Case-Based Student Model using Knowledge Markup Language for Intelligent e-learning Systems SunGwan Han, JungSeop Yoon, GeunSik Jo Inha University Dept. of Computer Science and Engineering, Inchon, Korea {fish, jsyoon }@eslab.inha.ac.kr
[email protected] Abstract. The knowledge management in e-learning systems is classified into knowledge about students, strategies of teaching and learning, management of the learning content, and the system management. The construction of the student’s knowledge, namely the student model, is a core component used to develop an intelligent e-learning system. The student model can be used as an effective method of learning by constructing a knowledge base in the distributed e-learning system. However, existing e-learning systems have many problems sharing knowledge in a heterogeneous student model and a distributed knowledge base. Because the methods of knowledge representation are different in each e-learning system, the accumulated knowledge cannot be used or shared without a great deal of difficulty. In order to share this knowledge, existing systems must reconstruct the knowledge bases or must incur an extra-cost to convert a knowledge base. Consequently, we propose a new a Case-based student model and a knowledge markup language based on XML in order to overcome these problems. A distributed e-learning system can have the advantage of easily sharing and managing the heterogeneous knowledge base proposed by the student model and CaseML. In this paper, we have done our research based on the design and development of the case-based student model in order to construct an intelligent e-learning system. Furthermore, we have designed and developed a CaseML by using a knowledge markup language.
1. Introduction Information Technology has created revolutionary changes in business. Of late, the influence of the digital revolution is also spreading into education. E-learning is considered the part which will grow most rapidly and make the greatest profits. Elearning has changed not only the technology of the education environment but also the educational paradigm itself. The advantages of e-learning are twofold: we can overcome the restrictions of time or space, and we can study individually and cooperatively. In e-learning, collaborative learning is the main learning method whereby various students are studying by using various learning materials in a common learning space to gain the effects of synergy[20][23]. These e-learning systems demand various methods of knowledge management to
perform effective learning. Knowledge management in an e-learning system is classified into knowledge about the student, strategies of teaching and learning, management of the learning content, and the system management. Especially, the student’s knowledge is represented by individual information about the student’s knowledge related to learning, processing of cognitive change, finding the knowledge on learning, and identifying the group knowledge for collaborative learning. Constructing this student knowledge is, namely, a student modeling, which is the core component at an intelligent e-learning system[27][28]. The best and easiest approach in constructing a student modeling is to use Case-Based Reasoning(CBR)[26]. Generally, case-based student modeling is the method to be constructed that uses knowledge reasoning and machine learning by cases[19]. The knowledge base of the student model in an e-learning system should be shared in advance in order to have for collaborative learning in the e-learning system. However, there have some problems in sharing the knowledge base in existing elearning systems. Because e-learning systems have a heterogeneous knowledge base and a distributed environment, these bring about problem such as partial restrictions in reusing the knowledge in the e-learning system, entire reconstruction of the knowledge base and extra-costs required to upgrade the system. However, if these knowledge-based systems employ knowledge markup language(KML) based on XML, we have the advantage of easily sharing and managing in a heterogeneous knowledge base and a distributed environment[5]. Moreover, we can share the distributed student’s knowledge and perform cooperative learning more effectively when we share the student model by using KML. In our research, we have presented a method to design and implement a Casebased student model for developing an intelligent e-learning system. To share these Case-based student models, we propose a new definition and structure of the CaseML using a knowledge markup language based on XML. In order to demonstrate the efficiency of the proposed the student model and CaseML, we has been designed and implemented the distributed e-learning systems for mathematics and considered the relative importance of KML.
