Abstract: The paper provides a review of adaptive and intelligent technologies in a context of Web-based distance education. We analyze what kind of technologies are available right now, how easy they can be implemented on the Web, and what is the place of these technologies in large-scale Web-based education.
Web-based education (WBE) is currently a hot research and development area. Benefits of Web-based education are clear: classroom independence and platform independence. Web courseware installed and supported in one place can be used by thousands of learners all over the world that are equipped with any kind of Internet-connected computer. Thousands of Web-based courses and other educational applications have been made available on the Web within the last five years. The problem is that most of them are nothing more than a network of static hypertext pages. A challenging research goal is the development of advanced Web-based educational applications that can offer some amount of adaptivity and intelligence. These features are important for WBE applications since distance students usually work on their own (often from home). An intelligent and personalized assistance that a teacher or a peer student can provide in a normal classroom situation is not easy to get. In addition, being adaptive is important for Web-based courseware because it has to be used by a much wider variety of students than any "standalone" educational application. A Web courseware that is designed with a particular class of users in mind may not suit other users.
Since the early days of the Web, a number of research teams have implemented different kinds of adaptive and intelligent systems for on-site and distance WBE. The goal of this paper is to provide a brief review of the work performed so far in his area. The review is centered on different adaptive and intelligent technologies . We stay on the level of technologies to provide compatibility with earlier papers on adaptive hypermedia [7] and Web-based ITS [6]. By adaptive and intelligent technologies we mean essentially different ways to add adaptive or intelligent functionality to an educational system. A technology usually could be further dissected into finer grain techniques and methods, which corresponds to different variations of this functionality and different ways of its implementation. In the next section we analyze what kind of technologies are available right now, and how easy they can be implemented on the Web. After that we discuss what is the place of these technologies in large-scale Web-based education.
Web-based Adaptive and Intelligent Educational Systems (AIES) are not an entirely new kind of systems. Historically, almost all Web-based AIES inherit from two earlier kinds of AIES: intelligent tutoring systems (ITS) and adaptive hypermedia systems . Most of adaptive and intelligent technologies applied in Web-based AIES systems were directly adopted from either the ITS area or the adaptive hypermedia area. As long as Web-based AIES research get more mature, it will produce original technologies inspired by the Web context. At least one of these Web-inspired technologies could already be identified (model matching). This section provides a review of existing technologies grouped by its origin. For each technology we list existing Web-based AIES and projects, which implements variations of this technology and discuss the ways to implement it on the Web.
Intelligent tutoring systems is a traditional area of research that investigates problems of developing AIES [13]. The goal of various ITS is the use the knowledge about the domain, the student, and about teaching strategies to support flexible individualized learning and tutoring. A review of existing intelligent tutoring systems performed by the author in 1990 helped to identify three core ITS technologies: curriculum sequencing, intelligent analysis of student's solutions, and interactive problem solving support. All these technologies were implemented in numerous ITS. Since 1990, only one new technology (example-based problem solving support) was added to the set to classify a functionality that was not covered by the core three. While the proposed set of ITS technologies could be considered subjective and incomplete, it turned out to be very useful for classifying existing Web-based AIES. Web-based AIES that use traditional ITS technologies are usually called Web-based ITS. First Web-based ITS were reported in 1995-1996 [6; 12; 34; 37]. These systems still constitute a rather small stream inside the ITS area.
