Case-Based Student Modeling in Multi-agentLearning Environment

Carolina Gonzalez1,2, Juan C. Burguillo1, and Martin Llamas1

1 Departamento de Ingenierıa Telematica, Universidad de Vigo, Vigo 36200, Spain{cgonzals, jrial, martin}@det.uvigo.es

2 Departamento de Sistemas. Universidad del Cauca, Popayan, Colombiacgonzals@unicauca.edu.co

Abstract. The student modeling (SM) is a core component in the de-velopment of Intelligent Learning Environments (ILEs). In this paper wedescribe how a Multi-agent Intelligent Learning Environment can pro-vide adaptive tutoring based in Case-Based Student Modeling (CBSM).We propose a SM structured as a multi-agent system composed by fourtypes of agents. These are: the Case Learner Agent (CLA), Tutor Agent(TA), Adaptation Agent (AA), and Orientator Agent (OA). Each stu-dent model has a corresponding CLA. The TA Agent selects the adequateteaching strategy. The AA Agent organizes the learning resources andthe OA Agent personalizes the learning considering the psychologicalcharacteristics of the student. To illustrate the process of student model-ing an algorithm will also be presented. To validate the Student Model,we present a case study based an Intelligent Tutoring System for learningin Public Health domain.

1 Introduction

Many developers of educational systems consider Intelligent Tutoring Systems(ITS) and Learning Environments as different and even contradictory ways ofusing computers in education. The recent success of such well-known IntelligentLearning Environments [1] showed that these ways are not contradictory, butrather complementary. ITS are able to control learning adaptively at variouslevels, but generally do not provide tools to support free exploration. Learningenvironments support exploratory learning [2], but they lack the control of anintelligent tutor. Without such control the student often works inefficiently andmay never discover important features of the subject.

ILEs can monitor students, help them to perform their tasks and providethem with feedback in a manner that contributes to their learning process. Forthe students to learn effectively and efficiently, ILEs should provide teachingstrategies according to the specific domain knowledge and objectives.

The Student Model is the main component within the Intelligent LearningEnvironment and, contains information about the student knowledge. It obtainsthe information by dinamically observing and recording the student’s behaviour,answers, problem-solving strategies, and analyzing them in order to deduct their

M. Pechoucek, P. Petta, and L.Z. Varga (Eds.): CEEMAS 2005, LNAI 3690, pp. 72–81, 2005.c© Springer-Verlag Berlin Heidelberg 2005

Case-Based Student Modeling in Multi-agent Learning Environment 73

level of understanding about the domain. This information is processed and usedto individually adapt the system to each student.

Intelligent agents have been quite successful at observing student’s behav-iour and, therefore, they have been widely used in learning environments in or-der to capture the characteristics of the student and perform student modelingtasks [3].

Building a student model involves defining; the ”who”, is modelled; the”what”, or the goals, plans, attitudes, capabilities, knowledge, and beliefs ofthe student; the ”how” the model is to be acquired and maintained; and the”why” , including student’s information to give assistance, to provide feedback,or to interpret the student behaviour [4]. The need for simplicity and ease ofunderstanding in Student Models is very high. It derives from the fact thatdistance education is addressed to students who vary greatly in their educa-tional background. Due to the lack of physical tutor-student contact, some-times the distance student has the feeling that the teacher is unreachable whenneeded. This is the reason why Student Models should provide bi-directionalbenefit to both instructors and students, by enabling students to monitor theirown progress and utilise the feedback provided by the model on a continuousbasis.

There are many techniques for generating student models; however most ofthese techniques are computationally complex and time consuming for exam-ple: Bayesian Networks [5], Fuzzy student modeling approach [6], the Dempster-Shafer theory [7]. Other techniques can only record what a student knows and notthe students’ behaviour and features. Examples are: overlay model [8], stereo-type and combination model [9]. A comparison of Case-Based Reasoning andBayesian Networks for student modeling is realized in [10]. This study showsthat CBR is the best and easiest approach for constructing a studentmodeling.

