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Application of neural network for implementing a practical student model
The student model plays a critical role in intelligent tutoring systems. With a student model, the tutoring system can adapt the learning process in order to satisfy the individual needs of every student. Here we use neural network methods to implement the student model. In the proposed approach, first a teacher has to apply the curriculum structure and crucial pedagogical steps for the learning process. These two data will be transmitted as proper weights and layers in the network. Then our neural network takes the student's responses as input data and performs learning. During learning, the network records the student's history and changes the corresponding weights in order to record his or her learning state. Finally, the system infers the student's misunderstandings and provides suggestions to the tutor. Our goal is to make a student model embedded in a neural network, and evaluate misunderstandings in the learning processes of learners. Based on the experiment, the proposed neural network can infer the topic where the student's under-standing is weakest as a reference for the next lesson.
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