Sixth International Conference on User Modeling, Chia Laguna, Sardinia, 2-5 June 1997

Trier, Germany

Phone: +49-651-201-2975

Fax: +49-651-201-2955

Abstract:In this paper we describe the Adaptive Statistics Tutor (AST), an adaptive courseware on the WWW. AST is based on a conceptual model of the domain of introductory statistics and uses the programmable WWW-server CL-HTTP (Mallery, 1994) to generate individualized courseware. The system monitors the interactions of learners with the learning materials in the course. Besides texts there are examples, so-called interactive playgrounds, and tests. The information gathered by the monitoring of the system is used to build up a probabilistic learner model. The learner model stores information about all interactions, their success, and individual instantiations of playgrounds. Learners can explore the curriculum or get recommendations by AST for each next best unit and learning material to work with depending on their previous interactions. Additionally, we have just begun to integrate cooperative modules in AST. Learners can ask and answer questions to other learners or the tutor and join discussion groups on any sections of the curriculum.

In the **domain expert module**, the concepts of the domain, their
interrelations
and dependencies are described. Basically, the knowledge base is built on
a conceptual
network with different types of units which are lessons, sections,
subsections, and
concepts. Several types of information are associated with each concept.
These are
different kinds of text (introduction, several stages of teachtexts,
summary), examples,
demonstrations, playgrounds, and tests. Besides, each unit has
prerequisites (units
that the student should be familiar with before working on the unit),
and consequences
(possible outcomes and effects on other units). The tests and
prerequisites are weighted
according to their importance for a unit.

The **pedagogical expert module** contains **pedagogical
strategies**
and **diagnostic knowledge**. Teachers follow different strategies to
teach different
types of concepts. Teaching strategies in this sense are comparable to
the teaching rules
in Vassileva (1994). For example, when teaching statistics, introducing
the arithmetic
mean can easily be done with a text, a formula, some tasks, or an
interactive playground.
In contrast to that, it is very hard to "explain" a more abstract
concept like different scale levels
with just a text or a formula.
For introducing scale levels it would be much easier to start with
concrete examples and then go on with
a more abstract text definition or a formula. Such different strategies
in the sense of a certain sequence of learning materials are defined in
the teaching strategies. The system has a default strategy for each
concept type and teachers can specify a preferred strategy for each
concept if needed. Moreover, rules are defined with each strategy which
allow the system to adaptively choose a teaching strategy depending on
critical learner features and on the class of the taught concept.

One problem with learning environments on the WWW is the reduced bandwidth of information. Due to the fact that a student loads a text page hardly anything can be inferred about the actual knowledge of the student. Integrating interactive tests in a WWW learning environment is one more valid way of getting information about the learner's knowledge. The diagnostic component stores the knowledge about several types of tests and how they have to be generated and evaluated similar to Nkambou and Gauthier's pedagogical resources (Nkambou & Gauthier, 1996). Each test in the test base can be connected to multiple concepts, rated on difficulty and on relevance for a concept. Depending on the difficulty and the relevance of a test, a student's solution has different impacts on the learner model.

The learner model stores the preferred settings of a learner and the domain units a learner worked on. A learned unit has information about the materials a learner has used and how successful he/she worked on the material (e.g., if tests are solved correctly, if the playground estimations were good or to what extent teachtexts were requested). Every action of a student has consequences for updating the learner model depending on the involved learning material and the previous experiences of a student. By accumulative information (Specht & Weber, 1996) stored with a learned unit a probabilistic conceptual overlay model for representing the student's domain knowledge is built up. The probabilistic overlay model can be used for several adaptive methods like adaptive sequencing, mastery learning, and adaptive testing (e.g., giving additional tests or teachtexts after wrong test solutions), or adaptive annotation (Weber & Specht, 1997).

Teachtexts: With every concept in AST a teachtext with three levels of detail is defined. The first level contains only basic information about a concept. The second level explains some concepts used in the first stage in more detail and the third stage gives detailed information and advanced hints. Depending on the detail level a student requests, the confidence value for the taught concept is increased.

Interactive Playgrounds in AST consist of shockwave plugins, and interactive html-forms (e.g., a spreadsheet simulation in which the learner can request data analysis and watch the computation stepwise in a formula filling up with the computed values). Request and usage of interactive html-forms increase the confidence value of a concept and are stored as a learning episode with the concept.

Examples can be requested only one by one. This is necessary to identify the examples a learner actually looks at. Displaying all examples for a concept at once would not allow any inference about how many of the examples a learner actually looked at. Requesting examples increases the confidence value of a concept.

Tests are the most important feedback for updating the model in AST. Four different types of tests are currently implemented: Yes-or-no-tests, multiple-choice tests, forced-choice tests, gap-filling tests and free-form tests. Every test has a general value for difficulty, and the relevance for the tested concept can be specified. Depending on the difficulty and the relevance the confidence value in the probabilistic learner model is increased. Besides the type of the learning material the last interaction with a concept, and the percentage of correct tests is taken into account for updating the confidence value of a concept. Based on the confidence values in the probabilistic learner model, adaptive navigation support, adaptive sequencing, and adaptive testing are implemented in AST.

Lindner, H. G. (1995). Ein Prototyp zur adaptiven Beratungsunterstützung des Versicherungsverkaufs. In U. Malinowsky (Eds.), Proceedings of Workshop Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen,. München: Siemens AG.

Mallery, J. C. (1994). A Common LISP hypermedia server. Proceedings of the First International Conference on the World-Wide Web (pp.

Nkambou, R. & Gauthier, G. (1996). Use of WWW resources by an intelligent tutoring system. In P. Carlson & F. Makedon (Eds.), Proceedings of ED-MEDIA'96 - World conference on educational multimedia and hypermedia (pp. 527-532). Charlottesville: AACE.

Specht, M. & Weber, G. (1996). Episodic adaptation in learning environments. In P. Brna, A. Paiva, & J. Self (Eds.), Proceedings of EuroAIED (European Conference on Artificial Intelligence in Education) (pp. 171-176)

Vassileva, J. (1994). A practical architecture for user modeling in a hypermedia-based information system. Proceedings of 4-th International Conference on User Modeling (pp. 115-120). Hyannis, MA: MITRE.

Weber, G. & Specht, M. (1997). User Modeling and Adaptive Navigation Support in WWW-Based Tutoring Systems.In Anthony Jameson, Cecile Paris, and Carlo Tasso (Eds.) User Modeling: Proceedings of sixth International Conference on User Modeling (pp 290-300). Springer, Wien New York.