Adaptive Distance Learning and Testing System

Suzana Markovi1, Nenad Jovanovi1, Aleksandar Jevremovi2, Ranko Popovi2

1Business School of Professional Studies, Blace,

2Faculty of Informatics and Management, Singidunum University, Belgrade

Abstract User models are essential to e-learning systems, giving students learning continuity, tutors evidence of students progress, and both a way to personalize students learning materials to their abilities and preferences. Personalizing information has long been the motivation behind developing e-learning systems. Adaptive educational systems attempt to maintain a learning style profile for each student and use this profile to adapt the presentation and navigation of instructional content to each student. This kind of system adapts the learning process on the basis of the students learning preferences, knowledge, and availability. One such Web-based tool is built at the Business School of Professional Studies in Blace (the system of intelligent evaluation using tests), which infers student knowledge using adaptive testing. The knowledge will not be evaluated according to fixed standards, but it will depend on individual characteristics of each student, therefore the learning style which is the most suitable. This system is part of the integrated assessment system inside of integrated curriculum. Keywords Student profile, adaptive system, adaptive testing, learning style, integrated assessment.

1. Introduction

Todays dominant e-learning systems are LMS (Learning Management Systems) systems like Blackboard [1], Moodle [2]. They are integrated systems which offer support for a large range of activities in the e-learning process. Teachers can use Learning Content Management Systems (LCMS) for creating courses and tests and LMS for communication with students, monitoring and assessment of their knowledge. Students can learn and communicate with each other through LMS. However, the problem lies in the fact that LMS usually does not offer services for personalization, which means that students have access to the same set of educational tools and resources, and personalization aspects like the differences in knowledge, interests, motivation and goals are ignored [3].

According to ALFANET [4], adaptability in the context of e-learning represents the creation of students experiences under different circumstances (personal characteristics, pedagogical knowledge, his/her interaction, the result of the previous learning process) in a certain period with a tendency of increasing the pre-defined criteria of success (efficiency of learning: result, time, the price, satisfaction of the user, etc.). Since the system is adapted to the person, i.e. the user, this type of adapting is called personalization.

There are three factors which must be taken in consideration when we speak about the adaptability of e-learning systems:

Student (whose characteristics are: degree of knowledge, technical education, educational goals, interests, motivation, learning style, personal characteristics, general knowledge, etc.)

Hardware and software platform (PC/laptop/PDA mobile phone, the size of the screen, available input devices, connection speed, the performance of the processor, memory size, operating system, Web browser, etc.),

Environment (physical environment in which we observe the interaction-lights, noise, geographical location and other external factors).

According to Edmonds [5], adaptation can take 3 forms:

Adapted systems the system is customized to a particular user profile, which is defined beforehand, at design time

Adaptable system adaptation is explicitly required by the user. The user can specify his own preferences, by manually creating his profile; thus the system is dealing with a fixed profile, which can only be modified by user's intervention.

Adaptive systems in which adaptation initiative belongs to the system itself, based on continuous observation of user preferences and needs. The user's profile is no longer static; it is dynamically updated by the system, after tracking and analyzing user behavior.

An adaptive web-based system is helpful for personalization. The structure of the web site and the learning strategies are two important issues involved in web-based learning, and courseware designers need to pay close attention to how web based courseware is constructed in the curriculum as well as how learners navigate through it [6].

Adaptation decision in adaptive web-based systems is based on the users characteristics represented in the user model. User model should be based on cognitive theory and educational methodology [7].

Managing a certain student profile is the basic requirement for creating the adaptive behavior and for providing support for adaptive learning.

Adaptive educational systems attempt to maintain a learning style profile for each student and use this profile to adapt the presentation and navigation of instructional content to each student.

According to Benadi [8] when you consider the profile of a student, you have to take into consideration his cognitive characteristics which fall under different categories: sensory affinities (verbal, spatial, logical, mathematical, kinetic); cognitive styles (impulsiveness/reflection, dependency/independency); personal characteristics (introvert personality/extrovert personality).

People have different learning styles as they take in and process information differently [9]. Students learn more effectively when the instruction is matched to their individual learning styles [10]. Students whose learning styles are compatible with the teaching style of a course instructor tend to retain information longer, apply it more effectively, and have more positive post-course attitudes toward the subject than do their counterparts who experience learning/teaching style mismatches [11]. In the end, the teacher must take into consideration the different learning styles of the students.

