Authors:
Harald R. Kisch
1
and
Claudia L. R. Motta
2
Affiliations:
1
Universidade Federal do Rio de Janeiro (UFRJ) and University of Applied Sciences, Brazil
;
2
Universidade Federal do Rio de Janeiro (UFRJ), Brazil
Keyword(s):
E-Learning, E-Learning Environments, E-Learning Assistance, Cognition Simulation, Recommender Systems, Brain Neuron Model, UFO.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
e-Learning
;
Social Context and Learning Environments
;
Theoretical Bases of e-Learning Environments
Abstract:
It is typically known that brain neurons are responsible for a significant part of our knowledge adaption process.
However, it is not yet fully understood how knowledge adaption works or what conscious intelligence is.
The aim of this research is to investigate how an E-learning environment can automatically identify learning
sequences to dynamically map them to specific learning types for suggesting course material, which makes
learning more individual, flexible and faster. For the purpose of this research, a neural ontology is created.
In this ontology, the characterization of one neural brain cell is meant to represent every neuron cell in our
brain as a specific part of a neural network to get closer to the answer how a simulation of brain functions
could be accomplished. This paper describes a neural network theory and how the conceptual model of a
neural brain cell could be interpreted through the concept of cognitive pattern match in relation to intelligence.
In conclusion, tw
o fundamental hypotheses for effective knowledge adaption in E-learning environments are
derived.
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