Research of Ontology-based Adaptive Learning System

Wang Xiao Min Beijing Information Science

& Technology University wxm@bistu.edu.cn

Cui Wei Beijing Information Science

& Technology University cuiw@bistu.edu.cn

Che Lei Beijing Information Science

& Technology University chelei@bistu.edu.cn

Abstract

Adaptive Hypermedia and Semantic Web are the

main implementation technologies of adaptive learning system. The learning system would be able to achieve individual optimized access to learning resource by ontology technology and provide friendly user interface by adaptive hypermedia. Taking the individual learning style and requirements of the users into account, in this paper we propose a framework for adaptive E-Learning system and show how the knowledge ontology model and learner ontology model are formulated, and how the adaptive learning strategies can be utilized to perform meaningful personalization and adaptation

1. Introduction

With the popularity of Internet and the constant increasing of network resource, people increasingly depends on Internet to complete or assist school study and professional learning. The digital learning platform has become one important educational tool. Web2.0 is in the ascendant, and Web 3.0 which based on Semantic Web Technologies seems ready to come out. In this situation, those learning websites and learning management systems with rigidly structure have became outdated obviously. Consequently, various research groups work on how to use latest theories and techniques to improve users' experience of E-learning. Nowadays, the personalized adaptive learning environment which combines technology with education theories is a hot research topic.

In constructivism theory, individual cognitive development is described as a process of self-construction. It stressed that the subject of learning cognitive the world is a dynamic, positive, active

construction process and the individual different cognitive structure leads to the different knowledge construction process. As far as the view of constructivism learning theory, the personalized E-learning system allows students learn by themselves so that it would improve learning effect and overcome the disadvantage of traditional class teaching. Generally, personalized learning environments adopt simple layered teaching mode. Students in the same level access similar learning path and resource, their interactions with system are also similar. Comparatively, adaptive learning has more intelligence. It can analyze students’ cognition style, learning goal, knowledge background, learning history and performance to provide adaptive learning content according with individual cognitive level.

Current researches about adaptive learning mainly focus on two technologies: adaptive hypermedia technology and semantic web technology. Adaptive hypermedia systems can provide adaptive presentation and adaptive navigation for users. Ontology technology of Semantic Web brings a new way to represent domain knowledge concepts on which application can process reasoning rules for some specific adaptation purposes.

Considering present flexible mode of talent training in Chinese university, an expanded knowledge ontology model and learner ontology model are proposed in this paper. A key concept introduced in the model is students’ learning goal which is categorized into 3 types: basic, research, and engineering. The information about which knowledge concepts fit for proper learning goal is stored in the course knowledge model. Then the personalized learning path can be established for individuals’ goal. In addition, students’ learning style, learning activities and evaluations are also record in learners’ model, thus the learning plan is adjusted to adapt for students’

2008 International Symposium on Computational Intelligence and Design

978-0-7695-3311-7/08 $25.00 © 2008 IEEE

DOI 10.1109/ISCID.2008.109

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2008 International Symposium on Computational Intelligence and Design

978-0-7695-3311-7/08 $25.00 © 2008 IEEE

DOI 10.1109/ISCID.2008.109

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personality and knowledge background. The combination of adaptive hypermedia and ontology technology give the support for these functions. The learning path is displayed to the users by adaptive hypermedia. The ontology technology facilitates to access data and business logic. 2. Architecture of adaptive learning system

An Adaptive Hypermedia System should include three subsystems: (1) Domain Model, which mainly includes the relations among the concepts of the application. (2) User Model, which is the description of user’s information. Generally it includes users’ background of knowledge, experience, characteristics, etc. (3) Adaptive Mode, which defines a set of rules by which the system fulfills adaptive contents and links and modifies the User Model. Based on this architecture, an improved architecture of adaptive learning system can be achieved if ontology is applied to represent the model. See Figure. 1.

Figure. 1. Layered architecture of adaptive learning system

The architecture of the system consists of 4 layers.

