**3. Adaptive levels and student-learning system interaction**

Within these levels, there are six categories of interactivity: Feedback; Control; Productivity; Creativity; Communication and Adaptation. Ensuring interactivity for each level and related categories requires the development of a comprehensive adaptive model to provide person‐ alization of the training through: the basic knowledge of the student; his plans and purposes; his cognitive characteristics; his preferences and habits; emotional profile, and so on (see [8]).

According to various aspects of the application and the use of e-Learning systems, the adaptability can be defined differently. We will define adaptability as a feature of the training system to be adapting itself and changed according to the requirements and the characteristics of the users before and during use of it. The main elements of the adaptive model are "condi‐ tion-action" rules that change the parameters of the environment and realize the adaptation to a user's knowledge, goals, abilities, preferences, etc. The different methods and frameworks

**•** Rule-based – we look at these systems from some aspects: as a declarative interpretation of rules; as a hybrid representation based on logical deduction; as a users' stereotypes; as an overlay model of connecting and co-interacting with the model of the relevant applied area. Presenting the cognitions is connected with the accommodation of the system conventions, the attitude and the convictions of the users, and the stereotypes and the user groups that

**•** Frame and Network-based – these models are associated with figuring the sciences as interrelations between separate facts of semantic net and frame structures. It can be used successfully on a small applied domain that can be easily identified and structured.

**•** Supposition-based – Such systems work with a multitude assumptions of the consumer that forms on the student's knowledge base and domain independent rules. The suppositions

can be activated dynamically with the particular conditions, etc.

for creation of adaptive models are as follows:

**Figure 1.** Iterative model of research methodology

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

Elementary adaptive level (EAL) – the adaptation in this level is connected by the use of static user information such as type of training, grade, class, name, access to learning material (by mobile or fixed device), etc. Here, we can use a stereotype approach. The authors of educational resources develop packages of lessons, tests, etc., in accordance with government educational requirements and standards for the typical student, school subject, the class and form of training. The created educational resources are common for all students in the described groups.

We can realize an adaptation on this level by the preparation of training materials, common for all students in the phase before the start of the learning process. These characteristics prove the relationship with the first interactive level – Standard Experience, because the physical structure and hierarchy of the learning content remains unchanged. However physical and cognitive interaction occurs for the users. The student receives the entire information inde‐ pendently if he knows it. At this level, users are an abstract group of people with common characteristics – background knowledge, preferences, cognitive performance, and more. This personalization is the lowest formal level. Although users have their accounts in the school e-Learning environment, they can work in it only on the predetermined way for all other users in the same stereotyped group.

Static adaptive level (SAL) – this level is based on the elementary level and is directly related with mechanisms to provide adequate learning materials for individual students according to their knowledge base, personal goals, and plans.

Before we present the adaptive mechanisms of this level, we will comment on the concept of persona as an aggregated user type. The persona is a description of a fictitious learner. This description is based on different methods, including the personal experience of the teachers, hypotheses, statistical methods, and heuristic analysis [10].

Adaptability of this level is realized through the collection preparation of educational materials and services, foreseeing the actions and behaviour of the persona. The realization is based on the log-information about past interactions between the student and LMS according to the set of rules defined by the teachers. It is necessary to define the background knowledge of this student. We can determine the knowledge in different ways – by initial testing, by results from completed to this moment training sessions, etc. Based on these values, the system joins this student to the persona, who is closest to these characteristics. The system compares the individual characteristics, plans, and objectives of the student with the typical didactic aims, defined in BES. As a result, this lesson that most closely matches with the basic knowledge, goals, plans, and personal characteristics of the student that is associated with this persona starts from the Lesson repository.

Moreover, special attention will be paid to the creation of courses by pedagogical specialists in the article. In accordance with pedagogical theory, this process is cyclical and begins by placing the main didactic aims, passes through specifying learning tasks, develops the profiles of aggregated user types (personas), the establishes learning scenarios with different personas, develops prototypes of the training process, shares this prototype among specialized peda‐ gogical community of educators, experts and heuristic evaluation of this prototype testing in real learning environment, and corrects existing errors and inaccuracies. Each stage of this process requires a qualitative evaluation and heuristic analysis of the pedagogical community and the implementation of appropriate tools in a common environment – ex. Integrated Learning Design Environment (ILDE) [11] (Figure 2).

