Multi-layered knowledge-based architecture of the adaptable distance learning system/Daugiasluoksne, ziniomis grindziama adaptyvaus nuotolinio mokymo sistemos architektura.

Dzemydiene, Dale ; Tankeleviciene, Lina

1. Introduction

Software engineering methods offer new semantic e-service technologies, ontology management possibilities, and intelligent agents' integration techniques. Synchronous, asynchronous, and blended study models are applicable and widely described in the e-learning process realizations (Karampiperis and Sampson 2005; Zavadskas et al. 2008; Kaklauskas et al. 2007a). However, personalised learning with the use of distributed information and a dynamic and heterogeneous learning environment is still problematic. Therefore, intellectualization of the distance learning system, using software agents, is important in order to support student's activities and the distance learning process, as well as instructor's activities. Our research aims at using domain ontology in order to acquire a general/particular understanding of the learning domain between users and software agents.

The educational virtual environments are being developed by the means of emerging new technologies enforcing us to reflect once again the theoretical background of how people learn and try to find innovative ways of adaptable learning realizations (Kaklauskas et al. 2007b; Stukalina 2008). The use of Learning Management System (LMS) is a common practice and it is widely analysed, but information seeking as a process may require new skills and strategies from the e-learner. On the other hand, a lot of extensions of functionality of LMS are oriented towards increasing the support, provided of a system to the e-learner.

Semantic web technologies, such as ontologies, agents, web services, can help in these cases and intensive research is carried out in the field (Stojanovic et al. 2001; Allert et al. 2006; Alsultanny 2006). Student's modelling and adaptation processes, based on ontologies are analysed in (Munoz and Oliveira 2004; Dolog et al. 2004; Cristea 2004; Karampiperis and Sampson 2005). Domain ontology plays an important role in approaches, presented for e-learning systems (Angelova et al. 2004; Deline et al. 2007), but the examples showing their usability are concerning more primitive domains.

Traditional distance study courses usually have predicted by lecturer sequence of learning resources and activities (Mockus 2008). This fact sometimes does not correlate with pedagogical strategy used, where free exploration is preferred. Despite of knowledge receiving or exploring method used (e.g. reading topics coherently, starting from different point and deepening the knowledge or looking all around, browsing in order to acquire the overall view of a material), students need to understand the structure of the information space, in order to better navigate through it and achieve their goals. In this case, the meta-cognitive support can be employed. Meta-cognition deals with understanding, managing, planning the own learning process. Specific methodologies and tools are proposed for seeking to support knowledge building, e. g. meta-cognitive maps (Lee and Baylor 2006), Did@browser System, which poses meta-cognitive questions during browsing (Chiazzese et al. 2006). The difference of our framework is the automation of the resource linking process at run time. However, developing ontology and resources, and mapping ontology concepts with resources remains manual work.

The goal of this research is to develop distance learning environment by improving the architecture of the traditional learning management system (LMS) while integrating domain ontology by generating scenarios with the reasoning components in adaptable learning process. The aim of the paper is to analyze application of reasoning mechanisms in the domain ontology, and to propose a framework for conceptual linking of educational resources.

2. The architecture of the distance learning management system for adaptable purposes

The multi-layer architecture of the e-learning system integrates the components of the usual learning management system (LMS) and extends it with intelligent components. The proposed architecture of an adaptive learning management system shows the structure of the main components and packages integrated for supporting the main functional organizational tasks (Dzemydiene et al. 2006):

--Subsystem of development of distance courses, intended for course planning, material creation or importing, integrating activities;

--Subsystem of supporting a study process, intended for process organization, users' activities control, communication, and assessment organization;

--Subsystem for organizing realization and control which identifies the registered users, analyses their rights, offers access to the objects of the environment, and ensures the safety of the data used;

--Subsystem of logistics meant for planning the personnel, resources, finances, equipment, and information of the institution.

In essence, we extend the existing LMS with 2 layers. One layer--intelligent decision support components--should act as a mediator between the core LMS elements and user interface. We prefer software agents as independent components. However, the previous intelligent learning systems were oriented towards the behavioural learning model.