It is very important that the teacher recognizes which learning style lies in each student and that he helps him to discover it himself. In this manner the teacher helps the student, not only to recognize the style which suits him the best, but to bring knowledge acquiring to the maximum. Quality of education significantly will be enhanced if instructors modified their teaching styles to accommodate the learning styles of all the students in their classes. When the teacher creates the lesson plan, it is desirable that he or she puts as many activities as possible which will reflect different learning styles. That means that beside the theory it is necessary to include the application of the theory, practical experience and the possibility of creating ideas.

Besides functionalities included in most LMSs such as content authoring, delivering learning content and activities to students, discussion forums, administration of classes and groups of students, schedule planning, and many more, many systems provide additional features automatic evaluation of knowledge (fixed, randomly, adaptive). It is one of the most important usage of e-learning systems - ability to evaluate or asses the knowledge of a student.

Testing is directly related to education and training as a way to measure the performance levels of students. Different methods of assessment have been used in different contexts, but the most common tools are oral test and the paper-and-pencil test. Given that the computer has been an educational tool in the last few decades, there are a number of benefits in using computers for assessing performance [12]: large numbers can be marked quickly and accurately, students response can be monitored, assessment can be offered in an open-access environment, assessments can be stored and reused, immediate feedback can be given, assessment items can be randomly selected to provide a different paper to each student.

Software tools and web-based sources are frequently used to support the learning process, so it seems reasonable to use similar computer-based technologies in the assessment process.

The basic motive of our research is to build a flexible system which will depend on the performances of students (the differences in levels of previous knowledge, competence, learning styles, communication abilities, cognitive style, etc.).

In this paper, the starting point is the fact that the traditional model of knowledge assessment does not have enough influence on the motivation for success. Namely, the teacher does not content himself with ranking students according to their knowledge but he wants to show with the grade how they are different from each other. The suggested model of knowledge assessment should lie on the success as the final experience, because teachers should establish the requirements which only determine positive goals.

This model will be based on knowledge evaluation, where knowledge will not be evaluated according to fixed standards, but it will depend on individual characteristics of each student, therefore the learning style which is the most suitable.

The suggested model will be presented in the continuation.

2. Literature review

User models are essential to e-learning systems, giving students learning continuity, tutors evidence of students progress, and both a way to personalize students learning materials to their abilities and preferences. Personalizing information has long been the motivation behind developing e-learning systems.Few attempts have been made to model user cognitive and affective attributes in order to achieve systems adaptively according to the needs of individual user. And while researchers agree on the importance of adaptation towards user cognitive and affective characteristics, there is little agreement on which features can and should be used and how to use them [13].

Guidelines and examples on content adaptation and presentation depending on various learning style in combination with instructional design theories are presented in [14]. Lessons are designed based on combinations of educational material modules, supporting several levels of adaptation towards individual learning style. Paper [15], gives guidelines for preparing learning materials according to different learners characteristics, based on pedagogical strategy and motivation factor with a strong psychological background, applying categories of Kolbs learning styles.

The most generic ITSs (Intelligent tutoring systems) architectures suggest building a good student model that reflects systems beliefs about learners mastery level in certain concepts. Moreover, such architecture is based on different domains, pedagogical strategies and enables systems to perform individualized tutoring for learners [16].

With the Internets evolution, researchers have attempted to deploy ITSs on the Web. These Web-based systems retain most generic ITS architecture features, such as AH (Adaptive Hypermedia), which generates content with different levels of detail according to users knowledge. This is known as adaptive presentation. AH system can offer adaptive navigation by giving users directional assistance in selecting the most relevant link. Such adaptive methods main purpose is to support students in hyperspace orientation and navigation [16]. Two well-known examples are: ISIS-Tutor [17] and Hypadapter [18].

In addition, Web-based adaptive and intelligent educational systems (AIESs) have begun adopting and benefiting from AH technologies. Examples include Web-administered multiple- choice tutors [19]. The three most popular techniques used in Web-based AIES are direct guidance, adaptive link annotation, and adaptive link hiding. Representative examples reflecting these include ELM Adaptive Remote Tutor (ELM-ART), Adaptive Statistics Tutor (AST), and InterBook.

