Intelligent tutoring system for real estate management.

ABSTRACT. The review on the worldwide intelligent tutoring systems and their application possibilities is presented in the paper. The intelligent tutoring system for real estate management developed by the authors is described. This system is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management. Besides the common components--student model, domain model, pedagogical model and graphical interface, the new developed system has testing model, decision support subsystem and database of computer learning systems. Domain model includes knowledge with the supplemental audio and video material for 63 modules being taught in Vilnius Gediminas Technical University. Student model enables to adapt to a learner needs and knowledge level. Decision support subsystem is used for all components of intelligent tutoring system giving them different level of intelligence. Database of computer learning systems enables using the following web-based learning systems: construction, real estate, facilities management, international trade, ethics, innovation, sustainable development, building refurbishment, etc. Tutor and testing model provide a model of the teaching process and support transition to a new knowledge state. Graphic interface is used to create an effective system-user dialogue.

KEYWORDS: Intelligent tutoring system; Life long learning; Real estate education

INTELEKTUALI NEKILNOJAMOJO TURTO VADYBOS MOKYMO SISTEMA

Straipsnyje pateikiama issami intelektiniu mokymo sistemu bei ju taikymo galimybiu analize. Aprasoma nekilnojamojo turto vadybos intelektine mokymo sistema, sukurta autoriu. Ji taikoma Vilniaus Gedimino technikos universiteto Statybos ekonomikos ir nekilnojamojo turto vadybos katedroje. Be bendru intelektinems mokymo sistemoms komponentu--studento modelio, pedagoginio modelio, disciplinu duomenu bazes ir grafines sasajos, i nauja sistema itrauktas sprendimu paramos posistemis, kompiuteriniu mokymo sistemu duomenu baze ir ziniu vertinimo posistemis. Disciplinu duomenu bazeje pateikiamos 63 moduliu, destomu Vilniaus Gedimino technikos universitete, zinios spausdinta, vaizdo bei garso forma. Studento modelis sudaro galimybe pritaikyti mokyma prie studijuojanciojo poreikiu ir ziniu lygio. Sprendimu paramos posistemis taikomas visuose intelektines mokymo sistemos komponentuose, suteikia jiems skirtingo lygmens intelektualumo savybiu. Kompiuteriniu mokymo sistemu duomenu baze leidzia naudotis siomis internetinemis mokymo sistemomis: statybos, nekilnojamojo turto, pastatu ukio valdymo, tarptautines prekybos, etikos, inovaciju, subalansuotos pletros, renovacijos ir kt. Pedagoginis ir ziniu vertinimo modelis pateikia kita--mokymo proceso modeli, padeda pereiti i kita zinio lygmeni. Grafine sasaja sukuria efektyvu dialoga tarp sistemos ir vartotojo.

1. INTRODUCTION

The once acquired education does not assure successful career for the whole life in the rapidly changing today's market, globalisation and information world. Professionals in the real estate field must learn all life long. Distance learning proves to be very suitable, enabling graduates to study at their working place, home or just any time and place convenient for them. Getting more and more popular distance learning provides not only plenty of advantages, but also the challenges. In order to create necessary conditions for individualised learning, to increase quality and effectiveness of distance learning, the intelligent tutoring systems are applied.

