User modelling and system adaptation in the interactive anatomy tutoring system anatom-tutor

Symbiosis of Human and Artifact Y. Anzai, K. Ogawa and H. Mori (Editors) © 1995 Elsevier Science B.V. All rights reserved.

U s e r M o d e l l i n g a n d S y s t e m A d a p t a t i o n in t h e I n t e r a c t i v e Anatomy Tutoring System Anatom-Tutor Ian H. Beaumont Fraunhofer Inst., St. Ingbert, Germany. e-mail address: [email protected] Keywords: adaptive hypermedia, intelligent tutoring systems, ITS, user modelling, computer aided instruction, CAI 1. ANATOM-TUTOR 1.1. What it is and w h a t it's for. ANATOM-TUTOR is an anatomy tutoring system for use at university level, combining ITS (intelligent tutoring system) and hypermedia technology. The aim has been to produce a tutoring system which can a) reduce the workload on university staff by automating the presentation of basic knowledge, and b) improve the effectiveness of student study by allowing the system to respond in an individual way to problems encountered by the learner, a feature lacking in the currently widely used drill-and-practice teaching programs. ANATOM-TUTOR is suitable for use both as a reference work and as an active hypertext-based teaching aid, and its self correcting modelling productions enable it to adapt rapidly to the individual user. Its knowledge domain is a section of brain anatomy including the visual system, the pupillary light reflex system and the accommodation reflex system. 1.2. S y s t e m o r g a n i s a t i o n ANATOM-TUTOR's domain knowledge is located in two modules: 1) a frame-based knowledge base, accessible via menus and mouse-sensitive diagrams, and 2) an adaptive hypermedia component. These together constitute a detailed "encyclopaedia" of the domain. They are combined with a rule-based user-modelling module and a didactic module, which allow the material to be presented, and its use illustrated, in a manner adapted to the actual knowledge of the user. Three teaching modes are implemented in ANATOM-TUTOR: 1) The browsing mode. This mode supports self-directed (or discovery) learning, and allows the user free access to the information in the knowledge base. Neither the user model nor the didactic component is used in this mode.

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902 2) The question mode. This mode is a simulation of an examination taken by second or third year medical students. Here, the system poses questions, including actual previous examination questions, and offers individually tailored responses to the user's misconceptions. 3) The hypermode. This mode provides guided (as opposed to the selfdirected approach t a k e n in the browsing mode) h y p e r m e d i a - b a s e d instruction through several topics in brain anatomy. Hypertext is adapted at both the content and link level (see below) to the needs of the learner. The latter two modes make extensive use of the user model for adaptation, and will be discussed in the sequel. 2. THE QUESTION MODE 2.1. Modelling The User Two types of acquisition of knowledge about the user are commonly distinguished, explicit and implicit. Explicit information is that entered by the user as a direct answer to a request for information while implicit information is that gleaned by the system from the user's interaction with the system, possibly using previously entered explicit information. Explicit information is obviously more reliable than the implicit variety, but is also far more time-consuming for the user, moreover, repeated questioning can interrupt the user's current cognitive process, for which reasons it is not suitable in many applications. The natural and expected occurrence of questions in a teaching situation enables ANATOM-TUTOR to freely employ explicit modelling techniques, resulting in a detailed and accurate base model which can then be extrapolated on using implicit techniques. ANATOM-TUTOR's user modelling module uses rules representing valid laws of the domain and socalled (self correcting) stereotypes, as developed by Rich [Rich 89], for this extrapolation. 2.2. The user m o d e l in u s e r / s y s t e m dialog The user modelling component extrapolates on explicitly entered information to produce a user model which is closed under the active domain rules and stereotypes. Should later interaction with the user show that the user model does not correspond with the user's actual knowledge and beliefs, the system can respond by offering explanations or posing follow-up questions, thus entering into a dialog with the user. From the user's point of view this dialog is interesting since it both allows the system to respond to the user's problems, and illustrates how the material is reasoned with to produce new information. The dialog is also essential for the maintenance of the user model, since it allows the system to correct implicit assumptions in the user model with explicit information. The selfcorrecting feature of the stereotypes decreases the certainty with which wrongly assumed information will be included in the model in future. Fig. 1 shows a sample dialog in ANATOM-TUTOR's question mode. (The full functionality of this mode is also available in the hypermode for answering questions included in the hypertext lessons.)

