FROM THEORETICAL AND EXPERIMENTAL CONSIDERATIONS TO THE DESIGN OF AN ADAPTIVE DISTANCE LEARNING

ENVIRONMENT: THE CASE OF ARDOVIA

Cherkaoui Chihab (ccherkaoui@yahoo.fr) Megder El Hassan (megderel@yahoo.fr) Laboratoire CERDIE, Université IBN ZOHR Ecole Nationale de Commerce et de Gestion

BP.37/S Hay Essalam, – Agadir – Maroc. http://www.encg-agadir.ac.ma/

Mammass Driss (Driss_mammass@yahoo.fr) Laboratoire LIMI, Université IBN ZOHR

Faculté des sciences B.P.28/S – Agadir – Maroc.

KEYWORDS: Distance learning, Dynamic Planning, Multi-agent, Adaptability. Abstract In most of the existing platforms for distance education, the taking into account of individual differences between students is nearly non-existent or it is often based on an instructor’s presence. But, an instructor can sometimes be overflowed by the different electronic mails received. An efficient distance-learning environment must be able to adapt the content to all students by planning sequences of activities. The main goal of this communication is to show some aspects bound to such a planning in the ARDOVIA environment. INTRODUCTION

The internet is one of the major technological innovations of this century. After affecting sweeping changes in the way people communicate and do business, the internet is poised to bring about the way people learn. One of the major consequences of this tremendous growth has been the rapid growth of technology-mediated distance learning at each level of education, and in particular the higher education. Recently, there has been a large number of educational applications (tutorials, course notes, etc) delivered through the World Wide Web (WWW). The design and the implementation of such applications is mainly based on platforms – called LCMS (Learning Content Management Systems), like WebCt (WebCT 2002), or the freeware tools like Claroline (Claroline 2002). The LCMS tools are more suitable to rapid design and management of online courses (Rasseneur 2003; Faerber, 2003). Besides, a LCMS system allows the user (teacher or administrator) to configure the system by changing some parameters and the system adapts its behaviour accordingly. One limitation of these platforms is that they propose to students the same page content, the same set of links and a "poor" individualised help. The access to WWW educational applications, does not guarantee effective learning, as many students lack the abilities to find their way through a vast amount of accessible knowledge. Students need guidance, either from human or computerized tutors. But the human tutor can sometimes be overflowed by the different demands he receives, notably in asynchronous mode, when it is about answering a big number of electronic mails, in particular for a large group of students. For a student, the first quality of a learning system is to be adaptable, and especially

able to adjust teaching to his rhythm, and to his style of learning. These different aspects lead us therefore to review the different elements bound to distance learning in a larger conceptual setting by combining the Intelligent Tutoring Systems (ITS) Technology with Hypermedia concepts, as underlined in (Brusilovsky 2000). This paper introduces ARDOVIA (ARchitecture de production de DOcuments Virtuels Interactifs pour l’Apprentissage): an environment expected to be used for distance learning to support algorithmic and Visual Basic programming course for a big group of students at our University. The first and most important capability assigned to the ARDOVIA environment is to be adaptive and able to decide what and how to teach next. It must be able to generate plans, monitor the execution of plans, and generate new plans (McCalla 1992). The next sections of this paper presents briefly the methodology used in this research, the ARDOVIA environment, in particular its planning mechanism to deal with adaptability of the online courses and finally, the implementation issues of the ARDOVIA environment. METHODOLOGY AND DATA-GATHERING Methodology At our university there is a large number of students that fail programming. At 1999, of the 160 students who were registered, only 90 succeeded the exam of the programming module. Concerned about this, we asked students why, in their opinion, programming has a high degree of failure. The student’s answers were very diverse, but all of them agreed that they only had a few hours of practice. They also agreed that when they were at home, it was more difficult to do exercises because the books didn’t have answers. When we studied answers, we thought that online courses and human tutoring could be a solution to the presented problem, so we decided to develop ARDOVIA. The first version was achieved in 2001. This environment was first created to allow the students to complete courses delivered in face-to-face mode by a number of online resources and activities. Actually, the system offers, some general information on the Visual Basic programming and algorithmic course, that enables the different students, to get administrative information. It also gives educational information, for example: the statements and the correction of practical works, guided works, past exams, and the correction. Various links with educational Web sites are also offered. A library of exercises with the corrected exercises is proposed. The first prototype of the system was tested with 30 students and 3 human tutors during a two-hour class session in the fall semester 2001. Students had a choice of working in 10 different chapters and a total of 50 exercises, with 10 quest-tests. Data-Gathering The system kept a detailed log of student interactions, in addition, students and tutors filled out a questionnaire after each session. In one questionnaire, for example, students responded that they liked the system for a number of reasons: first, they felt that the immediate feedback and syntax practice of instructions was very valuable. Second, in one of the questions students where asked whether they noticed that the feedback proposed by tutors adjusted to their level of expertise. 75% of the participants stated that they noticed and they preferred this scheme and found it useful. Tutors are asked, for example, to note the frequent mistakes and the proposed feedback. Tutors are also asked to notify, in particular, their teaching strategies and their interventions (or manual planning) when proposing help, choosing examples or exercises. In the system, the access to a manual planner is possible. In this case, tutors can plan a sequence of activities for