**Figure 2.** Creation of learning course in ILDE

training. The created educational resources are common for all students in the described

We can realize an adaptation on this level by the preparation of training materials, common for all students in the phase before the start of the learning process. These characteristics prove the relationship with the first interactive level – Standard Experience, because the physical structure and hierarchy of the learning content remains unchanged. However physical and cognitive interaction occurs for the users. The student receives the entire information inde‐ pendently if he knows it. At this level, users are an abstract group of people with common characteristics – background knowledge, preferences, cognitive performance, and more. This personalization is the lowest formal level. Although users have their accounts in the school e-Learning environment, they can work in it only on the predetermined way for all other users

Static adaptive level (SAL) – this level is based on the elementary level and is directly related with mechanisms to provide adequate learning materials for individual students according to

Before we present the adaptive mechanisms of this level, we will comment on the concept of persona as an aggregated user type. The persona is a description of a fictitious learner. This description is based on different methods, including the personal experience of the teachers,

Adaptability of this level is realized through the collection preparation of educational materials and services, foreseeing the actions and behaviour of the persona. The realization is based on the log-information about past interactions between the student and LMS according to the set of rules defined by the teachers. It is necessary to define the background knowledge of this student. We can determine the knowledge in different ways – by initial testing, by results from completed to this moment training sessions, etc. Based on these values, the system joins this student to the persona, who is closest to these characteristics. The system compares the individual characteristics, plans, and objectives of the student with the typical didactic aims, defined in BES. As a result, this lesson that most closely matches with the basic knowledge, goals, plans, and personal characteristics of the student that is associated with this persona

Moreover, special attention will be paid to the creation of courses by pedagogical specialists in the article. In accordance with pedagogical theory, this process is cyclical and begins by placing the main didactic aims, passes through specifying learning tasks, develops the profiles of aggregated user types (personas), the establishes learning scenarios with different personas, develops prototypes of the training process, shares this prototype among specialized peda‐ gogical community of educators, experts and heuristic evaluation of this prototype testing in real learning environment, and corrects existing errors and inaccuracies. Each stage of this process requires a qualitative evaluation and heuristic analysis of the pedagogical community and the implementation of appropriate tools in a common environment – ex. Integrated

groups.

in the same stereotyped group.

starts from the Lesson repository.

Learning Design Environment (ILDE) [11] (Figure 2).

their knowledge base, personal goals, and plans.

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

hypotheses, statistical methods, and heuristic analysis [10].

Creation of the training course, according to the developed scenario is realized based on the selected standard for e-Learning. In developing the DeLC-system, we use the standard SCORM2 [12]. The team developed a special SCORM-editor for the teachers and authors of the educational content – SELBO [13].

We will concentrate our attention on the two basic characteristics of the lesson – the content and the structure. The content of lessons is related with specific topics that are connected with some school subject domains. The e-lesson presents a semantic structure of the knowledge that is connected with some school subjects. The formalization can be realized through the creation of ontologies in which each concept from the respective area are associated with real infor‐ mation resources that represent them in the lesson. According to the main characteristics of the school subject domain, the authors of e-content in accordance with didactic aims define the structure of the lesson. The didactic aims are related with type of the lesson (for new knowledge, for exercises, summary, and testing). We use Bloom's taxonomy to formalize these

<sup>2</sup> SCORM- Sharable Content Object Reference Model

aims with the cognitive levels – knowledge, comprehension, application, analysis, synthesis, and evaluation [14]. The teacher could structure the lesson in different ways depending on the predefined didactic aims. We came to the conclusion that there is a correspondence between the different types of e-lessons and the cognitive levels of Bloom's taxonomy. Therefore, we can formalize the different types of e-lessons according to didactic aims by creating standard scenarios for training and templates that describe them. The template is a combination of structure and learning scenarios.

To create electronic lessons by using algorithm requires a thorough knowledge of the standard SCORM, which creates objective problems for teachers who are not IT specialists. To partly solve this problem, we can use the SCORM Best Practices Guide for Content Developers (BPG) [15], which offers a number of basic templates and models that correspond to different educational scenarios.

