Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability

Personalization can be addressed by adaptability and adaptivity, which have different advantages and disadvantages. This study investigates how digital library users react to these two techniques. More specifically, we develop a personalized digital library to suit the needs of different cognitive styles based on the findings of our previous work (Frias-Martinez, et al., in press). The personalized digital library includes two versions: adaptive version and adaptable version. The results showed that users not only performed better in the adaptive version, but also they perceived more positively to the adaptive version. In addition, cognitive styles have great effects on users’ responses to adaptability and adaptivity. These results provide guidance for designers to select suitable techniques to develop personalized digital libraries

An E-Learning Investigation into Learning Style Adaptivity

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

I presented our paper at the IEEE International Conference on Advanced Learning Technologies, in Sousse, Tunisia. The presentation of our paper was scheduled on July 7, 2010, in the “Adaptive and Personalized Technology Enhanced Learning” Session, which I was also invited to chair. There were about 25 people listening to my presentation, including very well-known researchers in the area of adaptivity and personalization in technology enhanced learning. My presentation was well received and there were many questions and comments. One comment I found especially interesting for our future research was from one of the keynote speakers at ICALT, elaborating on possibilities for extending our research with respect to more fine-granular adaptivity of learning material. After this session, I had discussions with two researchers about my presentation and possible collaboration opportunities which we will follow up. Overall, attending ICALT2010 was a very valuable experience with respect to my future research and my reputation. During ICALT, I had many discussions, some leading to concrete ideas for collaborations. Besides presenting a paper, I was also organizing the Doctoral Consortium and a workshop on “Design Centered and Personalized Learning in Liquid and Ubiquitous Learning Places – Future Visions and Practical Implementations”. Both events were very well received, with lot of discussion during and after the events (for the workshop, we received an award for “Outstanding Performance” as workshop organizers from the general co-chairs of ICALT).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

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Adaptive development and maintenance of user-centric software systems

A software system cannot be developed without considering the various facets of its environment. Stakeholders – including the users that play a central role – have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems

Investigation of behavior and perception of digital library users: A cognitive style perspective

Cognitive style is an influential factor in users’ information seeking. The study presented in this paper examines how users’ cognitive styles affect their behavior and perception in digital libraries. Fifty participants took part in this study. Two dimensions of cognitive styles were considered: (a) Field Dependence/Independence; (2) Verbalizer/Imager. The results showed that Intermediate users and Verbalizers have not only more positive perception, but they also complete the tasks in effective ways. Implications for the design of personalized digital libraries are also discussed