Adaptive feedback selection for intelligent tutoring systems

In this work, an adaptive method for feedback strategy selection is proposed in the context of intelligent tutoring systems. This uses a combination of machine learning methods to automatically select the best feedback strategy for students engaging in a foreign language learning context. Experiments show that our adaptive multi-strategy feedback model allows students to achieve correct answers by reducing their errors. Results also show the promise of the method compared with traditional methods of feedback generation. The approach is not only capable of dynamically adapting a feedback strategy, but also guiding the tutorial conversation so that student's correct answers can be obtained with a minimum feedback. Our approach also suggested that combining SVM and CRF models are promising to get effective feedback correction from student tutoring, showing that our multi-strategy selection approach outperformed the traditional meta-linguistic rules based feedback strategies. Experiments also showed a good correlation between the best strategy generated by our model and the decision taken by a human tutor.