The notion of intelligent machines for teaching purposes can be traced back to 1926 when Pressey built a machine with multiple choice questions and answers [15]. Intelligent tutoring systems (ITS) are an outgrowth of the earlier computer-aided instruction, which usually refers to a frame-based system with hard-coded links, i.e. hypertext with an instructional purpose [8]. However the start of artificial intelligence in education history is said to be 1950-1960. The scientists of this decade believed that the computers soon will think the same way as humans: Turing: Computing Machinery and Intelligence [22], Minsky: Neural networks, Symbolic systems and mind society; McCarthy: Logical artificial intellect [18]). Programming works led to computerised teaching systems, that developed task sets, designed to support student learning [24, 25]. In the 1960's, researchers created a number of Computer Assisted Instructional systems that were generative [23]. By the late 1960's and early 1970's, many researchers had moved beyond merely presenting problems to learners while collecting and tabulating their responses, to considering the student a factor in the overall instructional system [24]. In 1970 the computer assisted instructional systems were improved by student models, enabling system to predict student answers. However in the seventies and early eighties the limitation of computerised systems was realised and investigation in artificial intelligence education decreased. The computerised tutoring systems were analysed mostly by educational and psychology experts. In 1982 a book ,,Intelligent tutoring systems" was published by Sleeman and Brown, where the modern computer assisted instructional systems were reviewed and the term Intelligent Tutoring System was introduced for the first time. It was defined as the system, that monitors, instructs and tutors students. Improving the intelligent tutoring systems further on, the computerised knowledge assessment function was proposed, as the mean for more effective learning [10]. In the early nineties when internet was started to be applied widely for transfer of information, the properties and actions of users were fixed and the information used to improve adaptive functions [3]. In the beginning of the 1990's, early ITSs focused their efforts on lesson navigation, or a kind of electronic page-turner presenting frames of text or graphics. This type of ITS is often referred to as a first generation ITS. Second generation ITSs use the model-tracing algorithm [1] to create a model of the student and trace student thinking [19]. ITSs where a model of both the student and the tutor are created in an effort to improve performance were the natural extension to second generation systems. Different researchers [9, 11] have developed third generation ITSs that model the tutor as well as the student.

At first intelligent tutoring systems were mostly applied in the courses of mathematics, later on they were adapted for more complicated topics and subjects. Recently the systems are designed also for history, philology or social sciences [9]. However the authors were not able to find any information about application of intelligent tutoring systems in the field of real estate. It seems that distance learning of real estate management is still performed in an old way, without benefits of new technologies.

When analysing the essence of intelligent tutoring systems (ITS), it is worth-while to take into account the research of other authors and institutions [4, 6, 7, 8, 20, 21, 26, 27]. One reason that ITSs are such a large and varied field is that "intelligent tutoring system" is a broad term, encompassing any computer programme that contains some intelligence and can be used in learning [8]. Therefore, in order to increase the degree of objectivity, we shall rely on the research of specialists and institutions working in this field. There are various intelligent tutoring system definitions such as:

* A learning technology that dynamically adapts learning content to objectives, needs, and preferences of a learner by making use of his expertise in instructional methods and the subject to be taught [27].

* The system that is using more articulate representations of the domain knowledge, so that the computer can reason about the knowledge it incorporates, besides merely presenting it, encoding didactical knowledge, so that the computer can reason about how it should communicate the appropriate information, including capabilities to model learner behaviour, for the purpose of monitoring, diagnosing, and curriculum planning, and providing an adaptive interface, which may include capabilities for language processing or graphical communication is called an intelligent tutoring system [21, 26].

[check] A tutoring system is software whose aim is to communicate the knowledge of a domain (mathematics, language, etc.) to its user. Such a system is named "intelligent" mainly if it can adapt the interactions to the learner. Therefore, a tutoring system must have, among other things, some information about the user [7].

[check] Intelligent Tutoring Systems (ITS) are software programs which provide instruction for a learner with guidance and insight in the way a teacher would guide a student. In an ITS program the knowledge of "how to teach" is distinct from that which is to be taught and from that which the student knows. Each of these areas of knowledge may be captured in a knowledge base or at least some form of an abstraction which the program operates upon to control its execution [14].

[check] Broadly defined, an intelligent tutoring system is educational software containing an artificial intelligence component. The software tracks students' work, tailoring feedback and hints along the way. By collecting information on a particular student's performance, the software can make inferences about strengths and weaknesses, and can suggest additional work.

According to Freedman [8], the traditional ITS model contains four components: the domain model, the student model, the teaching model, and a learning environment or user interface. Wenger [26] presents the following model of ITS: Domain Expertise, Student Models, Pedagogical Expertise and Interface. Beck et al. [2] have identified five major components of ITS: Student Model, Pedagogical Module, Domain Knowledge, Communications Module and Expert Model. Each ITS must have these three components: knowledge of the domain, knowledge of the learner, knowledge of teacher strategies [15].

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