Besides the main databases (data on users, courses, and users' activities within courses), we propose deeper knowledge layer. We give the top priority to domain ontology integration. The layer of intelligent decision support components (2) has to act as a mediator between the elements of LMS (3) and different types of user interfaces (1). This layer plays the role of interaction regulations. We prefer software agents in the implementation stage. The schema of agents' relationships with the other components of e-learning system is presented in Fig. 1.

[FIGURE 1 OMITTED]

Agents must transfer right knowledge within the right context. Therefore agents must not only use knowledge, but also they need relevant, real time information, the so-called context. Context-related information (data on users, courses, and users' activities within courses) is stored in the core information component of databases. If we are aware of the subject domain ontology and information about goals of a student, we can adapt educational resources to students with different levels of e-learning progress (Fig. 2).

[FIGURE 2 OMITTED]

The goals are formulated by the teacher, who has subject domain ontology and information about student's goals. The learner does not reach the educational resources directly, but through the interacting with multi-agent system, which works on the intelligent level (Fig. 2). The example of division/extraction of abstract subject areas according to ontology levels by lecturers to become the agents later on. Scenarios for personalized learning path generation have to be specified and introduced into the intellectual layer. Due to language compatibilities with different platforms, such as Java-based, agents can be integrated with each form of the distance learning system used.

In order to gain more benefit from automating some processes, we have to find some exclusive tasks which are important in the aspect of being automated. In (Becks 2001) a task is regarded to be important if it is:

--typical, i.e. it occurs frequently;

--difficult to perform and thus can benefit from suitable support;

--valuable for the user to solve it;

--information technology can possibly support the problem solving process.

Typical users of e-learning system and their functions are listed in Table 1.

More precisely students' activities can be formulated ranging them by learning methods used:

--information transfer: lecture, studying learning material, instruction, illustration, analysis of case studies and examples;

--practical-operational: exercise, task, coursework;

--creative: working with scientific-technical literature, search for information and analysis of results, group work.

Simplified model of learning, as the process of acquiring experience, can be depicted as the sequence of 3 steps: 1) Absorb knowledge; 2) Do practice; 3) Connect to a life or work. In other words, these steps can be described as: 1) Introduce; 2) Apply; 3) Summarise. We don't consider here further use of acquired knowledge and abilities, therefore, we analyse only the first 2 steps.

In the modern society it is postulated that absorbing knowledge happens using different sources: lecturers, colleagues, virtual communities, libraries, internet, etc. Also the role of learner is emphasized. In the same time the role of the lecturer transforms into the collection of the following: consultant, expert, facilitator, mentor, etc. But despite of that we as academic staff must support learning processes and, if it possible, we try to employ intelligent systems in this support. Students' support concerns guidance and encouragement of the students both from the instructional material and from the communication channels in all steps of learning processes.

We present particular users', i.e. learners, tasks, typical tasks and ontology-based activities in Table 2.

The interpretation of data is as follows: for example, we would like to support browsing process in order to develop meta-cognition skills. We must realize typical tasks: details-on-demand, relate, history. Details-on-demand requires the following ontology-based activities: getting (picking) class (from name), inferring about properties and upper properties, retrieval of instances.

Analysis shows that tasks of the type absorb knowledge require all mentioned ontology-based activities except for checking equivalence and consistency check. On the other hand, these activities are useful in automating practice processes.

[TABLE 2 OMITTED]

3. Conceptual linking of educational resources in e-learning space

The overall structure of learning process from e-learner perspective can be described as a part of the conceptual model of IMS Learning Design (IMS-LD) (Fig. 3).

Learning process happens in virtual environment, face-to-face activities are also incorporated in blended studies. In "step-by-step" methodology learning circle is quite short, e.g. one academic hour. But if we seek the competences of a higher level, sometimes we cannot project our proposed sequence for studying learning material. Some order or hierarchy is provided to the learner, but it is not compulsory to follow. In this case, it is more useful to support free exploration better, and here the problem of linking resources arises.

We distinguish several important types of linking educational resources according to the technology used.

