A computational model for developing semantic web-based educational systems

Knowledge-Based Systems 22 (2009) 302–315

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Knowledge-Based Systems journal homepage: www.elsevier.com/locate/knosys

A computational model for developing semantic web-based educational systems Ig Ibert Bittencourt *, Evandro Costa 1, Marlos Silva 1, Elvys Soares 1 Federal University of Campina Grande, Rua Aprigio Veloso, 882 Bodocongo, CEP: 58.109-900, CEP: 58.109-970, Campina Grande-PB, Brazil Computing Institute – Federal University of Alagoas (UFAL), Av. Lourival Melo Mota, s/n - Tabuleiro do Martins Maceió, CEP: 57.072-970, Alagoas, Brazil

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Article history: Available online 6 March 2009 Keywords: Semantic web based educational systems Agent-based software engineering Artificial intelligence in education

a b s t r a c t Recently, some initiatives to start the so-called semantic web-based educational systems (SWBES) have emerged in the field of artificial intelligence in education (AIED). The main idea is to incorporate semantic web resources to the design of AIED systems aiming to update their architectures to provide more adaptability, robustness and richer learning environments. However, the construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This paper presents a computational model for developing SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Current artificial intelligence in education (AIED) systems have tried to incorporate semantic web resources to their design and architecture. The main idea behind this purpose is the attempt to represent information on the Web so that computers can understand and manipulate it, leading to more adaptable, personalized and intelligent learning environments. In fact, there is a significant interest within the AIED community on the evolution of e-learning systems in this direction. The new generation of AIED systems comes from the combination of two broad modalities of web-based educational systems: (i) e-learning systems (or learning management systems – LMS), which provide interaction between students and teachers through the use of information technology (by using synchronous and asynchronous tools) to ensure this communication, and (ii) AIED systems, which use artificial intelligence techniques to provide personalized interactions, aiming to improve the learning and problem solving processes. The result of such combination are the so-called semantic web-based educational systems (SWBES). The construction of SWBES, however, is a rather complex task which faces challenges in terms of software engineering and artificial intelligence aspects, such as: extensibility, interoperability, contextualization and consistence of metadata, dynamic sequence of learning and contents, integration and reuse of content and artificial intelligence techniques, distribution of services and new models of learning [15]. Such issues have been influenced by the * Corresponding author. Tel.: +55 82 3214 1401; fax: +55 82 3214 1700. E-mail addresses: [email protected], [email protected] (I.I. Bittencourt). 1 Tel.: +55 83 3310 1124, fax: +55 83 3310 1273. 0950-7051/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.knosys.2009.02.012

aim of representing information on the Web in a way computers can understand and manipulate it. Therefore, SWBES are assumed as the new generation of Web-based educational systems that uses semantic web technologies to generate more personalized, adaptable and intelligent educational systems [13]. This paper presents a computational model for the development of SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. Before, a reference model is introduced in order to be used in the definition of the computational model. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available. The remainder of this paper is structured as follows. An overview about the evolution of intelligent educational systems and some problems for building them are presented in Section 2. Section 3 presents a reference model adopted as the next generation of educational systems, here assumed as the semantic web-based educational systems. Section 4 presents a computational model for developing SWBES. A case study to illustrate the computational model is presented in Section 5. Section 6 presents a discussion about the results regarding the computational model construction. Related Work on SWBES is detailed in Section 7. Conclusions and future work are presented in the final section. 2. Overview of educational systems and research problems This section provides a review of the computer-based educational systems, aiming to present sufficient background and open issues to understand the proposed model. It starts with a discussion about classical approaches to these systems before embarking