In order to increase the formalization level of templates and models, we created a system for its parameterization. Thus, we received a number of different groups of templates that more fully meets the requirements and objectives of the learning process. We can use the following for the parameterization of templates:


Let's define two operatios:


In dialogue with the SCORM-based authoring tool for generating of electronic lessons, the teacher will determine the values of these parameters and the system will generate the structure of the desired template. If specific values of the parameters are not mentioned, the system will get the default values. After the parameterization, the teacher will receive the

<sup>3</sup> SCO- Sharable Content Object

parameterized template with the SCORM rules that served as a guide to the sequence of educational activities in the educational scenario depending on the behaviour of the individual student.

For example, if we get parameter values: number\_of\_SCOs = 10; has\_test = "yes"; (objective\_n; 0.75) for each n <10; (SCO\_n, template\_2), for each n <10; Set (SCO10 (asset\_n), objective\_n) for each n <10; Read (SCO\_n, objective\_n) for each n <10, we get the following chart describing the scenario of the lesson (Figure 3):

**Figure 3.** Parametrized Content Structure Diagram

aims with the cognitive levels – knowledge, comprehension, application, analysis, synthesis, and evaluation [14]. The teacher could structure the lesson in different ways depending on the predefined didactic aims. We came to the conclusion that there is a correspondence between the different types of e-lessons and the cognitive levels of Bloom's taxonomy. Therefore, we can formalize the different types of e-lessons according to didactic aims by creating standard scenarios for training and templates that describe them. The template is a combination of

To create electronic lessons by using algorithm requires a thorough knowledge of the standard SCORM, which creates objective problems for teachers who are not IT specialists. To partly solve this problem, we can use the SCORM Best Practices Guide for Content Developers (BPG) [15], which offers a number of basic templates and models that correspond to different

In order to increase the formalization level of templates and models, we created a system for its parameterization. Thus, we received a number of different groups of templates that more fully meets the requirements and objectives of the learning process. We can use the following

parameter value Has\_test is "yes", the number of questions Num\_Quest =Number\_of\_SCOs

**•** Has\_test – type Boolean to determine whether there is a final test or not in the template.

**•** The ordered pair (objective\_n, min\_value\_n) connects each target variable (objective) and the minimum value for which LMS will mark it as successfully passed (n<Num‐

**•** The ordered pair (SCOn, template\_num), for each n <Number\_of\_SCOs and template\_num

**•** Set (SCO\_Number\_of\_SCOs (Asset k); Objective\_k) – for setting values of k-th target

**•** Read (SCO\_k, Objective\_k) – start-up of the information SCO\_k if Objective\_k has a value

In dialogue with the SCORM-based authoring tool for generating of electronic lessons, the teacher will determine the values of these parameters and the system will generate the structure of the desired template. If specific values of the parameters are not mentioned, the system will get the default values. After the parameterization, the teacher will receive the

<= 10, which connects each SCO with instance of the main BPG-template;

variable for the k-th issue of the last SCO, where k <Number\_of\_SCOs, and

in the template. If the

**•** Number\_of\_SCOs – type Integer, to describe the number of SCOs3

Defaults to "yes" and is realized with the last SCO;

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

**•** Num\_Quest – number of questions in the final test

structure and learning scenarios.

for the parameterization of templates:

educational scenarios.

– 1;

ber\_of\_SCOs);

Let's define two operatios:

3 SCO- Sharable Content Object

less than the predetermined.

Therefore, based on the Bloom taxonomy of didactic purposes, lesson types formalization of their structure and navigation rules can be created by the step by step algorithm for creating of e-lessons.

The teacher in some school subjects create e-lessons in a specialized development environment, which is in dynamic interaction with the respective ontology. This author's tool has to maintain information about the compulsory concepts in the relevant discipline according to Bulgarian Educational Standards. Concepts that are mandatory taught according to BES and those that are determined from the teacher for this lesson are marked with AND and those that contain additional information are marked with OR. The algorithm includes the following steps:


E-Learning resources (SCOs) are associated with concepts of relevant ontology. They are stored in some online SCOs Repository. In ontologies and related items, SCOs are presented into the development environment for creating electronic lessons [16]. The authors determine the structure of the e-lesson by using the parameterization of some basic templates. In this way, they create an instance of the template in which there are no free parameters. LMS manages educational processes and determines the training scenario in accordance with the structure of the lesson and learning scenario, which are related to the didactic aims, behaviour, and basic knowledge of the students. The e-lesson will be presented in the system as a specific instance of some basic template, which, by setting the values of parameters, is associated with specific learning resources.

