Educational analytics and adaptive training using student model in the intellectual learning systems
Main Article Content
Abstract
For support of adaptive training and educational analytics in the intellectual learning systems, it is necessary to collect, process data on progress of the student and his various individual characteristics. It can be realized by means of the student model. The analysis of approaches to modeling of the student has shown that application of several types of models is an optimal solution, considering requirements to the learning system. Three approaches were chosen and united into one model: overlay, Bayesian network, error model. Use of overlay model allows to build individual trajectories of student training. Bayesian networks realize competence-based approach in training. The model of mistakes keeps track of wrong knowledge of the student and helps the student to correct them at early stages. The student model uniting in itself these approaches is more suitable for realization of the personalized training, allows to keep track of progress of the student according to various characteristics and also gives the chance to easily submit the card of subjects, knowledge, competence of the student of various areas in the form of the count that is quite convenient and clear representation.
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References
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