ANALYZING AND CLASSIFYING A RANGE OF INCORRECT ACTIONS MADE BY STUDENTS DURING AN EDUCATIONAL PROCESS USING AN INTERVAL TEMPORAL BEHAVIOR OBSERVATION
Pages: 117-126 Published: 13 Oct 2019
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Abstract: Training is an incremental process involving slow and methodical achievement of small goals over a given period of time. Each step in the process involves assimilation of matter by accepting a set of errors, the correction of which leads to the improvement of knowledge and deepening the understanding of the problem area and matter as a whole. The classical learning approach, where the facilitator is part of the learning process and has the technical ability to test the work of trained agents, allows for easy and effective classification of a set of errors to serve as the basis for changing and improving the material. E-learning suffers from this disadvantage that the process supervisor does not have this opportunity due to the lack of real-time action as well as the huge scale of these training sessions. In this article, we will look at a mechanism for classifying a set of errors used in the UniPlayground project to classify the behavior of trained agents based on their omissions, errors, or failure to conform to the particulars of the course material. The article examines learning in the context of software development.