2. Overview of Student Model The student’s knowledge in an e-learning system is represented by including the basis information of the student, pre-knowledge of the student before learning, postknowledge of the student after learning, and the change of the cognition state during the learning process. Constructing the student’s knowledge in knowledge base is called “student modeling” and the knowledge represented in the knowledge base is called the “student model”. A student model is an essential part of an intelligent elearning system. A student model can be described as the information that an intelligent e-learning system keeps about the knowledge of a student. It is used to drive instructional decisions in order to make an adaptable e-learning system for
individual students. Many researchers have tried to classify and formalize the student model in a unified framework. VahLehn[27] used three dimensions - bandwidth, target knowledge type, and differences between the student and the expert - to construct the space of the student model and classify them within the learning space. Ragnemalm[24] regards the student modeling problem as the process of bridging the gap between the student’s input in the tutoring system, and the system’s conception and representation of correct knowledge. Self[25] tried to provide a theoretical, computational basis for student modeling, which is psychologically neutral and independent of applications. Generally, the student model is divided into two main types represented by the way of which the student gained his knowledge. One is the overlay model, and the other is the bug model [14][28]. In addition, there is a combined hybrid model. Using another classification method, the student model is divided into the model tracing and the knowledge tracing by inferring the way in which the student gained his knowledge. Each time the student has the opportunity to apply a rule in the ideal student model, the tutor updates a probability which estimates that the student has learned the rule, contingent upon the accuracy of the student’s response, which may be viewed as knowledge tracing on barrier modeling process. In the case of model tracing, by comparing the student’s response to the set of possible legal actions and the set of known erroneous actions, the tutor is able to recognize whether the student is on a correct solution path, appears to be suffering from a known misconception, or has typed something unrecognizable [27]. As for acquiring methods of the student’ knowledge, the student model is divided into an explicit model and an implicit model [1]. The implicit model is a method whereby the system gains student information and interest, determined by observing a student’s actions. The explicit model is a method whereby the student gives and feeds it to the system with gives what demographic or personal information or is allowed to have system learning condition, knowledge state and level in person. The method of student modeling includes representing student’s knowledge, and reasoning. Moreover, it represents how the student acquires knowledge in order to perform intelligent learning. The best and easiest approach in constructing an intelligent tutoring system is through modeling by case-based reasoning(CBR)[8], which uses the process of problem solving to teach the student. Case-based reasoning is a paradigm for problem solving in many respects. Instead of relying solely on general knowledge of a problem domain or making associations along the generalized relationships between problem descriptors and conclusions, CBR is able to utilize the specific knowledge of previously experienced, concrete problem situations or cases [8][9]. A new problem is solved by finding a similarity to the formal approach of the past case and reusing it in the new problem situation. A second important difference is that CBR also is an approach to incremental, sustained learning, since a new experience is retained each time a
problem has been solved, making it immediately available for future problems. The processes involved in CBR have been described as a cyclical process comprising the 4REs i.e. retrieve, reuse, revise and retain [2]. The process of problem solving describes a memorable change in the cognitive situation and illustrates the consequences of that problem solving. Kolodner[19] defines a case as a contextualized piece of knowledge representing an experience that teaches lessons fundamental to achieving goals. Case-based intelligent tutoring systems that structure and present streams of other operators’ experiences may assist in collecting and sharing valuable operational knowledge. The examples of student modeling research using Case Based Reasoning for an intelligent tutoring system are explained in research by Shiri[21] for constructing student modeling and by Alan R. Chappel [9] for teaching an expert system and in ELMART[7] for Lisp tutoring.
3. Student Modeling by Case-Based Reasoning Generally, the learning domains represented by the case-based method in an elearning system are as follows: Case of the student’s basic information needed to assess knowledge level, Case of error-based bug library for constructing student model, Case of learning content by learning level, Case of question and solving process to construct an item bank, Case of student matching strategy for collaborative learning and P2P learning, Case of content structure searching for and sharing of suitable content. In this study, case is represented with two types. One is used to find out the basic student’s information to assess the level of the student’s knowledge, the other is used to construct an item bank with questions and solving processes. Figure 1 represents the architecture of a case-based and a rule-based student model in an intelligent elearning system. The student model has constructed a bug library based on rules, an item based on the case and a temporary student model with the local environment. Intellgent e-Learning system Student