One example is the Lesson "Summary on complex verb tenses» for 7th grade students for independent distance training". The didactic aim is to reach higher levels of Bloom's taxonomy – application, analysis, synthesis, and evaluation. The system offers BPG-templates №7, 8, and 10, and the teacher chooses Template 7. In this template, SCOs containing learning information are grouped in a separate Aggregation B. The student must answer questions from the preliminary test and if wrong (i.e. target variables that monitor test results are less than the minimum values), he has to become familiar with the educational content of the information SCOs. Then, he will make the final test in the last SCO. The template can be used in the creation of educational resources, which is necessary to verify and ensure a certain volume of back‐ ground knowledge. It is essential to fill the gaps and to allow the student to successfully pass the final test. LMS manages the values of the variables (objectives) and only if they are larger than the specified minimum, the training is considered to be successfully completed. The teacher gives the following values of the parameters through a dialogue in a step-by-step process: Number\_of\_SCOs=9; Has\_pre\_test="yes"; Num\_Quest=9; Has\_post\_test="yes"; (Obj\_n,1) – i.e., answered correctly for all questions from 1 to 9; (Obj\_n; 0,75) – gave a very good answer to the questions for ∀n∈ 10, 18 ; (SCOn, pattern\_2) for ∀ °n∈ 3, 11 ; Set(SCO1 (Asset\_k); Obj\_k) for ∀k∈ 1, 9 ; Set (SCO2 (Asset\_k); Obj\_k) for ∀k∈ 10, 18 ; Read (SCO (k +2), Obj\_k) for ∀k∈ 1, 9 . The system generates a CAM- model and S & N rules. The teacher

<sup>4</sup> CAM-SCORM Content Aggregation Model

<sup>5</sup> S&N Model- SCORM Sequence and Navigation Model

writes the test questions and puts the SCOs in Aggregation B. These data objects (SCOs) include both basic information on various complex verb tenses as well as tasks and exercises for students who will have to pass successively through the levels «application» – «analysis» – «synthesis» – «evaluation». The teacher makes a SCORM-package of the lesson and uploads it in the SCORM-based school education portal (http://sou-brezovo.org). These characteristics prove the relationship with the second interactive level – Personal Experience – because the hierarchy of content changes and adapts to the user's behaviours and selections.

**3.** In a step by step process, the teacher determines the values of parameters such as the number of data objects (SCOs), presence of preliminary and final test, minimum values of the target variables for their passing, number of attempts to solve the tests, etc. As a result, the system generates a parameterized template, which includes the structure of the

**4.** The author connects the SCOs with nodes of the structural graph in the template of the

E-Learning resources (SCOs) are associated with concepts of relevant ontology. They are stored in some online SCOs Repository. In ontologies and related items, SCOs are presented into the development environment for creating electronic lessons [16]. The authors determine the structure of the e-lesson by using the parameterization of some basic templates. In this way, they create an instance of the template in which there are no free parameters. LMS manages educational processes and determines the training scenario in accordance with the structure of the lesson and learning scenario, which are related to the didactic aims, behaviour, and basic knowledge of the students. The e-lesson will be presented in the system as a specific instance of some basic template, which, by setting the values of parameters, is associated with specific