SIETTE [20] is an example of a Web-based adaptive testing system. The only kind of learning material it possesses is questions. The system generates an adaptive sequence of questions to assess student's knowledge. SIETTE is not complete AIES but it has to be used as component in distributed Web-based AIES.

2.1. Learning and Cognitive Styles

The terms learning styles and cognitive styles have been often used interchangeably in literature. Jonassen and Grabowski [21] distinguish between learning and cognitive styles by explaining that learning style instruments are typically self-report instruments, whereas cognitive style instruments require the learner to do a task which is then measured to some trait or preference. However, there is a difference between their uses. Cognitive style deals with the form of cognitive activity (i.e., thinking, perceiving, remembering), not it's content. Learning style, on the other hand, is seen as a broader construct, which includes cognitive along with affective and psychological styles.

Learning style is a distinctive and habitual manner of acquiring knowledge, skills or attitudes through study or experience while learning preference is favouring of one particular mode of teaching over another [22].

There are a lot of types of learning styles. Some of them are:

Kolbs Learning Styles - The Kolb Learning Style Inventory (LSI) is a commercially available questionnaire (www.learningfromexperience.com) with twelve items where respondents rank-order four sentence endings that correspond to the four separate learning styles: diverger, converger, assimilator, and accomodator. These four styles of learning are assessed by two dimensions (abstract/concrete and active/reflective), which describe four abilities required to be an effective learner. Scores on the Kolbs Learning Style Inventory identify the learners preferred style of receiving and organizing [23].

Gregorc Learning/Teaching Style Model based in phenomenological research as well as Kolbs experiential learning cycle, that defines learning style as distinctive and observable behaviours that provide clues about the mediation abilities of individuals and how their minds relate to the world and, therefore, how they learn [24]. The Gregorc Style Delineator (GSD) is commercially available (www.gregorc.com) and asks the respondent to rank order ten sets of four words that correspond to the four poles of the two mind qualities.

Dunn and Dunn Learning Styles - there are 5 main categories (environmental, emotional, sociological, physiological, and psychological) and 21 elements in considering a learning style when using their model [25]. iWeaver [26] uses Dunn & Dunn model - the application of a Bayesian network is planned to predict and recommend media representations to the learner.

The Honey and Mumford Approach - A more promising alternative than the Kolb LSI may be a measure developed by Honey and Mumford [27], named the Learning Styles Questionnaire (LSQ). The Kolb model is the theoretical background to Honey and Mumfords LSQ, which has four styles: theorist, activist, reflector and pragmatist. INSPIRE [28] uses Honey and Mumford model.

According to the teaching model of Felder-Silverman [29], learners can be divided into four categories according to their characteristics: active learners (they like team work) and reflective learners (students who think and like individual work); sensing learners (facts, details and practical work is important to them) and intuitive learners (they like abstract materials, innovations, revealing connections and possibilities); visual learners (they remember what they see: pictures, diagrams etc.) and verbal (they use spoken or written words), sequential learners (they solve the problem by using small steps) and global learners (they learn in large leaps, details are unclear to them but they can create connections between objects).

Tangow [30] uses sensing-intuitive dimension of Felder-Silverman model. Hong and Kinshuk [31], develop a mechanism to model students learning styles and present the matching content to individual student, based on Felder-Silverman Learning Style Theory. CIMEL-ITS [32] is a novel pedagogical framework that facilitates integrating the Felder-Silverman Learning Style Theory in an intelligent tutoring system (ITS).

VARK Model: The acronym VARK stands for Visual (V), Aural (A), Read/Write (R), and Kinesthetic (K). Fleming [33] defines learning style as an individuals characteristics and preferred ways of gathering, organizing, and thinking about information. VARK is in the category of instructional preference because it deals with perceptual modes. It is focused on the different ways that we take in and give out information. The only perceptual modes, or senses, it does not address are taste and smell. The VARK Inventory provides metrics in each of the four perceptual modes, with individuals having preferences for anywhere from one to all four.

Cognitive styles refer to the preferred way an individual processes information. Unlike individual differences in abilities (e.g., Gardner, Guilford, and Sternberg) which describe peak performance, styles describe a person's typical mode of thinking, remembering or problem solving. The research literature classifies cognitive styles in two major subgroups [21]: Information Gathering and Information Organizing. Within the subgrouping of Information Gathering, there are visual/haptic, visualizer/verbalizer, and levelling/sharpening styles [34]. Within the subgroup of Information organizing, there are serialist/holist and analytical/relational styles.

