Tytuł pozycji:
Learning style recognition based on an adjustable three-layer fuzzy cognitive map
Identification of learning styles supports Adaptive Educational Hypermedia Systems
compiling and presenting tutorials custom in cognitive characteristics of each individual
learner. This work addresses the issue: identifying the learning style of students, following
the Kolb’s learning cycle. To this purpose, we propose a three-layers Fuzzy Cognitive
Map (FCM) in conjunction with a dynamic Hebbian rule for learning styles recognition.
The form of FCMs is designed by humans who determine its weighted interconnections
among concepts. But the human factor may not be as reliable as it should be. Thus, a
FCM model of the system allowing the adjustment of its weights using additional learners’
characteristics such as the Learning Ability Factors. In this article, two consecutively
interconnected FCM (in the form of a three layer FCM) are presented. The schema’s efficiency
has been tested and compared to known results after a fine-tuning of the weights
of the causal interconnections among concepts. The simulations results of training the
process system verify the effectiveness, validity and advantageous characteristics of those
learning techniques for FCMs. The online recognition of learning styles by using threelayer
Fuzzy Cognitive Map improves the accuracy of recognition obtained using Bayesian
Networks that uses quantitative measurements of learning style taken from statistical samples.
This improvement is due to the fuzzy nature of qualitative characterizations (such as
learning styles), and the presence of intermediate level nodes representing Learning Ability
Factors. Such factors are easily recognizable characteristics of a learner to improve
adjustment of weights in edges with one end in the middle-level nodes. This leads to the
establishment of a more reliable model, as shown by the results given by the application
to a test group of students.