903 Questions 1, 4, and 13 in fig. i are from a list of questions designed to test the user's u n d e r s t a n d i n g of the main points of a topic covered by the system. The user answers questions 1 and 4 correctly and question 13 incorrectly. System response 15 contains the information that user response 14 is incorrect plus a follow-up question designed to test the user's knowledge of the law used by the system in deducing the correct answer, and which the system assumes the user knows. (This follow-up question is not contained as 'canned text' in the question list, but is generated using the rule and premises occurring in the actual deduction.) [1] S Please indicate the nucleus Edinger-Westphal (dext) in the diagram. [2] U [User shows nucleus Edinger-Westphal (dext).] [3] S Correct. [4] S To which object class does the nucleus Edinger-Westphal belong? [5] U Parasympathic object. [item in multiple choice list] [6] S Correct. [ 13] S What type of object is the ganglion ciliare (dext)? [14] U Sympathic object. [item in multiple choice list] [ 15] S Your answer is incorrect. To which class does an object belong if it gets afferences from a parasympathic object? [ 16] U Parasympathic objects. [item in multiple choice list] [17] S Correct. You know that any object receiving afferences from a parasympathic object has to be parasympathic itself. Do you know a parasympathic object with efferences to the ganglion ciliare (dext)? [ 18] U [User types in Nucleus Edinger-Westphal (dext).] [19] S Correct. Your answer [to question 13] was incorrect, would you still like an explanation? Figure 1. A sample dialog in ANATOM-TUTOR's question mode. Attention has, with the follow-up question, been shifted (from question 13's correct answer) to the domain law. (A discussion of the focus-based approach to discourse used in ANATOM-TUTOR is beyond the scope of this article.) The user responds correctly (16) and the system evaluates the response ("correct") and repeats the law (17). System response 17 contains a second follow-up question shifting attention to the premises of the law. The user types in a correct answer (18) to the follow-up question (there may be

904 several equally valid answers). System response 19 evaluates the user's answer and shifts attention back to question 13. If, in response to question 19, the user answers that he would like an explanation, the system starts at the correct answer to question 13 and proceeds, in a top down manner, to explain how information in the user model was used for deducing the correct answer.

3. THE HYPERMODE 3.1. Adaptive Hypermedia Hypermedia systems have recently become popular as an aid to user driven learning and information retrieval. By browsing along hyperlinks, users can freely explore 'hyperspace' and come across information which they might never have found using formal querying methods. Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia systems build a model of relevant aspects of the user (e.g. goals, preferences, domain knowledge, etc.) and use this for producing output adapted to the needs of that individual user.

3.2. Why adaptation Users of a hypermedia system can differ substantially in their goals and in their knowledge of the subject covered by the system. While an ordinary hypermedia system provides the same hypermedia pages and the same set of links to all users, users with different goals and backgrounds will generally be interested in different pieces of information and may use different links for navigation. Adaptation techniques can be used to tailor both the information and the links being presented to the individual user. Furthermore, the problem of the user becoming disoriented in huge hypertext applications (i.e. lost in hyperspace) has received much attention recently. Model based adaptivity can be used here for hiding or deactivating irrelevant links, providing links with helpful comments, colour-coding links, or imposing some sort of ordering on the links. Finally, adaptive hypermedia systems can provide users with unobtrusive assistance in their work with the system. By creating adaptively dynamic links or just by highlighting some existing links the system can suggest to the user a way to proceed, while leaving him the freedom to make his own choices.

3.3. Adaptation in ANATOM-TUTOR's hypermode From the above remarks it will be seen that we can basically distinguish two types of hypermedia adaptation, which we will refer to as adaptive presentation (or content-level adaptation) and adaptive navigation support (or link-level adaptation). Since ANATOM-TUTOR's hypertext component is fairly small, becoming 'lost in hyperspace' is not a problem, so that adaptive navigation support is confined to link deactivation. Adaptative presentation in ANATOM-TUTOR involves both the expository style of the text and the informational content of the hypertext page. People at different levels of proficiency talk about their subject material in different ways, as anyone who has compared the conversations of experts on a subject

