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Weekly UpdatesHowever because of high development and maintenance costs, lack of components reuse and standards usually only some research idea is investigated and the rest of a system is left just as infrastructure of components for only supporting research. The authors of publication [5] have proposed a mathematical model facilitating integration of computing and domain areas. Huang et. al. [12] have proposed learning parameter improvement mechanisms that calculate the students' effective online learning time, extract the portion of a message in discussion section which is strongly related to the learning topics, and detect plagiarism in students' homework, respectively. The authors of publication [28] have created an intelligent tutoring system capable of carrying on a natural language dialogue with a student who is solving a problem. The study performed by the scientists of publication [13] proposes a conceptual map model, which provides learning suggestions by analyzing the subject materials and test results.
However scientists are analysing different aspects of intelligent tutoring systems, there are thousands of the systems created, but not so many used. The reason--no general tutoring systems on real estate management does not exist. The authors have developed the intelligent tutoring system, from the beginning to the end, having all the interrelated components. The system is designed for tutoring real estate management.
This paper is structured as follows. Following this introduction, Sections 2-9 describe the Intelligent Life Long Learning Tutoring System for Real Estate Management: Structure (Section 2), Domain Model (Section 3), Student Model (Section 4), Decision Support Sub-system (Section 5), Database of Computer Learning Systems (Section 6), Tutor and Testing Model (Section 7) and Graphic interface (Section 8). Finally, some concluding remarks are provided in Section 9.
2. STRUCTURE OF THE INTELLIGENT LIFE LONG LEARNING TUTORING SYSTEM FOR REAL ESTATE MANAGEMENT
The Intelligent Life Long Learning Tutoring System for Real Estate Management (ILLLTS-REM) has been created by authors in order to help employees to improve their qualifications in the field of real estate throughout their active professional life. This system enables learners to navigate in the information and knowledge variety and dynamically adapts learning content to objectives, needs, and preferences of a student by making use of his/her available expertise.
The Intelligent Life Long Learning Tutoring System for Real Estate Management consists of six subsystems:
* Domain Model,
* Student Model,
* Tutor and Testing Model,
* Database of Computer Learning Systems,
* Decision Support Subsystem,
* Graphic Interface.
The interrelationships among the components of the system are shown in Figure 1.
[FIGURE 1 OMITTED]
The subsystems are briefly analysed below.
3. DOMAIN MODEL
Domain Model (Domain Knowledge) component contains information the tutor is teaching, and is the most important since without it, there would be nothing to teach the student [7].
Since 1999 the e-learning Master degree studies "Real Estate Management" have been introduced in Vilnius Gediminas Technical University (VGTU), Master degree studies "Construction Economics" from 2000, and Master degree studies "Internet Technologies and Real Estate Business" from 2003 as well (See http:/ /odl.vtu.lt/). There are currently 220 master students from all over Lithuania studying in these three e-learning master programs.
63 modules are studied within the above programmes. All 63 modules with their supplement audio and video material are available at the ILLLTS-REM Domain Model. Usually, the amount of the material for one subject in text form varies from 100 to 500 pages.
Different Web-based links are in the presented teaching material. These links provide better conditions for lesson navigation and acquiring more related information and knowledge.
After registration, students mark the sections of 63 modules they want to study in the electronic questionnaires. If a student has already participated in these e-learning studies, then the modules he/she has studied before are considered. The system can also offer study materials to students according to the repetitive key words in different modules. Mixed approach is also possible. The received information is used for the action plans: "mini curricula" that are used to lead the learner. Naturally, different students receive different study materials. For instance, if a student is working as a real estate broker, he/she receives study materials related to real estate buying, selling, broker activities, real estate contracts, etc.
A student may also find courses using the search page. He/she enters a phrase he is interested in and the system performs a search in course titles as well as in the learning material (Figure 2).
[FIGURE 2 OMITTED]
The courses may also be found in the catalogue that could be open by pressing "browse catalogue" (Figure 2 and 3).
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Next to every course in the search page or catalogue, the link "Order" is provided. Clicking the link the course is included to the user basket (Figure 4).
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If a student selects at least one course, a new link "Next step" appears, leading to the next ordering stage--submitting the user contacts (Figure 5).
[FIGURE 5 OMITTED]
Entered the requested contacts the user clicks "Finish" and the order is saved in a database. A student and the responsible university employee receive the notes about ordered courses by e-mail (Figure 6).
[FIGURE 6 OMITTED]
The university employee reviews the orders. Clicking the link "Ref. No." he/she may revise the content of an order and decides which solution to take. He/she can reject an order, eliminate or change some courses or confirm the list of courses and proceed to the development of teaching plan. By clicking "Change" the coordinator may change the course list indicated the reasons of changes (Figure 7).
[FIGURE 7 OMITTED]
The revised course list is sent to a student by e-mail (Figure 8).
[FIGURE 8 OMITTED]
Opening the indicated link and entering the identification code the student may confirm or reject the changes. After the list of courses is reconciled the coordinator creates a learning timetable offered to a student (Figure 9).
[FIGURE 9 OMITTED]
After developing the plan, coordinator may click "Reorder", then all the parts of a plan will be sorted in a chronological order. When the work is finish, a coordinator clicks "Send offer" and the learning plan is sent to a student by e-mail (Figure 10).
[FIGURE 10 OMITTED]
Now the procedure is exactly the same as the aforementioned. A student clicking the link and entering an identification code may revise the learning plan and decide to accept or to reject an offer.
After a student user confirms the learning plan, further instruction is provided (Figure 11).
[FIGURE 11 OMITTED]
The username and password are sent by e-mail. Entering the sent data, a student may connect to the system and automatically directed to his/her learning plan. The plan provides links to the available learning material. Selecting it a student may obtain the necessary parts of electronic books (Figure 12) and solve self-control tests (Figure 13).
[FIGURES 12-13 OMITTED]
4. STUDENT MODEL
ITS 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].
The intelligent tutoring system starts by assessing the student knowledge of the subject or what the student already knows. Student Model uses that data to create a representation of the student's knowledge and learning process and represents the student's knowledge in terms of deviations from an expert's knowledge. On the basis of these deviations the system can decide what curriculum module, or chapter (subchapter) of module should be incorporate next, and how it should be presented (text, multimedia, computer learning system, etc.).
The Student Model stores data that is specific to each individual student. This information can be explicit (year of born or university completion) or tacit. Explicit knowledge, i.e. information is widely used in information technologies. The main student knowledge is tacit. Tacit knowledge is comprised of informal and non-registered practice and skills. This knowledge is vitally important because it defines the abilities and experience of learners.
The Student Model is used to accumulate information about education of a student, his/ her study needs, training schedule, results of previous tests (if he/she has studied in the above-listed e-learning MSc programmes or qualification improvement courses before) and study results. Thus the Student Model accumulates information about the whole learning history of a student.
Since the purpose of the Student Model is to provide data for the Tutor Module, all of the data gathered should be able to be used by the Tutor module.
5. DECISION SUPPORT SUB-SYSTEM
Decision Support Sub-system is used in mostly of all components of ILLLTS-REM (Domain Model, Student Model, Tutor and Testing Model, and Database of Computer Learning Systems) by giving different level of intelligence for these components.
Decision Support Sub-system aid in and strengthen some kind of decision process. Decision Support Sub-system is computer-based system that brings together information from a variety of sources, assist in the organization and analysis of information, and facilitate the evaluation of assumptions underlying the use of specific models. Decision Support Sub-system comprise of the following four constituent parts. These parts are: data (database and its management system), models (model base and its management system), user interface and message management system.
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