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on Adaptive e-learning environments and problems for building them. 2.1. Intelligent educational systems The use of AI techniques in the development of computer-based educational systems emerged as an important step towards providing individualized interaction with particular students. Generally, AI in education is concerned on providing systems with capabilities that include issues related to topics in AI such as knowledge representation and reasoning, planning, diagnosis, learning, uncertainty in knowledge and dialogue processing [24]. Thus, Intelligent Tutoring Systems (ITS) as stated in [28], may be seen as a first good characterization of such systems. Traditionally, the major components that comprise a typical ITS are: (i) expert or domain module, (ii) learner modeling module, (iii) pedagogical module and (iv) communication module. The expert module basically contains a domain knowledge base and some mechanisms to reason about this knowledge. Generally, this module is responsible for problem solving tasks, using some resources from AI, like logic, production rules, semantic network, frames and bayesian networks. Learner modeling module is composed of mechanisms that acquire and represent the learner’s knowledge about a specific subject domain. Pedagogical module is responsible for selecting resources from a domain as well as deciding about the pedagogical action to be accomplished during the interaction process with the learner. Finally, the communication module is responsible for directly managing the interactions with the learners. As a matter of fact, this kind of systems is also known by the community as artificial intelligence in education (AIED) systems, which use artificial intelligence techniques to assure personalized interactions in order to improve the learning and problem solving processes. Moreover, additional modern approaches have been characterized by a different educational perspective mainly adopting the notion of collaboration, sometimes conducting to the socalled interactive learning environment that provides the learner with a flexible interaction style and a more active role. As a result, such features make the system complex, been necessary a multiagent based approach [18]. In the last ten years, the design of web-based e-learning systems started to move to a direction that meets with the main concerns and results of the artificial intelligence in education (AIED) field, designing the so-called adaptive web-based educational systems (AWBES) [15]. Indeed, the research related with web-based educational systems has been playing an important role in the services’ quality improvement, such as the quality of educational contents, pedagogical approaches in e-learning environments and technological frameworks [27]. Currently, web-based systems are more complex and face several challenges in terms of software engineering and artificial intelligence aspects. A discussion about adaptive web-based educational systems and some problems are provided in the next subsections. 2.2. Adaptive web-based educational system An adaptive web-based educational system (AWBES) is a system that changes its configuration in order to improve the students learning. In other words, its goal is to provide adaptive interactions to the learners aiming to improve the quality of services [27]. The adaptation types are described as follows [34]:  Instructional model adaptation: this adaptation form allows the student to have different content, activities and services according to the specifications made by the course author. At project

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time, the author may only specify which attributes a user may have, aiming to receive certain content, activity or access to a service. At execution time, the student model has to be checked for specified conditions agreements in order to decide which content, activities and services will be provided to the student. The referred content, activities and services shall be properly modeled to apply this adaptation at execution time;  Adaptive interactions: this adaptation form provides support to the students whilst they interact in a certain course. This support is addressed to the student and the tutor, possessing several services, contents and activities to work in the interaction. Besides, user support is given by considering the information stored in different models, especially the user, group and service models;  Presentation adaptation: this adaptation form presents a different user interface for each student according to his or her model. This adaptation does not refer only to what the user has customized, but also to what the system has learned from previous interactions of that and other users. This is one of the most efficient ways of building the presentation for certain user learning.

2.3. Problems for building AWBES During the educational systems evolution, several problems have appeared. The partition of problems is an interesting way to make the resolution of each problem easier. Some relevant aspects that researchers [22,21,15] have approached for building and maintaining intelligent tutoring systems are  High development costs: researchers are forced to design their own system architecture, implementing all the system components, developing knowledge representation strategies and reasoning mechanisms, and acquiring and encoding all relevant domain and instructional knowledge. In addition, several aspects have to be considered regarding the pedagogical strategies and student information;  Complexity to develop artificial intelligence algorithms: the complexity related to artificial intelligence techniques makes it difficult to develop;  Integration of artificial intelligence techniques: the development and maintenance of hybrid artificial intelligence systems is hard;  Interactive tools: provide resources in order to enable students to interact between themselves. Consequently, students can learn more;  Scalability: educational systems have to be scalable in order to allow the simultaneous access of a high number of users;  Difficulty of sharing materials: the current intelligent tutoring systems use knowledge representation formalism, architecture, internal data, and idiosyncratic flow controls. That is, they have their own structure that makes it difficult to share and integrate material in a general way;  Consistence of metadata: domain ontologies have to be constructed and maintained. In a realistic setting which is a collaborative authoring, this may be difficult: several ontologies may exist and these ontologies are in constant evolution and are gradually refined in collaboration with the domain experts and through experiments with learners;  Extensibility: it considers two types of problems: (i) extensibility of the system in order to add new functionalities, and (ii) extensibility of standards to use more expressive languages;  Interoperability: the integration of e-learning systems and AIEd systems makes necessary the interoperability of data and services;

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Fig. 1. Reference model of semantic web-based educational systems.

