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Publikační činnost
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Record type:
stať ve sborníku (D)
Home Department:
Katedra informačních a komunikačních technologií (45080)
Title:
ADAPTIVE E-LEARNING AND ADAPTIVE ALGORITHMS FROM THE PERSPECTIVE OF M-LEARNING
Citace
Klubal, L. a Gybas, V. ADAPTIVE E-LEARNING AND ADAPTIVE ALGORITHMS FROM THE PERSPECTIVE OF M-LEARNING.
In:
EDULEARN22: EDULEARN22 Proceedings 2022-07-04 Palma.
Palma: IATED, 2022. s. 5068-5072. ISBN 978-84-09-42484-9.
Subtitle
Publication year:
2022
Obor:
Informatika
Number of pages:
5
Page from:
5068
Page to:
5072
Form of publication:
Elektronická verze
ISBN code:
978-84-09-42484-9
ISSN code:
2340-1117
Proceedings title:
EDULEARN22 Proceedings
Proceedings:
Mezinárodní
Publisher name:
IATED
Place of publishing:
Palma
Country of Publication:
Sborník vydaný v zahraničí
Název konference:
EDULEARN22
Místo konání konference:
Palma
Datum zahájení konference:
Typ akce podle státní
příslušnosti účastníků:
Celosvětová akce
WoS code:
EID:
Key words in English:
adaptive e-learning, m-learning, adaptivity, education
Annotation in original language:
Adaptive e-learning is one of the proven directions of ICT use in education. The wide availability of ICT tools makes the use of adaptive learning more accessible than before. Adaptivity can be ensured both in terms of adapting to the learning style of the learner, specifically in presenting learning materials that are more suitable for their personal profile. However, this type of adaptivity places greater demands on the teacher's preparation of the whole curriculum. In some cases, however, it is already possible to make use of the functions of mobile devices and, for example, to read classical text materials automatically. Another direction where the insights of adaptive e-learning are applied is the fixation of learning through practice. This is particularly useful in areas where the learner needs to fix fixed diagrams or obtain factual data. In these cases, the choice of a mobile device is particularly appropriate due to the personal nature of the device. Currently, adaptive algorithms can be found in many commercially available products, some of which are specifically targeted for use on mobile devices. It is these devices that ensure the rapid penetration of adaptive e-learning into mainstream practice. This paper analyses currently available products from the perspective of adaptive algorithms and mobile technologies.
Annotation in english language:
References
Reference
R01:
RIV/61988987:17450/22:A2402G7H
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