We propose a multi-agent approach to student modeling in which each stu-dent model has a corresponding Case Learner Agent. This agent uses the CBRparadigm [11] to generate the student profile. The CBR paradigm is simpleand do not require complex inference algorithms, moreover offers well-foundedmethodologies and experiences with respect to both mathematic and algorith-mic aspects. In our approach, we included an Orientator Agent to customize thelearning considering the psychological characteristics of the student. In order toconstructing the knowledge of the students, we used CaseML [12] a semanticenriched markup language.

In our approach the student model is improved because: it is easy to handleand to maintain beneficiating to both the tutor and the student; to promote stu-dent reflection because reporting the student’s misconceptions and the reasonswhy they have happened; and to facilitate the supervision of the students by en-abling the tutor to have a solid and continuous view of the student performance.

The outline of this paper is as follows: Section 2 describes an overview onStudent Models. Section 3 presents the Student Modeling Process. In Section4, the construction of the Student Model by Case-based Reasoning is described.

74 C. Gonzalez, J.C. Burguillo, and Martin Llamas

Section 5 presents the modeling algorithm. Section 6 exposes a case of study: AnIntelligent Tutor System for learning in Public Health. Finally in the Section 7some conclusions are presented.

2 An Overview on Student Models

Many researchers have tried to classify and formalize the student model in aunified framework. VanhLehn [13] uses three dimensions (bandwidth, knowl-edge type and differences between student and expert) to construct the studentmodel. Ragnemalm [14] regards the student modeling problem as a process toconnect the student’s input in the ILE, the conception of the system and therepresentation of the correct knowledge. Self [15] tries to provide a theoricalcomputational basis for student modeling, which is psychologically neutral andindependent from the applications.

Generally the student models are classified into three traditional model typesaccording to the assumptions about the student’s knowledge: (1) overlay, (2) an-alytical, and (3) predictive models [16]. Most ITS use the overlay model. Itconsiders the student’s knowledge as a part of the expert’s knowledge and use aset of concept-value pairs to represent the student’s knowledge. The analyticalmodel makes a distinction between the student’s knowledge and the expert’sknowledge. The system determines whether students have knowledge or not bychecking how the student uses the knowledge that the system defines. An expe-rience using this model is WEST [17]. The predictive model takes into accountthat the student’s knowledge can be extended beyond the expert knowledge.This model provides more flexibility as new perturbations can be added into anexisting model when needed, while the overlay and differential models alwaysconsider the student’s knowledge as a subset of the expert knowledge. However,the perturbation model brings more difficulty. This model was implemented inDEBUGGY and IDEBUGGY systems [18].

These traditional models have some disadvantages (1) the student may followdifferent problem solving approaches; (2) cannot predict what student knows; (3)may hold different beliefs that are not a subset of the domain knowledge; and (4)most models represent knowledge with procedural net increasing the complexitymodel. Case-based paradigm is another approach to student modeling, whichhas been used by some authors to conceive and develop a student model for In-telligent Tutoring Systems. We propose a case-based student modeling (CBSM)structured as a multi-agent system that takes into account several componentsthat are essential for efficient adaptive teaching process. They are: (1) knowledgelevel, (2) learning style, (3) learning goals, and (6) psychological characteristics.

3 Student Modeling Process

In order to construct the Student Model, information about student should beacquired.

Case-Based Student Modeling in Multi-agent Learning Environment 75

Fig. 1. Content of the Student Model

3.1 Content of the Student Model (SM)

A comprehensive student model should contain information about the previousstudent’s knowledge, the student’s progress, preferences, interests, goals, per-sonal information and any other information related to the student. Based onthe dependence upon the subject domain, the content held in student modelsconsists of two parts: domain specific information and domain independent in-formation.

– Domain specific information (DSI): it is also named student knowl-edge model (SKM) which represents a reflection of the student’s state andlevel of knowledge in term of a particular subject domain.