| System | Ref. | Adaptive sequencing | Adaptive navigation support | Problem solving support | Intelligent solution analysis | Adaptive presentation |
| ELM-ART | [12] | Page | Annotation | Partial | Server | Some |
| ELM-ART-II | [53] | Course, tests | Annotation | Partial | Server | Some |
| PAT-InterBook | [11] | Page, remedial | Annotation | Partial | Server | Some |
| VC Prolog Tutor | [39] | Task, remedial | Server |
The goal of the curriculum sequencing technology (also referred to as instructional planning technology) is to provide the student with the most suitable individually planned sequence of knowledge units to learn and sequence of learning tasks (examples, questions, problems, etc.) to work with. In other words, it helps the student to find an "optimal path" through the learning material. The classic example is the BIP system [5]. There are two essentially different kinds of sequencing: active and passive. Active sequencing implies a learning goal (a subset of domain concepts or topics to be mastered). Systems with active sequencing can build the best individual path to achieve the goal. Passive sequencing (which is also called remediation ) is a reactive technology and does not require an active learning goal. It starts when the user is not able to solve a problem or answer a question (questions) correctly. Its goal is to offer the user a subset of available learning material, which can fill the gap in student's knowledge of resolve a misconception. For active sequencing systems, it makes sense to distinguish systems with fixed and adjustable learning goal. Most of existing systems can guide their students to the fixed learning goal - the whole set of domain concepts. A few systems with adjustable learning goal let a teacher or a student to select a subset of the whole set of concepts as the current learning goal. In most of ITS systems with sequencing it is possible to distinguish two levels of sequencing: high and low. High-level sequencing or knowledge sequencing determines next learning subgoal: next concept, set of concepts, topic, or lesson to be taught. Low-level sequencing or task sequencing determines next learning task (problem, example, test) within current subgoal. High and low level sequencing are often performed by different mechanisms. In many ITS systems only one of these two mechanisms are intelligent, for example, a lesson is selected by a student, while learning tasks within this lesson are adaptively selected by the system. Some systems can only manipulate the order of task of one particular kind: usually problems or questions. In this case it could be also called problem or question sequencing.
Sequencing is currently the most popular technology in Web-based AIES. Almost all kinds of sequencing mentioned above were already implemented on the Web. Active sequencing is a dominated type of sequencing. Only a few systems (InterBook, PAT-InterBook, CALAT, VC Prolog Tutor, and Remedial Multimedia System) can perform passive remedial sequencing. Among active sequencing systems, only a handful of systems such as ELM-ART-II, AST, ADI, ART-Web, ACE, KBS-Hyperbook, and ILESA are able to perform intelligently both high and low level sequencing. Others, like Manic, leave a choice of activity within a topic to the user. Vice versa, some systems, like Medtec, leave a choice of a topic to the user but can generate an adaptive sequence of problems within the topic. Most of the systems supports sequencing with fixed learning goal (equals to the whole course). Only a few systems support adjustable learning goals enabling a teacher (as in DCG) or a student (as in InterBook and KBS Hyperbook) to select an individual goal. The student can choose a goal as a subset of domain concepts (InterBook) or a project (KBS Hyperbook).
Active sequencing in most of the systems is driven by the students knowledge (more exactly, by the difference between student's knowledge and global goal). A few systems and projects, however, experiment with the use of students' preferences on the type and media of available learning material to drive sequencing of tasks within a topic [14; 15; 45]. Two interesting cases of sequencing could be found in DCG and SIETTE systems. DCG [49] can perform advanced sequencing of educational material adapted to a learning goal. However, the sequencing is performed before students start working with the system producing a static Web-based course. SIETTE [40] is an example of a Web-based adaptive testing system. The only kind of learning material it possesses is questions. The only thing it can do is to generate an adaptive sequence of questions to assess student's knowledge. Systems like SIETTE are incomplete by their nature and have to be used as components in distributed Web-based AIES.
While curriculum sequencing could be considered as the oldest ITS technology (it was implemented in almost all first ITS), for about 20 years it was a Cinderella among other technologies. Very little attention was devoted to it. Mainstream ITS research were centered around problem solving support technologies (which will be analyzed below). Problem solving support was considered as a main duty of an ITS, while delivery and sequencing of education material was though to be performed outside the system (usually, by a human teacher). Naturally, almost no ITS includes educational material itself (other than a set of problems). The situation with Web-based AIES is very different. In the context of Web-based education a solid amount of educational material (usually structured as a hyperspace) is one of the main attractions of an educational system. In this context (with its "lost in hyperspace" problem), curriculum sequencing technology becomes very important to guide the student through the hyperspace of available information. This technology is also natural and easy to implement on the Web: all knowledge could be located on the server and all sequencing could be done by a CGI-script. It's not surprising that, it is not only the oldest, but also the most popular technology of Web-based AIES.
