E-Learning Models Analysis for Lifelong Learning

• Дата: 2017
• Автор: Synytsya, K.M.
• Формат: Стаття
• Мова: English
• Опубліковано: Міжнародний науково-навчальний центр інформаційних технологій і систем НАН України та МОН України 2017
• Назва видання: Кибернетика и вычислительная техника
• Теми: Информатика и информационные технологии
• Онлайн доступ: http://dspace.nbuv.gov.ua/handle/123456789/131494
• Назва журналу: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Цитувати: E-Learning Models Analysis for Lifelong Learning / K.M. Synytsya // Кибернетика и вычисл. техника. — 2017. — Вип. 4 (190). — С. 19-32. — Бібліогр.: 26 назв. — англ.

Introduction. E-learning models reflect various aspects of ICT application in education but they are not intended for reflecting long periods of time, multiple sources of information and knowledge involved or dynamic control from the learner’s side. These and other features are important for lifelong learning modeling aimed at raising the efficiency of learning by the use of individual learner’s data. The purpose of the paper is to review e-learning models that describe frameworks, abstract architectures or reference models for identification of potential prototype for lifelong e-learning model and to outline the requirements to its construction. Results. The study reveals typical features of the e-learning models grouped according to the level of abstraction and connection to technology and pedagogy. The paper presents an overview of lifelong learning specifics and models that could be considered during the lifelong e-learning modeling. Although none of the discussed models could serve as a unique prototype, some of them could either be integrated in a lifelong e-learning model or guide the modeling. A set of requirements to lifelong e-learning model is suggested. Conclusion. Lifelong e-learning model should be presented as a set of views that are relevant to actors in e-learning and reflect longevity, multiple sources, context of learning, management and learner’s control, collection and sharing the data about learning. The main purpose of it could be in identification of components, tools and processes that should be implemented for intelligent and efficient lifelong learning support