Decision-theoretic planning for content planning in ITS

In this paper we focus our work to determinate the mechanisms of decision-making that permit to calculate an objective in working session for one learning particular (dynamic content planning). This work comes within the framework of AMICAL1 project, which is an informatics environment for teaching and learning to read in French Language, for student at the beginning of learning. our approach is to use Bayesian network for represent tutor’s uncertain beliefs about the student state for each knowledge to be acquire, and decision theoretic planning to select the optimal objective by using dynamic decision network to help children (five years) for lear ning to read in French Language. Our aim in this work is to specify a method to manage the matter contents to be taught, within the framework of an intelligent tutor system for the training of the reading of the French language to 5-6 year old schoolchildren. The choice of the reading is justified on the one hand, by the lack of the adaptive systems in this field of training and on the other hand, by its role for the 5 yea r old children in the school, and it is regarded as the base of any training, and a failure i n this matter will introduce a school failure. In the literature, the most of research in instructional planning i ITS are focused on aspect of delivery planner and interaction planning. But little are interest s of content planning, it (content planning) considered as such as goals organization problem with graph (And/Or/Ispart. ) [9], consequently planning content consist in traversin g the nodes of the graph. This method remains insufficient to exceed ad-hoc and especiall y to carry out an individualized training, adapted for each learning profile in the fie ld of the reading. Because, on the one hand the training of the reading is not linear and non sequentia l: the progression is not made a basic element, the letter, with the most complex elem ent which is the text. The four objects of the language (letter, word, sentence, text) are approached in parallel in their relationships, and on the another hand with uncertainty on the state of know ing of learning. It 1 AMICAL : Architecture Muti-agents Interactive Compagnon pour l’Apprentissage de la lecture Conference ICL2008 September 24 -26, 2008 Villach, Austria is for that we chose in our research to apply decision-theoretic a ppro ch to planning dynamically the contents. This approach was used in other work like DT Tutor[7],CAPIT [6], iTutor [5]for interaction planning. In this paper, we consider the organization of the contents as a ta sk of planning with decisionmaking which consists in determining starting from knowledge to ma ke acquire and the student model what to make with him. i.e. to determine the objective i n working session, “basic element of learning object LO [4]”, this objective was de fined as a set of the units of objectives (UOs)[2] and each UO = ( Action, Kow, Context), Action: specify the intention of teaching, Kow determines knowledge to make acquire and Context: a context for specify the level of acquisition of this knowledge K (knowledge, application,. ) withi n e meaning of Bloom [1]. That says that the calculation of an objective in working session will depend on: • choice of knowledge to be acquire • choice of the intention of teaching (Action) • And the choice of the context in which this knowledge will be treated For that, the first stage consists to organize knowledge of the rea ding ccording to a causality graph. And to present “under uncertainty” the student state for eac h knowledge to be acquired, by basing on the probabilistic approach. Thus the graph and the probability distribution -obtained starting from the empirical datafor each node , constitute a Bayesian network [8], who presents the tutor beliefs on learning state. Conseque ntly planning content can be treated as a partially observable Markov decision proble m (POMDP)[3] , and the action (context) choice can be determined by maximising the expe cted utilities for sequence of actions (contexts). This theoretical approach used, we permit to develop a rational agen t to determine an objective in working session with three components: • Component belief that constitute beliefs on the state of knowledge of the learner model is using the network Bayesian • Component decision: this component is modelled using the approach to decisiontheoretic planning • Component learning for update the agent beliefs Conference ICL2008 September 24 -26, 2008 Villach, Austria References: [1] Bloom, B. S. , Englehart, M.D., Furst, E.J., Hill, W.H. , & Krathwohl, D.R. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals: H ndbook I. The Cognitive Domain. New York: David McKay Company, Inc. [2] Cleder C. (2002) « Planification didactique et construction de l’objectif d’une s ession de travail individualisée : modélisation des connaissances et de raisonnement mis jeu » thèse Université Blaise Pascale 237p [3] Lovejoy, W. (1991). Computationally feasible bounds for partially observed Markov decision processes. Operations Research, 39, 162--175. [4] Koper, R (2001): Modeling units of study from az pedagogical perspektive. The pedagogical meta-model behind EML, Educational Thechnology Expertice Centr e, Open University of the Netherlands, 2001. Online. [5] Kiat.P 2003 « decision theoretic Intelligent tutoring System » thèse univers ité de Singapore 194p [6] Mayo . M & al (2001) : « Optimising ITS Behaviour with Bayesian Networks a nd Decision Theory »; International Journal of Artificial Intelligence in Edu cation (2001), 12, 124-153 [7] Murray, R. C. & VanLehn, K. (2000). DT Tutor: A decision-theoretic, dynamic approach for optimal selection of tutorial actions. In Proceedings of ITS 2000. Berlin: Spri ngerVerlag. [8] Pearl J. 1988 “Probabilistic Reasoning in Intelligent Systems”: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman Publishers [9] Vassileva J.(1995) « Reactive Instructional Planning to support Interacting T eaching Strategies » AI-ED 95, August 16-19 1995. AACE Charlottesville, VA. , 334-342