Preparing the Camera Ready Paper for

MODELING OF ADES-ESP SYSTEMSuzana Markovi1, Ranko Popovi2Abstract This paper outlines a multimedia adaptive system of

distance learning where the main emphasis is laid on the student and corresponding student profile. System has several advantages, which improve teaching and facilitate the learning process compared to existing systems. Teachers play significant roles in the system. Web-based system is easily available to students. The contents are automatically adaptive based on student performances and teacher preferences. Finaly, system is divided into the several modules. This approach provides great flexibility to adapt to future needs.Keywords Distance learning, student profile, adaptive system, testing.

I. Introduction

Rapid development of technological achievements and opportunities available in the information era impose new rules of conduct in all fields, including education.

As the use of the computer as a standard instruction medium has begun, researchers have tried to integrate the instruction delivery media in an adequate way and use the possibilities the same offer. On the other hand, most teachers still rely on well-established primitive aids. For example, the chalkboard - one of historys earliest teaching tools - remains the preferred exposition medium in many scientific disciplines. Since the advent of computational devices in education, researchers have sought the means for properly integrating them and taking advantage of their capabilities [1].

Numerous computer-based systems have been used in education to increase student performance and motivation.

There are distance education models based on students behaviour and their interactions with the system [2]:

Computer-Assisted Instruction (CAI or CBT Computer - Based training).

Intelligent Tutoring System (ITS),

Integrated Learning System (ILS),

Adaptive Integrated Learning.

In traditional learning environments, teachers monitor students and control their learning strategies and performances. Nowadays, many computer-based learning systems provide students with personalized learning and enable them to be responsible about their learning, while the teachers roles is ignored. So, the motivation behind our research is to develop a flexible system which works according to a teachers decisions and student performance. Thus, our proposed model is adaptive and it will be presented on the following pages.1Suzana R. Markovi is with the Advanced Business School, Kralja Petra I, Blace, E-mail: msuzana@vpskp.edu.yu

2Ranko M. Popovi. is with the The Faculty of Business Information Science, Singidunum University, Danijelova 32, Belgrade, E-mail: rpopovic@singidunum.ac.yu

II. Literature review A. Adaptive Models And Student ProfileTo improve online learning environments, number of theories and practices are proposed, such as the content management system with authoring tools (help teachers implementing their instructional strategy), learning objects and digital repository to share and reuse educational resources, ontology and student profiles to achieve personalized learning, and learning community study. Above all, the significant change is the learning paradigms shift - from teacher-centred to student centred [3].

For the purpose of implementing a qualified and useful teaching course it should be adapted to a student. Therefore a student model should be developed while implementing such kind of systems. Student model includes information about a certain student knowledge level, skills level, tasks performance ability, psychological and other characteristics that are needed for effective adaptive teaching process organization [4]. That model also includes components that are essential for effective teaching process organization: knowledge level, psychological characteristics, learning speed/style, tasks performing, learning ability, skills level, teaching strategy, method and knowledge graph.

The flexibility of computer-based learning systems is one of the major advantages over traditional learning methods. System does not give students full control over predefined learning directions, but it offers teachers the flexibility to monitor learning activities and automatically guide students.

The process of learning is carried out in 3 stages [5]. On the first stage the learner studies theoretical material. The second stage is intended for checking the learners knowledge of theory. During the third stage the learner solves the problems on the subject under the system adaptive control.

The typical learning scenario is proposed by Microsoft with Windows SharePoint Services as the base portal layer. On top of this layer, a framework of educationally relevant Web parts from any vendor can be added to customize a solution. SharePoint Learning Kit (SLK) provides the basic ability to assign, track, and grade e-learning content to learners. SLK provides basic distribution of electronic documents to students and final grade capability. SLK is a SharePoint-based Web application that leverages a core technology called Microsoft Learning Components (MLC). Developers can use MLC to add e-learning functionality to new or existing applications [6].B. Models of instructional presentation

One of the advantages of a computer based learning system over the traditional learning systems is its flexibility. These systems do not give full control over the learning process to the student, but also include the teacher in the process, who oversees his/her activities and automatically leads him/her through the learning process aided by appropriate tools. Therefore, there is a need for a collaborative system that can let a teacher know how learners are progressing so that the teacher can modify and disperse near real-time course content if learners are struggling with current content. Such systems provide an innovative method of instruction that adapts to the learners unique learning style [7].

