Environnements informatiques pour l'apprentissage humain

Résumé

Les environnements informatiques pour l'apprentissage humain (EIAH) sont des environnements informatiques qui ont pour objectifs de favoriser ou susciter des apprentissages, de les accompagner et de les valider. Les réalisations sont nombreuses, mais les succès très inégaux. La recherche dans ce domaine est née avec l'informatique mais s'est surtout développée dans le sillage de l'intelligence artificielle dans les années 1970. De nombreuses dénominations ont été utilisées. Le terme EIAH est né dans les années 1990, avec le souhait de souligner l'interaction entre les deux pôles source de la complexité du projet technologique et scientifique : l'informatique (avec la modélisation computationnelle qu'elle exige et son inscription matérielle) et l'apprentissage humain (pour lequel on ne dispose encore que de modèles très partiels). La recherche sur les EIAH est fondamentalement pluridisciplinaire, en appelant à la coopération de différents secteurs de l'informatique (génie logiciel, rés

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CoPainter: Interactive AI drawing experience

Pawel Golinski

Google AI Experiments [3] is a set of simple experiments that showcase innova- tive uses of AI in UX. As part of this collection, Google shared Quick Draw, where user draws simple sketches and intelligent agent tries to guess what it depicts, and Sketch- RNN interactive demo, an experience where user starts a sketch and recurrent neural network model completes it [4]. These experiences provide fun and engaging drawing activities which have potential to be used in more educational context. This report describes CoPainter - an interactive AI drawing experience which aims to provide an educational activity based on learning by teaching paradigm - the application asks the user to draw a painting of some object in cooperation with an intel- ligent agent (which can either be a robot or an abstract entity). The activity is based on Sketch-RNN model mentioned above. Main contributions of this project are porting the Sketch-RNN web app experience to Qt, which enables integration with CoWriter code- base and makes it possible to collect more data on user activities. An additional draw in turns game mode is also introduced. The activity may possibly be extended to enable it to be used in diagnosing and treating dysgraphia, which is briefly mentioned in this re- port and is aim of further research on this project.

2019

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Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing (BKT). BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

Springer2014

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Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clustering pipeline that can be applied to learning data, aiming at improving cluster stability over multiple training sessions in the presence of noise. Our model selection is designed such that relevant cluster evolution effects can be captured. The pipeline can be used as a black box for any intelligent tutoring system (ITS). We show that our method outperforms previous work regarding clustering performance and stability on synthetic data. Using log data from two ITS, we demonstrate that the proposed pipeline is able to detect interesting student behavior and properties of learning environments.

EDM2016

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