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Top2. Theoretical Background
If one looks at the history of individualized education and cybernetic approaches, one learns that in the 1960s algorithmization started to be implemented to the education process, which laid the foundation of the so-called programmed learning. This theory was systematically elaborated by B. F. Skinner (Nye, 1979). The curriculum is divided into small, content-compact units, which enable effective optimization of education according to the student’s personal characteristics.
N. A. Crowder complemented Skinner’s principle of linear program (Watters, 2015) by a branched multiple answer program (a process of diagnosing errors with the possibility of individual correction). In order for the managed education systems to be effective, they need to meet three main requirements. The first is the input sensitivity. The second is output effectiveness of the control system. And the third is a program of the impact on the managed system. This program works as a connecting link between the system’s “sensors and effectors” (crowder.org, 2010).
The combination of adaptive and adaptable systems offers an optimal solution for adaptive education through eLearning. The theory of adaptive eLearning is a topic that touches at least 3 areas – pedagogy, psychology and informatics (Kostolanyova, 2012).
As far as the Czech Republic is concerned, the Pedagogical Faculty of the University of Ostrava in cooperation with VŠB-Technical University of Ostrava deals with the theory of adaptive eLearning. The main idea of the theory is personalization of education, which consists in the education process being adapted and personally tailored to every student’s personal characteristics. It is an adaptation, searching and compiling of the educational content (Kostolanyova, 2013). The proposition of the complex adaptive model of education is based on finding the student’s input characteristics, creation of a suitable adaptable study material and the formulation of adaptive algorithms. The theory of programmed learning has been included among pedagogical-algorithmic solutions of the adaptive eLearning principles (Kostolanyova, 2012).
The educational environment is considered adaptive when it is able to monitor and interpret the users’ activities, deduce their preferences and requirements according to the interpreted activities and – on the basis of this information – dynamically modify the education process (Chapelle, 1998; Chen and Chung, 2008).