 Distribution of services: the combination of e-learning systems and AIEd systems requires a distributed architecture;  High maintenance costs: Adaptive e-learning systems are built as complex, integrated AI applications, and several modules (interactive tools, agents and ontologies). Consequently, several parts of the system require some changes. Indeed, it is very clear that the specification, development, maintenance and evolution of such systems are very complex and time-consuming. For this reason, one of the best ways to manage the complexity of these systems is the decomposition of its problems. In addition, such challenges are related with the attempt to represent information on the web in a way computers can understand and manipulate it. The research in this field is known as semantic web research. As a result, the next section describes a reference model for semantic web-based educational systems.

3. Reference model The semantic web (SW) extends the classical web in the sense that it allows a semantic structure of web pages, giving support to humans as well as artificial agents to understand the content inside the web applications. As a result, Semantic Web provides an environment that allows software agents to navigate through web documents and execute sophisticated tasks. SW itself offers numerous improvements in the context of Web-based educational systems contributing to the upgrade of learning quality. According to Anderson [1], the educational semantic web is based on three fundamental affordances. The first is the capacity for effective information storage and retrieval. The second is the capacity for non-human autonomous agents to augment the learning and information retrieval of human beings. The third affordance is the capacity of the Internet to support, extend and expand communications capabilities of humans in multiple formats across the bounds of time and space. In general, a broad question concerning SWBES is the one involving the interaction among, at minimum, two players, which are a machine/educational system (responsible for providing infor-

mation according to a learning context or learning domain) and a user (with a specific role) [6]. Fig. 1 describes a reference model of a semantic web-based educational system [13]. Each component concerning semantic web-based educational systems is discussed as follows:  Role: several educational activities are enclosed on semantic web-based educational systems, such as teaching, learning, cooperation, collaboration and authoring. Moreover, these activities are distributed according to the roles of each player. Some of them are discussed as follows: – Teacher’s role: teachers are required to monitor learners’ interactions (problem solve, assessment, etc), configure learners’ strategies; – Learner’s role: the main interest of learners is to interact with the system in order to improve their knowledge and fulfill their learning goals. This interaction occurs based on personalized and adaptable educational content; – Author’s role: authors are responsible for structuring the educational content. Also, the authoring activities can be divided into (i) educational content, (ii) instructional process and (iii) adaptation and personalization [4]; – Group’s role: several applications take into account the learning process through groups. They are interested in collaborative learning, interaction with others students in order to reach personal goals, sharing cognitive, meta-cognitive, motivational and emotional functions with others learners; – Developers’s role: they are responsible for developing and adding new functionalities to the semantic web-based educational systems. Moreover, several efforts have been done to build ontology in order to specify the methodologies as scripts to deploy the applications.  Interface environment: a context defines the learning domain on which drives the interaction between the SWBES and the users;  Educational resources: educational resources represent the learning objects concerning a specific educational system, such as examples, problems, counter-examples and units of activities;

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Fig. 2. Multilayers architecture.

 SWBES: it supports the numerous users in an effective way to guide and help them reaching their goals. Thus, it represents the new generation of web-based educational systems that aim at providing several improvements on the quality of services (QoS) through the use of semantic web technology: – Ontologies: they aim to more carefully define parts of the data world and to allow interaction between data held in different formats [26]. Additionally, ontologies have been addressed by the community as an important requirement to assure the interoperability between educational systems (AIED systems, pervasive learning systems, e-learning systems, adaptive educational hypermedia systems); – Tutoring agents: these agents help the learning process in several ways. For instance, it evaluates similarities between profiles in order to recommend educational content; – Tools: several tools can be used in a web-based educational system. Some of them are collaborative simulation, intelligent, and authoring tools; – Services: semantic web services (SWS) provide a number of different educational activities transforming a static collection of information into a distributed way on the basis of semantic web technology making content within the WWW machine-processable and machine-interpretable. Some examples of SWSs are personalization of educational content and interfaces, assessment, collaboration and recommendation.  Semantic web environment: it represents the interaction environment available to SWBES and users to discover, browse, select and invoke resources on the web according to several technologies and architectures semantically described.

