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Seidel, N., Haake, J. M., Burchart, M. (2021). From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment. eleed, Iss. 14. (urn:nbn:de:0009-5-52421)

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%0 Journal Article
%T From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment
%A Seidel, Niels
%A Haake, Jörg M.
%A Burchart, Marc
%J eleed
%D 2021
%V 14
%N 1
%@ 1860-7470
%F seidel2021
%X Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.
%L 370
%K Adaptive Personalized Learning Environments
%K Adaptive Systems
%K LMS
%K Learning Management Systems
%K Personalized Learning
%K e-learning
%U http://nbn-resolving.de/urn:nbn:de:0009-5-52421

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@Article{seidel2021,
  author = 	"Seidel, Niels
		and Haake, J{\"o}rg M.
		and Burchart, Marc",
  title = 	"From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment",
  journal = 	"eleed",
  year = 	"2021",
  volume = 	"14",
  number = 	"1",
  keywords = 	"Adaptive Personalized Learning Environments; Adaptive Systems; LMS; Learning Management Systems; Personalized Learning; e-learning",
  abstract = 	"Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.",
  issn = 	"1860-7470",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-5-52421"
}

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TY  - JOUR
AU  - Seidel, Niels
AU  - Haake, Jörg M.
AU  - Burchart, Marc
PY  - 2021
DA  - 2021//
TI  - From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment
JO  - eleed
VL  - 14
IS  - 1
KW  - Adaptive Personalized Learning Environments
KW  - Adaptive Systems
KW  - LMS
KW  - Learning Management Systems
KW  - Personalized Learning
KW  - e-learning
AB  - Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.
SN  - 1860-7470
UR  - http://nbn-resolving.de/urn:nbn:de:0009-5-52421
ID  - seidel2021
ER  -

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<b:Comments>Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners&apos; needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.</b:Comments>
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ISI

PT Journal
AU Seidel, N
   Haake, J
   Burchart, M
TI From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment
SO eleed
PY 2021
VL 14
IS 1
DE Adaptive Personalized Learning Environments; Adaptive Systems; LMS; Learning Management Systems; Personalized Learning; e-learning
AB Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.
ER

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Mods

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    <title>From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment</title>
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    <namePart type="given">Jörg M.</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Burchart</namePart>
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  <abstract>Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a  five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.</abstract>
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    <topic>Adaptive Personalized Learning Environments</topic>
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    <topic>e-learning</topic>
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Full Metadata

Bibliographic Citation	e-learning and education, Iss. 14
Title	From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment (eng)
Author	Niels Seidel, Jörg M. Haake, Marc Burchart
Language	eng
Abstract	Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services.
Subject	Adaptive Personalized Learning Environments, Adaptive Systems, LMS, Learning Management Systems, Personalized Learning, e-learning
Classified Subjects	ddc data processing - psycological principles, 004.019 methods of learning, 153.152 transfer of learning, 153.154 electronic distance education, 371.358 methods of instruction and study, 378.17 pacs multimedia, C6130M it trainings requirements, D1030 human aspects of it, D1040 personal applications of it, D2110
DDC	370
Rights	fDPPL
URN:	urn:nbn:de:0009-5-52421