Metadata about: This study attempts to use a deep neural network to assess the acquisition of knowledge and skills by students. This module is intended to shape a personalized learning path through the e-learning system. Assessing student progress at each stage of learning in an individualized process is extremely tedious and arduous. The only solution is to automate assessment using Deep Learning methods. The obstacle is the relatively small amount of data, in the form of available assessments, which is needed to train the neural network. The specifity of each subject/course taught requires the preparation of a separate neural network. The paper proposes a new method of data augmentation, Asynchronous Data Augmentation through Pre-Categorization (ADAPC), which solves this problem. It has been shown that it is possible to train a very effective deep neural network with the proposed method even for a small amount of data.

About: This study attempts to use a deep neural network to assess the acquisition of knowledge and skills by students. This module is intended to shape a personalized learning path through the e-learning system. Assessing student progress at each stage of learning in an individualized process is extremely tedious and arduous. The only solution is to automate assessment using Deep Learning methods. The obstacle is the relatively small amount of data, in the form of available assessments, which is needed to train the neural network. The specifity of each subject/course taught requires the preparation of a separate neural network. The paper proposes a new method of data augmentation, Asynchronous Data Augmentation through Pre-Categorization (ADAPC), which solves this problem. It has been shown that it is possible to train a very effective deep neural network with the proposed method even for a small amount of data.

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http://ns.inria.fr/covid19/graph/entityfishing	7
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