ANÁLISIS DE LOS DATOS OBTENIDOS

A number of adaptive systems use learning styles to adapt the learning environment to the user. These adaptive systems include Adaptive Courseware Environment (ACE), Carmona (2007), CAMELEON, SMILE (Stoyanov and Krommers (1999)), INSPIRE (Intelligent Instruction System for Personalised Instruction in a Remote Environment), iWeaver and APeLS. These systems will now be described in further detail including the particular learning styles used and how they are adapted for the learner.

Specht and Oppermann (1998) state that ACE is a web-based tutoring framework combining methods of knowledge representation, instructional planning, and adaptive media generation to deliver individualised coursework via the web. The ACE system adapts content to the learner based on the Felder SivermanÕs Index of learning styles. They describe experimental studies within ACE, which showed that for successful

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application of incremental linking of hypertext, it is dependent on a studentÕs prior knowledge and their learning style. Carmona and CAMELEON also adapt content to the learner based on this learning style.

The SMILE system is a web-based knowledge support system providing intelligent support for dealing with open-ended problem situations (Grigoriadou et al. (2001)). The SMILE system incorporates the Honey and Mumford learning style. The system utilises a learner profile that takes into consideration the learnerÕs learning style, following the Honey and MumfordÕs categorisation (Stoyanov and Kommers (1999)).

Within INSPIRE the system aims to generate different lessons for each individual learner, to meet his/her goals. Papanikolaou (2003) describes the learner model within the INSPIRE system which controls the adaptive behaviour and has:

o An overlaid model that records the learnerÕs knowledge level in the various goals

o The ability to record information that describes the learnerÕs interaction with any content

o The ability to store information about the learner including the learning style preferences

o Transparency to the learner and so therefore the learner can manage the stored information

o The dynamic updating of the user model during the interaction so that the learnerÕs current interactions can be stored in the database

The INSPIRE system also uses the Honey and MumfordÕs learning style and adapts the presentation to the learner based on their learning style. The learner initially completes the Honey and Mumford style questionnaire and the learner model records the categories: activist, reflector, theorist and pragmatist. Within the system, the learner can update the user model and the learner can have the ability to make decisions about the lesson content. The iWeaver system is described as an interactive adaptive learning environment. The system adapts the presentation of the learning material

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based on the learnerÕs style and follows the Dunn and Dunn learning style model. The system uses the categories of the Dunn and Dunn model and recommends the representation file type accordingly. iWeaver supports the teaching of a programming language and it offers a combination of adaptive navigation and adaptive content presentation techniques.

A system developed according to the APeLS framework matches the user model with content metadata in order to select the learning objectives that are most relevant to the userÕs learning style given certain alternatives in the pool of resources (Brusilovsky 2007). This system presents learning material based on style and presenting sequencing. The learning styles used within this system are VARK, Kolb and Honey and Mumford.

It was found during the research that one of the most common uses of learning styles within e-learning systems is to adapt the learning material presented to the user based on their learning style category (summarised in the information in Table 2).

During the research it was also found that some systems for example the Adaptive Hypermedia Architecture (AHA) system (De Bra and Calvi (1998)) and the Mulimedia Asynchronous Networked Individualised Courseware (MANIC) system (Stern et al. (1997)) go further and propose systems that provide mechanisms for inferring learnerÕs preferences.

Despite the systems that have been developed, Brusilovsky and Mill‡n (2007) state, Òthere are no proven recipes for the application of learning styles in adaptationÓ. Brusilovsky and Mill‡n (2007) also state that it is still unclear which aspects of learning styles are worth modelling, and what can be done differently for users with different styles. Furthermore, Carmona (2007) states that the reason for this may be that the tool used gives some grade of uncertainty and that the assumptions made about the learning style are not updated in the light of the students interaction with the system. Also, Carmona (2007) states that the rules defined are fixed even when the behaviour of the student shows something wrong with these rules.

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Therefore, they state that it is unable to adapt itself in the light of new information.

Brusilovsky and Mill‡n (2007) therefore state that to progress with this area, it is necessary to learn more about the relationships between user traits and possible interface settings, or to develop other techniques for the adaptation.

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Table 2: Learning styles, systems and purpose

A further research project undertaken by Graf and Kinshuk (2007) has adapted content to the user based on their learning style. The learning style used within this project is the Felder Silverman style model. This project evaluated the effect of the adaptability on the learners and the outcome was that the adaptability had a positive effect on the learners. The study demonstrated that the adaptive model helped the students to learn more effectively and so therefore this facilitates learning.