Barber and Milone [35] developed a simpler instrument to measure cognitive styles (VAK Instrument), which simply classifies learners as being visual, auditory and kinesthetic or a combination of these. Cognitive styles are recognized as describing learner traits and are mostly related to their preferred way to process information. Persons with a visual preference tend to show a greater ability to analyze and integrate visual information. An auditory learner would prefer to process information in the form of verbs, either written or spoken [34]. Kinesthetic learners prefer to process information through tactile means such as interactive media.

Cognitive characteristics of students in fact affect the learning process. However, a very small number of adaptive systems are created to take into consideration these cognitive characteristics because it is difficult to gather this type of data and then design the adaptive rules according to them.

Developing e-learning systems that adapt to student learning style is not a trivial task. Design and development challenges include selecting the appropriate learning style model and instrument, creating course content consistent with the various learning styles, and determining the level and degree of adaptation of domain content. The generic pedagogical framework would allow easy integration of learning styles in an e-learning system.

Hawk and Shah [36] performed comparative analyze of five learning style instruments (the Kolb Learning Style Indicator, the Gregorc Style Delineator, the FelderSilverman Index of Learning Styles, the VARK Questionnaire, and the Dunn and Dunn Productivity Environmental Preference Survey). But only two Web-based instruments that the students could do on their own time and report the results to the instructor would be the Felder Silverman and the VARK, with only the VARK having a moderate support for validity and reliability. An advantage for using the Felder Silverman would be it is an instrument designed for engineering students.

In the end there are inevitable questions:

Can separate dimensions be considered independent and is it possible to reduce the mutual overlaps onto the smaller number of more basic styles?

Are tests and questionnaires used for the estimation of different students styles valid and reliable?

The task of the practical teachers is, on the basis of the presented ideas, to ensure and create as diverse as possible forms of lectures, as well as to monitor carefully which of them suit the concrete conditions best.

3. Methodology of implemented solutions

This paper describes a Web-based LMS system ADES-TS (Adaptive Distance Education System Testing System) developed at the Business School of Professional Studies in Blace - Serbia [37]. The three layer system architecture is realized with Microsoft's ADO.NET technology using the programming language C# and MS SQL database.

The user's profile is dynamically updated by the system, after tracking and analyzing user behavior. Regarding process of testing, system is adaptive, as no one evaluates the same test (system supports the delivery of user-specific content - questions).

As with other standard LMS, system has several groups of tools that are used by students and/or instructors. The most important sets of tools are for student assessment, collaboration support for student groups and class management.

Student Module is the key component in adaptive and intelligent learning systems. Student Profile is an abstract representation of learners in the adaptive learning system, which is the systems belief about learners [38].

First of all, the system must collect the data about students, and then process these data in order to set up the Student Profile. Next, the system must act according to the Student Profile, and adapt the system.

Information about the student is obtained in many ways, using different techniques [39]. Our system not support constructing the student model from the student's reading and navigation, but from external sources, various kinds of tests (psychological and knowledge assessment).

Generating Student Profile begins with students filling out their basic data and tests. The data are:

Fill out their personal data, data about the school and the major they are attending (registration),

Fill out VARK questionnaire (available on address www.vark-learn.com) offers sixteen statements in order to determine their learning style. On the basis of these data the system recommends the student teaching materials which would suit best his learning style.

Figure 1. Student Module

VARK questionnaire (version 7.0) is used as it is free and available. The total of all four scores ranges from 13 to 48, with individuals having a preference for one, two, three, or all four of the learning channels. Students and faculty can self-administer, self-score, and self-interpret the VARK Inventory.

The basic course is distributed online in different forms: as text (doc and pdf format) and as a multi-media course (presentation with audio and video file and simulations).

Instructors modified their teaching styles to accommodate the learning styles of all the students in their classes. When a lecture plan is created, it is desirable that as many activities as possible are utilized which reflect different students learning styles. This means that beside the theory (text and pictures) it is necessary to include the application of the theory (multimedia simulation). Web laboratory [40] completely accomplishes those requirements.

WnetSim [41] presents an environment which allows students to visualize and simulate a process in computer network with any topology. The purpose of the simulator is to help students comprehend basic principles of acting of one TCP/IP computers network as a part of the educational system of computer network.

