Modeling learner-controlled mental model learning processes by a second-order adaptive network model

Rajesh Bhalwankar, Jan Treur*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Learning knowledge or skills usually is considered to be based on the formation of an adequate internal mental model as a specific type of mental network. The learning process for such a mental model conceptualised as a mental network, is a form of (first-order) mental network adaptation. Such learning often integrates learning by observation and learning by instruction. For an effective learning process, an appropriate timing of these different elements is crucial. By controlling the timing of them, the mental network adaptation process becomes adaptive itself, which is called second-order mental network adaptation. In this paper, a second-order adaptive mental network model is proposed addressing this. The first-order adaptation process models the learning process of mental models and the second- order adaptation process controls the timing of the elements of this learning process. It is illustrated by a case study for the learner-controlled mental model learning in the context of driving a car. Here the learner is in control of the integration of learning by observation and learning by instruction.

Original languageEnglish
Article numbere0255503
Pages (from-to)1-21
Number of pages21
JournalPLoS ONE
Volume16
Issue number8
Early online date24 Aug 2021
DOIs
Publication statusPublished - Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 Bhalwankar, Treur. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Fingerprint

Dive into the research topics of 'Modeling learner-controlled mental model learning processes by a second-order adaptive network model'. Together they form a unique fingerprint.

Cite this