students facing problems. At this issue, the tutor must choose a pedagogical objective and the associated activities to achieve. This experimentation permitted us to acquire a different knowledge on students and tutors. For students, we can note their preferences, their ways to navigate and the consulted items of the online course, their behaviour facing the different used media, their questions about the courses, their mistakes, etc. For teachers, we underline, in particular the tutoring protocols acquired which constitute important features of dynamic planning in the ARDOVIA environment. The pedagogical strategy and pedagogical objectives described in the next section, constitutes some of the elements identified at this stage of the study. SYSTEM DESCRIPTION AND ADAPTIVE COMPONENTS ARDOVIA represents knowledge about units to be learned as individualized activities. Each activity, introduces new concepts or offers problems to be solved. The choice of an activity or a sequence of activities is dynamic and depends on several elements including the individual learner model, and the pedagogical objective of the session (Fig. 2). When a sequence of activities is selected, it can be adapted to a particular student according to different aspects (content, help, next links, interfacing, etc.). This can be done by what we called here the Individualization procedures. For example, for interfacings of activities of type course document (Fig.1), we developed an individualization procedure that uses colours to facilitate the navigation; this issue is inspired from the ADAPT-TUTOR system (Czarkowski 2000). The red colour proposes an itinerary not counselled, whereas the green links proposes ready web pages which are ready to be learned. All prerequisites to the student and Web pages that have already been visited are coded in the colour blue. In this context, a simple activity constitutes a type of activity that can be individualized for each student. That’s what we present in a didactic plan (Fig. 2.a) as an activity type (ACTT). Three classes of online activity types exist on our system: course documents, exercises and examples. For each of these activity types, we have subclasses of activity. For example, for exercises we propose currently four types of exercises: the text pages, the Multiple-choice questions permitting to evaluate the basic knowledge of students, exercises permitting a specific work at the instruction level in programming, and others having for role to allow students to combine the different instructions to write some longer computer programs.

Figure 1– Sample visualization of the interfacings in ARDOVIA

From the data gathered in the first experimentation of the ARDOVIA environment, each activity can be related to an operator of the pedagogical strategy. That is, an activity can simply introduce a concept, fix its acquirement, or evaluate if the student possess the required knowledge. To illustrate this presentation, we propose here a brief description of the pedagogical objectives, the nature of the pedagogical strategy and the adaptive components.

Pedagogical objectives A pedagogical objective corresponds to a particular concept of the Visual Basic programming language. A space of objectives corresponds to knowledge to be learned and it is represented in terms of a conceptual network (Kay 1994; Specht 1997). The structure of an objective is proposed by our teachers and tutors, as described above. It is composed of elements called factors. A factor is characterized by its name and other components presented in (Cherkaoui 2003). For example, factors that can intervene for a first group of sessions are: "F_Interface", "F_Procedures", "F_Events", "F_Objects", "F_Variables", "F-Printing ". In Visual Basic, the factor F-Interface, for example, consists in introducing the main elements of interfacings (TextBox, labels, buttons,…). Pedagogical operators and pedagogical strategy The pedagogical operators represent types of actions available to the system in order to reach a pedagogical objective. For instance, we can mention four main operators: MIS (MIse en Situation), FIX (FIXation), CONT (CONTrôle) and APPR(APPRopriation). The MIS operator has for main role to allow students to acquire the basic concepts of algorithmic and programming. The FIX operator is a stage in which the student begins to fix some ideas of the courses proposed. The CONTROL operator permits the assessment of the student through exercises at the instruction level of programming, but also through the Multiple-choice questions. The APPROPRIATION operator takes place in the face-to-face context when the teacher tried to show deficiencies of students as showing examples of the possible mistakes and bad conceptions of the students raised either on the Web or in exams. This stage is done by explanations, examples and against-examples, but also with the help of returns toward the Web. User modelling The new Students connected to ARDOVIA are asked about their background, preferences, and goals in an introductory questionnaire. Students can specify learning materials and can select a preferred strategy like learning by example, reading texts, or learning by doing. They also indicate the level of detail of Web pages. A Web page for a concept is displayed with levels of detail, for instance, the “Exercises” and the “Examples” buttons (Fig.1) which can be enabled or not depending basically on the learning strategy selected in the questionnaire. The actions of a students (e.g., if tests are solved, if the response to an exercise is correct, etc.) has consequences for updating the learner model at three levels of detail. The historical representation level is an inventory of the student’s interactions corresponding to recorded events (number of trials before result, right or wrong results, response delays, for instance). The strategic representation integrates the hypothesis of the strategies applied by the student in order to solve exercises, for example the interpretation of the response time, on the rate of trial-error, on number of choices made in web quests (Multiple-choice tests). The cognitive representation directly concerns the believes made by the system of the student’s knowledge according to the factors which compose the pedagogical objectives, in terms of beliefs Formula as described in (Singh 1994). The adaptation mechanism Adaptive Navigation Navigation in ARDOVIA is supported through adaptive annotation of links and the highlights of buttons. A colour is associated to each link, which holds information about its state. This issue is inspired from the ADAPT-TUTOR system. The red colour proposes an itinerary not counselled, whereas the green links proposes accessible pages to the student. Pages that have already been visited are coded in the blue colour and an orange colour stands for a link to the students lacks

