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Integrating Expert Systems in Authoring Systems for Curriculum and Course Building Roger Nkambou*, Gilles Gauthier**, Claude Frasson*, Myriam Antaki* *Universite de Montreal, Departement d'Informatique et de Recherche Operationnelle C.P. 6128, Succ. Centre-Ville, Montreal, H3C 3J7 (Canada). Email:
[email protected] **Universite du Quebec a Montreal, Departement d'Informatique C.P. 8888, Succ. Centre-Ville, Montreal, H3C 3P8 (Canada) Email:
[email protected] ABSTRACT Experience has shown that users of authoring systems do not completely master the process of building curriculums and courses through the use of authoring tools. As a consequence, existing authoring tools provide the user with the necessary on-line information for using the tool. This assistance is limited to the general knowledge relative to the functionality of the tool, ignoring the expertise required to build an adequate curriculum or course. In order to provide users with support that focuses on the expertise for building curriculums and courses, we propose a validation system that is an expert-based assistant integrated in the authoring environment.
KEYWORDS: Expert system, Authoring system, Knowledge representation, Intelligent tutoring system, Curriculum, Course, Instructional design. INTRODUCTION Expert systems have proved helpful in advising and assisting users (Dear, 1987). Where we lack instructional design experts, expert systems can be useful. In order to provide users with support that focuses on the expertise for building curriculums and courses, we propose to use an expert-based assistant integrated in the authoring environment. This expert-based assistant has a knowledge base that is editable and constraint-based. The aim of this system is to assist novice and intermediate instructional designers in the curriculum and course building process. For instance, verifying the suitability of the instructional objectives the designer has created, advising him about what type
2 of learning material might be e ective to achieve a particular instructional objective (or to acquire a particular skill). This assistance is accomplished by integrating an expert system in our authoring environment. The expert system validates curriculums and courses produced using the authoring tool. The validation technique uses strategies from learning and teaching theories. The strategies take advantage of knowledge models for curriculums and courses which have been used for intelligent tutoring systems and authoring tools as reported in Nkambou, Gauthier & Frasson (1994). Many sources provide guidelines for instructional design: Gagne & Briggs (1979), Gagne (1985), Spector et al. (1993). Special expertise is involved in the instructional design process (design methodology, learning principles, ...). Including such expertise in an authoring system would be very useful for the users (useful recommendations and error detection). Existing authoring systems fail to provide needed guidance such as: { What is mandatory when de ning a concept? { What type of resource should be used. For instance: when should we use multiple choices, and when short answers? Merrill (1987), through ISD Expert, was one of the rst to experiment the use of an expert system in the instructional design context. The purpose of ISD Expert is to guide instructional design decisions so that resulting products can more adequately implement the precepts of learning and instructional design. However, ISD Expert is not an authoring system. It does not include editors or other tools for developing computer-based learning materials. The system we are presenting in this paper is an authoring environment that integrates a validation system that is an expert system. The goal of this validation system is to validate a designer's work, and to give him gradual guidelines on this work. We describe in the following pages, the architecture of this expert system and its integration in the authoring environment. This integration is described by two validation approaches: onthe- y validation, in which the validation system acts as a coach or an advisor that reacts according to the user's actions, and post-construction validation in which the system acts as a critiquing system (Silverman, 1992). We also try to assure that the
3 validation system's intervention will not disturb the instructional designer's progress (that it will limit itself to what should be said and not more).To insure this kind of assistance, we introduce di erent intervention levels. INTRUCTIONAL DESIGN THEORY AND AUTHORING SYSTEMS:The Need For An Expert System The primary purpose of the instructional design process is to structure the learning environment so as to provide the learner with conditions that will support the learning process. However, this process is not simple. For instance, courseware design and development are time-consuming, tedious and somewhat repetitive. There is material to create, there are lesson plans to develop, and so on. Thus, the automation of the instructional design process (Spector et al., 1993) might make courseware production more cost-e ective, and more simple. There exist authoring systems that serve that purpose (Paquette et al., 1994). These systems o er a set of tools that are used in the instructional design process. We have developed such an environment (Nkambou, Gauthier and Frasson, 1995) for curriculum and course building. This environment includes design tools such as knowledge browsers, graphical editors and visualization and simulation tools. These tools are integrated in the