User Models

One actual major aspect is the collection of data characterising certain users to provide a substantial basis for system adaptivity [Kabassi and Virvou, 2003]. In e-Learning such a learner profile collects data e.g. about learning credentials (student grades and perfor-mance in certain courses, . . . ), learning preferences, learning style and learning habits [Jafari, 2002]. Such adaptivity may for example result in individual suggestions for pro-vided learning packages, for special courses or certain learning paths; best suited for the personal needs and preferences. Therefore a user model can be defined as:

an individual on various aspects, and these beliefs can be decoupled from the rest of the system [Kobsa and Wahlster, 1998].

Definition 47 “User modelingis the process of building data structures and inference mechanisms that allow an application to assess certain properties of its user and tailor the interaction accordingly” [Giotopoulos et al., 2005].

There many human characteristics that evoke such differences and individuality.

[Belanger and Jordan, 2000] listed a few:

◦ “Humans receive information via sensory input and/or physical interactions.”

◦ “Humans are not reliable receivers of information.”

◦ “Humans are diverse and unpredictable receivers of information.”

◦ “Humans are nomadic – they learn at different places and learn different over time.”

◦ “Humans are self-aware and can give advice on themselves.”

◦ “A single human can play several roles; several humans can play a single role.”

◦ “Several learning experiences may be occurring simultaneously.”

Other reasons are e.g. ethnicity, gender, religion, disability, language, culture, communities, prior domain knowledge, pre-determined learning style, individual ap-proach to learning, personal motivation, expectations, social contexts of education, and learner’s personal life style ([Dimitrova et al., 2003b], [Wild and Quinn, 1998], [Soloway, 1998]).

Self identified four major goals of user models: prediction and planning; diagno-sis and remediation; negotiation and collaboration; interaction and communication [Self, 1994]. Following Eklund and Zeilinger [Eklund and Zeiliger, 1996] the main tasks of a user model are:

◦ Identification of the current and relevant goals of the user.

◦ Saving and actualisation of the user’s knowledge about the system and its usage pos-sibilities.

◦ Saving and actualisation of the user’s background knowledge.

◦ Analysis of the user’s experience that can be useful for knowledge transfer.

◦ Saving and actualisation of the user’s preferences and interests.

Several distinct information can be stored, including user data, usage data and envi-ronment data. User data are e.g. goals, tasks, background, experience, preferences com-bined with their progress; cognitive states such as knowledge, preferences and goals, non-cognitive states like emotions and personality traits. Usage data can be data from interaction with a system by monitoring, behaviour patterns, etc. They can be used as basis for e.g. decisions about future lectures. Environment data may include the position in time and space, socio-political aspects, the state of external resources and technological information ([Cannataro and Pugliese, 2004], [Kernchen, 2005]).

Cannataro and Pugliese describe a classification of user models [Cannataro and Pugliese, 2004].

80 2 Foundations on e-Learning

Initialisation Updating

System Adaptation

User Data Usage Data

Environment Data

Figure 2.13:The three processes involved with a user model (according to [de Vrieze and van Bommel, 2004])

◦ Overlay models: depict relevant aspects of the user by quantitative or qualitative metrics and compare them with a domain model.

◦ Stereotype models: can be differentiated into pure, multiple and mixed stereotype models. Pure stereotypes models attribute the user to one group meanwhile multiple stereotypes allow affiliations to several groups. Mixed stereotypes use attributes for the description of affiliations.

Practical implementations often use a mixture of both models. They start with a stereotype model and with growing data they segue into an overlay model.

Data about the user can be gathered by implicit and explicit methods. Mostly implicit approaches are used, because users are not inclined to answer too many questions ex-cept they see an explicit advantage. Initialisation may be an exex-ception. A user model can be implemented e.g. via Bayesian belief networks or decision tree models. The first possibility follows a propabilistic approach and aims to automatically build a user model using learner history data [Ueno, 2005]. They are useful to model and process uncertainty involved in student modelling, but are still limited in the number of vari-ables ([Ueno, 2001], [Ueno, 2005], [Giotopoulos et al., 2005]). Decision trees are an approach to overcome this limitations.

2.4.1.1 IEEE Personal and Private Information Project

The IEEE Personal and Private Information Project (PAPI) was developed with a spe-cial focus on the user’s learning performance [IEEE LTSC, 2002b]. That results in the depictable categories. Within this model personal information and preferences (ob-ject types used by the learner), performance, security-related aspects, a portfolio and relations to other people can be modeled. A differenciated presentation is possible for a role-based access. So a tutor can access different information than the learner or ther institution. Reusability in different systems was another goal of this standard.

IEEE PAPI was once a standard developed by IEEE LTSC, but was submitted to ISO IEC/JTC1/SC36 WG 3 for further development.

IMS Learner Information Package (IMS LIP) is a management-focused approach to create user models [IMS Global Learning Consortium, Inc., 2001]. Main goals are the recording and management of learning-process-related events, goals and capabilities, learner support as well as the highlighting of learning advantages to the learner. IMS LIP is oriented towards interoperability between user models and Web-based learning systems. A special Learner Information Server is the backbone of this standard, it manages information about the users as well as the rights for its usage. The categories of IMS LIP are:

◦ Access properties of the user,

◦ Learning activities,

◦ Relations among categories,

◦ User membership in special groups,

◦ Competences (knowledge, capabilities, etc.),

◦ Interests and goals,

◦ Identificators (necessary biographic and demographic data),

◦ Certificates, qualifications and licenses (about acquired knowledge),

◦ Security keys for system access and

◦ Summaries about achievements.