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A concept approach to use of the EU-SENSE system in exercises based on the Kolb's learning cycle

Gikiewicz Magdalena, Kozioł Joanna

Security and Defence Quarterly

2022

Vol. 37, No. 1

94--104

The aim of the article is to present a conceptual approach to the use of the EU-SENSE system in exercises based on Kolb’s learning cycle. The methodology of the research conducted in 2018–2021 was based on an analysis of literature in the field of teaching adults and conducting training, chemical and ecological rescue, analysis of domestic and foreign materials and procedures in the field of chemical and ecological rescue, direct observation of the way of the State Guard Fire Service respond to CBRNe threats, taking place in the measurement test dams of the EU-SENSE system and for the analysis of the training module, which is an element of the EU-SENSE system. After completing the exercises, the participant should achieve learning outcomes in terms of knowledge, abilities and skills. The acquired knowledge and practical skills will allow firefighters and civilians to conduct effective and safe rescue operations in the field of chemical rescue during incidents involving hazardous chemicals in the future. Exercises in the field of chemical rescue with the use of the EU-SENSE system will lead to an improvement of skills within the State Fire Service and make it possible for it to cooperate and coordinate activities with entities cooperating in the field of crisis management activities.

Learning style recognition based on an adjustable three-layer fuzzy cognitive map

Georgiou D. A., Botsios S., Mitropoulou V., Papaioannou M., Schizas C., Tsoulouhas G.

Journal of Artificial Intelligence and Soft Computing Research

2011

Vol. 1, No. 4

333--347

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.