learning styles are increasingly being incorporated into technology-enhanced learning. Appropriately, a great deal of recent research work is occurring in this area. As more information and details about learning styles becomes available, learning styles can be better accommodated and integrated into all aspects of educational technology. The aim of this paper is to analyse data about learning styles with respect to the Felder-Silverman learning style model (FSLSM) in order to provide a more detailed description of learning style dimensions. The analyses show the most representative characteristics of each learning style dimension as well as how representative these characteristics are. As a result, we provide additional information about the learning style dimensions of FSLSM. This information is especially important when learning styles are incorporated in technology-enhanced learning.

Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

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.

A fully personalization strategy of E-learning scenarios

The personalization in E-learning systems has been the subject of many recent research efforts. While a large number of systems have been implemented, many of these systems allow the application of very few if not just one predefined personalization strategy. This is a constraint for providing effective E-learning experience and for rationalizing the personalization needs of the pedagogues, the professors and the learners. In this paper, we propose a new approach for personalization of learning scenarios based on two levels: The first level allows the personalization of learning scenarios according to a predefined personalization strategy. The second level allows teachers to select personalization parameters and combine them flexibly to define different personalization strategies according to the specifics of courses. The proposed solution is a step to federate the research efforts on the E-learning personalization by integrating and combining the personalization parameters. Concerning the technological aspect, Web service technology constitutes an operational solution for implementing our approach and for the interoperability with other E-learning personalization systems. Beside the implementation of an interoperable solution, we also aim to enable teachers to provide proper personalized learning scenarios.

Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization

Heterogeneity in learning groups is said to improve academic performance. But only few collaborative online systems consider the formation of heterogeneous groups. In this paper we propose a mathematical approach to form heterogeneous groups based on personality traits and the performance of students. We also present a tool that implements this mathematical approach, using an Ant Colony Optimization algorithm in order to maximize the heterogeneity of formed groups. Experiments show that the algorithm delivers stable solutions which are close to the optimum for different datasets of 100 students. An experiment with 512 students was also performed demonstrating the scalability of the algorithm.

Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems

Different learners have different needs; they differ, for example, in their learning goals, their prior knowledge, their learning styles, and their cognitive abilities. Adaptive web-based educational systems aim to cater individual learners by customizing courses to suit their needs. In this paper, we investigate the benefits of incorporating learning styles and cognitive traits in web-based educational systems. Adaptivity aspects based on cognitive traits and learning styles enrich each other, enabling systems to provide learners with courses which fit their needs more accurately. Furthermore, consideration of learning styles and cognitive traits can contribute to more accurate student modelling. In this paper, the relationship between learning styles, in particular the Felder-Silverman learning style model (FSLSM), and working memory capacity, a cognitive trait, is investigated. For adaptive educational systems that consider either only learning styles or only cognitive traits, the additional information can be used to provide more holistic adaptivity. For systems that already incorporate both learning styles and cognitive traits, the relationship can be used to improve the detection process of both by including the additional information of learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.

An Approach for Detecting Learning Styles in Learning Management Systems

Detecting the needs of learners is a challenging but essential task to be able to provide adaptivity. In this paper we present a tool that enables learning management systems (LMS) to detect learning styles based on the behavior of learners during an online course. By calculating the learning styles and filling the student model of LMS with such personal data, a basis for adaptivity is provided.

A Flexible Mechanism for Providing Adaptivity Based on Learning Styles in Learning Management Systems

While today’s learning management systems (LMSs) provide lot of support for teachers to assist them in holding online courses, they typically do not consider students’ individual differences in the composition and structure of courses. In this paper, we introduce a mechanism for extending LMSs’ functionality to provide learners with courses that fit their individual learning styles, using adaptive sorting and adaptive annotation in order to highlight the learning objects (LOs) that support students’ learning process the best. The mechanism enables teachers to add adaptivity to their already existing courses, using a flexible course structure in order to avoid limiting the richness of the learning resources and materials. Besides being flexible to teachers’ needs, the adaptive mechanism aims at asking teachers for as little as possible additional effort when using it, requiring teachers only to choose the corresponding type of LO when creating an LO in the authoring tool of the LMS.

Analysis of Felder-Silverman Index of Learning Styles by a Data-Driven Statistical Approach

In this paper a data driven analysis of Felder-Silverman index of learning styles (ILS) is given. Results, obtained by multiple correspondence analysis and cross-validated by correlation analysis, show the consistent dependencies between some styles; some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of ILS questionnaire. Both the results and the comparisons show the effectiveness of data driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for an effective, precise and accurate modeling

Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

Typical learning management systems consider only little or, in most cases, no adaptivity. In this paper, we introduce an adaptive mechanism which enables such systems to provide students with courses that fit their individual learning styles. The adaptive mechanism is based on an advanced student modelling approach which identifies learning styles by automatic, dynamic, and global student modelling. Based on the identified learning styles, the adaptive mechanism composes courses that match the students’ learning styles, aiming at making learning easier for students. Furthermore, the adaptive mechanism aims at being easy to use for teachers by being generic and adaptable for teachers, allowing them to adjust the mechanism to their course structure and preferences.