One example is the Lesson "Summary on complex verb tenses» for 7th grade students for independent distance training". The didactic aim is to reach higher levels of Bloom's taxonomy – application, analysis, synthesis, and evaluation. The system offers BPG-templates №7, 8, and 10, and the teacher chooses Template 7. In this template, SCOs containing learning information are grouped in a separate Aggregation B. The student must answer questions from the preliminary test and if wrong (i.e. target variables that monitor test results are less than the minimum values), he has to become familiar with the educational content of the information SCOs. Then, he will make the final test in the last SCO. The template can be used in the creation of educational resources, which is necessary to verify and ensure a certain volume of back‐ ground knowledge. It is essential to fill the gaps and to allow the student to successfully pass the final test. LMS manages the values of the variables (objectives) and only if they are larger than the specified minimum, the training is considered to be successfully completed. The teacher gives the following values of the parameters through a dialogue in a step-by-step process: Number\_of\_SCOs=9; Has\_pre\_test="yes"; Num\_Quest=9; Has\_post\_test="yes"; (Obj\_n,1) – i.e., answered correctly for all questions from 1 to 9; (Obj\_n; 0,75) – gave a very good answer to the questions for ∀n∈ 10, 18 ; (SCOn, pattern\_2) for ∀ °n∈ 3, 11 ; Set(SCO1 (Asset\_k); Obj\_k) for ∀k∈ 1, 9 ; Set (SCO2 (Asset\_k); Obj\_k) for ∀k∈ 10, 18 ; Read (SCO (k +2), Obj\_k) for ∀k∈ 1, 9 . The system generates a CAM- model and S & N rules. The teacher

**5.** The author ccreated lesson (the system generates zip-package and imsmanifest.xml)

**6.** He upload created lesson in SCORM-environment of the education portal.

).

) and the rules that will manage the learning process (SCORM

lesson (SCORM CAM4

lesson.

learning resources.

4 CAM-SCORM Content Aggregation Model

5 S&N Model- SCORM Sequence and Navigation Model

Sequence & Navigation Model5

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

Dynamic adaptive level (DAL) – This level is related to the dynamic interaction between students and the system during the training process (in run-time). After selecting the most appropriate e-lesson from the Lesson DB, the LMS starts the learning process according to the training scenario. The learning scenario is realized by a sequence of actions that is previously defined by the author of the lesson. The system observes the intermediate results during the training and information from the already completed training sessions. Based on this infor‐ mation, the LMS adapts itself dynamically to the changing characteristics of the learning environment as it generates new "condition-action" rules and either continues the training process or stops it. If the parameters are not appropriate, the system has to choose and to start a new and more appropriate e-lesson.

We are convinced that in the process of dynamic interaction between the learners and the training system, it is essential to use intelligent agents who interact with the system and with each other to provide a flexible change of training scenarios depending on the behaviours and actions of the individual student. For the managing of the dynamic adaptation of LMS, we can use Interval temporal logic (ITL) and policies.

Morris Sloman in [17] defines the policies as a set of rules for activating different states and actions, depending on the behaviour of the consumers or the current state of the system. There are different techniques to formalize the policies – graphical modelling, using the objectoriented methods for defining of policies, etc. We will use the opportunities provided from ITL [18] as it builds on a classical logic tier and allows to describe dynamic processes in the course of their implementation. It is a flexible notation for handling events that varied in time intervals, allows series, and parallel compositing using a well-defined mathematical proof system. ITL includes four components – logic tier, temporal structures, conditions, and intervals. Classical logic manages variables, constants, functions, and predicates. If we want to describe the dynamic processes, it is necessary to add temporal structures as skip, chop, and chopstar. The states are specific transmission of values to the observed variables and the intervals are sequences of states.

We will describe the next three sets: S-set of students, O-set of available objects or resources, and A-actions that can be performed with these resources. Then, we can introduce the user authentication as one of the Boolean variables:

**•** Autho+(S, O, A) – Positive identification of the user S, who has right to use the resource O by performing action A. For example Autho+(Ivan, Lesson1, Read) or Autho+(Ivan, Test1, Write);

**•** Аutho<sup>−</sup>(S, O, A) – Negative identification – the user S refusal to use the resource O by performing action A. For example Autho<sup>−</sup> (Ivan, Lesson1, Write).

Upon the initial start-up of the system, these variables have a default value of "false". The mathematical model of **Autho** is a matrix with 3 columns – users, objects, actions, and nnumber of lines for all users in the system. The access to resources will be allowed if they satisfied certain "condition-action" rules of the type: F→W i.e., F always followed by W in the final state of the observed subinterval. According to this definition, the Access Rules take the following form: F→autho+(S, O, A)– rule for positive identification and F→Autho<sup>−</sup>(S, O, A) – rule for negative identification. For example: If in the initial step the access was denied, but in the next moment, it is authorized in the duration of 10 time units then: ((Autho<sup>−</sup>(S, O, A)∧skip) ∨(Autho+(S, O, A) ∧ len < = 10) → Autho +(S, O, A)). If two users M and N are grouped and one of them has access, then the second one also receives access: In(M, N)∧Autho+(M, O, A) →Autho+(N, O, A).