4. Computational model This section describes a computational model, called e-Mathema, for building semantic web-based educational systems based on the proposed reference model. Some applications have been created by using e-Mathema, such as [9,7,10,5]. This model has three layers which are presented in Fig. 2 followed by the description of each layer. The architecture was developed as a multi-layer architecture whose layers are described below [14]:  Framework: it is maintained by developers who can add new functionalities. The inputs of this layer are three ontologies: (1) Mathema ontology: it represents the educational specification, defining the pedagogical, student, and domain models; (2) Inference ontology: it represents the ontology used by knowledge engineers to configure inference mechanisms and

(3) Interaction Ontology: this ontology is responsible for the interaction between the agents. The output of this layer is an instance of the framework;  Application: this layer represents the user application and is used to: (i) define the requirements of the desired system, where these requirements regard fundamental information for personalized tutoring systems and (ii) final users as students, teachers, and others;  Authoring: this layer is responsible for providing authors with a user-friendly interface which is used in the ontologies specification. The inputs of this layer are the requirements of the desired educational application and the output represents the ontologies populated with individuals according to these requirements.  Distribution connector: this architectural element intermediates the communication between clients and servers by implementing distribution and load balance among the available servers.

4.1. Ontologies The ontologies which are used by the three architectural layers are described as follows: 4.1.1. Domain model ontology The characteristics of the domain knowledge are provided through a multi-dimensional view of this knowledge. The domain model ontology is shown in Figs. 3 and 4. Some classes of the ontology are  Domain: this class has details about domains of teaching;  LO:LearningResource: it represents the reuse of learning objects into the environments. In order to ensure the use of learning objects, the IEEE LOM RDF has been used as standard. These learning objects can be, for example, an evaluation, a counterexample or an example;  LOM:LearningObject: it represents the RDF Class imported from IEEE LOM RDF;  IMS:QTI: this class has information about the standard IMS Questions and Test Interoperability;  LO:Problem: it defines information about problems through the reuse of the standard IMS–QTI;  CD:Curriculum: Mathema Model makes the mapping of a partition of the domain into a curriculum structure. For this reason, a class to provide such information is defined. 4.1.2. Student model ontology The Student Model has the knowledge about who will be taught, that is, this model contains information about the student

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Fig. 3. Ontological modeling of the domain.

being taught. The construction of the model was developed through the evaluation of [16,17], capturing some elements of static and dynamic information about the student. These elements are explained below: The types of information necessary to this model are

such as the time average for answering question or how many attempts to solve a question. Although all the classes have been used to model the cognitive abilities of the student, the main information used is the ProblemSolving class.

 Static information: the student information that does not change during the student–system interaction. It’s important to note that this ontology is integrated to IMS LIP Standard;  Dynamic information: the student information that change during the student–system interaction. Usually, this information is associated with the domain information. Fig. 5 shows the interaction features between the student and the system. All the information about system and student actions are recorded into the SystemStrategyRecord and LearnerActivityRecord. In addition, the information from LearnerActivityRecord class is based on a curriculum structure (defined in domain model ontology). Moreover, the AggregateDomainData has aggregate information

4.1.3. Pedagogical model ontology It contains the knowledge about how to teach, serving as a resource for conducting the interaction. Usually, this interaction occurs through an instructional plan that takes into account cognitive aspects of the students. The pedagogical model construction was based on the works [19,20], such as pedagogical strategies. Moreover, the instructional plan makes use of pedagogical strategies that corresponds to the way a student or a group of students are taught. To define the instructional plan, a domain ontology composed by pedagogical strategies and tactics is necessary.

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Fig. 4. Ontological modeling of the domain curriculum.

4.1.4. Interaction ontology This ontology aims at describing multi-agent systems, which is based on GAIA methodology [36] through an ontology abstraction. From this perspective, the system designer is only expected to have its knowledge focused on the domain. As a result, the specification of the domain provides the automatically generation of the multiagent system, as described in Fig. 6. 4.2. Framework layer A semantic web-based framework was developed to facilitate the development of multi-agent educational systems. It has three goals, as follows. First, assure the low time cost for building such systems, with a minimal amount of code modification. Second, provide an adaptive application according to the user needs. Third, evolve the autonomous tutoring agents knowledge and inference capabilities [11]. The tools used in the development of the framework were Tomcat, Jade, Protégé, OWLSEditor, Mindswap, and OWL–DL. Fig. 7 shows the proposed framework architecture. Details about the entities presented in the architecture are described as follows:  Persistence services: they represent the implementation of persistence mechanisms by using Jena and hibernate;  Agents: the agents assure the adaptive way of the learning process. They are composed by controller agent, mediator agent and an agent society: – MA (mediator agent): this agent has two functionalities, which are: (i) recommend agents according to the requester

agent necessities; (iii) coordinate the complex problem solving process; – CA (controller agent): it has three fundamental skills, which are (i) start agents, (ii) add, remove and update society agents, and (iii) add, remove and update the agents’ services; – Agent society: it represents a heterogeneous agent society composed by (i) support agents: they have information to infer in accordance with a preconceived mechanism and (ii) autonomous tutoring agents (ATA): these agents have information about educational aspects (cognitive, motivational and affective).  Semantic web services: they have the functionalities in order to ensure the automatic discovery, compositions and invocation of the services by the agents.

