Comparative Study of the Mobile Learning Architectures

Baccari, Sameh; Mendes, Florence; Nicolle, Christophe; Soualah-Alila, Fayrouz; Néji, Mahmoud

doi:10.1007/978-3-319-49625-2_23

Cited by 10 publications

(25 citation statements)

References 8 publications

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“…The widespread availability of mobile devices, offers the opportunity to bring mobile learning directly to the workplace. This allows flexible, time and device independent, on-demand access to educational resources, experts, peers and services from any place [2]. Additionally, it should enable enhanced worker autonomy and flexibility [32].…”

Section: Related Workmentioning

confidence: 99%

“…Through new capabilities of the mobile devices, the working context can be determined as well as the machine states observed. This enables a context-aware learning and personalizes the learning experience [2].…”

Section: Related Workmentioning

confidence: 99%

“…These publications describe a) a generic technical architecture for implementing mobile learning or b) the architecture of a specific mobile learning implementation. Baccari et al [2] give an up-to-date overview of different projects dealing with mobile learning architectures.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture

Engelmann¹,

Schwabe²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Manufacturing companies have to meet a lot of challenges in continuing training for employees. Especially on the way towards industry 4.0 the workforce needs to be able to handle fast changing environments and ever-changing working contexts. Furthermore, they have to be familiar with constantly new technologies (e.g. complex user interfaces, mobile devices) that are introduced during the process of company development. Due to this, working people are facing a lifelong learning process and need to evolve to knowledge workers. To fulfill these requirements new concepts are necessary for human resources development directly at the workplace and therefore adequate artifact designs. In this paper we design a layered architecture for mobile learning at the workplace. This layered approach offers the possibility to educate employees with different qualifications and skills using an integrated solution. Further, we propose to implement appropriate components at each layer to support different kinds of learning.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture

Engelmann¹,

Schwabe²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…Different architectures are surveyed in [6] where they are finally evaluated and compared with respect to a set of factors that include: 1) device mobile support, 2) heterogeneity support, 3) wireless communication technology, 4) adaptability, 5) traceability and control, 6) learning as a collaborative process and 7) learning integrated with formal or realising informal learning. The reason for selecting these factors is that they are tightly coupled with the user requirements for contemporary mobile learning scenarios and foremost learning process personalisation.…”

Section: Mobile Learning Architecturesmentioning

confidence: 99%

5G technologies boosting efficient mobile learning

et al. 2017

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Abstract. The needs for education, learning and training proliferate primarily due to the facts that economy becomes more and more knowledge based (mandating continuous lifelong learning) and people migrate among countries, which introduces the need for learning other languages, for training on different skills and learning about the new cultural and societal framework. Given that in parallel, time schedules continuously become tighter, learning through mobile devices continuously gains in popularity as it allows for learning anytime, anywhere. To increase the learning efficiency, personalisation (in terms of selecting the learning content, type and presentation) and adaptation of the learning experience in real time based on the experienced affect state are key instruments. All these user requirements challenge the current network architectures and technologies. In this paper, we investigate the requirements implied by efficient mobile learning scenarios and we explore how 5G technologies currently under design/testing/validation and standardisation meet these requirements.

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“…Some of the important capabilities that should be provided by the learning device m-Learning in [9] is the ability to connect to other devices, the ability to present information about learning and the ability to realize the communication between teacher and learner interaction. Learning system using m-Learning in Kalimantan, Indonesia, which is still relatively new makes us study and learn more about the application of the m-Learning system.…”

Section: Introductionmentioning

confidence: 99%

Performance Rate for Implementation of Mobile Learning in Network

Budiman¹,

Haryaka²,

Watulingas³

et al. 2017

EECSI

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This paper discusses the availability of mobile networks and develops mobile learning software. The measurement Approach is testing from the user experience side to some point where potential users are located. Data clustering is divided into six location of measurement points, with different time sessions; morning, daylight, afternoon, and evening. In each time session, the measurement process is done as much as 10 times test for each the card service packs from Internet Service Providers (ISPs). The measurement process is carried out continuously for 21 days (three weeks), this is done to ensure the availability of mobile networks in the location. The results of measurement and application testing, have given conclusion and contribution, that in of the research explains that although performance levels such as download and upload speed, latency, jitter and packet loss metrics are appropriate, not necessarily the level of network availability is appropriate. Because each parameter is influenced by a certain factor. The average value of network availability measurement experienced an internet connection failure rate on one of ISP 84.046% or as many as 10 to 11 times failed to connect from 70 attempts for internet connection.

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Comparative Study of the Mobile Learning Architectures

Cited by 10 publications

References 8 publications

Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture

Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture

5G technologies boosting efficient mobile learning

Performance Rate for Implementation of Mobile Learning in Network

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