– Domain independent information (DII): it is slightly different fromsystem to system. The domain-independent information about a student mayinclude learning goals, cognitive aptitudes, measures for motivation state,preferences about the presentation method, factual and historic data, etc.

We propose a student model that includes individual and cognitive character-istics grouped in a component named Knowledge Component. This componentcontains information related to the (1) knowledge level of the student, (2) per-sonal information, (3) learning preferences, and (4) psychological characteristics.Figure 1 shows the content of the student model.

4 Constructing the Student Model by Case-BasedReasoning

The student modeling has been recognized as a complex and difficult but im-portant task by researchers. The method of student modeling includes a repre-sentation of the knowledge and reasoning of the student, and the way how thestudent acquires new knowledge in order to perform intelligent learning.

Case-Based Reasoning (CBR) is a problem-solving paradigm that is able toutilize the specific knowledge gained from previous experiences in similar situ-ations (cases) to solve a new problem. Instead of relying on exact reasoning in

76 C. Gonzalez, J.C. Burguillo, and Martin Llamas

a well ordered world, CBR focuses on inexact reasoning by a similarity mea-surement among objects. The process involved in CBR has been described as acyclic process that integrates four phases: Retrieve, Reuse, Revise and Retain.

4.1 Student Model Initialization

The initialization of the Student Model is a task of great importance to makesinitial estimations of the new knowledge level of the student. When a studentstarts a new learning session, the system has no previous knowledge about hislearning skill.

In this study, the information about the students is regarded as cases. Whenthe student starts learning, the information about the students is extracted fromthe student model and is converted into a new case. When there is a new student,he is asked to take some tests, then the system analyses his tests results to gatherinformation and initialize the student model. For representing cases, we haverevised several types of methods from unstructured cases to structured problemsolving episodes and we had selected CaseML because it is a semantic enrichedmarkup language.

Additionally, CaseML solves the problems presented in traditional case repre-sentation as: (1) needs a human interpreter, (2) fails to describe complex objects,and (3) needs of approaches for similarity assessment that allow to compare twodif-ferently structured objects. Basically CaseML define a Case Ontology for describ-ing cases, it defines a set of classes and properties between classes. The

Fig. 2. CaseML Scheme

Case-Based Student Modeling in Multi-agent Learning Environment 77

Fig. 3. Student Model Stages

figure 2 shows a scheme for representing cases that include the classes and their rela-tionships. The student model presented here is structured as a multi-agent systemintegrated by: (1) Case Learner Agent (CLA), (2) Tutor Agent (TA), (3) Adapta-tion Agent (AA), and (4) Orientator Agent (AO). The student modeling processby these agents taking account the student model stages presented in Figure 3.

Case Learner Agent (CLA): It is an CBR-Agent. It is responsible for retrievethe information about the student and identifies his profile. In the retrievalprocess, the CLA agent evaluates cases and uses the k-nearest algorithm [19]to determine the matching grade. After the evaluation, the most similar casesare selected (if there is more than one case, the case with the highest rankis selected and prepared for adaptation). After the process is completed, theCLA agent storages the new case in the casebase. Additionally, this agent keepscommunication with the Tutor Agent and updates the student model.

Tutor Agent (TA):It selects a specific teaching strategy for the different stu-dents profiles, personalizing the learning process. It interacts with the CLA to getinformation about the students and to produce changes in the teaching paradigm.

Adaptation Agent (AA): It organizes the learning resources according to theteaching strategy implemented by the TA. It takes into account the studentprofile to present the contents and information, making a customized learning.

Orientator Agent (ACG): It gives an emotional guide to the student whenhe/she fails in the learning process due to pshychological problems as: memory,motivation, personality, and learning ability.

5 The Modeling Algorithm

Let us illustrate a modeling algorithm. Below we explain the process referencingevery line in the pseudo-code. Figure 4 shows the modeling algorithm. Line 3.