The aim of adaptive systems is precisely the connection of instruction and learning into one integrated dynamic knowledge-transfer environment [8] which uses learning objects made according to a valid standard, such as SCORM (Sharable Content Object Reference Model).

One of the existing e-learning multimedia systems which synchronize presentation slides with instruction video files is made at School of Engineering - University of South California - USC. Online Webcast demo version of the DEN system (Distance Education Network) can be found at http://den.usc.edu [9].

E-Chalk software [10] simulates a chalkboard using a touch-sensitive computer screen on which the lecturer works using a pen. System enhances classroom teaching by integrating the multimedia features of modern presentation software with the traditional chalkboard. This approach preserves the didactical properties of the traditional chalkboard while helping instructors create modern multimedia-based lectures.III. Methodology of implemented solutions

The topic of this paper refers to the implementation of a solution in the development of web applications. It is a realization of an adaptive distance learning system, since it provides student with: attending online classes (synchronized video instruction presentation), real-time communication and defining the student profile (with general information: information about courses completed, examinations passed, and individual personality). Within this module testing is carried out to assess the knowledge of students on the basis of which a student is assigned to a corresponding group. This grouping affects the level of the teachers instruction.

The layers of designed application three-layer architecture are:

Data layer - necessary data are placed in MS SQL database together with stored procedures (Fig.1).

Middleware layer describes business logic and access to data implemented by ASP.NET. The selected web server is IIS.

Presentation layer - client side covering user PC desktop applications, Web applications, mobile applications, etc. depending on technologies used for the presentation layer, such as ASP.NET, MOBILE.NET, PHP, etc.

Fig.1. The part of the Main SQL database diagram

System enable authorized student access to instruction material. Namely, the entire system is so designed to be divided into following modules:

1. The instruction module takes the central part of the application. The module is implemented by means of ASP.NET technology. The basic achievement course is just distributed online as a multimedia course. That is, video file lectures (Avi format) are transformed into many .swf format files (by applying Cantasia studio tools), dividing a huge file i.e. capacity is divided into many small ones. The obtained files can be downloaded more easily and quickly from the Internet, which was exactly the purpose.

The realization of this module is given in Fig. 2. The module saves its data in the database. The selected RDBMS is MS SQL. This module is directly connected to the communication module.

Fig.2. Instruction module realization

2. The goals of online learning environments are to achieve adaptive learning and help learners to create their own knowledge. Given the proposed adaptive learning environments, learners can be uniquely identified and their learning processes can be monitored with their respective learner profiles.

The next module represents the student's profile. A student's profile is determined by various parameters. When a student registers to the system for the first time all relevant data are stored in the database. Every transition which is made by the student (course enrolment, testing) affects his/her profile. That transition is stored in the database and the teacher has access to them at every moment.

This module comprises the following information:

general student data (name, surname, address, e-mail, age, etc.),

cognitive level: the appropriate group to which the student belongs on the base of preliminary assessed level of knowledge,

his/her performance at specific courses he/she completed, learning ability, learning style (learned, not learned, current learning) and

a number of different methods of knowledge testing applied depending on the group to which the student belongs.

3. The final module is the one by which the communication in the system is achieved. In order for the communication to be truly synchronized, the participants must be able to find one another in the network and exchange messages sufficiently quickly to lend credibility to the term synchronized communication.

A part of real-time video communication together with messenger will enable synchronized communication between teacher and student on current instruction material. If required, the communication can be textual only. A. Knowledge assessments

In order to assign a student to the appropriate group, it is necessary to carry out appropriate knowledge assessment. Unlike static knowledge assessment, which usually has a fixed number of questions presented to all students, the adaptive model uses dynamic knowledge assessment.