[FIGURE 3 OMITTED]

Manual linking. All educational resources and links to external resources are compiled by human, lecturer. Typical web server/web client architecture is used. By clicking hyperlink user requests some data. Application from Server side renders it for the user. This model invokes problems of social type: a) developing of learning resources and linking them to each other is time consuming; b) links to external resources must be often revisited in order to guarantee their availability.

Automatic linking. Technically this model of linking is usually implemented by analyzing possible resources or their metadata in a syntactical level. In this case the problem of relevance, quality and trust arises, because all resources found are treated as of the same quality.

Conceptual linking. The main problem of automatic linking is that semantics usually is ignored. Semantics concerns the relation of signs to real world entities they represent. Concept is an abstraction, formed in mind. It corresponds to the real world entities and is designated by signs (e.g. word). For example, in Cognitive Linguistics theory (Hoek 1999) it is stated, that "the meaning of an expression is the concepts that are activated in the speaker or hearer's mind. In this view, meaning is characterized as involving a relationship between words and the mind, not directly between words and the world". Here subjectivism is emphasized, and it means that we are dealing with conceptualization--abstract representation of domain.

Therefore, in order to implement conceptual linking of educational resources, the humans (and the computer, if we want to automate and/or support some tasks) must accept a) common vocabulary of some domain, and b) the meanings of syntactical elements.

Ontology concept has various meanings in different sources. Some of the definitions, used in computer science field, are presented in (Guizzardi 2005). We assume the following definition of ontology: "Ontology is a conceptual specification that describes knowledge about a domain in a manner that is independent of epistemic states and state of affairs" (Guizzardi 2007). This definition emphasizes that ontology are universal models of domains or models of known knowledge in a domain.

Therefore, we adopt a formal definition of ontology from (Guizzardi 2005), derived from logic and set theory. Ontology is a 4-tuple <C, R, I, A>, where C is a set of classes (concepts), R is a set of relations, I is a set of instances, and A is a set of axioms. Classes (other synonymous terms: concepts, categories, types) represent important concepts of the domain. Classes in the ontology are usually organized in taxonomies, where generalization specification mechanisms are applied. Relations (properties, slots, attributes, roles) represent associations between the concepts of a domain. Most often is-a and consist-of relationships are used. However, the taxonomical structure is not the only one possible. Ontology usually contains binary relations. The attributes are sometimes distinguished from relations. Instances (individuals) represent individuals in ontology. Instances can be defined in ontology or in database of factual data. Formal axioms are used for expressing propositions that are always true, e.g. in the eLearning course the same person cannot perform the role of a lecturer and student at the same time. Formal axioms are used to infer new knowledge. If axioms are not included into ontology itself, reasoning mechanisms must be implemented in program part of the system (in the code).

Domain ontology can be used in the 2 main processes of the eLearning life cycle: development and delivery, as shown in Table 3.

4. Scenarios generation using reasoning over domain ontology in adaptable distance learning system

One of the commonly used reasoning definitions is as follows: "Reasoning is computing the implied relations" (Brusse and Pokraev 2007). We restrict ourselves with reasoning using knowledge, represented by the means of ontology. Differently from traditional AI systems, reasoning over ontology tends to be deductive, not inductive. It is a limitation, because deductive reasoning does not allow us to learn something new. However, the purpose of domain ontology is to represent domain and support retrieval of data. Therefore, the use of ontology unconsciously provides better capabilities and ontology driven information system differs qualitatively.

Usually the reasoning must be conducted over 2 elements: the ontology itself (which contains terminology) and the knowledge base, in which instances described (and contains assertions).

Two main reasoning techniques are used in the Semantic Web: a) Query languages; and b) Logic-based formalisms (Walton 2007). Query-based reasoning comes from popularity and wide spreading of Relational Data Base Management Systems (RDBMS). It employs the main idea used in RDBMS: the use of structured query language in order to extract data, which matches a given pattern. The last standard for querying ontology is SPARQL--Simple Protocol and RDF Query Language.

