Design of an SOA-based ubiquitous learning environment

Wang, Ching-Shu; Wang, Yung-Hsin

doi:10.1109/grc.2011.6122682

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Wang and Wang [115] (S) Ontology-based (U), (P), (ST.L) RFID CAMLES [88] (UIn) n/a (U), (ST) -Scott and Benlamri [98] (S), (D), (UIn) Ontology-based (U), (T), (ST) IR ELLA [124] (S) Ontology-based (U), (T), (ST.T) RFID, Camera, Mic., GPS TANGO [90] (S) n/a (U), (ST Rule-based APALS [12] Provide personalize learning LC Selection (U) Substitut. Rule-based Learning Java [122] Recommend learning content LC Selection (U), (E), (ST) Substitut.…”

Section: System Context Acquisition Context Modeling Context Entitiesmentioning

confidence: 99%

“…to Loc. (U), (T), (E), (P), (ST) Structural Rule-based IWT [20] Improve learning Structure of learning content (U), (T), (P) Structural Algorithmic [115] n/a LC Selection (U), (P), (ST.L) Substitut. n/a CAMLES [88] Improve learning LC Sequence, Navigation (U), (ST) Substitut.…”

Section: Rule-basedmentioning

confidence: 99%

“…Consequently, some of the ACALEs covered by this survey can be categorized as smart environments (e.g. a smart museum learning environment [22,24,56,115]); however, we refer to them simply as ACALEs for consistency. Smart television systems and vehicle infotainment systems that adapt the user interface to the user behavior are further demonstrations of the broadness of the category of smart environments, which includes adaptive context-aware systems [11].…”

Section: Introductionmentioning

confidence: 99%

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A survey of adaptive context-aware learning environments

Hasanov

Laine

Chung

2019

AIS

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Adaptive context-aware learning environments (ACALEs) can detect the learner's context and adapt learning materials to match the context. The support for context-awareness and adaptation is essential in these systems so that they can make learning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they do not consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this, a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were published between 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones) are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context modeling approach, (iv) context data typically originates from the learner profile or the learner's location, (v) rule-based adaptation is the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we conducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of context categories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results, directions for future research in the field were given. These results can be of interest to educational technology researchers and to developers of adaptive and context-aware applications.

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Section: System Context Acquisition Context Modeling Context Entitiesmentioning

confidence: 99%

Section: Rule-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey of adaptive context-aware learning environments

Hasanov

Laine

Chung

2019

AIS

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“…Mobile learning. In mobile learning (m-learning) scenarios [48], learners can share resources such as videos, audio, documents and comments. Participants in m-learning may also assume one or more roles including learners, mentors and peer-tutors.…”

Section: Potential Applications Of Mobile Servicesmentioning

confidence: 99%

Mobile Web Services: State of the Art and Challenges

Elgazzar¹,

Martin²

2014

IJACSA

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Abstract-For many years mobile devices were commonly recognized as Web consumers. However, the advancements in mobile device manufacturing, coupled with the latest achievements in wireless communication developments are both key enablers for shifting the role of mobile devices from service consumers to service providers. This paradigm shift is a major step towards the realization of pervasive and ubiquitous computing. Mobile Web service provisioning is the art of hosting and offering Web services from mobile devices, which actively contributes towards the direction of Mobile Internet. In this paper, we provide the state of the art of mobile service provisioning as it currently stands. We focus our discussions on its applicability, reliability, and challenges of mobile environments and resource constraints. We study the different provisioning architectures, enabler technologies, publishing and discovery mechanisms, and maintenance of up-to-date service registries. We point out the major open research issues in each provisioning aspect. Performance issues due to the resource constraints of mobile devices are also discussed.

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Ubiquitous and Ambient Intelligence Assisted Learning Environment Infrastructures Development - a review

Kanagarajan¹,

Ramakrishnan²

2017

Educ Inf Technol

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Design of an SOA-based ubiquitous learning environment

Cited by 4 publications

References 13 publications

A survey of adaptive context-aware learning environments

A survey of adaptive context-aware learning environments

Mobile Web Services: State of the Art and Challenges

Ubiquitous and Ambient Intelligence Assisted Learning Environment Infrastructures Development - a review

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