Student Model Bug Library Rule Base
Pattern Matching New Case
Item Case Base
Matching Case
Inference Engine CBR reasoner
CaseML
Interface Temporary Student Model Case Base
Fig. 1. Case-Based Student model in intelligent e-learning system
If a student starts new a session of learning a subject, the student’s information in the temporary student model is extracted by an inference engine. A new case is sent to the intelligent e-learning system which is the learning server. At this time, the student’s information is regarded as a new case and the case base reasonser retrieves the most similar case among the existing cases in the case base. Retrieving a similar case means that it selects a student of a similar type among a learning history of past students. The algorithm to compute similarity generally uses simple the Nearest Neighbor Algorithm[3]. The Nearest Neighbor Algorithm is a formula(1) as shown here. n
Similarity( N , S ) = ∑ f ( N i , Si ) × wi
(1)
i =1
where: N is the new case(new student) S is the source case(past student history) n is the number of features in each case i is an individual feature from 1 to n f is a similar function for feature i in cases N and S w is the importance weighting of feature I Fig. 2 The Nearest Neighbor Algorithm New learning is approached by finding a case that is a similar to the learning domain of a past student. A similar learning domain means that the new student found getting suitable content, questions and lessons. If the new student solves supplied the learning content or the question, then the e-learning system compares the student’s solving process with a correct solving process stored in the case base. At this time, the inference engine reasons the state of the student’s knowledge through the student’s solving process. If a new solution occurs when the student solves a problem, the casebased reasoner stores the new case or solution in the case base after the validation of consistency. The case-based system treats this process as machine leaning. Current CurrentKnowledge Learning Pre-score: 70 Information Post-score:80 Pre-score: 70 Pre-learning : figure Post-score:80 Learning time: 65min Pre-learning : Geometry Subject: circle 65min Learning-time: Error_rate: 20% Subject: circle Error-rate: 20% Recommanded Learning Content: Item 4
Feature Pre-score Post-score Pre-learning Learning-time Subject Error-rate
learning1 70 80 Geometry 65min circle 20%
learning2 60 80 Geometry 120min square 24%
Solution
Item 4
Item 5
New Case 70 90min square 18% ?
Figure 3. Example of retrieval and representation of cases to construct a case base The represented case domains needed to infer student’s knowledge are divided into the student information case and the item case. The case in figure 3 presents the student’s information case. The student’s information case is composed of current learning information and recommended learning. Current learning information
contains pre-scores, post-scores, pre-learning, learning-times, learning-subjects and error-rates for providing a suitable content or problem. The recommended learning represents contents or items for new learning. The table in figure 3 shows values for the features of each case. In the following figure, an item case is organized as a sample of a problem and a solving process. The problem is shown with the indexed information, since it will be used for indexing the problem, a statement(a description of the problem), and the solution. In addition, the problem is represented as a relation of statement type and similarity type. Likewise, the item case has been built with the indexed information, a statement(a description of the case), and the solution process. The case of the problem is represented as a statement type(ST_type) and a solution type(SIM_type). The student model consists of the knowledge component, the knowledge level, the capability (analogy and adaptation), the solution, the goals and the plans, the attitudes, and the beliefs. The usage method and the processing of the problem case will be described in more detail in chapter 6.
Item Case Base Problem Statement
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SOL_type:Inductive ST_type=relation of statement type SIM_type=relation of similarity type SOL_type=relation of solution process f(x),g(x)..=function of similarity
Solution SOL_type:Deductive Solution
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Figure 4. Representation of a problem and case
4. Knowledge Markup Language and Case-based Markup Language 4.1 Introduction of the Knowledge Markup Language Existing HTML concentrates on data and management of information in the distributed web environment, but Markup languages based on XML are extending into knowledge management. This markup language which manages the knowledge based on XML is called Knowledge Markup Language(KML) [17]. We were able to
make use of the knowledge of the distributed data and the information in the heterogeneous systems in the distributed environments. Recently much research has been conducted to try to combine XML and KML with artificial intelligence(AI) technologies to construct an intelligent network environment. Figure 5 shows the research cases that can manage a knowledge base in a distributed environment using a knowledge representation such as rules, frames, logic, and XML technologies such as schema, RDF, RDFS and XOL[5]. The merits of XML to represent knowledge are to describe data on a standardized web by itself and to exchange structured materials and knowledge in various fields. Moreover, it can integrate information from other resources. The strong point of a knowledge markup language is that it has a reliable infrastructure for constructing the knowledge base on the web. Specifically, it enables one to save data to a knowledge base(KB), load data from a KB, exchange data in a KB between other AI languages, and exchange the data of a legacy system, an existing database system and application programs. Moreover, by using XML, we can translate or convert a pure AI source easily. Much research in agent-related KML is going forward in DARPA and in research institutes in many universities. Related research consists of a Rule Markup Languages (RuleML, DFML, HornML), the DARPA Agent Markup Languages(DAML, ACML) and a markup language for a Semantic Web[4][18]. Knowledge Markup Language FRODO Ontobroker ACML DAML-L DAML OIL