2.1. Application layer

Learning materials and resources are provided to meet learners’ adaptive needs. The task of the layer is to present the learning objects transferred from the Semantic Reasoning Layer with adaptive hypermedia. 2.2 Semantic reasoning layer

Rules defined in SWRL can describe the business logic. Then rule-based reasoning can be achieved to generate learning paths. Moreover, new learning goal and contents are adjusted according to learner’s learning history and performance during his/her learning process.

2.3 Semantic layer

The semantic models of the learning system including domain knowledge model and learner model are defined by ontology language OWL. The ontology model is completed by the ontology editing tools, which can also check the consistency of the model. 2.4 Meta data layer

It includes the Metadata Standards related to the learning system. Semantic Layer should refer to the standards when the learning resources are annotated and the learning objects are packaged.

As Figure. 1 illustrates, the domain model and user model of the adaptive hypermedia system are described in the Semantic Layer. Both Semantic Reasoning Layer and Application Layer are responsible for adaptive model implementation.

3. Knowledge ontology model and learner ontology model

Learners can choose their own learning paths and learning contents in personalized E-learning environment. To reach the goal, we have to solve the problems as how to represent domain knowledge and how to organize learning resource. Generally, learning materials are annotated by metadata to benefit the different requirements of accessing, querying and exchanging learning object. LOM and Dublin Core metadata standard are chosen to be the annotation metadata. Though the method annotating learning documents with metadata can promote the reusability and interoperability of learning resources, this sort of structural features of documents can not show essential relationship completely, such as interrelationship of learning materials, domain concepts and learning goal. Especially it is hard to reveal the complete knowledge structures in one subject.

The Semantic Web provides the foundations for semantic descriptions of knowledge and learning resources. In The Semantic Web, data is allowed to be shared and reused across application, enterprise, and community boundaries. Because ontologies can represent shared and agreed-upon conceptual models in a domain, Ontology was brought into focus in the Semantic Web. That means ontology techniques can also facilitate to present the knowledge concepts and their relationships in a subject and more a semantic knowledge structure can be constructed. Further,

Domain Knowledge

Ontology

Learner Features

Ontology

Rule

Personalized Learning Path

and Learning Object

LOM SCORM Dublin Core

Application Layer

Semantic Reasoning Layer

Semantic Layer

Metadata Layer

Adaptive

Hypermedia

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Reasoning techniques can work on this model to infer personalized learning decisions.

Using Web Ontology Language (OWL), this paper presents the domain knowledge consisting of several concepts (classes in OWL): Course, Chapter, Section, Learning Resource, Knowledge Concept and Learning Goal. There are various relationships among these concepts. Each relation means a predicate named as object property in OWL, such as “is part of”, “is prerequisite to”, “is fit for”, “explains”, etc. Figure. 2 illustrates classes and their relationship. Each object property of class is drawn in arrows which begin with domain class and end with range class.

subclass ofis specialized for

is fit for

LearningGoal

is explained byis prerequisite to

explainscontains

is part of is part of

CourseChapterSection

Learning Resource

KnowledgeConcept

Lecture Test ReferenceMaterial

Example VideoMaterial

Figure. 2. Domain knowledge ontology model

As pointed before, Learning Goal (class in OWL) has 3 categories (individuals in OWL): basic, research, engineering. Each concept is only fit for one goal. If the concept is fit for “basic”, it also fits for “research” and “engineering”. Some Learning Resource is specialized for a goal. Learning Goal is the key class driving the reasoning rules and algorithm described in the follow part of the paper.

A short piece of database knowledge ontology model is selected as below:

<Concept rdf:ID="primary key"> <is_fit_for rdf:resource="#basic"/> <is_prerequisite_to> <Concept rdf:resource="#entity integrity"/> <Concept rdf:resource="#referential integrity"/> </is_prerequisite_to> </Concept> <Concept rdf:ID="foreign key"> <is_fit_for rdf:resource="#basic"/> <is_prerequisite_to> <Concept rdf:resource="#referential integrity"/> </is_prerequisite_to> </Concept> <Concept rdf:ID="entity integrity"> <is_fit_for rdf:resource="#basic"/> </Concept> <Concept rdf:ID="referential integrity"> <is_fit_for rdf:resource="#basic"/> </Concept>