| System | Ref. | Adaptive sequencing | Problem solving support | Intelligent solution analysis |
| CALAT | [35] | Course, remedial | ||
| Medtec | [19] | Tasks | ||
| Manic | [47] | Topic | ||
| DCG | [49] | Course | ||
| SIETTE | [40] | Question | ||
| ILESA | [30] | Lesson, problems | Server, Java | |
| PAT-Online | [41] | Partial | Server | |
| PAT-Java | [42] | Java | ||
| WITS | [37] | Server | ||
| WITS-II | [27] | Server, Java | ||
| Belvedere | [48] | Server, Java | ||
| ADIS | [50] | Server, Java | ||
| (Yang-Akahori) | [54] | Server | ||
| D3-WWW-Trainer | [20] | Server, Java | ||
| AlgeBrain | [1] | Server, Java | ||
| ADELE | [44] | Server, Java | ||
| TEMAI | [38] | Partial | Server, Java |
As it is mentioned above, for many years, problem solving support was considered as a main duty of an ITS system and a main value of an ITS technology. We have identified three problem solving support technologies: intelligent analysis of student solutions, interactive problem solving support, and example-based problem solving support. All these technologies can help a student in a process of solving an educational problem, but they do it by different ways.
Intelligent analysis of student solutions deals with students' final answers to educational problems no matter how these answers were obtained. To be considered as intelligent, a solution analyzer has to decide whether the solution is correct or not, find out what exactly is wrong or incomplete, and possibly identify which missing or incorrect knowledge may be responsible for the error (the last functionality is referred as knowledge diagnosis). Intelligent analyzers can provide the student with extensive error feedback and update the student model. The classic example is PROUST [Johnson, 1986 681. As it could be seen from the Tables 1 and 2, a number of Web-based AIES implement intelligent analysis of student solutions.
Interactive problem solving support is a more recent and a move powerful technology. Instead of waiting for the final solution, this technology can provide a student with intelligent help on each step of problem solving. The level of help can vary: from signaling about a wrong step, to giving a hint, to executing the next step for the student. The systems which implement this technology (often referred to as interactive tutors ) can watch the actions of the student, understand them, and use this understanding to provide help and to update the student model. The classic example is the LISP-TUTOR [2]. This technology is also represented by a number of Web-based AIES (Tables 1 and 2).
The example-based problem solving technology is the newest one. This technology is helping students to solve new problems not by articulating their errors, but by suggesting them relevant successful problem solving cases from their earlier experience (it could be examples explained to them or problems solved by them earlier). An example is ELM-PE [51]. In the Web context, this technology is implemented in ELM-ART [12] and ELM-ART-II [53].
In the area of traditional ITS, the interactive problem solving support technology absolutely dominates. Interactive problem solving support is an ultimate goal of almost any ITS, while intelligent analysis of student solutions is often considered imperfect (and example based problem solving support is too rare to consider as a competitor). Again, the Web context changes the situation. Both intelligent analysis of student solutions and example based problem solving support appears to be very natural and useful in Web context. Both technologies are passive (works by student request) and can be relatively easy implemented on the Web using a CGI interface. Moreover, an old standalone AIES, which uses these technologies, could be relatively easy ported to the Web by implementing a CGI gateway to the old standalone program. It is not surprising that these technologies were among the first implemented on the Web. An important benefit of these two technologies in the Web context is their low interactivity: both usually require only one interaction between browser and server for a problem solving cycle. This is very important for the case of slow Internet connection. These technologies can provide intelligent support when a more interactive technology will be hardly useful. Currently, these technology dominates in Web context over more powerful and interaction hungry interactive problem solving support.
Interactive problem solving support technology is the last ITS technology migrated to the Web. The problem here is that the "fast-track" approach of implementing Web-based ITS (developing a CGI interface to an older standalone ITS) used in pioneer systems does not work properly for this technology. It could be well illustrated by the PAT-Online system [41], which was probably the first trial to implement interactive problem solving support on the Web. This system uses a form-based CGI-AppleScript interface to a standalone Practical Algebra Tutor (PAT) system. Since CGI interface is passive, the Web version of the system had to provide a "submit" button for the student to get the feedback from the system. Naturally, it also added another feature, which was essential for students with a slow Internet connection: a possibility to request a feedback once after performing several problem solving steps. As a result, PAT-Online moved to the category of an intelligent problem analyzers, more exactly, to a subcategory of analyzers that are capable to analyze incomplete solutions (ELM-ART also belongs to this subcategory). The intelligent analyzers of this subcategory can be placed between traditional analyzers and interactive tutors (in Tables 1 and 2 they are marked with keyword "partial", however, they can't be considered as real interactive tutors).
