Building intelligent campus environment utilizing ubiquitous learning

Nelaturu, Sarat Chandra Babu; Kambham, Radhika; Karna, Nava Jyothi; Parupalli, Ramu; Mandula, Kumar

doi:10.1109/t4e.2010.5550103

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…In the remaining eight documents (see Chen & Huang, 2012;Chu, Hwang, Tsai, & Tseng, 2010;Hsieh, Jang, Hwang, & Chen, 2011;Hwang, Chen, Chu, & Cheng, 2012;Nelaturu, Kambham, Karna, Parupalli, & Mandula, 2010;Song, Guimbretiere, Ambrose, & Lostritto, 2007) it is pointed out how a more extensive connection between the real and the digital world could be achieved by using distinct technologies in learning scenarios. However, Chen and Huang (2012), Chu, Hwang, Tsai and Tseng (2010) and claim that students might feel excited about such learning scenarios, but their learning success could still be disappointing.…”

Section: Reasoned By Case Studiesmentioning

confidence: 99%

Technology Enhanced Learning and the Digital Economy

Schweighofer

Grünwald

Ebner

2015

International Journal of Innovation in the Digital Economy

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Throughout history, the ongoing technological progress has caused the economy to change in many aspects; furthermore, at times it triggered the dawn of a new era. In the 1980s, this happened for the last time and the digital economy has emerged over the last decades. Since then, the significance of approaches to technology enhanced learning (TEL) has increased rapidly. However, the relations between the digital economy and technology enhanced learning are hardly investigated. Therefore this study points out the known relations between technology enhanced learning and the digital economy by reviewing 1089 publications. Thereby one could recognize that the relations between the regularities of the digital economy and the field of technology enhanced learning have not been thoroughly researched yet.

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Section: Reasoned By Case Studiesmentioning

confidence: 99%

Technology Enhanced Learning and the Digital Economy

Schweighofer

Grünwald

Ebner

2015

International Journal of Innovation in the Digital Economy

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“…Examples on emerging ubiquitous learning systems are presented in [10] [8] [5]. The three systems have one common idea, gathering sensor data from the user or the user's surroundings, do some processing and adapt the output of the system, which is mostly learning content, based on this collected data.…”

Section: Introductionmentioning

confidence: 99%

Cloud computing and mapreduce for reliability and scalability of ubiquitous learning systems

Gad

2011

Proceedings of the Compilation of the Co-Located Workshops on DSM'11, TMC'11, AGERE! 2011, AOOPES'11, NEAT'11, &Amp; VMIL'11

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Ubiquitous learning research is about seamlessly enabling learning through the use of sensors that gather data from the learner surroundings and adapt learning contents accordingly. Nowadays, mobile devices play a big part of these systems due to their advanced capabilities, like communicating with other devices through different methods and standards, and the ability to connect other gadgets expanding functionality even further. On the other hand, sensors integrated with these systems are very advanced and sophisticated making it possible to gather tremendous amount of data. In this paper, a new Ubiquitous Learning system architecture is presented. This new architecture enabled solutions for some major challenges in the field. A Ubiquitous Learning system design and implementation is presented as a use case. The system adapts learning contents based on applying an understanding degree detection algorithm on an input of brain signals collected from the learning student. The adapted learning contents are then sent back over to be displayed on a mobile device. The main focus of this paper is to show how the new architecture supports the necessary reliability and scalability for such systems. I'm proposing in this paper that using Cloud Computing and MapReduce as the architecture main building blocks lead to better approaches and solutions for these two problems. Evaluation showed the effectiveness of using the proposed architecture to support systems with an increasing number of users.

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