4.2.1. Semantic web services Semantic web services (SWS) have the functionalities in order to ensure the automatic discovery, composition and invocation of the services by the agents. To this paper, SWS have been used to personalize educational content, assessment, problem solving, and the use of artificial intelligence techniques (case-based reasoning and rule-based reasoning). Furthermore, the Service Manager ensures the automatic discovery, compositions and invocation of the services by the agents. It uses a matchmaking algorithm [35] to allow this process.

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Fig. 5. Ontological modeling of the curriculum of the domain.

Fig. 6. Mapping between GAIA methodology to ontology and to Jade code.

4.2.2. Agents This subsection discusses each agent of the architecture. They are composed by controller agent, mediator agent, persistence agent, and an agent society, as shown in Fig. 8. The extensibility of Jade occurs by the Agent Class where ForBILEAgent extend it. ForBILEAgent is an abstract class that implements some default functionalities, like the register of services, sensors and actuators. The sensor is responsible for perceiving the environment and the actuator is responsible for acting in the environment. Aiming to discuss functionalities of the agents presented in the framework, the next subsections specify each agent.

4.2.2.1. Controller agent. This agent is responsible for ensuring the organization and execution of agents. In addition, all the information about the agents and its functionalities are acquired through the manipulation of the AgentInteraction Ontology. It has four fundamental skills, which are: (i) start agents: to build all the agents when the system is started, each agent requires the triple hAgent,Service,Abilityi; (ii) add, remove, and update agents of the society: to kill or start an agent in the system, the agent has to be validated by the evaluation of the triple in the interaction ontology; (iii) add, remove, and update the pair hService,Abilityi of the agents: each agent can change their services and abilities dynamically, in this sense, the controller agent provides the functional-

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can define the services provided by each one, defining the triple hAgent,Ability,Servicei. 4.2.2.2. Mediator agent. The complexity involved with the interaction management of multi-agent systems motivated the use of a mediator agent (MA) to ensure and organize as best as possible the interaction process. This agent has three functionalities, which are: (1) recommend agents according to the necessities of the solicitor agent. The recommendation occurs when autonomous tutoring agents (ATA) needs to interact between them. The recommendation is possible due to the communication protocol and the DomainModel Ontology. Moreover, the ATA ask the mediator agent to identify which agent has the ability to execute a specific task and (2) coordinate the complex problem solving process. In other words, identifying which agent can cooperate in the problem solving process through its capabilities. Each ATA has a set of problems presented in a pedagogical unit (see DomainModel Ontology). When an ATA ATA1 do not know how to solve a specific problem, then, it sends a message to the MA, informing the requirements to solve the problem. Due to that, the problem is partitioned into several tasks. After that, the MA verifies in the ontology each agent ability to allocate each task according to the triple hAgent,Ability,Servicei. In other words, the MA is responsible for the coordination of the problem solving process, while each ATA cooperates to solve a specific task Ti. The success of the problem solving process occurs because of the capacity of all the agents to solve their tasks.

Fig. 7. Agent-based architecture of the framework layer.

ities to the agents; (iv) autonomous tutoring agents (ATA) management: The dynamic management of autonomous tutoring agents occurs in three sequential phases: (1) in the moment the system is initiated, the mediator agent is started and it verifies whether the ontology is populated with the autonomous tutoring agents features. If not, it queries to the persistence agent for the set of domain curricula; (2) with the curriculum list, the mediator agent populates the ontology, mapping each curriculum in abilities and (3) the third phase is responsible for managing autonomous tutoring agents through the pair hAgent,Abilityi. In addition, the agents

4.2.2.3. Agent society. The complex problem solving process is a feature from semantic web-based educational environments, being necessary for educational agents and intelligent agents use. For this reason, an heterogeneous agent society was built to achieve this process. These agents are described as follows: (1) Autonomous tutoring agents: they were modeled based on the Mathema Model, through the development of a top ontology. The relevant features of the tutoring modelling are the same specified in the methodology used by the author, and (2) Support agents: they have additional functionalities that could be used by the educational system when it is necessary (e.g. mining agents). In order to use support agents, two aspects must be considered: (i) to define/construct which functionality performed and (ii) to associate this functionality with the agent through the AgentInteraction Ontology.