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Figure 5. XML-based Knowledge Markup Language The example below shows that the rules and the facts of the Horn clause in Prolog language are embedded in XML. As a result, the inference engine can use it in reasoning without much effort or many processes. This HornML was used to construct the rule in the bug library in this study. With an item basis fact: item(solve,student_1). A rule is usable to dynamically derive study assertions as needed, without having
to store them all-inclusively and statically: study(someone,content_1) :-item(solve,someone). That is, its earlier XML version is useable by a Horn-logic interpreter for inferential queries such as someone=student_1
study(student_1,content)=>item(solve,someone)=>true content=content_1 Rule: study someone content_1 item solve someone
content=content_1 Fact: item solve student_1 Inference result true
4.2 The Design of a Case-Based Markup Language(CaseML) Existing research on Case-based reasoning and XML [10][15][[16] has described CBR applications that receive cases in an XML format. We present a proposal for CaseML, a case description language based on XML. XML is a description language that supports meta-data descriptions for particular domains and these meta-data descriptions allow applications to interpret data marked up according to this format[6]. The meta-data description is the Document Type Declaration (DTD). For instance, a DTD for real estate will attach semantics to a document marked up in that format. We propose a generic DTD for CBR called CaseML that allows cases to be marked up in an XML-based format. The major drawback of this approach was that data needed to be marked up in this specific CBR format. However since then the evolving potential of XML has improved on this idea. This study proposes four files which show that cases can be represented in XML. The four files are CaseFrame.xml to represent the structure of a case, CaseFrame.dtd to define the structure of the case document, CaseContent.xml to represent the content of the case-base, and CaseContent.dtd to define the structure of the document. Figure 6 presents the structure of the CaseML document in detail. The source files below show the steps needed to represent and construct the entire case-base.
Case Frame
CaseFrame.xml: Specific domain containing the features in a case-base, their type and their weight CaseFrame.dtd: Define a CaseFrame file
Case Base
CaseContent.xml: A case-base containing cases with features and values CaseContent.dtd: Define a CaseContent file
Figure 6. Structure of a CaseML document The first file (CaseFrame.xml) contains information about the features in the case-base - their type constraints and weights. The extract below shows three features from the student model case base, now marked up in CaseML. CaseFrame.xml …. Set Logic Proposition Number Equation Geometry Function Algebra Trigonometric Differential Integral Matrix Vector Limit Probability Statistics ...
The first two lines of the above file simply state that the version of XML being used is 1.0, and that the DTD (Document Type Definition) can be found in the file CaseFrame.dtd. A full explanation of DTDs is outside the scope of this section, but in essence, a DTD allows you to define the tags to be used in your language. In the DTD
we also indicate the contents permitted on each tag (either character data, or another nested tag), and its allowed attributes. In the above example we can see that , , and are some of the tags defined in CaseML. The tag contains character data while the tag contains either the tag or the tag. The tag has an attribute called name. An XML document for which there is a DTD, and which conforms to that DTD, is termed "valid". A partial DTD for the , and tags is given below (taken from CaseFrame.dtd). CaseFrame.dtd : # weight tag and #constraint tag are both optional #type consists of optional range tag # type has attribute "kind" with certain allowed values : :
In the example below, we see partial contents of the second file, CaseContent.xml. Because the DTD is so short, it is enclosed within the file instead of being referenced as an external file. This example gives a brief indication of what cases marked up in CaseML will look like. CaseContent.xml ]> 70 80 Geometry 65min
Circle 20% problem 4 ...
5. Advantages of a CaseML approach in an e-learning system The proposed CaseML in this study was easily designed to share the knowledge base in an e-learning system. The CaseML provides an especially efficient learning environment by sharing the case-based student model. Therefore, the CaseML integrating with XML and the KB in an e-learning system provides the following benefits: Learner P2P Learning
Application
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Figure 7. Advantages of a CaseML approach in an e-learning system
Easy management for the student: When the system and learning are changed, the student’s knowledge can be modified easily. Outsourcing with other systems: The system can accept the student’s information without having to convert between the content provider and the learning enabler.