<Section rdf:ID="integrity constraint"> <contains rdf:resource="#entity integrity"/> <contains rdf:resource="#referential integrity"/> </Section> <Lecture rdf:ID="DB_id331"> <explains> <Concept rdf:resource="#entity integrity"> <is_explained_by rdf:resource="#DB_id331"/> </Concept> </explains> </Lecture> To achieve adaptive goals, learner model should

store learners’ information, including characteristic, learning goal, learning history, mastered concepts and performance grades. A learner’s characteristic can reflect his/her learning preferences and cognitive style. For example, what kinds of materials does the learner prefer, text or video? Which type of learning style does the learner have, intuitive, thinking or practical type? That characteristic information is determined by some personality test before the learners begin his/her course. On the basis of Learner model, reasoning can be run to recommend proper learning goal and learning resources. Fig. 3 shows the model.

Figure. 3. Learner ontology model

4. Personalized adaptive reasoning

Once the ontology models are constructed, learning environment can provide various learning strategies for learners during their learning process.

4.1 Individual learning path Generation

When learners logon at the first time, they can set up a learning goal by themselves. A learning path initialization should be executed as the Fig. 4 shows.

The first learning page displayed for user is a knowledge tree consisting of chapters, sections and concepts. Each concept in the tree has a link which is valid if all prerequisite concepts were mastered by user, or it hides.

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The first timea learner login

Choosing a goal

Get all prerequisiteconcepts by reasoning

Finish characteristic test

Learner select a individuallearning path?

Learner selectlearning content

Run the tests

Learner ontologymodel

Knowledgeontology model

Generate defaultlearning path

NY

Generate individuallearning path

Want to have tests onprerequisite concepts? NY

End

Displaylearning path

Figure. 4. Learning path initialization

4.2 Semantic navigation

The process of knowledge acquisition is not a stiff linear process. Following the people's cognitive laws, learning environment should offer context-sensitive semantic navigation to users. The useful navigation includes links of related concept and learning materials which can be queried by using the SPARQL query language. Furthermore, by means of rule-based reasoning, the system can usually provide the links of the best learning resources fitting for users. Then the problems of information overload and getting lost in network learning will be solved.

Some rules for the best resources recommendation are given bellow.

is_explained_by(?A,?B) /\ is_specilized_for(?B,?C) /\ is_fit_for(?A,?C) -> is_the_best_of(?B,?A)

When Concept A is explained by Resource B, and B is specialized for Goal C, and A is also fit for C, then B is one of the best resources of A.

has(?A, “vidual_prefer”) /\ choosed(?A, ?B) /\ is_fit_for(?C, ?B) /\ explains(?D, ?C) /\ Video(?D) -> is_best_of(?D,?C)

A learner who has the “video_prefered” characteristic can get the best learning materials of each concept contained in learners’ learning goal.

More complex rules may be discovered when the requirement is put forward in more detail. For example, the best resources should be recommended by taking account of their learning history and performance grade. Thus users would have the experience of personalized service. 4.3 Learning path adaptation

Adaptive learning path is used by two ways. One is the adaptive link of the learning tree. With the more knowledge is mastered by learners, the link hidden before will be opened. Then learners have more selections of reviewing old concepts or beginning new contents. Another is the adaptive path. Firstly, learners’ performance grade must be evaluated by testing system, and then the minimum performance grade of each learning goal has to be determined by rules. Finally, an adaptive learning goal can be inferred from learner model and rules. Only if the learner unable to meet the standard, a suggestion will be given for user (e.g. suggest user changing the goal from research to engineering). Consequently, a new learning path would be generated following the new goal.

As a result it is necessary to build an online testing system. The testing system should have 3 modules: (1) Test item bank, which provides a set of examination and/or practice questions in the subject. As a kind of learning resources, each test item relates with a concept and is specialized for a learning goal. (2) Test paper generation System, which is responsible for setting up a quiz. It can accept a testing request containing appointed concept and learning goal from a user. The test paper will be produced according the request. When the goal is “research” or “engineering”, both basic test items and corresponding test items will be chosen in different ratio. (3) Testing System, which is used by students to take the quiz and score the quiz. Once the user finishes the quiz, a score will be calculated and returned. Then the user’s performance grade will be summarized in one or two scores (users with “basic” goal only have one score of basic, users with “research” goal have a score of basic test and a score of research, so as “engineering”).

