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Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates some of the limitations of similar systems. The adaptive methods used as well as their implementation in a dedicated system (WELSA) are presented, together with a thorough evaluation of the approach. The results of the experimental study involving 64 undergraduate students show that accommodating learning styles in WELSA has a beneficial effect on the learning process.

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Individualizing the learning experience for each student is an important goal for educational systems and accurately modeling the learner is the first step towards attaining this goal. This chapter addresses learner modeling from the point of view of learning styles, an important factor for the efficiency and effectiveness of the learning process. A critical review of existing modeling methods is provided, outlining the specificities and limitations of current learning style based adaptive educational systems (LSAES). The controversy regarding the multitude of partially overlapping learning style models proposed in the literature is addressed, by suggesting the use of a complex of features, each with its own importance and influence (the so called Unified Learning Style Model). An implicit modeling method is introduced, based on analyzing students’ behavioral patterns. The approach is validated experimentally and good precision rates are reported.

This paper deals with the use of learning styles in technology-enhanced learning by introducing a “Unified Learning Style Model” (ULSM). The article aims at providing answers to three main questions: “What is ULSM?”, “Why do we need it?” and “How can we use it?” First, a critical analysis regarding learning styles is provided; the identified challenges are addressed by proposing the use of a new model, specifically designed for TEL use. This model integrates learning preferences extracted from several traditional learning style models, related to perception modality in a way for processing and organizing information, as well as motivational and social aspects. A detailed description of the ULSM components is provided together with its rationale and its advantages. The practical applicability of the model is also shown by briefly introducing an adaptive web-based educational system based on it (called WELSA).

Abstract. Adaptive learning systems may be used to provide personalized content to students based on their learning styles which can improve students' performance and satisfaction, or reduce the time to learn. Although typically questionnaires exist to identify students' learning styles, there are several disadvantages when using such questionnaires. In order to overcome these disadvantages, research has been conducted on automatic approaches to identify learning styles. However, this line of research is still in an early stage and the accuracy levels of current approaches leave room for improvement before they can be effectively used in adaptive systems. In this paper, we introduce an approach which uses artificial neural networks to identify students' learning styles. The approach has been evaluated with data from 75 students and found to outperform current state of the art approaches. By increasing the accuracy level of learning style identification, more accurate advice can be provided to students, either by adaptive systems or by teachers who are informed about students' learning styles, leading to benefits for students such as higher performance, greater learning satisfaction and less time required to learn.

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