The knowledge testing (Figure 4) may be: self-evaluation, preliminary (assessing the students general level of knowledge), progressive (assessing student knowledge at different stages of the learning process) and final (final exam).

Each knowledge assessment has different parameters defining: the assessment termination (completion) criterion, number of questions to be offered to the student, etc.

A student evaluates his/her own knowledge of instruction fields by self-evaluation. As a result the system gives statistical data evaluating a student's success.

In preliminary testing, time as a parameter is not taken into consideration, since the testing is intended only to determine the level of the students knowledge and to assign him/her to the appropriate group accordingly. This assessment is used first to evaluate students level of knowledge. It is the static testing with a fixed number of questions and a test is the same for all students. The teacher (initiator) prepares questions for this kind of tests (Figure below).

Fig.3. Interface for tests preparing

Preliminary assessment assigns a student to an appropriate group: group A students with best results, group B students with average results, group C students with bad results. In this assignment stage, the system automatically assigns students to an appropriate level of knowledge.

In the phase of progressive testing, the adaptive system automatically determines the students level of knowledge and, if required, dynamically generates the areas he/she has to learn again.

The teacher accomplishes progressive assessment when he/she has finished an appropriate instructional section module. The teacher prepares questions and assorts them into categories easy (E) and hard (H). The questions are stored into the database. When the student enters for assessment, the system generates a certain number of randomly selected questions. But the weight of questions will depend on the group a student belongs to. So, the system is adjusted to student and send him/her questions depends of group. The number of easy and hard questions is variable with course progress (Table 1).TABLE I

The number of easy and hard questionsGroup C

E (%)H (%)

5644

6238

6832

7426

8020

As the course progresses, students from group C will have less hard questions and their number will depend on number of progressive assessment. So the number of questions will be determined by formula (80-50)/n, where n is a number of all progressive assessments (in the beginning, we suggest that the number of hard and easy questions should be equal). Table 1 shows the case of 5 assessments. The proportion of easy and hard questions for students from group A will be reversed. The system is realized so that the number of easy questions (group C) in the last assessment is always 80%. Students from group B will have an equal number of easy and hard questions.

The final result of an assessment enables student transition between groups. The system summarizes the scores from assessments; divides them with the mentioned number and rates the student in the following way:TABLE IIStudents rateScore

ABC

76-10051-750-50

Therefore, a change in the number of students in a group affects the system dynamics and a number of questions with different weight affect the system adaptively.

For example, if the student from group C, on the first progressive assessment gets 53 points, on the second 79, then his/her total score will be (53+79)/2=66 points, so he/she will move to group B etc. So, students progressing (or degrading) continually influences changes of his/her profile. At the end of progressive assessment students in group A will have minimal ensured mark 8 on the final assessment, even if they do not make assessment well.

Final assessment of students is carried out at the end of course. The system generates random questions from the same database as questions for progressive assessment. Final assessment will enable poor students to improve their score which is made during the progressive assessment. Better students will confirm their success. At the end of the test the system will provide a summary score as well as a suggestion for learning those modules for which the answers were wrong.

The following flowchart shows type of testing regard to initiator, stage and content of the tests.

Fig.4. Type of testingIV. ResultsIn this part, the statistical results obtained through the monitoring of the students progress by the teacher are shown. Information system designing was the subject of the testing of students who must have certain previous knowledge of information systems, acquired in the Introduction to Information Systems course as a prerequisite to attend this course.

1. First stage Preliminary assessment (Figure 4). This assessment is used first to evaluate students level of knowledge.

Preliminary assessment assigns a student to the appropriate group: group A students with best results (6 students), group B students with average results, (10 students), group C students with bed results (9). In this assignment stage, the system automatically assigns students to an appropriate level of knowledge.

Teaching according to the student level of knowledge is an important issue in adaptive learning. This statistics indicate to teacher that level of course has to be normal, otherwise it can be elementary or advanced.

Progress assessment is used to estimate the student's acquiring of knowledge following the completion of any module in the concept learning.