A simple rule is written as below. Once all the pre-conditions are satisfied, new learning goal would be recommended for a learner.

Selected(?A,”research”) /\ is_evaluated_by(?A, ?B) /\ hasBasicGrade(?B, ?C) /\ swrlb:lessThan(?C, 70) /\ hasResearchGrade(?B, ?D) /\ swrlb:lessThan(?D, 60) /\ has(?A, “practical”) -> recommend_goal(?A, “basic”)

Some of adaptive hypermedia techniques such as link annotation and link hiding are used in A, B and C. Furthermore, color annotation could be used to show how much degree a learner has mastered the domain knowledge. That can help user to achieve self-control and self-adjustment. 5. Conclusion

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In this paper, we have proposed an approach for using ontology and reasoning to set up an adaptive learning system. The main feature of the approach is it can generate individual learning path according to different learning goal and give learning goal suggestion based on learners’ profile through rule-based reasoning.

A simple prototype system which only has one fixed subject was carried out with Protégé3.2 and Jena2.5.2 by the research team. The homepage has three frames. The left frame displays a hierarchical structure which consists of chapters, sections and knowledge points. The right-up frame shows recommended resource ordered by learners’ fond. The right-down frame lists all concepts link to the selected knowledge points. At the beginning, learner can only access a few nodes in the hierarchy because of most nodes have one or more prerequisites. Each time when the learner finished a knowledge point, the hierarchy will be refreshed to make sure proper hyperlink will be generated. The prototype has worked as exportations to prove the feasibility of the approach. More reasoning rules have to be discovered in the future research. 6. References [1] Xing Liren, Zhao Wei, Li Xiaohong. “Analysis on Present Situation and Prospect of Web-based Adaptive Educational Hypermedia System”, China Educational Technology, Beijing, 2007.3, 100-103. [2] http://www.elena-project.org/ [3] http://www.prolearn-project.org/ [4] Henze, N., Dolog, P., & Nejdl, W. “Reasoning and Ontologies for Personalized E-Learning in the Semantic Web”, Educational Technology & Society, International Forum of Educational Technology & Society, 2004, 7 (4). 82-97.

[5] Thanh Tran, Philipp Cimiano, Anupriya Ankolekar. “A Rule-Based Adaption Model for Ontology-Based Personalization”. Advances in Semantic Media Adaptation and Personalization, Springer, Berlin, 2008, pp. 117-135. [6] Zeng Ling. Study on System Model Design and Key Techniques Application of Ontology Based Adaptive Learning System(OBALS). Doctorate Dissertations. Hua Nan Normal University, GuangZhou, 2007 [7] Cui Guangzuo, Li Shufang. “A Study of Intelligent Education Architecture”.GCCCE2006, Tsinghua University Press, Beijing, 2006. [8] Zhang Jianxing. “The Summary of piaget's Educational Psychology Theory”. Forum on Contemporary Education, Changsha, 2007(7), 142-144. [9] IEEE Learning Technology Standards Committee. IEEE standard for learning object metadata (IEEE 1484.12.1{2002). http://ltsc.ieee.org/ [10] W3C, http://www.w3.org/RDF [11] W3C, http://www.w3.org/2004/OWL [12] Lora Aroyo, Darina Dicheva. “The New Challenges for E-learning: The Educational Semantic Web”, Educational Technology & Society, 2004, 7 (4), 59-69. [13] Dolog, P., Nejdl, W. “Challenges and Benefits of the Semantic Web for User Modelling”. AH2003 Workshop, User Modelling Conference. [14] Peter Dolog. “Knowledge Representation and Reasoning in Personalized Web-Based e-Learning Applications”. Znalosti 2006, Hradec Kralove, 2006.2, Keynote speech. [15] De Bra, P.& Calvi, L.. “AHA! An open Adaptive Hypermedia Architecture”, The New Review of Hypermedia and Multimedia, Taylor & Francis, 1998(14), 115-139.

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