A real interactive tutor is expected to be not only interactive, but also active. It should not sleep from one help request to another, but instead should be able to monitor what the student is doing and instantly react to errors. It simply can't be implemented with the traditional server-side CGI interactivity and requires client-side interactivity based on Java. Java technology has matured very recently. Two years ago the review [8] named it as a prospective platform for Web-based AIES and mentioned only three Java-based systems. Now Java provides a reliable solution for Web-based interactive tutors. To be more exact, Java offers two different solutions. One solution is a tutor implemented completely in Java. It could be a Java applet working in a browser, or a Java application. Another solution is a distributed client-server tutor where a part of functionality is implemented in Java and works on the client side, and another part works on the server side. The parts communicate over the Internet. While the pure Java solution looks simpler (just a new language to build an AIES), the client-server architecture offers a more attractive choice for developing Web-based tutors. It is a definite choice for porting a standalone interactive tutor on the Web. D3-WWW-Trainer [20] and AlgeBrain [1] demonstrate how to re-use the intelligent functionality of an earlier standalone tutor by changing it to a server-side application and developing a relatively thin "brainless" Java client that implements interface functions and communicates with an intelligent server. Event relatively small newly implemented interactive tutors such as ADIS [50] and ILESA [30], which could be easily implemented in pure Java, can benefit from client-server architecture for such reasons as central student modeling. Finally, an overhead of the client-server approach (the need to have a distributed system) is not very big since Java naturally supports several ways of client-server communications - HTTP/CGI, sockets, or RMI/CORBA. We think, that the client-server architecture will become very popular in the coming years as a standard way of implementing Web-based interactive tutors and a way to implement all kinds of highly interactive Web-based AIES. We already see examples of using it for implementing pen-based interface in WITS-II [27] and an animated pedagogic agent Vincent in TEMAI [38].
| System | Ref. | Adaptive sequencing | Adaptive navigation support | Adaptive presentation |
| InterBook | [9] | Page | Annotation | Some |
| AST | [46] | Course | Annotation | Some |
| ADI | [43] | Course (knowledge+interests) |
Annotation | Some |
| ART-Web | [52] | Course, tests | Annotation | Some |
| ACE | [45] | Course (knowledge+interests) |
Annotation, Hiding | Some |
| Remedial Multimedia System | [4] | Course, remedial | Hiding | |
| PT | [28] | Hiding | Yes | |
| AHA | [16] | Annotation, Hiding | Yes | |
| WEST-KBNS | [18] | Annotation | ||
| MetaLinks | [33] | Page | Some (intro) | |
| KBS Hyperbook | [23] | Course | Annotation |
Adaptive hypermedia is a relatively new research area [7]. Adaptive hypermedia systems apply different forms of user models to adapt the content and the links of hypermedia pages to the user. We distinguish two major technologies in adaptive hypermedia: adaptive presentation and adaptive navigation support. Education always was one of the main application areas for adaptive hypermedia. A number of standalone (i.e., non-Web-based) adaptive educational hypermedia systems was built between 1990 and 1996. First Web-based AIES that use adaptive hypermedia technologies were reported in 1996 [12; 17]. Since that the Web has become the primary platform for developing educational adaptive hypermedia systems.
The goal of the adaptive navigation support technology is to support the student in hyperspace orientation and navigation by changing the appearance of visible links. Adaptive navigation support (ANS) can be considered as a generalization of curriculum sequencing technology in a hypermedia context. It shares the same goal - to help students to find an "optimal path" through the learning material. At the same time, adaptive navigation support has more options than traditional sequencing: it can guide the students both directly and indirectly. In a WWW context where hypermedia is a basic organizational paradigm, adaptive navigation support can be used very naturally and efficiently. There are several known ways to adapt the links [7]. Two examples of ANS-based standalone systems are ISIS-Tutor [10] with adaptive hiding and adaptive annotation and Hypadapter [24] with adaptive hiding and adaptive sorting. The three ways that are most popular in Web-based AIES are direct guidance, adaptive link annotation, and adaptive link hiding.