Fig. 8. Class diagram and package of the Kernel.

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Examples of this kind of agent are the support to Case-based reasoning and rule-based reasoning. 4.3. Application layer This layer was developed to facilitate the automatic deployment of the framework instances. The goals of this layer are two. First, to assure the automatic construction of web-based applications (based on the framework layer). Second, to provide an adaptive hypermedia application according to the necessities of the user. The tools used in the development of the framework were Portlets, Liferay, Jade, Protégé, and OWL–DL. Fig. 9 shows the application architecture. Details about the entities present in this layer are described as follows:  Portal: it represents the interface environment to interact with the users according to a specific role: – SAKAI: it is a collaborative and learning environment for distance learning [25]; – SIA (society of interface agents): these agents are responsible for perceiving the information of the users in the application. In addition, each agent is defined for each portlet; – AA (adaptation agent): this agent is represented by a support agent (framework layer) and it uses the semantic web services to infer the users’ interactions. 4.4. Authoring layer This layer is responsible for providing an authoring system based on a methodology for building such educational systems. Moreover, this layer has interfaces with a good usability to be used by authors in order to configure the set of ontologies present in the computational model [8,12]. The tools used in the development of the framework were Portlets, Liferay, Jade, Protégé, Jena and OWL– DL. Fig. 12 shows the authoring architecture. Basically, both application and framework configuration entities described in Fig. 10 are configured through the specification of ontologies.

Fig. 9. Agent-based architecture of the application layer.

Fig. 10. Agent-based architecture of the authoring layer.

This methodology provides the author with facilities to systematically establish the objectives and the viewpoints associated to a set of activities in order to define the agents and their possible relationships. Thus, authoring model should present an easy way to specify these models. To this point of the development, the methodology proposes four main steps as follows: 1. Domain model definition:  specify the domain and partitions of the domain;  define the curriculum structure and pedagogical activities;  build and maintain learning objects to be used;  define problems to be solved by the students;  specify behavioral knowledge of the problems. 2. Student model definition:  specify the learning goals of the students for each curriculum within a partition domain;  define information to be used in the student–system interaction. 3. Pedagogical model definition:  define strategies and tactics to be used;  specify the student’s evaluation;  define the instructional plan. 4. Interaction model definition:  specify the roles and services;  define the agents.

5. Case study The aim of this section is to illustrate the features of the proposed computational model through the development of an intelligent tutoring system. The system is applied at legal domain, providing Law students with real cases, rules and different viewpoints of a given body of knowledge. The main idea is to engage Law students into interactions with the system based on the resolution of Legal problems and their consequences on other tutorial activities, concerning the penal Law. The main features of the system are described as follows:  The learning process happens according to three different types of knowledge: – Normative knowledge: it is the most important element of the legal systems. A norm can be defined as a statement to the effect that something ought to, ought not to, may or can be done; – Doctrine: it represents a theoretical foundation with regards to concepts and legal themes; – Jurisprudence: it represents the legal cases that were previously judged.  The interaction process occurs through the student and the system;  The pedagogical approach is problem-based learning [37];  The system can evaluate the student answer and provide a solution to the student;  The student can require to the system a solution of a specific problem. This way, the interaction occurs in two ways: (i) when the system sends subject content and a problem to be answered by the

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Fig. 11. Adaptation of the problem structure.

student and (ii) when the student sends a problem to be solved by the system. As a result, two kind of solution must be provided by the stakeholders: (1) to specify the legal problem: it is a specific problem that must be added into the system through the framework extension. (2) to configure the ontologies: it represents the general configurations of the system. The next subsections define the view of the author and the developer in the system, showing how easy and useful they are for them.

extension (addition of the Class LegalProblem, Legal, Jurisprudence, and NormativeKnowledge). After that, the developer has to extract this knowledge to the framework. This extraction process is able through the ontology-object mapping, provided by Protégé-OWL API. The mapping is made as shown in Fig. 11. Finally, the core of the execution must be defined by the integration with the inference mechanisms (rule-based reasoning and casebased reasoning). The characteristics that were taken into account are described as follows:

5.1. Developer view

 Personages;  Personages age: it is an important attribute when the personages involved are minor (age < 14);  Personages deficiencies: specify if some personage has any kind of deficiency that can be considered, for example, the victim could not defend herself;  Personages conditions: define if some personage is, for example, drunk, drugged or other condition;  Fact (success attempt): define if the crime was materialized;  Used weapon: the weapon is a fundamental part, because it characterizes how serious was the crime;  Motive: specify if the crime was a revenge, ordered, and others.