Logic-based reasoning is performed, after ontology has been translated into a description logic representation. The last standard for implementing logic-based reasoning is compliance with DIG (shortened: DL Implementation Group).

The categorization of reasoning over ontology can also be conducted considering formalisms used. The types of reasoning according to (Brusse and Pokraev 2007) are listed as follows:

1. Property level reasoning. Means inferring implied triples (subject, predicate, and object) from the stated ones.

2. Class level reasoning. For example, it concerns checking whether a class B is a subclass of class A. A separate case is classification--constructing a class hierarchy.

3. Individual level reasoning. For example, it oncerns checking if an individual can exist in some model (consistency check). Two distinguishable cases are: 1) Realisation is finding the classes of which an individual is a member; and 2) Instance retrieval is finding all the known instances from the class.

The list of the main reasoning tasks as: class membership, equivalence of classes, consistency, and classification is analysed by (Antoniou 2007).

Summarising the analysed literature, we can state:

--The query-based reasoning is simpler, more efficient and easier to use. Logic-based reasoning provides more possibilities, it is more powerful, but it is harder to implement.

--Since our solution is oriented towards extending present LMS, which already uses current web technologies, including Relational Databases, we choose the query-based reasoning for further use.

We distinguish between lightweight and heavyweight ontologies. Lightweight ontologies include concepts with properties and taxonomies, but do not include axioms. Heavyweight ontologies are richer in expressiveness, but they are harder to manage. Since the lightweight ontologies are less restrictive, they are usually acceptable wider, which is very important for knowledge sharing and reuse. The less expressiveness the language provides, the better reasoning mechanisms are implemented. This is very important in the context of immediate feedback generation and increasing the efficiency of a system in common and simple tasks.

Emphasizing the importance of using formal semantics, we allow humans and systems to reason about the knowledge (Antoniou 2007). Reasoning support is usually provided by system components. The reasoning mechanisms can be realized, for example, based on Description Logic (RACER, FaCT++, Pellet) or rule-based (Jena, Bossam). Reasoning is necessary because the ontology is static in its essence. According to our proposed architecture (Dzemydiene and Tankeleviciene 2006), reasoning processes are planned to realize an intelligent layer, intended for active components that automatically perform functions previously performed by the lecturer.

4.1. Framework for conceptual linking of educational resources

In our proposed knowledge-based architecture of the adaptable distance learning system, we use a framework for conceptual linking of educational resources. This framework supports our foreseen improvement of the architecture of the traditional LMS (Dzemydiene and Tankeleviciene 2008; Tankeleviciene and Dzemydiene 2009), while integrating domain ontology by generating scenarios with the reasoning components in adaptable learning process.

The goal of our framework is automated support in eLearning activities, based on pedagogical background. We follow these instructional requirements:

--Provide learners with the learning material and guidance towards the accomplishment of their goals;

--Stimulate learners for active participation in learning and to take control over learning results;

--Support adaptability, personalization and information retrieval.

Often adaptability is analysed in the context of learning, and is oriented towards knowledge transfer. For example: "The main goal of adaptation in educational systems is to guide the students through the course material in order to improve the effectiveness of the learning process" (Gaudioso and Montero 2005). It means that the constructive view is not taken into account. On the other hand, we can consider adaptability and personalization as "guiding students" rather than forced intervention, but also support in simple and time consuming tasks, and provision of learners with alternative ways of learning.

Educational resources represent knowledge to the learner in the form suitable for learning. Therefore, in the static view, we can distinguish 2 levels (Fig. 4):

--Domain level, which concerns the domain knowledge, is called Domain space.

--Course level, which concerns the practical implementation of e-learning. The course consists of a set of educational resources, including both teaching/learning materials and activities. This level is called Media space.

[FIGURE 4 OMITTED]

Therefore, subject domain ontology can be developed despite the educational resources and services, which may vary at different moments. Obviously, some design/development processes must be performed beside typical processes, such as development of educational resources or definition of distance study course structure. These additional processes are presented in Fig. 5.

