Francky Trichet

DSTM: a framework to operationalise and refine a problem solving method modeled in terms of tasks and methods

Abstract In this paper we present DSTM, a framework that enables the operationalisation and the refinement of problem-solving methods modeled within the Task–Method paradigm. DSTM proposes an operational kernel, i.e. operational but flexible high-level constructions: modeling primitives, such as task or method, and manipulation mechanisms, such as select a method. These constructions can be customized in order to better capture the paper-based model to be operationalised. This permits the construction of an implemented system that is an explicit reification of the paper-based model, and, therefore, enables to analyze the model by means of the analysis of the system. In order to support this analysis, DSTM proposes tools that allow the knowledge-engineers to inspect the implemented model from different points of view. This facilitates the conciliation of the initial modeling phase, the model refinement phase and the operationalisation phase that are achieved when constructing a KBS.

Modelling the Knowledge-Based Components of a Learning Environment within the Task/Method Paradigm

We show that the Task/Method paradigm is adapted for modeling the problem-solving method that the teacher wants to communicate to the student as well as the pedagogical strategies that have to be put into practice for managing the interaction with the student.

Training Strategies and Knowledge Acquisition: Using the same Reflective Tools for Different Purposes

The Emma educational system embodies a Knowledge Based System (KBS) that models a problem-solving method defined at an abstract level. In order to allow different explicit problem-solvings, the KBS is based on a Task-Method modelisation. The objectives of this paper are (1) to explain how we take advantage of the flexibility provided by the modelisation by using analysis-modules that can study what the influence of the students propositions on the rest of the solving is; (2) to highlight that elaborating a knowledge-base that allows such a flexibility requires the use of automated tools that help the modelling-team to keep a precise understanding of how the different notions interact one with another; and (3) to demonstrate that these different tools can be constructed over the same set of reflective primitive modules, modules that are linked to the problem-solving modelisation.