Operating with a legacy system without having to change: There is easy operation with the existing system or a single user system. Operating with other learning systems: The student is able to learn by using the contents or his own information without changing to another e-learning system. Collaborative learning: The students in cyber space can be a collaborative learner or work with an agent. P2P learning: The peculiar information(case) of each student can be connected to the peer to peer learning environment. Using data for business: The student’s knowledge is accepted, applied, and estimated by operating with business computers in enterprise learning. The Knowledge-base resource, which is implemented by another KB processing language, can be operated by simple conversion and be implemented to create a better learning system using a different KB processing language. Consequently, the integrating case-base is very effective because the e-learning system operates with other DB applications easily. Moreover constructing a new system is more cost effective because it shares knowledge-related programs and the resources of the KB. This strong point is depicted in Figure 7.
6. Design of Case-Based Student modeling in an e-learning System An e-learning system for case-based student models is divided into three parts. It consists of an ITS(Intelligent Tutoring System), a student interface module and the XML processing engine to match a student for the system. A typical ITS is divided into a student module, an expert module, a tutor module, and an interface module. The student module consists of a Case Base which is based on a bug library. The case base works with a student model and is used when the system decides on the learning steps and the reason for the student’s knowledge. The tutor module has the contents and the item bank. It provides learning contents for the student’s knowledge level and creates problems through an item bank in order to diagnose the student’s progress. The expert module includes a CBR engine and uses the Nearest Neighbor algorithm to search for similar cases. In addition, it supplies appropriate contents and an item bank that is provided by the tutor module. The XML processing engine consists of CaseFrame.xml, CaseContent.xml, CaseFrame.dtd, and CaseContent.dtd which have been described in the previous chapter, and fifa_acl.xml, fifa_acl.dtd composed of ACML. It has the XML parser and the translator to analyze cases and KQML messages. The student interface has a partial student model and a case converter.
e-Learning system
Learner Interface
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Expert Module CBR engine (Reasoner)
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Fig. 8. Case-Based Student model in intelligent e-learning system The process whereby the student’s knowledge is processed by the student model is depicted in Figure 9. When the student starts learning, the student’s information extracted from the existing student’s model is converted into a new case. During this time, the student’s information is constructed on the basis of the elementary data and previous learning situation, and the most similar information to present to the student is extracted. The system recommends learning for new students by extracting learning logs. Therefore, the system provides relevant learning contents to a new student. In this paper, problems in an item bank are more suitable than the learning contents. When provided with appropriate problems, the student starts learning. The student solves the problems according to the process that is suggested in each problem and the provided answers. The system assesses the student’s knowledge on the basis of the transferred results. The system confirms the student’s answers. In case of a wrong answer, the system provides another problem which is similar to the previously provided problem. The student’s knowledge is diagnosed in this process. The diagnosed result is saved in the student’s information and the bug of the student model. If, however, the answer is correct, the answer is compared with the prepared solving process in the system. If the similarity is greater than 90%, it is estimated that the student understood the problem and the system save the student’s information in the student model. However, if the method differs greatly, it is estimated that the case is a new case of solving the problem and this case adds to the existing an item bank and the problem-solving case. The process of the case-based student modeling is similar to the CBR cycle. new case selection of a problem solving of a problem by the student solution generation and storage analyzing the student’s solution achieving the knowledge component of the student model
Similarity Retrieve
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5 if learner find solution if learner get mistake then offer suitable content
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Fig. 9. Processing diagram of case-based student modeling When there is a distributed environment, many solutions can be collected by CBR. Moreover, the CaseML makes sharing the KB easily in different the KB systems. Cycling these process continuously as the cycle of the CBR model can implement a better intelligent e-learning system. In order to process the knowledge in a distributed environment, agents need a standard communication protocol. The communication protocol between the typical agents uses the ACL proposed in the FIPA. The Lisp-like message below represents an example of an ACL message. The ACL message encoded in XML is called an Agent Communication Markup Language(ACML) [4]. An ACML file is represented in the ACL messages which include a CaseML to be sent to an agent. Figure 10 represents case sharing between two agents. The hansg_agent sends a new case to the gsjo_agent using the CaseML and the ACML. The source below presents an example of a FIPA ACL message encoded in XML. The case in a content performative is linked by XLink and XPointer as an example in the following file. The XLink can be used to simultaneously link various resources(or cases) and search various cases. The XPointer links a fragment of the XML document or a specific case in CaseContent.xml. Consequently, e-learning systems can share and exchange the information of distributed knowledge with the ACML and the CaseML KQML message (inform :sender hansg_agent :receiver gsjo_agent :ontology mathematic :language CaseML :content (
]> 70 80 … ))
acl.xml not-understood hansg_agent gsjo_agent mathematic CaseML XML documanet
Facilitator Agent
...... ...... ...... ......