Web-based educational computer system simulator SIMAS [42] provides for entering and compiling an assembly language program, as well as for loading and executing the resulting machine code. The simulator enables user to visualize the whole execution process of an instruction and to compare side-by-side assembly and machine language instructions.

Table below summarizes a number of learning activities to support each learning style [33]. Visual Aural Read/WriteKinesthetic

Diagrams Debates, Arguments Books, Texts Real-Life Examples

GraphsDiscussions Handouts Examples

Colors Conversations Reading Guest Lecturers

Charts Audio Tapes Written Feedback Demonstrations

Written Texts Video+Audio Note Taking Physical Activity

Different Fonts Seminars Essays Constructing

Spatial Arrangement Music Multiple Choice Role Play

Designs Drama Bibliographies Working Models

Table 1. Learning activities for learning style support

4. Adaptive testing module

Grading students is usually conducted in a traditional manner, through oral exams and tests on paper. However, today a great number of tests for learning and grading exist, which can be done over the computer. Computer tests provide a very efficient way of checking knowledge. These tests require less time for checking students knowledge and awarding grades. In reality, in the moment when the student finishes the test, the system generates a report (grade or percentage, and in some systems recommendations for further studying in areas where students did not perform well, etc.).

Numerous testing systems do not distinguish between individual characteristics of each student, neither from the aspect of assessment, nor from the aspect of expression, all students are treated in the same manner, i.e. they all do the same tests.

Considering the fact that the process of individualization is strengthening in many aspects of human development, we suggest a model which is based on knowledge assessment through adaptive testing, where knowledge will not be assessed according to constant and unchangeable standards for its participants. With the tests it is necessary to determine students preferences, i.e. which manner of passing exams will be the most suitable for each student and all in order to accomplish the final goal-making those demands from students which determine only positive goals.

According to Brusilovsky [43], the "life cycle" of a question in online assessments has been divided into three stages: preparation (before active life), delivery (active life), and assessment (after active life).

The knowledge assessment system enables creation of questions through the module for question authoring. Using appropriate interface (Fig.1), it is possible to create different types of questions (multiple choice, fill in the blank, etc.). Already stored questions can be altered - question text, answer text and other question parameters can be changed. In the process of question authoring, the ability of creating complex question texts is very important. The text editor should support authoring of questions that consist of pictures, tables, animations, etc. Apart from the above mentioned, when creating questions, the teacher must mark the following parameters (Fig.2): the complexity of the question (hard H, easy E), the number of points the question is worth depending on the complexity and the module it belongs to. Marking modules enables the system to, when generating a report at the end of testing, give the recommendation for learning those areas (modules) for which the questions were mostly wrongly answered. Parameter Answer Type refers to the following possible options: multiple choices, false/true, fill in the blank and drag and drop. Each of the options dynamically generates the form for filling in the answer. Figure 3 illustrates the use of multiple choices.

Figure 2. Interface for question management

As soon as questions and correct/incorrect answers are defined and tests cases are supplied, it is possible to perform the assessment in the delivery stage.

The delivery stage includes a question presentation, an interface for the student to answer the question and the retrieval of the answer for evaluation. This stage depends on the technology used for the learning system. For the delivery stage of an assessment in ADES-TS system, there are: windows and web-based learning environment. An assessment starts as soon as a student has logged on to the system and has entered a user name and password. The module for the presentation of an assessment question displays different question types differently but preserves the unique assessment page presentational characteristics. An assessment can be automatically evaluated at any moment. Student can check his/her answer at any time (it depends on teacher decision). After the check, the student cant change his answer and can submit the assessment for automatic evaluation.

At the assessment stage, the system should evaluate answers as correct/incorrect, deliver feedback to student, grade the question. System should provide a summary score as well as a suggestion for learning those modules for which the answers were incorrect and record the students' performance. When an assessment is submitted, students cannot access it anymore. After the automatic assessment evaluation, students are provided with summary assessment results containing all the assessment questions together with the number of points acquired question by question and for the whole assessment.

Personalized Testing System Model is suggested in paper [44]. The model suggests four types of identification: preliminary (determines learning/cognitive styles of students), self-testing (self-assessment of knowledge conducted online, important interactive element for students), progressive (assessment of knowledge in different phases of the learning process) and final testing. This system is part of the integrated assessment system inside of integrated curriculum (Fig.3).