78 C. Gonzalez, J.C. Burguillo, and Martin Llamas

Fig. 4. Pseudo-code for the Student Modeling Process

Case-Based Student Modeling in Multi-agent Learning Environment 79

Initialize the student model: When the student interacts with the system for thefirst time, the information is acquired.

Line 8. The ProcedureStudenProfile generates the student profile. First theCLAAgent evaluates and filters the cases. This agent combines searching andmatching techniques. In line 36, the new case is analyzed, and evaluated (e.g.cases with the same goals than the current case).

Line 47. The ProcedureMatching is implemented to check the correspondingfeatures in the cases stored using the k-nearest neighbour algorithm. Based onthe result of the matches, CLA identify those that best address the requirementsof the new situation, ranking the cases from highest to lowest, getting the studentprofile.

Line 10. The CLAAgent sends a request to the TAAgent with the studentprofile. Then the TAAgent selects the teaching strategy in line 11.

Line 12. The TAAgent sends a request to AAAgent in order to organizethe learning resources according to the student preferences. This information isreturned to the students through the CLAAgent in line 14.

Line 15 and 16. The CLAAgent monitorizes the students’ tasks and evaluatesthe students’ answers.

Line 17. If the students’ answers corresponding to misconceptions or fails, theProcedureMisconceptions will be called. If the fail is related with an inadequateteaching strategy, the CLAAgent sends a request to the TAAgent in order tomodify the teaching strategy in line 22. If the fail is due to personality problemsthe ProcedureEmotionalGuide is called in line 25.

Line 31. If the students’ answers are correct, the session finished and theCLAAgent updates the student model in line 26.

6 Intelligent Tutoring System for Learning in PublicHealth

The ITS was developed within the SINCO project [20] considering the MAS-CommonKads methodology [21]. The system is developed under a multi-agentapproach compatible with the FIPA standards [22]. In the development Java,JavaScript and XML are used. The ITS modules are distributed and dividedin smaller parts called agents. These agents work like autonomous entities, andact rationally in accordance with their environment perceptions and knowledgestatus.

The principal purpose of the Intelligent Tutoring System in Public Healthwas the learning improvement and the decisions making process, by means ofthe use of personalized tutoring, letting adaptation to new teaching strategiesaccording to the student profile. For this purpose, the system used an EvaluatorAgent (EA) that was responsible for evaluating the student behaviour.

We have considered necessary to redesign the student model to improve thestudent performance. For this, we structured the student model as a multi-agentsystem that uses the CBR paradigm to obtain the student profile.

80 C. Gonzalez, J.C. Burguillo, and Martin Llamas

The kernel of the new student model contains student individual characteris-tics together with psychological aspects like level of concentration, intelligence,motivation, etc.

7 Conclusions

The aim of this paper is to show the use of case-based reasoning with multiagentsystems in student modeling within a Intelligent Learning Environment. Withour approach is possible to categorize students according to their knowledgelevel and learning preferences, to motivate them to learn in user friendly envi-ronments that suits with their learning style. The multiagent system integratesa set of agents that realizes continuous student assistance and tutoring duringthe learning sessions. The use of an Orientator Agent is very important to givean emotional guide to the students when misconceptions or fails are reported.

Acknowledgments

We want to thank “Ministerio de Educacion y Ciencia” for its partial support tothis work under grant “MetaLearn: methodologies, architectures and languagesfor E-learning adaptive services” (TIN2004-08367-C02-01).

