**4. Personalization and user modeling**

User modelling is an important feature of any e-learning system, to personalize and tailor the e-Learning to individual characteristics, knowledge, didactic aims, and the preferences of the students [19], [20], [21]. On the basis of the previous section, we can describe the adaptability of the system for e-Learning to knowledge and the preferences of students in elementary, static, and dynamic levels [22].

The Elementary Adaptive Level is guaranteed by the profile information about the student before starting his training process in the system. Based only on this adaptive level, the elearning system offers only learning resources that are common to all students of the same grade and form of training.

The Static Adaptive Level is based on the model for selecting the most appropriate lesson from the Lessons DB as the student is joined to a particular persona in the stereotypical hierarchy. By personas, students with similar characteristics are presented in the e-learning system together. The lessons are prebuilt by the parameterization of the basic BPG-templates and models from the authors of e-learning content in a special authoring environment. These lessons are placed in a special repository – Lesson DB – and are described in metadata as described above.

The Dynamic Adaptive Level is implemented through the Policies of Preferences and Policy-Engine, which dynamically monitors the behaviours of student and his preferences with the relevant lesson in the actual learning process and can replace the current lesson with another that is more appropriate for the individual student.

In the adaptation process in terms of the user modelling, we will look at: the information athering about the learner, processing the information and its update, and finding and presenting the appropriate training resources for the considered student. The model describes the notion of the e-learning system for user knowledge, for his preferences, and aims. This model must be continuously updated according to the dynamic changes in the process of accumulation of knowledge about the particular student (Figure 4). The algorithm includes the following steps:


**Figure 4.** User modelling and personalization

**4. Personalization and user modeling**

142 E-Learning - Instructional Design, Organizational Strategy and Management

that is more appropriate for the individual student.

the group in the assumption of the individual user.

submits it to the LMS for implementation.

and dynamic levels [22].

grade and form of training.

described above.

the following steps:

User modelling is an important feature of any e-learning system, to personalize and tailor the e-Learning to individual characteristics, knowledge, didactic aims, and the preferences of the students [19], [20], [21]. On the basis of the previous section, we can describe the adaptability of the system for e-Learning to knowledge and the preferences of students in elementary, static,

The Elementary Adaptive Level is guaranteed by the profile information about the student before starting his training process in the system. Based only on this adaptive level, the elearning system offers only learning resources that are common to all students of the same

The Static Adaptive Level is based on the model for selecting the most appropriate lesson from the Lessons DB as the student is joined to a particular persona in the stereotypical hierarchy. By personas, students with similar characteristics are presented in the e-learning system together. The lessons are prebuilt by the parameterization of the basic BPG-templates and models from the authors of e-learning content in a special authoring environment. These lessons are placed in a special repository – Lesson DB – and are described in metadata as

The Dynamic Adaptive Level is implemented through the Policies of Preferences and Policy-Engine, which dynamically monitors the behaviours of student and his preferences with the relevant lesson in the actual learning process and can replace the current lesson with another

In the adaptation process in terms of the user modelling, we will look at: the information athering about the learner, processing the information and its update, and finding and presenting the appropriate training resources for the considered student. The model describes the notion of the e-learning system for user knowledge, for his preferences, and aims. This model must be continuously updated according to the dynamic changes in the process of accumulation of knowledge about the particular student (Figure 4). The algorithm includes

**•** Step 1. Filling the static profile information. According to the grade and form of training, the student is associated to any persona in a stereotypical hierarchy. The initial parameters are filled in interactive mode or the system gets the default values from the general stereo‐ type model. Stereotyping and personas are used to transfer more general information about

**•** Step 2. According to the persona, which is associated with the student, the system deter‐ mines the common characteristics of the group and includes default values. Then, in the dialogue mode,the school subject, topic, and personal didactic aims of the student are determined. The rules are updated on the basis of collected information. The Policy-Engine launches the Searching Mechanism for the more appropriate lesson from the Lesson DB and The information in this user model can be considered as information specific to the school subject domain and information that is independent of it. The first type includes the data connected with the Dynamic Adaptive Level as an evaluation of the student; his background knowledge and records of his behavior (number of passed lessons, number of errors during solving test, number of inappropriate lessons, etc.). The information that is not dependent on the some subject domain is related to the personal goals of the learner, with his motivation, experience, preferences, interests, and personal data such as name, years, type of training, etc.

The presented algorithm provides a continuous actualization of information. Such one is independent from the specific school subject domain and one that is domain-dependent. The model is continuously updated to correctly present the student in the e-Learning environment. We created several versions of SCORM-based e-Learning portal of the secondary school "Hristo Smirnenski"-Brezovo, which is based on the conceptual framework of the system DeLC and supports SCORM RTE [23]. The latest version of the environment ensures the personali‐ zation in the elementary and static levels. We developed the mechanism of parameterization of the basic BPG-templates and models, and created an authoring tool for the designing and packaging of SCORM-based e-lessons. Ontologies provide developers with predefined resources covering a specific school subject domain that can be used directly in the content. The establishment of educational environment is based on the adaptation of the corporate portal of the Delphi group. For the realization of the educational portal, we used the portal framework Liferay (http://liferay.com), which has implemented LMS of SCORM RTE [24]. There are many services implemented into the portal that supports the training process in different subjects and raises the level of interactivity in learning.
