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Simulation of learning effects of adaptive learning
Author(s) -
Keisuke Kuniyoshi,
Setsuya Kurahashi
Publication year - 2020
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2020.09.253
Subject(s) - computer science , artificial intelligence , learning effect , adaptive learning , machine learning , human–computer interaction , economics , microeconomics
In this research, we propose a teaching simulation on the complex doubly structural network with adaptive learning and verify the effect of the adaptive learning environment. The teaching simulation on the complex doubly structural network is a method to verify the learning effect in the learning environment. The simulation model has an internal network and a social network. As a result of experiments, we found that the adaptive learning model has a significantly lower average number of teachings than the lecture model. Lessons tailored to individual’s understanding lead to improved learning effects. And adding the support of teacher to adaptive learning contributes to improving learning efficiency. It is important to properly design the entire learning environment while combining various mechanisms. And there are cases where the placement effect is large and cases where the placement effect is small depending on the learning environment. The learning effect of the mechanism varies depending on the learning environment.

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