Profiling and categorising the individual

Profiles have been applied and adapted to many different environments to facilitate and reduce information overload by taking into consideration user interests, themes, pedagogical learning theories, and software sharing. The profiles enable students to adjust learning environments according to how they learn and filter the repository for appropriate learning objects. According to Subramaniam (2006), profiles can capture and store information about users’ personal data (e.g. name, contact address, etc.), relations (i.e. with their classmates and teachers), performance (i.e. their learning progress), and specific learning needs. According to Hummel et al., (2003), Zahedi (2003), Sinha et al., (2004), Tzouveli et al., (2005) and Subramaniam (2006), using profiles has enabled environments to adapt to groups’ with similar interests, skills, projects, location and personalised settings. This presents the opportunity for the individual to experience a more specific group surrounding and better correlation between group/collaborative environments. De Meo et al., (2007) agrees with the research of Hummel et al., (2003) and Kabassi et al., (2004) that using profiles in on-line learning can enable e-learning environments to adapt to the specific needs of the individual. However, a profile can offer more than just the personality traits of the individual.

Research conducted by Bloedom et al., (1996) incorporates weight-based algorithms to facilitate learning experience by exploiting key terminologies within the user profile, which can be applied to the filtering mechanisms to reduce information overload. Bloedom et al., (1996) indicates that user profiles can be adapted for the World Wide Web and academic use by incorporating weight-based algorithms that interrogate key terminologies in the learner profile and the retrieval of relevant web-based documents for comparison purposes. The comparison takes place by using a weight-based algorithm that exploits the evidence

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and compares the relativity, thus providing a mechanism for the user to filter out the unnecessary retrieval of documents.

The idea of using learning profiles that can quickly adapt to the individual has been incorporated into the design of AIPL, which can be found within Chapter 4 and also Chapter 5 PAFS. Learning profiles are used to store a detailed description of how an individual prefers to learn and also what behaviours they have. Over time ones own learning style adjusts according to experiences gained, and within AIPL, the learning profile will prompt the individual to carry out their learning style questionnaire again every 2-3 months to ensure that individuals have correct matching patterns, for more information on the learning profile please see Chapter 5 (PAFS). Machine learning of changes over time of student learning style could be a place for further research.

Categorisation within learning profiles can enable greater flexibility when designing course contents with the pedagogical approach that creates a correlation between the learning experience and the learner. Kolb et al., (1999) suggests that experimental learning helps to define flexible learning experiences at a more comprehensible level that encourages guidance, support, and facilities to aid learners. Heery et al., (2000) expand on educational learning profiles by incorporating a relationship between the Semantic Web specifications and the data elements that are used to describe documentation within heterogeneous environments and communities. These profiles enable different communities to access learning profiles to retrieve knowledge through specific repositories to facilitate mechanisms for file sharing, peer-to-peer sharing and documentation. According to Aroyo et al., (2006) when applying learning profiles to e-learning environments research has discovered that using this particular technique has unearthed issues surrounding adaptability.

According to Simon et al., (2002) learning profiles have been applied to adaptive e- learning frameworks to facilitate and maintain personalised learning. Adaptive environments provide access to all kinds of educational resources. Simon et al., (2002) suggests that the LP can be trained to facilitate a fully electronic educational service that enables a tutoring system to assist the learner when encountering problems. The LP can be fully integrated into a web-based adaptive environment, which automatically registers learning details, and a personal record of achievements. To achieve the LP, matching

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ontologies of additional academic services are required to incorporate various types of educational services that model the learner’ s previous knowledge; this enables the LP to describe the capabilities of the student.

Simon et al., (2002) indicates that matching ontologies must be used to provide educational resources that correlate to the most used LP and to additional repositories with the use of mainstream Semantic Web technologies. According to Simon et al., (2002) and Thalmann et al., (2007), a learner profile can be used to assist the learner experience by adjusting the requirements to fit the need of the current learning situation; however, other researchers King et al., (2005), Stash et al., (2006), and White et al., (2006) indicate that matching the learner needs involves a mixture of techniques and theories to provide a more efficient and effective way of improving the learning experience.

3.4.3 Stereotyping and categorising the individual

Another type of profiling found within Computer Science is that of the work from Elaine Rich, which introduced a type of individual/group classification called stereotypes. Stereotypes are based on modelling groups of users who share common interests or characteristics, which can be extracted to form clusters of group-paradigms.

According to Rich (1979) the use of stereotypes, can be achieved by using a small set of words (simple self description) to enable a system to adapt to individual needs. Rich (1979) suggests that to treat users as individuals stereotyping can be used to identify distinct personalities and goals, which will provide a useful mechanism for building models of individual users on the basis of a small amount of information.

Elaine Rich in 1979 suggests that “there are many theories about why people use

stereotypes, but one of the most certain explanations is that people use stereotypes as a means for dealing with the fact that the world is far more complex than they can deal with without some form of simplification and categorization” (Rich 1979, P3) .

According to Melia et al., (2009) and Brusilovsky et al., (2010) the use of stereotyping within on-line learning enables a system to adapt to a variety of individual needs like retrieving relevant information; knowledge of a subject, and learning style.

As stated by Brusilovsky et al., “to create and maintain an up-to-date user model, an