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Modeling learner-controlled mental model learning processes by a second-order adaptive network model.
Bhalwankar, Rajesh; Treur, Jan.
Affiliation
  • Bhalwankar R; Work and Social Psychology Department, Maastricht University, Maastricht, Netherlands.
  • Treur J; Social AI Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
PLoS One ; 16(8): e0255503, 2021.
Article in En | MEDLINE | ID: mdl-34428234
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Automobile Driving / Adaptation, Physiological / Models, Statistical / Neural Networks, Computer / Learning / Models, Psychological Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2021 Document type: Article Affiliation country: Países Bajos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Automobile Driving / Adaptation, Physiological / Models, Statistical / Neural Networks, Computer / Learning / Models, Psychological Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2021 Document type: Article Affiliation country: Países Bajos