authoring environment. Although such an environment exists, diculties remain with the following facts: { The design process is dicult for humans, { It requires dicult-to-acquire expertise, { Human actions introduce unacceptable errors, { There is a lack of human experts. It is to solve these problems that traditional authoring systems (that were nothing more than tool sets) have evolved towards more intelligent systems trying to integrate instructional design expertise. Systems such as ISD Expert (Merrill 1987, 1991, 1993), IDE (Pirroli & Russell, 1991), ISD Expert (Tennyson, 1993), the GAIDA project (Gagne, 1993) and AGD (Paquette et al., 1994) have been speci ed to that e ect. The need is to incorporate instructional design expertise with regard to the selection, sequencing and presentation of materials in support of various lesson objectives and subject-matter
4 domains. Our system covers the selection and sequencing aspect, that is, the curriculum and course design. Several integrating approaches of the expertise in an authoring system were proposed. For example, Gagne (1993) argues for automating only the guidance framework. The advantage of this sort of automation is that it is relatively inexpensive. Merrill's proposal incorporates instructional transactions, that are intelligent object-oriented frameworks for creating speci c kinds of instructional interactions, in a computer-based environment. His system contains rules for prescribing instructional strategies, which in turn will prescribe transactions. Tennyson (1993) proposed to incorporate a model of the user. Our approach is based on encapsulating the curriculum and course building expertise in an expert system that is integrated in the authoring environment. This expert system reasons on a constraint base that contains constraints on curriculum and course design. These constraints come from di erent instructional design theories (Gagne & Briggs, 1979; Gagne, 1985; Merrill, 1991). THE VALIDATION SYSTEM The validation system includes a constraint base, an inference engine and a validation interface. The inference engine uses metarules that reason on the constraint base. When a constraint is not satis ed, a message is given to the user through the validation interface. The Constraint Base There exist four types of constraints: 1. Knowledge constraints: this kind of constraint incorporates the conditions in which a knowledge de nition is adequate. We use the CoLan constraint language (Bassiliades and Gray, 1994) to specify all our constraints.
Example of knowledge constraints: For all d in discrimination discriminationFactors of d 6= .
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2. Relationalconstraints: this kind of constraint is used to validate the links de ned between knowledge units in the curriculum model.
Example: De nition-analogy relation between two concepts For all x in de nition-analogy Source of x is-a concept and destination of x is-a concept and intrinsicAttributes of source of x are-similar-to intrinsicAttributes of destination of x
3. Properties constraints: properties constraints concern the properties of the different types of relation. These properties are written into the class of each relation. For instance, the prerequisite relation is antisymmetric and transitive. Example: Antisymmetry of a relation R: For all x in R Such that source of x 6= destination of x There does not exist y in R Such that source of y = destination of x and destination of y = source of x
4. Methodology constraints: this kind of constraint concerns the design process. Our system allows the user to add new constraints and edit (modify or delete) existing constraints. The constraints are grouped into sub-bases themselves organized in a hierarchy. Each sub-base corresponds to a class of object and the constraint hierarchy is the same as that of the classes to which the constraints are related.
6 The Inference Engine We explored di erent possibilities to integrate rules in Smalltalk: 1. MEI-Prolog (Katsuhiro, 1994): MEI-Prolog is a small subset of DEC-10 Prolog. It has facilities of uni cation and backtracking. According to its authors, it can handle any Smalltalk object and send Smalltalk messages to it. But we found this important attribute to be lacking. We noticed that Prolog cannot handle a Smalltalk object that was created in a di erent context (an object that is passed as an argument for example) than that in which it is handled by Prolog. This is what we would like to do but does not work: validate: anObject engine:= MeiPrologInterpreter new. engine refute: constraintBase. \This is a string containing prolog predicates" engine refute: `?- valid(fanObject aMessageg).'. \valid is a prolog predicate"
Note: Curly brackets are used to delimitate a Smalltalk expression within a Prolog expression.
2. NeOpus (Pachet, 1995): NeOpus is a rst order inference engine integrated in the Smalltalk environment. This engine uses the forward chaining strategy. Its advantages are: { The capability to manipulate all Smalltalk objects { The possibility to use any Smalltalk expression, in rule premisses or in the action part of a rule. We nally chose NeOpus because it can actually handle all Smalltalk objects. When a new object is created, the following message is sent to the validation system: ValidationSystem validate: anObject. In turn, the next messages are sent to anObject's constraint sub-base: anObjectsConstraintSubBase setNaturalTyping.
7 anObjectsConstraintSubBase executeWithSingleObject: anObject. These instructions re all the constraints related to the class of anObject and its superclasses.