Figure 3. Specialized module in integrated system

Integrated assessment system is assessment part of integrated curriculum. In this, integrated assessment system provides information about which assessment modules are included in that particular assessment (Fig 4.). Each of assessment modules are specialized for some type of assessment practice single/multiple choice, language dictation, specific problem based learning, etc.

Fig. 4 Global integrated assessment architecture

Each assessment module has its own database. Those databases store two different groups of data. In the first group are data used to create questions questions, answers, parameters, ranges, audio/video materials, etc. The second group are data generated for specific testing instance: student's details, answers, problem's parameters, etc. Despite this, communication between system and module during the assessment is limited to assessment request (system gives student id to logon to a specialized module) and a test result response (quantitative score, percents). Detailed testing instance results can be requested from specialized module after the end of testing.

Fig. 5 System/module communication

Each of the mentioned types of testing is conducted in school (windows application) except the online testing. Short quizzes in the form of multiple choice questions are made available online for students who want to self-test their knowledge or learning. Students, therefore, receive immediate feedback on whether their answers are correct, and what the correct response should be for each question.

To create an assessment, the following parameters have to be determined: accessibility - students use the test to help them study (online test), grading - knowledge assessment, timing (limit for answering) - the criteria of stopping the test, assessment scale, question selection criterion - for dynamic selecting of questions from database (fixed, random, or adaptive) and test-finalization. In the progressive and the final test the number of easy and more difficult questions is varied (adaptive testing).5. Influence of learning styles on the testing process

Adaptive tests for knowledge testing using computers present a very efficient method of knowledge testing which adopts according to student abilities. This adaptability is reflected in the fact that the ongoing test questions are generated as a direct consequence of students ability to correctly answer previous questions. Main advantages of this testing method are: an individual test for each participant, the smaller number of mistakes in case of different examiners, grading consistency, a higher level of test security (since it is not known which questions the individual student will get), the possibility of adapting time and acquiring a wide range of different question types, faster testing with the same level of security, instant feedback to students, giving precise results for the examined student etc.

The newest research in the field of adaptive testing which was conducted in the school, is based on the learning styles which represent different approaches or ways of learning. While learning, every student gives priority to the information that he gets through a certain sensory modality, and using that information learns in the most efficient manner.

The first research question asked student whether it is more beneficial to learn with a choice of media experiences, or to learn with only one medium experience, matched for their most-preferred learning style. Benefit was measured objectively by assessing the learning gain and subjectively by asking participants about their perceived enjoyment, progress, and motivation. It was expected that giving learners a choice of media experiences would have a positive effect.

The distribution of the four perceptual styles visual (V), auditory (A), read/write (R) and tactile-kinesthetic (K), is displayed in Fig 6. The light bars represent percentages for all 63 students, whereas the dark bars represent percentages for the group of 25 students used for the statistical analysis. It was interesting to note that 12 of all 63 (19%) participants expressed equivalent scores for multiple (two or three) most-preferred styles compared to 6 of 25 (24%) of the analyzed students.

The R style was rare: only 2% of all students expressed R style and none of the analyzed students. Similarly, only 6% of all students expressed an A style, compared to 8% of the analyzed students. The second most common perceptual style was V with 40% of all students and 36% of the analyzed students. Learners were most commonly assessed with K as their most-preferred perceptual style: 52% of all students, compared to 54% of the analyzed students. In summary, 94% of the students expressed either K or V as their most-preferred style.

Figure 6. Distribution of the four perceptual styles

Examining the influence of learning styles on the learning process itself, i. e. the adoption of new knowledge, we reached the conclusion that the testing process itself would crucially influence the learning style preferred by the student.

Students, who during the learning process take notes and draw pictures or diagrams in order to remember the information more easily, use the visual learning style. They prefer to learn alone. They prefer to get questions formulated through picture illustrations, diagrams and the like.

The auditory learning style is dominant for those who easily learn when information is presented through a text (written or spoken). They learn in the easiest way by listening to lectures, discussions, and exchange of ideas. They find the textual questions most suitable. Learning in a group or pair is a good way to learn for these students. That is why they cooperate perfectly during the group work (e.g. group projects).

Tactile/kinesthetic students learn the best through movement, game, acting and action, actively researching the world around them. They prefer practical work.

