Ubiquitous Learning Environment: Smart Learning Platform with Multi-Agent Architecture

Temdee, Punnarumol

doi:10.1007/s11277-014-1730-2

Cited by 29 publications

(13 citation statements)

References 10 publications

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“…As higher access to low-cost mobile devices allows more people to adopt them for learning, they will benefit from the emerging trend of ubiquitous learning, where learners are able to obtain educational information at anytime and anywhere, whenever they are needed [24,54]. Moreover, as the technology used to implement ubiquitous learning increases in sophistication, the line between the physical and digital learning environment becomes less pronounced.…”

Section: Ubiquitous Learningmentioning

confidence: 99%

Designing a ubiquitous sensor-based platform to facilitate learning for young children in Thailand

Putjorn

Siriaraya

Ang

et al. 2017

Proceedings of the 19th International Conference on Human-Computer Interaction With Mobile Devices and Services

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Education plays an important role in helping developing nations reduce poverty and improving quality of life. Ubiquitous and mobile technologies could greatly enhance education in such regions by providing augmented access to learning. This paper presents a three-year iterative study where a ubiquitous sensor based learning platform was designed, developed and tested to support science learning among primary school students in underprivileged Northern Thailand. The platform is built upon the school's existing mobile devices and was expanded to include sensor-based technology. Throughout the iterative design process, observations, interviews and group discussions were carried out with stakeholders. This lead to key reflections and design concepts such as the value of injecting anthropomorphic qualities into the learning device and providing personally and culturally relevant learning experiences through technology. Overall, the results outlined in this paper help contribute to knowledge regarding the design, development and implementation of ubiquitous sensor-based technology to support learning.

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Section: Ubiquitous Learningmentioning

confidence: 99%

Designing a ubiquitous sensor-based platform to facilitate learning for young children in Thailand

Putjorn

Siriaraya

Ang

et al. 2017

Proceedings of the 19th International Conference on Human-Computer Interaction With Mobile Devices and Services

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“…To satisfy anywhere and anytime learning, early research in u-learning pays much attention to develop ULEs being able to provide seamless ways of content delivery [4][5][6][7] for a variety of connected devices [8][9][10][11]. To promote context-awareness, many recent works [12][13][14][15] aim to provide the suitable individual learning paces for the learners. In order to provide the personalization support appropriately, the learner's behavior is required to be described appropriately which is the main purpose of this paper.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…However, it is difficult for the designers to provide the suitable individual learning to each learner who has the different performance and preference. The current research area is thus to determine the method for understand context of each learner appropriately [15].…”

Section: Literature Reviewsmentioning

confidence: 99%

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Modeling of student and learning object interactions toward ubiquitous learning environment

Temdee

2014

2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace &Amp; Elec

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This paper focuses on modeling the interaction between the learners and the learning objects in the ubiquitous learning environment for personalization support. The empirical study is conducted with 30 students from School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand enrolling Artificial Intelligence subject for gathering the interaction data. Then, the interaction data is analyzed and explained by the measurement of Social Network Analysis. Consequently, the model is constructed by agent based modeling according to the measured interactions. The constructed model is verified in order to compare the similarity between the interactions of the real learners and the simulated agents to the learning objects. Consequently, the experiment with 20 students and the simulation with 20 simulated agents are conducted for verifying the constructed model. Finally, the results show that the constructed model can reasonably provide the accurate interaction model between the learners and the learning objects in ubiquitous learning environment.

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“…Currently, there has been the introduction of ubiquitous learning (u-learning) [12][13][14][15]. It employs the concept of ubiquitous computing [16] in which the computing can happen anywhere and anytime through the seamlessly connected small computing units.…”

Section: Introductionmentioning

confidence: 99%

Smart Learning Environment: Paradigm Shift for Online Learning

Temdee¹

2020

Multi Agent Systems - Strategies and Applications

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Online learning has always been influenced by advanced technology. The role of online learning is expected not only for delivering contents to massive learners anywhere and anytime but also for promoting successful learning for the learners. Consequently, this emerged role has introduced the concept of smart learning environment. More specifically, smart learning environment is developed to promote personalized learning for learners. Personalized learning focuses on individual learner and provides appropriate feedback individually. Currently, the advances of modern technologies and intelligence data analytics have brought the idea of smart learning environment into realization. Machine learning techniques are generally applied to analyze real-time dynamic learner behavior and provide the appropriate response to the right learner. In this chapter, the evolution of online learning environment from different points of technological overviews is first introduced. Next, the concepts of personalized learning and smart learning environment are explained. Then, the essential components of smart learning environment are presented including learner classification and intervention feedback. Learner classification is to understand different learners. Intervention feedback is to provide an individual response appropriately. Additionally, some machine learning techniques widely used in smart learning environment in order to perform smart classification and response are briefly explained.

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Ubiquitous Learning Environment: Smart Learning Platform with Multi-Agent Architecture

Cited by 29 publications

References 10 publications

Designing a ubiquitous sensor-based platform to facilitate learning for young children in Thailand

Designing a ubiquitous sensor-based platform to facilitate learning for young children in Thailand

Modeling of student and learning object interactions toward ubiquitous learning environment

Smart Learning Environment: Paradigm Shift for Online Learning

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