Here is the discrimination constraint as implemented as a NeOpus rule (it is a Smalltalk method): discrimination j Discrimination d. Local aViolation j d discriminationFactors isEmpty. actions aViolation := Violation new. aViolation object: d constraintName: `discriminationFactors' explanation: `The discrimination factors have not been speci ed' x: `Edit the Discrimination Factors eld and accept again'. ValidationSystem process: aViolation The Validation Interface The validation interface allows the user to set the validation mode. There are two validation modes: on-the- y (coaching) and post-construction (critiquing). For the rst mode, there are ve intervention levels. The rst level of intervention does not display anything, the second only warns the user that a constraint was violated, and at each next level, an element is added to the display: rst the violated constraint then a solution and nally, at the fth level, the cause of the violation is corrected if requested by the user. The second mode o ers two levels. The rst level o ers suggestions and the second also o ers to correct the problem. In any case, there is an on-line menu that allows the designer to ask more about a problem even if the intervention level is xed. For some problems, the system records them in a list and will intervene only if the designer does not correct them. For instance, when a knowledge unit is created but is not linked to any other, the system records this fact and will intervene only if the user tries to save his work. When a violation occurs, the validation system calls a module that processes this violation according to the level of intervention. The result of this processing is the
8 displaying of the appropriate message in the validation interface and/or the addition of the violated constraint to a list. This is done by sending the following message to the validation system: ValidationSystem process: aViolation CONCLUSION By integrating an expert system in our authoring environment, that gives appropriate hints to the designer, we contibute to the evolution from traditional authoring systems towards more intelligent systems that integrate pedagogical expertise. The seperation of the validation system from the authoring environment makes possible the modi cation of the constraint base without modifying the authoring environment. The validation system we have described is part of a global system for the construction of intelligent tutoring systems whose architecture was presented in Nkambou et al. (1995). Our system is fully functional, what is left to implement is the automatic correction. The constraint base should be revised with a more pedagogical perspective. ACKNOWLEDGEMENTS: We would like to thank the MICST (Ministere de l'Industrie, du Commerce, de la Science et de la Technologie du gouvernement du Quebec) for providing nancial support for the SAFARI project where this work was undertaken. REFERENCES { Bassiliades, N. and Gray, P.M.D. (1994). CoLan: A functional constraint language and its implementation. Data & Engineering 14 (3), 203-249. { Dear, B.L. (1987). AI and the authoring process. In IEEE-Expert 2 (2) 17-24. { Gagne, R. M. (1985) The Conditions of Learning (4th edition). Holt, Rinehart and Winston, New York. { Gagne, R. M. (1993). Computer-Based Instructional Guidance. In: Spector, J.M., Polson, M.C. and Muraida, D.J. (Eds): Automating Instructional Design: Concept and Issues.; pp. 133-146, ETD. Englewood Cli s, N.J. { Gagne, R.M., Briggs, L. (1979) Principles of instructional design. (2nd edition). Holt, Rinehart and Winston, New York. { Katsuhiro, W. (1994). How to run attractive demonstrations with Mei. User guide.
9 { Merrill, M.D. (1987). An expert system for instructional design. In IEEE-Expert 2 (2) 25-37. { Merrill, M. D. (1991) An introduction to instructional transaction theory. In Educational Technology, Vol. 31, No 6, pp. 7-12. { Merrill, M. D., (1993) An Integrated Model for Automating Instructional Design and Delivery. In: Spector, J.M., Polson, M.C. and Muraida, D.J. (Eds): Automating Instructional Design: Concept and Issues.; pp. 147-190, ETD. Englewood Cli s, N.J. { Nkambou, R., Gauthier, G. and Frasson, C. (1994). CREAM: un modele de connaissance pour le curriculum et le cours dans les STI. Publication interne #930. Departement d'informatique et de recherche operationnelle, Universite de Montreal. { Nkambou, R., Gauthier, G. and Frasson, C., Se ah, A. (1995). Une architecture de STI avec une composante curriculum explicite. In Environnement Interactif d'Appentissage par Ordinateur, pp.315-327, Eyrolles, Paris. { Pachet, F. (1995): On the embedability of production rules in object-oriented languages. In Journal of Object-Oriented Programming. 8 (4) 19-24. { Paquette, G., Crevier, F., Aubin, C., Frasson, C., (1994). Design of a KnowledgeBased Didactic Engineering Workbench, CALISCE 94, Paris. { Pirolli, P. and Russell, D.M. (1991). Instructional design environment: technology to support design problem solving. In Instructional Science, 19 (2), 121-144. { Silverman, B. G. (1992). Building Expert Critiquing Systems: A Situated Tutoring Alternative. In ITS `92 Tutorial, Montreal, Quebec. { Spector, J.M., Polson, M.C. and Muraida, D.J. (1993). Automating Instructional Design: Concepts and Issues. ETD. Englewood Cli s, N.J. { Tennyson, R. D. (1993). A Framework for Authomating Instructional Design. In Automating Instructional Design: Concept and Issues. ETD. Englewood Cli s, N.J., pp. 191-214.