The Access Rules determine whether the particular user is entitled to access this learning resource or service. To realize the access itself, the management passes the Implementation Rules, which has the following more general form: F→Autho(S, O, A). There are two alterna‐ tives in access: Open Access and Restricted Access. Open Access has low security – i.e., if access is not prohibited, it is allowed: ¬Autho<sup>−</sup>(S, O, A)→Autho(S, O, A). Restricted Access means the system checks whether access is allowed and it has meanwhile been prohibited i.e., (Autho<sup>+</sup> (S, O, A)∧ ¬Autho<sup>−</sup>(S, O, A))→Autho(S, O, A).

Another way to access learning resources is the delegation of rights to the unauthorized user. For example, the teacher gives access rights to other teachers for reading a lesson: Teacher(S, Lesson)→Candeleg+(S, \_, Lesson, Read). The rules for delegating access, which author A1 gives teacher T2 to make corrections in Lesson1 is: (Autho(A1, Lesson1, Write)∧Candeleg(A1, T1, Lesson1, Write))→Autho(T1, Lesson1, Write).

The policy P is a collection of rules: P≅(w∧ (Λ ri) ∧fin ), where w is the initial state, w' is the final state, and Λri is a conjunction of intermediate states. For example, the policy for Author of Lesson1 (Author), teacher, who use this Lesson1 (Teacher), and student (Student) is:

> (( ( ) ( )) ( ( ) ( )) ( ( ) ( )) ( ( ) ( )) ( ( ) ( )) P1 Author S, Lesson1 Autho S, Lesson1, Read Author S, Lesson1 Autho S, Lesson1, Write Teacher S, Lesson1 Autho S, Lesson1, Read Teacher S, Lesson1 Autho S, Lesson1, Write Student S, Lesson1 Autho S,Lesson1, Read + + + - + @ ® ® ® ® ®

 Auth ( o S, Lesson1, A Autho S, Lesson1, A Autho S,Lesson1, A ( ) ( )) ( )) + - ® The first step towards the creation of our school e-Learning system is the standardization of key processes associated with the personalization of access to e-lessons.

**•** Аutho<sup>−</sup>(S, O, A) – Negative identification – the user S refusal to use the resource O by

Upon the initial start-up of the system, these variables have a default value of "false". The mathematical model of **Autho** is a matrix with 3 columns – users, objects, actions, and nnumber of lines for all users in the system. The access to resources will be allowed if they satisfied certain "condition-action" rules of the type: F→W i.e., F always followed by W in the final state of the observed subinterval. According to this definition, the Access Rules take the following form: F→autho+(S, O, A)– rule for positive identification and F→Autho<sup>−</sup>(S, O, A) – rule for negative identification. For example: If in the initial step the access was denied, but in the next moment, it is authorized in the duration of 10 time units then: ((Autho<sup>−</sup>(S, O, A)∧skip) ∨(Autho+(S, O, A) ∧ len < = 10) → Autho +(S, O, A)). If two users M and N are grouped and one of them has access, then the second one also receives access:

The Access Rules determine whether the particular user is entitled to access this learning resource or service. To realize the access itself, the management passes the Implementation Rules, which has the following more general form: F→Autho(S, O, A). There are two alterna‐ tives in access: Open Access and Restricted Access. Open Access has low security – i.e., if access is not prohibited, it is allowed: ¬Autho<sup>−</sup>(S, O, A)→Autho(S, O, A). Restricted Access means the system checks whether access is allowed and it has meanwhile been prohibited i.e.,

Another way to access learning resources is the delegation of rights to the unauthorized user. For example, the teacher gives access rights to other teachers for reading a lesson: Teacher(S, Lesson)→Candeleg+(S, \_, Lesson, Read). The rules for delegating access, which author A1 gives teacher T2 to make corrections in Lesson1 is: (Autho(A1, Lesson1, Write)∧Candeleg(A1, T1, Lesson1, Write))→Autho(T1, Lesson1, Write).

