Kidlearn is a research project studying how machine learning can be applied to intelligent tutoring systems. It aims at developing methodologies and software which adaptively personalize sequences of learning activities to the particularities of each individual student. Our systems aim at proposing to the student the right activity at the right time, maximizing concurrently his learning progress and its motivation. In addition to contributing to the efficiency of learning and motivation, the approach is also made to reduce the time needed to design ITS systems.
Algorithms: We introduce two different algorithms ZPDES that uses a simple graph of knowledge and RiARiT that includes a more complex student model. The details of the algorithms and the user studies are presented in the following paper:
B. Clement, D. Roy, P.-Y. Oudeyer, M. Lopes, Multi-Armed Bandits for Intelligent Tutoring Systems, Journal of Educational Data Mining (JEDM), 2015 (arXiv:1310.3174 [cs.AI])
- git clone https://github.com/flowersteam/kidlearn.git
- python setup.py install
Check KidlearnStarter notebook
Check Seq_manager_explanation notebook
People involved: Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, Manuel Lopes https://flowers.inria.fr/research/kidlearn/
The Kidlearn software released under a dual-license model: GNU Affero GPL License v3 (AGPL3) and Commercial.