prerequisites. The highlight of buttons is used to allow the students to read more texts proposed in the pages, rather than do exercises, or consult examples or both of these. Adaptive planning In ARDOVIA, students can start to work on the selected activities, which are chosen by the process of plan generation. This process can first, compute the next best pedagogical objective in the curriculum depending on the overlay model of a learner and the prerequisite of possible concepts. In the simplest case, the algorithm is a top-down generation. For each factor of the chosen objective, a list of pedagogical operators is determined. Then, for each pedagogical operator of this list, it is subsequently an activity which is sought. This process is then repeated until a sequence of activities is found. The process of execution begin by applying individualisation procedures, which will transform activities into the individualized one by permitting help or not, colouring links, highlighting some buttons or not. This process, executes the activities by showing the corresponding web pages. The re-planning process can evolve at all levels of the plan, according to the prerequisites or to the type of gap between the expected representation of the student and the effected one.

Figure 2–(a) Structure of an instructional plan, (b) an example of portion of a plan.

An example of a plan In order to introduce the “printing” and the “the data entry” concepts, the objective “O-InputOutput” is selected by the system and contains factors like “F-Printing”, “F-Entry-Data”, “F-variables” or “F-Affectation” (Fig. 2b). The last two factors (mentioned in gray colour on the Figure 2b) are added to the plan if the student modeller shows that the student has problems using variables or must have prerequisite of the affectation instruction. When the factors are selected, the choice of the pedagogical strategy depends either on the user model, or on the prerequisite information of the initial state of the model, if it is about the first session. In this example, for the “F-printing” factor, three operators are selected: MIS, FIX and CONT. For each of these operators an activity is defined. For instance, as the MIS operator corresponds to introducing a new concept, the system choice is a Web text page with links and examples. The FIX operator consists to fix a concept, the activity chosen is an exercise asking the student to write a program that prints the string “Bonjour”. At this moment, the student must write the computer program in the form proposed by the system. If the answer is correct, the execution of the next activity is available, if not, the re-planning process adds to the plan a web page with examples giving more explanation. IMPLEMENTATION The architecture of ARDOVIA is conceived as a multi-agents system. Four Macro-agents are defined: the Planning agent, the student's modeller agent, the interfacing manager agent and the

Expert of the domain agent. For the implementation, we used at the server CGI scripts, the JAVA technology and the PROLOG language. Components permitting adaptability are also put on the server and arrange two knowledge bases, the knowledge base called BCA that contains information on the different interactions of the student and the knowledge base named BCR that contains resources (Exercises, Course, links). Each of these resources has potential of reusability and adaptability. Forms of interactions are coded with the help of the HTML and the JavaScript code that play the role of event manager and a dynamic generator of Web Pages. The mechanism of the Cookies is also used to capture interactions of the student. CONCLUSION In this paper we described a framework for adaptive learning environment for WWW. Integrating the dynamic planning (with hierarchical planning) and the student modelling (with a multi-level of representation of the student) into this framework allows several adaptive methods to be implemented. More advanced adaptive methods like the explanation of the solutions to the proposed exercises, of course require more elaborated approaches to student modelling. We think that ARDOVIA reached of brief way the fixed objectives. However, we underline that the present implementation endures a slowness, probably owed to the CGI scripts, and that we intend to make migrate toward the PHP language, faster in its execution and easy to maintain.

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