The policy P is a collection of rules: P≅(w∧ (Λ ri) ∧fin ), where w is the initial state, w' is the final state, and Λri is a conjunction of intermediate states. For example, the policy for Author of Lesson1 (Author), teacher, who use this Lesson1 (Teacher), and student (Student) is:

(( ( ) ( ))

+ + - + +

( ( ) ( )) ( ( ) ( )) ( ( ) ( )) ( ( ) ( ))

( o S, Lesson1, A Autho S, Lesson1, A Autho S,Lesson1, A ( ) ( )) ( )) + - ®

P1 Author S, Lesson1 Autho S, Lesson1, Read Author S, Lesson1 Autho S, Lesson1, Write Teacher S, Lesson1 Autho S, Lesson1, Read Teacher S, Lesson1 Autho S, Lesson1, Write Student S, Lesson1 Autho S,Lesson1, Read

® ® ® ®

@ ®

performing action A. For example Autho<sup>−</sup> (Ivan, Lesson1, Write).

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

In(M, N)∧Autho+(M, O, A) →Autho+(N, O, A).

Auth

(Autho<sup>+</sup> (S, O, A)∧ ¬Autho<sup>−</sup>(S, O, A))→Autho(S, O, A).

The teachers create e-lessons in specialized SCORM-compliant and ontology-based develop‐ ment environment, then publish them in the education portal in a special Lesson-DB. Further to SCORM-metadata, we will use some additional specifications such as:


Therefore, any electronic lesson in the education portal is a vector with the above dimensions:

( ( ) ( ) ) Info ID, title, domain, author,... , Subdomain concept,m , Num \_ Grade, Form \_ of \_ training, Lesson \_ status, Didactic \_ aims **Lesson**

For example, the lesson "Past imperfect tense of the verb", school subject "Bulgarian language", for 5th grade; author Sarafov, with concepts from matrix Subdomain, designed for regular students, free for use for all users in the education system, and is a lesson for new knowledge we get:

( ( ) ) Lesson1 Info ID, Past imperfect tense of the verb,Bulgarian language, Sarafov,... , Subdomain \* 5, 2, 4, 1 ,

where Subdomain\* is present with Table 1.


**Table 1.** Subdomain

When the student requires launching of a lesson around a chosen theme, the system checks the availability of the appropriate e-lesson from the Lesson DB. Lessons that meet the initial user requirements are usually more than one, so the system should provide an appropriate mechanism for selecting the most appropriate among them. After a dialogue with the student, the personal agent defines his personal aims, preferences, etc., and transmits this vector to the system for choices. After the comparison with the vectors of the uploaded e-lessons in the Lesson DB, the e-Lessons with the highest level of similarity are extracted. The result will be a number of e-lessons andthe system should choose the most appropriate. This selection can be realized by the use of some intelligent algorithm (ex. CBR-approach).

The preferences and personal goals of each student can also formalize the policy which defines the sequence of actions in this training scenario. After the identification of the student in the training environment, based on the profile and persona-stereotypical information and a dialogue with his personal agent, the system receives the necessary initial values of the observed variables. After determining the initial state, the policy management can be trans‐ ferred to a special Policy-Engine, which is part of the infrastructure of the run-time environ‐ ment of the educational e-Learning portal. Initially, based on the dialogue with the student, the Policy of Preferences registers in the Policy-Engine and then starts the Mechanism for Selecting of Lesson that makes a request to the Lesson DB. After the selection a particular lesson, this e-lesson is filed to SCORM-Learning Management System for implementation. The scenario, which will run activities in the learning process, are described and formalized in the SCORM Sequence & Navigation-model and the corresponding parameterized template by which is created this lesson. Policy-Engine can continually modify policies according to the information coming from the behaviour of the learner.

The learning scenario may include mandatory implementation actions (e.g. solving tests). If a student fails to successfully complete these actions, the learning process falls in a critical condition and the Policy-Engine has to choose more appropriate lessons. In this case, the learning process is temporarily interrupted and the LMS restarts the training process with the new lesson.

The Policy of Preferences is expressed by the rules of condition-action types. Conditions present a number of behaviors that trigger certain actions. The formal semantics of the model is based on ITL as the rules are the following:

when B increase | decrease preference in Lesson low | medium | high , where B is behaviour and Lesson is the e-lesson.

