Using Neural Network Mathematical Models to Solve Pedagogical Problems
M.V. Lapenok, O.M. Patrusheva, S.A. Hudyakova
Available Online 13 May 2020.
- https://doi.org/10.2991/assehr.k.200509.005How to use a DOI?
- artificial neural networks, intelligent systems, neural network mathematical modelling, pedagogical tasks
- This article describes the process of creating neural network mathematical models to solve such pedagogical problems as predicting the results of project activities of schoolchildren and developing recommendations for selecting a perspective project task; predicting student attendance based on their personal qualities, aims and lesson schedules; modelling the activities of student interns (musicians and future music teachers) related to their decision to attend theoretical classes, etc. The following stages of creating neural network forecasting systems are considered: formalization of the task, the formation of training examples, designing a neural network, its training and testing. To create neural network systems that implement mathematical models, special software was developed using a high-level cross-platform programming language Python.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - M.V. Lapenok AU - O.M. Patrusheva AU - S.A. Hudyakova PY - 2020 DA - 2020/05/13 TI - Using Neural Network Mathematical Models to Solve Pedagogical Problems BT - International Scientific Conference “Digitalization of Education: History, Trends and Prospects” (DETP 2020) PB - Atlantis Press SP - 22 EP - 26 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200509.005 DO - https://doi.org/10.2991/assehr.k.200509.005 ID - Lapenok2020 ER -