The developer uses the framework in order to extend it by adding a new requirement. This requirement takes into consideration the specification of a legal problem. An important aspect of Legal domain is the problem specification, because it takes into account learning resources, like doctrine, Jurisprudence or Legislation. Thus, a problem then is defined by a 3-Tuple hP, I, Fi, where  P: it represents a real penal situation;  I: it represents an interpretation set of the problem P. The interpretations are based on the Lawyer and Prosecutor views;  F: it represents a theoretical recital of the relation P I, and it can be a doctrine, Jurisprudence or Legislation. As a result, case-based and rule-based reasoning are used as problem solving mechanisms. These mechanisms are motivated by the ‘‘legal structure” which is based on the legislation and jurisprudence. It is important to say that the computational model provides these mechanisms. For this reason, the developer must only extend the framework to provide support to these kinds of problems. The first step of the developer is to extend the ontology in order to specify the structure of such problems. So, Fig. 12 shows the

Some rules inserted in the knowledge base that are executed by the inference mechanism are defined as follows: 1. Rule IF AccusedCondition = ’Puerperal’ and Victim = ’son’ and VictimAge <=0 THEN Article = 123 2. Rule IF VictimAge >= 18 and CrimeReason in [’recompense’, ’futile’,’nasty’,’betrayal’,’ambuscade’] THEN Article = 121 and Paragraph = 4

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Fig. 12. Ontology-object mapping.

The case-based reasoning characteristics are defined in accordance with the attribute-value representation formalism in which the attributes are equivalent to those defined in the rule-based reasoning. The main difference is that the attributes are defined as problem attributes and diagnosis attributes. Diagnosis attributes are equivalent to: view (Lawyer or Prosecutor), article (121, 122 . . .), paragraph, item and sub-item.

Table 1 Data of legal domain – Brazilian penal code. Class DomainModel LearningResource(Content) PartitionDomain Curriculum1 Pedagogical Unit1 Problem1 Problem1 Problem1 ... Curriculum11 Pedagogical Unit1 ... Curriculum12 Pedagogical Unit1 ... Curriculum2 Pedagogical Unit1 ... Curriculum3 Pedagogical Unit1 ... Curriculum8 Pedagogical Unit1 ...

Content Law Introduction Criminal Law Article 121 General Easy Normal Hard ... Simple Homicide Emotion ... Qualified Homicide Poisoning ... Article 122 Depth Corporal Lesion ... Article 123 During the parturition ... Article 128 Raper ...

5.2. Author view It consists of the specification of the legal educational system, where the methodology described in Section 4.4 was used. This methodology contains the following steps. 5.2.1. Step one: domain model definition In first step, which is the most complex, five concerns are identified (and described in Table 1)     

specify the domain and partitions of the domain; define the curriculum structure and pedagogical activities; build and maintain learning objects to be used; define problems to be solved by the students; specify behavioral knowledge of the problems.

The definition of the problems to be solved and the behavioral knowledge were defined by the developer, as described in Section 5.1.

Table 2 Data of legal domain – Brazilian penal code. Class Strategy1 LearningGoal1 levelKnowledge rate LearningGoal2 LevelKnowledge rate ... LearningGoal8 LevelKnowledge rate

Content Problem-based Learning Curriculum1(Art. 121) Advanced 0.80 Curriculum2(Art. 122) Advanced 0.80 ... Curriculum8(Art. 128) Advanced 0.80

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Fig. 13. Ontology description of an ATA.