During the progress assessment the system decides in what group a student should start to initiate the next progress test.

During the final exam system generates a response: students are assigned to concepts that they did not successfully master during the assessment stage. The goal of assessment is to know exactly what concepts a student needs to learn.On the basis of preliminary assessment the teacher defines from which layer to start the course. The layer of the course can be elementary, basic or advanced. Teacher composes the questions for each module of specified course and gives them appropriate weight. The questions can be easy and hard. The teacher has access to a students profile at every moment.

Also, system observes students progress during the teaching process. There are two types of students: students who have regularly attended lectures and those who have not attended lectures at all.

The research results show that students who have regularly attended lectures have better passing average than those who have not.

These results should be taken into consideration with reserve because of next problem. The same research that we have performed in a state-run high school in Blace, we have applied at a private faculty in Belgrade, where we have maintained a statistics following students' attendance and success. The studying expenses for this region are extremely high. Students have opportunity of distance learning using our programming packet ADES-ESP. Every fifth student did not attend instructions and practices of first year courses during first semester. We expected that students have been used distance learning system; however, no one has passed exams in the first term. With introduction of pre-exams duty and minimal number of points which students must gather in order to go in for an examination, this percentage of success did not change.V. Conclusion

Multimedia instruction software environment should offer something more than the management of individual instruction units. It must provide a complete solution offering the integration of educational mini applications and fundamental support to create and distribute generated contents.

This paper outlines a multimedia adaptive system of distance learning where the main emphasis is laid on the student and corresponding student profile. The system has several advantages, which improve teaching and facilitate the learning process compared to existing systems. Teachers play significant roles in the system. Web-based system is easily available to students. The system is automatically adaptive based on student performances and knowledge. Finally, the system is divided into several modules. This approach provides great flexibility to adapt to future needs.

This project is not fully completed, since several other features are being designed or in the process of being developed. For the future work, we intend to include hardware adaptability on different devices: desktop PC, mobile units (lap-top, PDA, cell phone) in the system.References

[1]G. Friedland and K. Pauls: Architecting Multimedia Environments for Teaching, Freie Universitt Berlin, Published by the IEEE Computer Society, June 2005.

[2] F. T. Alotaiby and J. X. Chen and H. Wechsler and E. J. Wegman and D. Sprague: Adaptive Web-based Learning System, Proceedings of the 12th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, 2005.

[3] Chi-Syan Lin and Ming-Shiou Kuo: Adaptive Networked Learning Environments Using Learning Objects, Learner Profiles and Inhabited Virtual Learning Worlds, Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT05).

[4] L. Zaitseva and C. Boule: Student Models in Computer-Based Education, Proceedings of the The 3rd IEEE International Conference on Advanced Learning Technologies, 2003.

[5] I. Galeev and L. Tararina and O. Kolosov: Problems of building adaptive integrated learning environments, Proceedings of the The 3rd IEEE International Conference on Advanced Learning Technologies, 2003.

[6] http://www.microsoft.com

[7] G. Vert, R. Yakkali: Towards A Collaborative Model Of An Automated Adaptive Content Delivery Training Utilizing Fuzzy Logic, 0-9785699-0-3, 2006 IEEE

[8] V. Adamchik, A. Gunawardena, Adaptive Book: Teaching and Learning Environment for Programming Education, Proceedings of the International Conference on Information Technology: Coding and Computing, 2005.

[9] http://den.usc.edu

[10] G. Friedland, L. Knipping, and R. Rojas: E-Chalk Technical Description, tech. report B-02-11, Dept. Computer Science, Freie Universitt Berlin, 2002._1241878953.unknown

_1232096767.vsdInitiate

Grouping students

Learning module- preparing test

Progressive - dinamic testing (adaptive content)

Prepare

Evaluate

Self evaluating of knowledge

Questions

Terminate

Level of teaching

Student level of knowledge

Finished?

Final exam

Preliminary - static testing (fixed content)

Students

Teachers

Learning materials

Yes

No