[FIGURE 5 OMITTED]

Mapping of ontology concepts and educational resources allow us to perform only elementary operations: to get class from its name, to get resource identifier from class name. Therefore, in step 3 further scenarios for reasoning over ontology must be defined. The scenarios that can be developed depend on pedagogical goal, ontology structure, and technology of reasoning used.

4.2. Workflow representation for providing studying scenarios

The workflow of run-time processes is presented in Fig. 6.

Instructional engineering in general and our framework in particular try to focus on 2 important processes (Paquette 2004): knowledge extraction and knowledge dissemination.

[FIGURE 6 OMITTED]

In order to realize ontology-based conceptual linking, the experimental domain ontology was developed, pedagogical goal was formulated and scenario was designed. E-learning tools were chosen for implementation, in order to implement the ontology and the framework, while working with distance study course "E-learning technologies". The course is implemented using Moodle: an open source e-course management system. Therefore, we already have learning material, which shall be further linked to the concepts from domain ontology. The main general concepts in our domain are: Software_Product, Manufacturer, Purpose, Curriculum_Level. The more specific concepts are: Multimedia_Processing_Tool, LMS, Web_Browser, etc. The example of description of part of taxonomical hierarchy from general concept Software_Product is presented in Fig. 7.

Also we employ a whole-part relationship in order to represent aggregation. The most general concepts are associated with relationships provides, isProvided, isSuitableFor, canBeAchievedWith.

An approach to choosing a tool suggests several steps of filtering, beginning from all possible alternatives. We may begin our analysis with defining a manufacturer, if we use some other its products; we can restrict ourselves only to the free tools or easy to use tools, if our competencies are on a quite low level. The possible scenario is presented in Fig. 8.

[FIGURE 7 OMITTED]

[FIGURE 8 OMITTED]

The scenarios for automated linking of educational resources are described by means of sequence diagrams of UML (Fig. 9) at run-time.

[FIGURE 9 OMITTED]

5. Conclusions

Development of multi-layered architecture for adaptable distance learning system offers some advantages including the use of scenarios which help in personalisation and individualisation of learning processes. Ontology has a great potential in distance learning systems, but the methodologies, describing how to do that, are developed from the instructional design viewpoint. The query-based reasoning over ontology is simpler and more efficient than logicbased reasoning; therefore, it is suitable in hybrid information systems, where current web technologies and ontology engineering are combined. The reasoning component must be designed, which provides API for integration with external parts of the system.

Despite of increased attention of researchers towards instructional ontologies and design of learning scenarios, design and development of domain ontology remain an actual problem, because domain ontology and learning resources, developed based on it, imply realisations of the mentioned learning scenarios. The approach to using domain ontology in the development and delivery of educational resources enables automating these processes, increasing effectiveness, interactivity, adaptivity and users' satisfaction.

List of important tasks, possible and worth to automate using ontology-based reasoning, have been made. It lets to gain more effectiveness from e-learning system.

Conceptual linking of educational resources and displaying different ways of achieving the learning goal provide us with a better trade-off between control and self-responsibility. There is enough to apply simpler reasoning mechanisms over domain ontology in order to support learner in simple tasks.

The proposed framework for the conceptual linking of educational resources is based on reasoning over structural parts of ontology: classes, instances and properties. We have demonstrated the applicability of this framework in a case study, where the field of E-learning tools was chosen as a problem domain, and supporting browsing and searching for the most suitable tools for realising project work were considered as a pedagogical goal. The same framework can be reused in other context for realizing other pedagogical goals.

The social impact of the proposed solution can be identified as supporting knowledge building process. From the managerial point of view on e-learning, it increases interactivity on student-study-material level and decreases the amount of workload of academic staff.

The analysis of the domain, the proposed framework, and practical experiments have allowed us to formulate a set of research problems: there is the need for collaboration of experts in instructional design and ontology engineering fields, because the design of scenarios must be based on sound pedagogic strategies. Compatibility of the traditional Learning management system (e.g. Moodle) with the ontology development tools and reasoning component over ontology is problematic due to interoperability problems between ontology and current web technologies. These problems shall be a subject of our further research.

doi: 10.3846/1392-8619.2009.15.229-244

Received 5 November 2008; accepted 4 May 2009

Reference to this paper should be made as follows: Dzemydiene, D.; Tankeleviciene, L. 2009. Multilayered knowledge-based architecture of the adaptable distance learning system, Technological and Economic Development of Economy 15(2): 229-244.