References

1. Moundridoud, M., Virvou, M.: Authoring intelligent tutoring system over the worldwide web. Proceedings IEEE First International Symposium in Intelligent Systems1 (2002) 160–165

2. Kashihara, A., Kinshuk, O.R., Rashev, R., Simm, H.: A cognitive load reductionapproach to exploratory learnig and its application to an interactive simulation-based learning system. Journal of Educational Multimedia and Hypermedia 9(3)(2000) 253–276

3. Crews, T.R., Jr: Intelligent learning environments: using educational technology toassist complex problem solving. Frontiers in Education Conference. 27th AnnualConference. ’Teaching and Learning in an Era of Change’.Proceedings. 2 (1997)911–916

4. Khuwaja, R.A., Evens, M.W., A, R.A., Michael, J.A.: Architecture of circsim-tutor:a smart cardiovascular physiology tutor. Proceedings IEEE Seventh Symposiumon Computer-Based Medical Systems 3 (1994) 158–163

5. Petrushin, V.A., Sinista, K.M.: Using probabilistic reasoning techniques for learnermodelling. World Conference on AI in Education (1993) 418–425

6. Hawkes, L.W., J, D.S., A, R.E.: Individualized tutoring using an intelligent fuzzytemporal relational database. International Journal of Man-Machine Studies 33(1990) 409–429

7. Bauer, M.: A dempster-shafer apporach to modeling agent references for planrecognition. User modelling and User-Adapted Interaction 5 (1996) 317–348

8. F, S.A., Kinshuk: Model for distance education system in maldives. Proceedingsof E-Learn ISBN 1-880094-9 (2003) 2435–2438

Case-Based Student Modeling in Multi-agent Learning Environment 81

9. Jeremic, Z., V, D.: Desing pattern its: Student model implementation. Proceedingsof the IEEE International Conference on Advanced Learning Technologies ISBN0-7695-2181 (2004) 864–865

10. Gonzalez, C., Burguillo, J., Llamas, M.: A comparison of case-based reasoning andbayesian networks for student modeling in intelligent learning environments. 16thEuropean Conference on Machine Learning (ECML) and the 9th European Con-ference on Principles and Practices of Knowledge Discovery in Databases (PKDD).(2005) Espera de publicacion.

11. Craw, S., Jarmulak, J., Rowe, R.: Learning and applying case-based adapatationknowledge. Lecture Notes in Computer Science 2080 (2001) 131

12. Huajun, C., Zhaohui, W.: On case-based knowledge sharing in semantic web. Pro-ceedikngs of the The 15th IEEE International Conference on Tools with ArtificialIntelligence ISSN 1082-3409 (2003) 200–207

13. VahLehn, K.: Student modelling. Foundations of Intelligent Tutoring SystemsLawrence Erlabum Associates Publishers (1988) 55–78

14. Ragnemlam, E.L.: Student diagnosis in practice:bridging a gap user modelling anduser-adapted interaction. 5(2) (1995) 93–116

15. Self, J.: Grounded in reality: the infiltration of ai into practical educational systems.IEEE Colloquium on Artificial Intelligence in Educational Software 313 (1988) 1–4

16. Yazdani, K.: Intelligent tutoring system: An overview. Artificial Intelligence andEducation 1 (1987) 183–201

17. Yang, A., Kinshuk, A, P.: A plug-able web-based intelligent tutoring system. Pro-ceedings of the Xth European Conference on Information Systems ISBN 83-7326-077-3 (2002) 1422–1429

18. R, B.R.: Diagnosing bugs in a simple procedural. Intelligent Tutoring SystemAcademic Press (1982) 116–125

19. Joussellin, A., Dubuisson, B.: A link between k-nearest neighbord rules and knowl-edge based systems by sequence analysis. Pattern Recognitizion Letters 6 (1987)287–295

20. Gonzalez, C., Burguillo, J.C., Vidal, J.C., Llamas, M.: Sinco: Intelligent systemin disease prevention and control. an architectural approach. Lecture Notes inComputer Science 3337 (2004) 129–140

21. Sanchez, A., Medina, M., Castellanos, N.: Ontological agents model based on mas-commonkads methodology. Proceedings of the 14th International Conference onElectronics Communications and Computers (2004) 19–23

22. Nicol, R., O’brien: Fipa-towards a standard for software agents. Inhttp://www.bt.com/publications/bttj/ 16 (2003)