Direct guidance implies that the system informs the student which of the links on the current page will drive him or her to the "best" page in the hyperspace (which page is "best" is decided on the basis of student's current knowledge and learning goal). Often, if a link to the next best page is not presented on the current page, the system can generate a dynamic "next" link. As we can see, adaptive navigation support with direct guidance is almost equivalent to curriculum sequencing technology. There are some differences though (in addition to the different origin). A page suggested by a direct guidance technology is always a page of the existing hyperspace. The student usually could reach this page in one or several steps without the system guidance. The guidance just helps the student to realize that this page is "best" and to get there fast. In an ITS with adaptive sequencing a "page" with next best task or presentation could be completely generated from system's knowledge, thus the student has no ways to get to this material others than using sequencing. Also, direct guidance usually applies a one level sequencing mechanism (in comparison with two-level sequencing in most ITS): the best page is simply selected from the set of acceptable pages using some heuristics. We refer to this way of sequencing as page sequencing. InterBook and ELM-ART provide good examples of this technology. However, the difference between these two technologies starts to disappear in the Web context. Web-based ITS systems are naturally moving to hypermedia platform representing at least some part of the learning material as a hyperspace. As long as some type of educational material (presentations, problems, and questions) is represented as a set of nodes in hyperspace, sequencing of it becomes indistinguishable from direct guidance. To stress this similarity we have represented adaptive sequencing and adaptive navigation support with direct guidance in the same column of the tables.
The most popular form of ANS on the Web is annotation. It was used first in ELM-ART [12] and since that applied in all descendants of ELM-ART such as InterBook, AST, ADI, ACE, and ART-Web as well as in some other systems such as WEST-KBNS and KBS HyperBook. ELM-ART and InterBook also use adaptive navigation support by sorting. Another popular technology is hiding and disabling (a variant of hiding that keeps link visible but does not let the user to proceed to the page behind the link if this page is not ready to be learned). The options are either to make the link completely non-functional (nothing happens when the user clicks on it) as implemented, for example, the Remedial Multimedia System [4] or to show the user a list of pages to be read before the goal page as done in Albatros [29]. Tables 1 and 3 list all major systems that use adaptive navigation support and indicates the type of adaptation.
The goal of the adaptive presentation technology is to adapt the content of a hypermedia page to the user's goals, knowledge and other information stored in the user model. In a system with adaptive presentation, the pages are not static, but adaptively generated or assembled from pieces for each user. For example, with several adaptive presentation techniques, expert users receive more detailed and deep information, while novices receive more additional explanation. Adaptive presentation is very important in WWW context where the same "page" has to suit to very different students. Only two Web-based AES implement full-fledged adaptive presentation: PT [28] and AHA [16]. Both these systems apply a flexible but low-level conditional text technique. Some other systems use adaptive presentation is special contexts. Medtec [19] is able to generate adaptive summary of book chapters. MetaLinks can generate a special preface to a content page depending on where the student came from to this page. ELM-ART, AST, InterBook and other descendants of ELM-ART use adaptive presentation to provide adaptive insertable warnings about the educational status of a page. For example, if a page is not ready to be learned, ELM-ART and AST insert a textual warning at the end of it and InterBook inserts a warning image in a form of a red bar. A very interesting example of adaptive presentation is suggested in WebPersona project [3] where an individualized presentation of information in an educational hypertext is performed by a life-like agent.
The last group of technologies is probably the most exciting one since these technologies has almost no roots in pre-internet educational systems. Currently this group include only one technology. We call this technology student model matching (or simply model matching) because the essence of this technology is the ability to analyze and match student models of many students at the same time. Traditional adaptive and intelligent educational systems has no opportunity to explore this technology since they usually work with one student (and one student model) at a time. On the contrary, in the WBE context this opportunity happens naturally because student records are usually stored centrally on a server (at least for administrative reasons). It provides an excellent framework for developing various adaptive and intelligent technologies that can make some use of matching student models of different students. So far, we have identified two examples of student model matching, which we call adaptive collaboration support and intelligent class monitoring . These examples quite differ from each other and probably could be considered as different technologies within the student model matching group.