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Dale Dzemydiene (1), Lina Tankeleviciene (2)

(1) Mykolas Romeris University, Ateities g. 20, LT-08303 Vilnius, Lithuania E-mail: daledz@mruni.lt

(2) Software Engineering Department, Institute of Mathematics and Informatics, Akademijos g. 4, LT-08663 Vilnius, Lithuania, e-mail: linat@splius.lt

Dale DZEMYDIENE. Professor, Doctor, Head of the Department of Informatics and Software Systems of Social Informatics faculty of the Mykolas Romeris University (Lithuania). She holds a diploma with honour of applied mathematics in specialization of software engineering in 1980, Dr. in mathematics-informatics in 1995, Habilitation Doctor procedure in the field of social sciences of management and administration in 2004, and long time works at the Department of Software Engineering (the Institute of Mathematics and Informatics). She has published about 100 research articles, 3 manual books and one monography. An organizer of international conferences in the area of information systems and database development. A head of the Legal informatics section of Lithuanian Computer Society (LIKS), member of European Coordinating Committee for Artificial Intelligence (ECCAI) and member of Lithuanian Operation Research Association. Her research interests include: artificial intelligence methods, knowledge representation and decision support systems, evaluation of sustainable development processes.

Lina TANKELEVICIENE. Doctoral student at the Software Development Department of the Institute of Mathematics and Informatics under the supervision of Prof. D. Dzemydiene; she works as lecturer at the Distance Learning Department of Siauliai University. Her research interests include: knowledge representation methods, ontology description methodologies, distance learning systems.

Table 1. Users and their functions

User type Functions

System administrator Controls the system, users, courses, defines
 users' rights, adapts system to separate
 computer or informs users, how to adapt
 computer to system (what to install, what
 values of parameters to change).

Author Creates new courses, while using previously
 accumulates materials and forming new
 learning content, renews content, provide
 learning scenarios.

Instructor Observes learning process, analyzes students'
 achievements, consults students, direct
 students to the proper direct, evaluates
 tasks. Usually authors become instructors of
 the course.

Student Picks the course according to his/her goals,
 seeks for realisation of defined goals,
 connects to the system using user name and
 password, studies material, evaluates progress,
 completes tasks, collaborates with others, etc.

Table 3. Two main types of processes, where domain ontology can
be used in distance learning system

Entitlement Description

Development of learning Constitutes of 2 different parts:
materials/activities using 1) Content and services discovery and
subject domain and assembly;
instructional ontology 2) Development of their own materials
(optional). and/or activities. Tasks that can be
 realized: verifying completeness,
 timeliness, compatibility, linking of
 learning materials.
Teaching/learning Includes adaptive course delivery,
process. adaptive sequencing, adaptive presen-
 tation, adaptive interaction, and
 adaptive support. For example:
 1) Adapting educational resources for
 students with different levels of
 learning progress;
 2) Different starting point. Concepts
 belong to overlapped groups, which
 are important differently to different
 students;
 3) Concept mapping, development of topics
 map (as evaluated task);
 4) Free exploration of content, based on
 conceptual linking.

Fig. 8. Description of steps of scenario for automated linking
of educational resources

Human part

Learner acquires information about Learner acquires information about
Adobe FlashCS3 some software product

System part

Concrete example Abstract example

1) The class of tools is 1) Realization: finding the most
 found--Animation-Tools specific concept, which
 describes it?

2) Other tools for animation 2) Instance retrieval: finding
 creation are found: all instances, described by
 GifConstructionSetProfessional, the given class.
 CoffeeCupGIF, UleadGifAnimator

3) Other tools from the same 3) Querying over triples
 manufacture (Adobe) are <Manufacturer, Provides,
 found: AdobeFlashPlayer, SoftwareProduct>, where
 AdobeDreamweaverCS3, etc. Manufacture is given.