The following table shows the students style and recommended representation of tests. Table 2. Recommended representation of tests dependence of learning style

According to this, students were divided into three groups (V-visual students, A-auditory, K-practical students).

The testing showed that the system described in this paper proved to be useful to all the three groups of students. We describe our research in [41].

6. Conclusion and future work

Development in information technologies resulted in improvement of the education process. The thing that should be especially pointed out is their application in knowledge evaluation, grading and ability for learning through computer supported tests and computer adaptive tests. Computer adaptive tests represent a more effective way to assess knowledge with the maximum usage of computers. Adaptability is conducted through the ability of adjusting to the knowledge of the student. Primary advantages in the presented method of examination are in creating individual and unique tests for every candidate, the smaller number of mistakes in case of different examiners, grading consistency, a higher level of test security (since it is not known which questions the individual student will get), a wide range of different question types, faster testing with the same level of security, instant feedback to students, giving precise results for the examined students with a wide range of knowledge, etc.

Computer assessment of knowledge has been conducted in the school in the last few years and it proved very successful. Above all, the weaknesses of a teacher as a grader (the same criteria of assessment for all students) were removed. Statistics has shown that the success of the students, from year to year, has a tendency to grow. There is a quantitative and qualitative progress in the sense of increasing the number of students who pass the exam and also with very good grades.

We have applied adaptive testing for more than 300 students when we started using SIMAS [42]. The quantitative evaluation shows that the percentage of students passing the exam increased steadily from 40% before 2005.

The newest research in the field of adaptive testing which was conducted in the school is based on the learning styles, which represent different approaches or ways of learning [40], [41]. While learning, every student gives priority to the information that he gets through a certain sensory modality, and using that information learns in the most efficient manner.

The course resulted in a significant increase in high marks and the learning experience feedback sheets showed that students were much happier about the course than in the previous years. However, there was no direct control group. Thus, the novelty effect cannot be excluded as an alternative explanation for the improvement.

Developing e-learning systems that adapt to student learning style is not a trivial task. Design and development include selecting the appropriate learning style model, creating course content consistent with the various learning styles, determining the level and degree of adaptation of domain content [48] and creating appropriate tests for each student according to knowledge level and learning style.

In order to offer the possibilities of contemporary testing to our students, the created system has been experimentally used at the school. The experiment has shown that in our circumstances it is also possible to implement testing by applying IT and logic of computerised adaptive testing and to open the doors to further research and improvement.

The next stage of our project will be the composition of the curriculum, module-connected courses as a specific basis of the knowledge of teaching lessons and questions. In order to realize them we will use different multimedia elements which would suit different learning styles of students.

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[16] Qing Li, Rynson W.H. Lau, Elvis W.C. Leung, Frederick Li, and Victor Lee, Benjamin W. Wah, Helen Ashman, Emerging Internet Technologies for E-Learning, Published by the IEEE Computer Society, 2009.

[17] cs.joensuu.fi/~mtuki/www_clce.270296/Brusilov.html

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[41] N. Jovanovic, R. Popovic, Z. Jovanovic, WNetSim: a web-based computer network simulator, International Journal of Electrical Engineering Education, 46/4, pp 383-396, 2009.[42] N. Jovanovi, D.Markovi, D.ivkovi, R.Popovi, SIMAS: A Web-Based Computer System Simulator, International Journal of Engineering Education, accepted for publication, 2009.[43] Brusilovsky, P., Miller, P., Web-based Testing for Distance Education, Proceedings of WebNet 99, Honolulu, Hawaii, United States , 1999 .[44]S. Markovi, R. Popovi, Modeling of ADES-ESP system, Proceedings of the Third International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services TELSIKS, pp. 441, Ni, september 2007, IEEE Catalog number: 07EX1875.[45] R.K. Humbleton, H. Swaminathan, H.J. Rogers, "Fundamentals of Item Response Theory", Sage Publications, Newbury Park, 1991.[46] Keeves, J.P., Alagumalai, S.: New Approaches to Measurement. In: Masters, G.N., Keeves, J.P. (eds.) Advances in Measurement in Educational Research and Assessment, Pergamon, pp. 2348. Pergamon, Oxford (1999).[47] G.Fleming Learning Styles: Know and Use Your Personal Learning Style,

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