Adaptive collaboration support is a very new adaptive technology which was developed within last 5 years along with development of networked educational systems. The goal of adaptive collaboration support is to use system's knowledge about different students to form a matching group for different kinds of collaboration. The pioneering non-WBE (i.e., non-Web, or non-educational) examples of adaptive collaboration support are known for already a few years. These examples include forming a group for collaborative problem solving at a proper moment of time [25; 26] or finding the most competent peer to answer a question about a topic (i.e. finding a person with a model showing good knowledge of this topic) [31]. Less than two years ago Brusilovsky [8] predicted that adaptive collaboration support will become a popular technology. This prediction came true almost immediately. Now we can list already several real examples of adaptive collaboration support in WBE context. The group from University of Saskatchevan has extended their original workplace-oriented peer-help technology developed for PHelpS system [21; 31] to the WBE context in their Intelligent Helpdesk system [22]. Another similar system was developed and evaluated in the University of Central Florida [32]. In addition to that, the group in the University of Duisburg known for their pioneering work on adaptive collaboration support [25] have recently suggested a complete framework for implementation of intelligent support techniques for distributed internet-based education. This framework can naturally support their original adaptive collaboration support techniques and provides a framework for exploring other model matching techniques.
Intelligent class monitoring is also based on the ability to compare records of different students. However, instead of searching for a match, it search for a mismatch. The goal is to identify the students who have learning records essentially different from those of their peers. These students may be different from others in many ways. They cold be progressing too fast, or too slow, or simply have accessed much less material than others. In any case, these students need teacher's attention more than others - to challenge those who can, to provide more explanations for those who can't, and to push those who procrastinate. In a regular classroom the teacher can simply track students attendance and activity to find students who need special attention. In a Web-based classroom, the teacher in the best case has only logging data - tables with numbers which are very hard to grasp. At the same time, the need to identify a small subset of students who need help more than others is more important. In WBE context, communication between teacher and students is usually more time consuming and a distance teacher simply can't individually address more than a small subset of the class. The system HyperClassroom [36] provides an interesting example of using fuzzy mechanisms to identify deadlocked students in a WBE classroom. At the time of writing, it is the only example of the intelligent class monitoring technology known to the author.
It should be clear to anyone who is familiar with the needs of Web-based education, that adaptive and intelligent technologies can enhance different sides of Web-based educational systems. Adaptive presentation can improve the usability of course material presentation. Adaptive navigation support and adaptive sequencing can be used for overall course control and for helping the student in selecting most relevant tests and assignments. Problem solving support and intelligent solution analysis can significantly improve the work with assignments providing both interactivity and intelligent feedback while taking a serious grading load from the teachers' shoulders. Model matching technologies can enforce both administration of distance courses and communication / collaboration between students and teachers.
From another side, adaptive and intelligent technologies have not found yet their place in "real" virtual classroom, i.e., as a part of real courseware used by hundreds of distance students. Most of the systems discussed above are typical "lab" systems, which have never been used for teaching real distance classes. The rest of them, a handful of systems mainly from ELM-ART and AHA families, were used in a few relatively small classes. At the same time, none of the dozens of commercial and "university-grown" Web courseware systems that are used in hundreds of real distance courses applies adaptive and intelligent technologies. Does it mean that research and practice in Web-based education area will never merge together?
The position of the author is the following. Web-based education itself is relatively young. Until now different companies producing Web-based education systems were able to compete on the market with their simple non-adaptive systems. However, a number of research level systems have already clearly demonstrated the benefits of adaptive and intelligent technologies. As long as the competition on the market of Web-based educational system will increase, "being adaptive" or "being intelligent" will become an important factor for winning the customers. Traditional Web-based education companies will start to include adaptive and intelligent functionality. Research teams with solid experience in using adaptive and intelligent technologies will found startup companies to bring their technology to the market. The first technologies to be used in commercial systems will probably be sequencing technologies (page sequencing and question sequencing) since they match very well to the current structure of Web-based education systems. Next will come the turn of adaptive navigation support and model matching. Problem-solving support technologies will stay on research level for longer, though we could expect the market debut of small Web-based tutors that are aimed to support teaching a fragment of some subject. I hope that the next five years will show us a number of examples of commercial-level adaptive and intelligent systems as well as many new and exciting developments on the research level.
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