5.2.2. Step two: student model definition In this step, two concerns are identified:  specify the learning goals of the students for each curriculum within a partition domain;  define information to be used in the student–system interaction. In legal domain, all curricula previously defined will be studied by students. These objectives are specified in just one step and the information is shown in Table 2. The second step informs which interaction information will be considered. For instance rates concerning problem solving process and pedagogical actions used by the autonomous tutoring agents. 5.2.3. Step three: pedagogical model definition The third, and last step, is the pedagogical model which take into account the following concerns:  define strategies and tactics to be used;  specify how students should be evaluated;  define the instructional plan. The first step is about the pedagogical strategy. In this case study, was chosen the problem-based learning. The learning evaluation (second step) is a set of actions that tries to build the system’s representation of the student. The author can define some kind of questions like subjective, multiple

choice questions and others. The choice of which evaluation to be used depends on the working domain. The instructional plan is strongly coupled with the pedagogical strategies defined. So, the pedagogical strategy must be first defined and after that, to define the instructional plan of the system. 5.2.4. Step four: interaction agent definition The agents of the system are, basically, the same previously provided by the framework. For this reason, the Fig. 13 shows an example of the configuration of the interaction agent ontology.

6. Discussion of the addressed problems This Section aims at discussing how each problem stated in Section 2.3 was addressed by the proposed computational model. During the system implementation and evaluation in a real scenario, the solutions adopted to these problems are discussed as follows.  High development cost: it was decreased through the reuse of an agent-based architecture, ontologies, and a methodology that specifies the main aspects of a SWBES;  Complexity to develop AI algorithms: it was solved through the reuse of some inference mechanisms and support to ease integrate new mechanisms through the use of semantic web services;

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 AI techniques integration: it was solved through semantic web services to ensure the discovering, composition, invocation, and execution of such mechanisms;  Interactive tools: SAKIA provides synchronous and asynchronous tools to support the users both in the learning objects definition and in the learning process;  Scalability: the distribution connector with load balance provides, on client and server sides, the necessary features to assure the systems scalability;  Difficulty of sharing materials: the use of ontologies enables the knowledge reusability and sharing;  Consistence of metadata: the use of OWL–DL supports consistence verification of the ontologies;  Extensibility: the framework assure the extension of the system and the use of OWL support the integration with new languages based on XML;  Interoperability: it was solved through the integrated use of ontologies to share the meaning of involved concepts and agents to ensure problem solving, interacting in distributed environments;  Distribution of services: web services are already distributed and autonomous business units;  High maintenance cost: the solution were (i) the authoring layer providing a user-friendly interface to authors maintain the learning resources of the environment and (ii) semantic web services through the automatic discovering, composition, and invocation. 7. Related work Some tools for building educational systems have been created. A relevant analysis of the state of the art can be viewed in [23] in a traditional perspective and other proposals were developed under the perspective of semantic web technologies [2,31,33]. However, recently, some new environments have been developed. Some of them, considering the proposals closely related to the presented proposal here are described below. Millard [29] proposes the use of semantic web services for building e-learning systems, where the gain in the use of semantic web services is discussed. However, just examples discussing the use of those services are provided. In other words, an architecture for building such systems are not provided. Lin [32] defines a multi-agent and service-oriented architecture for developing integrated and intelligent web-based education systems. However, this proposal is not extensible for adding new agents in the architecture and it does not ensure the interoperability between applications. Dietze [30] presents a semantic web service-oriented framework for developing adaptive learning environments. It supports a level of re-usability and a dynamic adaptation to different learning contexts. However, the proposal does not ensure the interoperability between applications and the use of autonomous agents (e.g. for solving complex problems in a distributed way). In addition, the dynamic discovering, composition, and invocation are not provided by the used frameworks. Aroyo [3] presents evolutional authoring support environment (EASE). It structures the complexity of the entire authoring process by providing the authors with guidelines to specify the following resources: domain model, learning objects, learning goals, instructional strategies, assessment strategies, learner model, and learning sequence. However, this environment does not support extensibility features. In addition, it does not offer inference mechanisms. The approach presented in this paper addresses all the problems presented in Section 6, while the proposals discussed above have only a partial coverage of such problems.

8. Conclusions In this paper a computational model for developing semantic web-based educational systems was described. The approach introduced in this work promotes an easy and efficient way to build such systems for both authors and developers. This computational model is characterized by offering low development costs, scalability, extensibility, interoperability, and low maintenance costs. Moreover, with this approach it is also possible to deal with the development of artificial intelligence, interactive tools, difficulty of educational resource sharing, distribution of services, and use of domain ontologies. The case study shows positive results concerning the facilities and effectiveness for building particular educational applications. The use of agents, semantic web services and ontologies technologies ensure the construction of semantic web-based systems. As a future work, new case studies should be developed in different domains, such as health and mathematics. Moreover, new semantic web services have to be developed to provide further features to developers and authors such as mechanisms for adaptive hypermedia and text mining.

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