Mobile Edition
Print
Get the Mag
Weekly UpdatesFor example, ILLLTS-REM focusing on intelligence in the Integrated Web-based Negotiation Decision Support System for Real Estate (Database of Computer Learning Systems, See http://dss.vtu.lt/realestate/) can create value in the following important ways: search for real estate alternatives, find out alternatives and make an initial negotiation table, complete a multiple criteria analysis of alternatives, make negotiations based on real calculations, determine the most rational real estate purchase variant, and complete an analysis of the loan alternatives offered by certain banks. Figure 14 shows an example how the market value calculations are presented in a graphical form.
[FIGURE 14 OMITTED]
Also by using a Decision Support Sub-system, the tutor can compare the learner's solution to the expert's solution, pinpointing the places where the learner had difficulties. Decision Support Sub-system have been developed by using multiple criteria methods [17, 29] as was developed by the authors.
6. DATABASE OF COMPUTER LEARNING SYSTEMS
The database of computer learning systems enables using of the following Web-based computer learning systems:
[check] E-negotiation computer learning system for real estate (http://dss.vtu.lt/ realestate/).
[check] Web-based innovation computer learning system (http://dss.vtu.lt/ default_eng.asp).
[check] Web-based construction computer learning system (http://smg.vtu.lt/ statyba/index.php?kalba1=en).
[check] Web-based computer learning system for public buildings renovation (http:/ /dss.vtu.lt/renovacija/ index_educational.asp).
[check] Web-based computer learning system for renovation of large panel buildings (http://dss.vtu.lt/renovacija/ index_eng.asp).
[check] Web-based computer learning system for sustainable development (http:// dss.vtu.lt/default_eng.asp).
[check] Web-based decision support system for facilities management (http://dss.vtu.lt/ default_eng.asp).
[check] Web-based computer learning system for loan analysis (http://dss.vtu.lt/ default_eng.asp).
[check] Web-based computer learning system for ethics alternative analysis (http:// dss.vtu.lt/default_eng.asp).
The above mentioned systems have been developed by using multiple criteria methods [17, 29] as was developed by the authors. Each computer learning system consists of a database, a database management system, model-base, a model-base management system and a user interface.
Application of multiple criteria computer learning systems developed by authors allows one to determine the strengths and weaknesses of analysed alternatives and its constituent parts. Calculations were made to find out by what degree one version is better than the other and the reasons disclosed why it is namely so. Landmarks are set for an increase in the efficiency of versions. All this was done argumentatively, basing oneself on indexes under investigation, on their values and weights. This saved students' time considerably by allowing them to increase both the efficiency and quality of e-learning. Below is a list of typical problems solved by graduates in their course and diploma projects:
* Multiple criteria analysis and determination of market value of a real estate (e.g. residential houses, commercial, office, warehousing, manufacturing and agricultural buildings, etc.).
* Analysis and selection of a rational real estate version.
* Multiple criteria analysis and determination of the highest and best use of a real estate.
* Determination of efficient investment instruments.
* Determination of efficient investment projects.
* Determination of efficient financing instruments.
* Multiple criteria analysis of a property's obsolescence.
* Alternative design of a project's lifetime process (i.e. one-family dwelling houses, agricultural buildings, cast-in-place buildings, prefabricated panel buildings, refurbishment of buildings, etc.), its multiple criteria evaluation, determination of utility degree and the selection of the most efficient version.
The use of multiple criteria computer learning systems in solving various problems encountered in the course and diploma projects was also aimed at determining: student's knowledge acquired at the university, student's general level of education, student's keenness of mind, student's ability of fast and adequate response to changing situation.
7. TUTOR AND TESTING MODEL
The Domain Model presents frames to the learner. Tutor and Testing Model provide a model of the teaching process and support the transition to a new knowledge state. For example, information about when to test, when to present a new topic, and which topic to present is controlled by this module. The Tutor and Testing Model reflect teaching experience of associate professors or professors. The Student Model is used as input to this component, so the Tutor and Testing Model decisions reflect the differing needs of each student.
The Tutor and Testing Model formulates questions of various difficulties, specifies sources for additional studies and helps to select literature and multimedia for further studies and a computer learning system to be use during studies.
Student can select the level of difficulty at which the teaching takes place. For example, the chapters of modules with mathematical orientation (i.e. mathematical methods used for estimation for market or investment value) are quite difficult for some students.
Traditional testing systems evaluate learner's state by giving them a mark and do not provide a possibility to learn about own knowledge gaps or to improve knowledge in any other way. The Tutor and Testing Model compare the knowledge possessed by a student (test before studies) and obtained by a student during studies (test after studies) and then it performs a diagnosis based on the differences. By collecting information on a history of a student's responses, the Tutor and Testing Model provide feedback and help to determine strengths and weaknesses of student's knowledge, new knowledge obtained during studies is summarized and various recommendations and offers are provided. After giving feedback, the system reassesses and updates the student skills model and the entire cycle is repeated. As the system is assessing what the student knows, it is also considering what the student needs to know, which part of the curriculum is to be taught next.
Also there is an option of selection of the following question in a test depending on the correctness of answers to the previous questions. Correct answers lead to the more difficult, incorrect--to the easier ones.
The obtained knowledge is the difference between the possessed knowledge (test before studies) and the final knowledge (test after studies). The Tutor and Testing Model also explain why one or another answer is correct/ incorrect and offers certain additional literature and multimedia related to the incorrectly answered question.
Applying ILLLTS-REM, a tutor does not renew tests for every learner. Questions are saved in a question database and hundreds of test alternatives are developed casually.
The questions base of the Tutor and Testing Model accumulates the following information:
* Questions according to modules,
* Possible answers to the question,
* Evaluation of correctness of possible answer versions. An incorrect answer is evaluated by zero and a correct is evaluated by one; intermediate answers get from 0 to 1,
* Difficulty of a question determined on the basis of the results of previous tests taken by other students,
* Link to the study material related to the question,
* Time allocated for testing.
Having such a question data base, it is possible to create tests also in a non random way, but to individualize it for each student according to the number of questions, their difficulty and proportion of questions of different topics. Received test results are saved in the results data base.
Using statistics provided by the Tutor and Testing Model, students can see the question difficulty, average evaluation of the whole group and learn about their position in the group before and after studies. Saving the data on question difficulty, the opportunity of giving the easier questions first of all later moving on to the more complicated ones occurs. Similarly the topics can be selected--from the simpler to the more difficult repeating the most complicated topics.
Thus on the basis of the compiled questions base, questions for tests are formulated not randomly; they are individually adjusted to each student according to the number of questions, their complexity and the proportion of questions from different modules. It is also possible to give easier questions in the beginning and then to proceed to more complex. Similarly, it is possible to select the taught subject from easier to more complex and to repeat subjects that are not mastered yet.
If a student has already participated in these studies, then the complexity of his/her test is determined by the Tutor and Testing Model on the basis of his/her average evaluation and interests. Thus after registration the system gives the student questions considering the average evaluation of his/her previous studies and his/ her interests (job of the student, cognition interests, etc.). Those who take qualification improvement courses for the first time are given average complexity questions by the Tutor and Testing Model. Therefore, students are passing tests of various complexities.
Student can select the level of difficulty at which the testing takes place. For example, the testing questions with mathema0tical orientation (i.e. mathematical formulas used for decision-making) are not desirable for some learners.
FEED