Brain-Based Intelligent Tutoring Systems

我们的目标是受到我们从以前的潜意识学习实验中获得的一些结果的启发。在这些工作中，我们发现脑电波分析可以在一瞬间区分认知推理和直觉推理之间的变化，换句话说，从有意识模式到潜意识模式。这种变化类似于学习者从认知任务切换到游戏时所观察到的变化。他们不是继续寻找解决方案，而是玩弄系统直到找到解决方案，但只是通过反复试验，没有任何认知努力。这导致了知识获取中的一个重要问题，它与大脑状态相对应：参与或脱离。在第一种情况下，学习者有兴趣（动机）去探索可能的解决方案，并可能收敛到最终解决方案。在第二种情况下，学习者对继续完成任务不再感兴趣（这有时解释了游戏的衍生）。我们的实验还强调了两种典型的推理：认知推理和直觉推理。虽然前者通常涉及大脑的意识部分，但后者更多地与潜意识部分联系在一起。有时，解决问题的情况会先后涉及认知和直觉推理，在两种模式之间来回切换。

Our objectives are inspired by some results we have obtained from previous experiments on subconscious learning . In these works we have discovered that brainwaves analysis allowed to distinguish a change, at a moment, between cognitive reasoning and intuitive reasoning, in other words from conscious to subconscious mode. This change is similar to what is observed when learners switch from a cognitive task to gaming. Instead of continuing to find a solution, they play with the system until they encounter a solution but just by trial and error without any cognitive effort. This leads to an important problem in knowledge acquisition which corresponds to a cerebral state: engagement or disengagement. In the first one, the learner is interested (motivated) to explore the possible solutions and could converge to the final solution. In the second one, the learner is no more interested to continue on the task (which sometimes explains the derivation to gaming). Our experiments highlighted also two typical kinds of reasoning: cognitive reasoning and intuitive reasoning. While the first involves generally the conscious part of the brain, the second is more linked to the subconscious part. Sometimes, a problem solving situation will involve successively cognitive and intuitive reasoning, switching back and forth between the two modes. In both cases emotions play an important role to facilitate or not learning. The emotional conditions of the brain allow for the transmission of information not only to the conscious part but also to subconscious region and this last one seems to contain more knowledge and to process the information at a higher speed. As we have seen in our previous studies, the affective part is extremely important as it prepares the brain to accept or not the information. Intuitive behaviour is more present in video games. For instance, in flight training or games the resulting experience and abilities will concern immediate reactions (reflexes) Following our previous works, we want to detect more precisely emotional conditions and cerebral states that lead to engagement or disengagement. This implies to detect the emotional condition (for instance a learner is excited or not) and learning condition (the learning objectives are too high) in which learning occurs, and measure the resulting cerebral state (for understanding more clearly the different steps of learning and the evolution of cerebral states). Moreover, a continuous control of learner’s brain activity could be used by an intelligent system to give advices to the learner and put him into the best emotional conditions for learning. As we can see the brain is at the center of this intelligent system. An intelligent personal adviser (agent) could follow the evolution of cerebral states and provide personalized advices to optimize learning. To reach this goal, we need to explore more possibilities to detect the different steps of engagement and disengagement (when a learner cannot understand the problem and find the solution) through EEG signals. The main problem remains to detect and monitor the learner emotional state with precision and in real time. Emerging device allow to have a direct interaction between the learner and his environment (eye tracking systems, brainwaves interaction with helmets) The general objectives of our research are (1) to build an integrated interactive Brain-based environment able to detect the conditions of engagement and disengagement both in cognitive and intuitive learning,using a video game, and (2) to build a complete Brain-based video game learning environment able to provide the best pedagogical strategies to foster learner’s memorization and knowledge acquisition.