The degree of preference can be expressed as an integer. The larger number represents a higher degree of preference. It is initially assumed that the student doesn't have any preferences and all values are 0. We define the meaning of low, medium, and high level of preferences as 1, 2, and 3. We will look at an example of training with two lessons on the same learning material. The first lesson is more difficult and presents the studying concepts in a higher level than the second one. The student initially has not decided what his preferences are. In the Policy-Engine, there are defined policies, which specify that the lessons that guarantee more than 70% results in the final test are preferable than those that only guarantee between 50% to 70% and the lessons that ensure less than 50% are not preferred. We can express the policy with the following rules:

Score (Lesson1, Lesson2):

**Conceptions Level of Studying**

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

When the student requires launching of a lesson around a chosen theme, the system checks the availability of the appropriate e-lesson from the Lesson DB. Lessons that meet the initial user requirements are usually more than one, so the system should provide an appropriate mechanism for selecting the most appropriate among them. After a dialogue with the student, the personal agent defines his personal aims, preferences, etc., and transmits this vector to the system for choices. After the comparison with the vectors of the uploaded e-lessons in the Lesson DB, the e-Lessons with the highest level of similarity are extracted. The result will be a number of e-lessons andthe system should choose the most appropriate. This selection can

The preferences and personal goals of each student can also formalize the policy which defines the sequence of actions in this training scenario. After the identification of the student in the training environment, based on the profile and persona-stereotypical information and a dialogue with his personal agent, the system receives the necessary initial values of the observed variables. After determining the initial state, the policy management can be trans‐ ferred to a special Policy-Engine, which is part of the infrastructure of the run-time environ‐ ment of the educational e-Learning portal. Initially, based on the dialogue with the student, the Policy of Preferences registers in the Policy-Engine and then starts the Mechanism for Selecting of Lesson that makes a request to the Lesson DB. After the selection a particular lesson, this e-lesson is filed to SCORM-Learning Management System for implementation. The scenario, which will run activities in the learning process, are described and formalized in the SCORM Sequence & Navigation-model and the corresponding parameterized template by which is created this lesson. Policy-Engine can continually modify policies according to the

The learning scenario may include mandatory implementation actions (e.g. solving tests). If a student fails to successfully complete these actions, the learning process falls in a critical condition and the Policy-Engine has to choose more appropriate lessons. In this case, the learning process is temporarily interrupted and the LMS restarts the training process with the

be realized by the use of some intelligent algorithm (ex. CBR-approach).

information coming from the behaviour of the learner.

Verb 3 Person of the verb 2 Tense of the verb 2 Communication moment 3 Moment of action 3 Main orientation moment 2 Additional orientation moment 2

**Table 1.** Subdomain

new lesson.

When (1: test\_result >= 70%) increase preference in Lesson to high

When (2: 50%<=test\_result <70%) decrease preference in Lesson to medium

When (3:test\_result<50) decrease preference in Lesson to low

The Policy-Engine determines the information needed for the implementation according to these rules. LMS through the SCORM RTE6 and the mechanism of the target variables (objectives) determines the outcome of the student in solving the test. After each experience, the Policy-Engine checks the assumption as defined by the rules and determines whether they are appropriate. Let's assume that the student has an aim to study the learning material at a high level (3). The system starts Lesson1 and the results of the three consecutive attempts to resolve the final test are 55, 49%, and 60%. After the first attempt to solve the final test, the Policy-Engine activates the second rule because the result is between 50% and 70%. This determines the preference in 2. The next value is <50%. According to rule three, the Policy-Engine reduces the preference from 2 to 1. The last attempt to solve the test starts again with rule two and increases the preference from 1 to 2. This result is unsatisfactory for the personal aims of the student and as a result, the Policy-Engine defines the lesson as inappropriate. The learning process suspends the former lesson and continues with Lesson 2. The student's results from solving the final test for this lesson are 64%, 68%, and 72%. At the first attempt, the preferences rise to 2, the second is retained the same level, while the third attempt increases it to 3, which is quite satisfactory for the student's personal aims that the student has set.

The dynamic adaptive levels most directly correspond to the third type of interaction – Open Experience – because the communication is dynamic with continuous engagement between the system and student.

<sup>6</sup> RTE- Run-Time Environment