905 with that of laypersons will readily admit, and texts prepared for a specific reader group orientate their expository style on that group. ANATOMTUTOR's hypertext component first chooses a default presentation style depending on the user's global classification, and can adapt this locally according to the user's (local) level of proficiency. The user's level of experience, the lessons and lectures he has already worked through, etc., are taken into consideration for choosing the default style. Two basic categories of user are distinguished, beginner and advanced, and two expository styles correspond to these two user categories. (This is also the approach taken by C.Paris [Paris, 89] in her system TAILOR.) Most users will fall somewhere between beginner and advanced, and the view taken by Paris [Paris 89] and ourselves is that a presentation adequate for the needs of the individual user can be obtained by combining these styles in accordance with the user's local knowledge of the concepts involved. The effect produced is that of a continuous range of user levels. In addition to this presentation style, ANATOM-TUTOR also locally determines the content of the presented text, i. e. known material can be left out and additional material included. Content adaptation ideally requires an associative procedure for determining exactly which representable items of domain knowledge are located where in the text. In a closed system such as ANATOM-TUTOR this can be effected by the system designers by adding a detailed documentation to the text; in hypertext systems which access external data sources this can be a problem, since the only documentation available for these texts is a keyword list. Several systems (e.g. [Boyle 94]) do, however, carry out keyword-based text adaptation. 4. COMBINING ITSs AND ADAPTIVE HYPERTEXT SYSTEMS

While both adaptive presentation and adaptive navigation support are in common use in current applications (e.g. [Boecker 90], [De Rosis 93], [Boyle 94] and [Boecker 90], [Vassileva 94], respectively), few systems presently combine ITS and adaptive hypermedia techniques. Indeed it has been noted by several authors that ITSs and educational hypertext systems are currently seen as two distinct approaches to using computers in education, and that this view has often tacitly implied the incompatibility of the two approaches. Systems such as ANATOM-TUTOR [Beaumont 94] and [Brusilovsky 92] show that this is not the case, and that indeed the two approaches can be combined to the advantage of both. From the point of view of educational systems, the inclusion of adaptive hypermedia can allow a much greater diversity in the material being presented, and allow the learner much more freedom in self-directed learning. Guided instruction applications can use adaptive hypermedia to provide hints for further study based on the needs and goals of the individual learner. On the other hand, the field of education can, as shown, provide the necessary feedback for more precise and reliable hypermedia adaptation. While there may be a question of the desirability and user acceptance of putting test questions in an on-line documentation system, hypertext-based tutoring systems such as ANATOM-TUTOR can use test questions to

906 increase the bandwidth of information available from the user without leaving the teaching paradigm. Adding questions relating to the material is obviously superior to trying to accurately update the model solely from the user's requests for more or less material, or from a "thumbs up" or "thumbs down" signal (indicating the user's contentment, or lack thereof, with the material presented). The author believes that the combination of ITSs and adaptive hypermedia technology will prove fruitful in the field of computer aided instruction, and that the next few years will see much activity in this direction. REFERENCES

[Beaumont 94] Beaumont I. (1994) User modelling in the interactive anatomy tutoring system Anatom-Tutor. In User Modeling and UserAdapted Interaction, 4: 1994, (p.21-45). [Boecker 90] Boecker H.-D., Hohl H. and Schwab T. (1990) Hypadapter Individualizing Hypertext. In Diaper D. et al (ed.) INTERACT'90. Proceedings of the the IFIP TC13 Third International Conference on HumanComputer Interaction. North-Holland, Amsterdam. (p. 931-936). [Boyle 94] Boyle C. and Encarnacion A.O. (1994) MetaDoc: an adaptive hypertext reading system. In User Modeling and User-Adapted Interaction, 4: 1994. [Brusilovsky 92] Brusilovsky P.L. (1992) Intelligent Tutor, Environment and Manual for Introductory Programming. Educational and Training Technology International, 29(1), (p. 26-34). [De Rosis 93] De Rosis F., De Carolis N. and Pizzutilo S. (1993) User tailored hypermedia explanations. In INTERCHI'93 Adjunct Proceedings, Amsterdam, 24-29 April, 1993. (p. 169-170). [Paris, 89] Paris, C. (1989) 'The Use of Explicit User Models in a Generation System for Tailoring Answers to the User's Level of Expertise'. In: A. Kobsa and W. Wahlster: 1989, User Models in Dialog Systems, Heidelberg: Springer Verlag. [Rich 89] Rich, E. (1989) 'Stereotypes and User Modeling'. In: A. Kobsa and W. Wahlster: 1989, User Models in Dialog Systems, Heidelberg: Springer Verlag. [Vassileva 94] Vassileva, J. (1994) A practical Architecture for User modeling in a Hypertext-Based Information System. Proceedings of the Fourth International Conference on User Modeling, (p. 115-120).