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Case XML translator Request
Case Base Reasoner Case Base
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Fig. 10. Sharing the Case between agents using CaseML and ACML
7. Prototype development for a Mathematics e-learning System An intelligent e-commerce systems lab is developing an intelligent e-learning system whereby student can efficiently learn higher mathematics. The foundation theory is the knowledge space theory. The target fields of study are students over eighteen, and the system covers the full branches of higher mathematics. We are developing the learning contents and the item bank to assess the student’s knowledge in a mathematic learning. We have also constructed the case-based student model for collaborative learning. The knowledge space theory was developed by Doignon and Falmagne[12][13] in 1985. If Q is a set of the items, the state of a student’s knowledge can be described as the subset of items this student masters. Due to the prerequisite relationships between items, the knowledge space is restricted to a subset of the power set of Q. One way to represent such prerequisite relationships is with a surmise relation. Two items x, y ∈ Q are in prerequisite relation ( x ⊆ y) if, from a correct answer to item y, we can surmise a correct answer to item x. Each surmised relation describes a unique knowledge space [12][13]. Using the theoretical features and stability of the knowledge space theory, a developer can easily construct a knowledge and bug library into a case base type. In addition, he/she can assess student by intellectual reasoning. The knowledge space theory was applied to the Aleks system [12] and the AdAsTra systems [11]. These systems are offered for students to learn high-level mathematics after building an assessment module and a tutor module. However, the pre-building systems have many problems which are designed for just a testing step and a learning process but do not
invoke the concepts of knowledge sharing and a knowledge base. Furthermore, the system does not gain the advantage of distributed network resources, because it treats only an individual student. Thus, the system we have developed for this research project has the following merits. First, it can gather a large amount of knowledge about higher mathematics. Second, student’s errors(or bugs) are stored in the case base. Third, it can assess the various knowledge level of the student by using CaseML, which was described in the previous chapter. We have constructed the prototype of the e-learning system by using the Java program and a CBR inference engine, which are embedded into PROLOG and JESS. Internet Information Server(IIS) and Servlets are used for the web server. JDBC and MS-SQL are used for DB programming. Xerces-J is used for an XML parser. For communication between multi-agents, we have embedded JADE(Java Agent DEevelopment) and ACML into the proposed system. Figure 11 shows an implemented system for our research. This screenshot represents an implementation of the CaseML agent used to communicate between agents.
Figure 11. Implementation of the CaseML agent to communicate between Agents
8. Conclusions and Future works In this study, we have described a method for constructing a case-based student model in an intelligent e-learning system. The system we have proposed has the advantages and roles of CaseML for sharing the knowledge base of the distributed learning systems. We have introduced a way to make an integrated XML and Case Base by representing knowledge markup language. With the purpose to construct real student modeling, we have designed and implemented a Case Base which divides a student’s knowledge and the content knowledge such as the given questions and solving process. Moreover, we have designed an XML-based CaseML for sharing the Case Base. This paper has presented four types of files – CaseFrame.xml, CaseFrame.dtd, CaseContent.xml and CaseContent.dtd. These files need to represent and define the knowledge of the Case Base. We have shown the CBR cycle which includes a method of case based reasoning and machine learning in a designed e-learning system. Finally, the practical prototype of a case-based student model in an e-learning system has been implemented and applied to the knowledge space theory. Furthermore, we are implementing a casebased e-learning system that can teach the full range of higher mathematics extracted by the knowledge space theory. Further work will be needed for sharing, translating, exchanging and searching for the learning content by using an intelligent agent. To perform such processes, this study will need to have an XML based design and a detailed method of communication between multi-agents. A current Case-based e-learning systems need to be integrated into the rule-based system and a computational machine learning with an existing case-based reasoning to elaborate on and assess the students’ knowledge.
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