A Ubiquitous Smart Learning Platform for the 21st Smart Learners in an Advanced Science and Engineering Education

Lee, Jongsuk Ruth; Jung, Young Jin; Park, Sun Rae; Yu, Ji‐Man; Jin, Du-Seok; Cho, Kumwon

doi:10.1109/nbis.2012.66

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…During Phase I, the conceptual features of the prototype HuMAR application were identified in order to enhance the efficiency of learning and hence offer a longer retention of information. A comparative analysis from (Jamali et al, 2013) has been taken through a few interfaces from other mAR technology applied in various industries; for instance education, advertising, entertainment, and tourism. As a result, based on the high success rate of selling product efficiently (Institute, 2004) in advertising using a multimodal interface this concept has received a very positive response from users.…”

Section: Functional Requirements Of the Prototype Humar Applicationmentioning

confidence: 99%

Utilising Mobile-Augmented Reality for Learning Human Anatomy

Jamali

Shiratuddin

Wong

et al. 2015

Procedia - Social and Behavioral Sciences

144

103

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Augmented Reality (AR) is a technology that augments reality with either two or three dimensional computer generated imagery (CGI), objects and/or information, and allows users to interact with them. AR on mobile devices are evolving and offer a great deal of potential in terms of learning and training. This paper discusses the development process of a mobile prototype learning environment that utilises mobile-Augmented Reality (mAR). The prototype is called the Human Anatomy in Mobile Augmented Reality or HuMAR, and the selected learning topic is the anatomy of the human skeletal structure. The main objective of HuMAR is to aid students and it could potentially enhance their learning process. There has been a report stating that there is a decline in retaining and generating long lasting information longer when learning the abovementioned topic. This paper describes the theory, concept, prototype development and results of HuMAR taken from a pilot test. The pilot test used the experimental method with science's students from three different universities. The objectives of the pilot test were to consolidate users' experience from a didactic and technical point of view. Based on the results of the pilot test, it is concluded that students were satisfied with HuMAR in terms of its usability and features; which in turn could have a positive impact in their learning process.

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Section: Functional Requirements Of the Prototype Humar Applicationmentioning

confidence: 99%

Utilising Mobile-Augmented Reality for Learning Human Anatomy

Jamali

Shiratuddin

Wong

et al. 2015

Procedia - Social and Behavioral Sciences

144

103

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“…Os autores em [6] [7][8] [9] defendem o uso de dispositivos móveis como a principal ferramenta de aprendizado nas salas de aula do futuro. Os autores em [6] investiga os desafios e oportunidades em se aplicar dispositivos móveis como ferramenta do processo de ensino-aprendizagem no ensino básico norte americano.…”

Section: Trabalhos Relacionadosunclassified

“…Tal estudo investiga aspectos como mudanças nas atitudes dos professores, crenças e percepções sobre o ensino e aprendizado utilizando tais tecnologias. Os autores em [7] desenvolvem um sistema de aprendizado ubíquo baseado em simulações computacionais chamado EDISON, específico para apoiar usuários no aprendizado de dinâmica de fluídos computacionais. Os autores em [8] investiga o nível de inclusão destes dispositivos móveis em salas de aula universitárias, além de analisar informações sobre a percepção dos estudantes nos anos finais de mestrado de uma respectiva universidade.…”

Section: Trabalhos Relacionadosunclassified

“…Para uma definitiva sala de aula futurística seriam necessárias diversas aplicações em todos os âmbitos do ensino, utilizando-se de conceitos de computação pervasiva, redes de computadores, inteligência artificial, computação multimídia e softwares de personalização [7]. Pensando nesta problemática, utilizaremos como estudo de caso um dos micromódulos trabalhados recentemente, sendo este um sistema tutor inteligente que utiliza tecnologia vestível para otimizar o aprendizado do aluno em aulas de música.…”

Section: Cenários De Aplicaçãounclassified

See 1 more Smart Citation

PB-SmartClass: Um Modelo Arquitetural Baseado em Projetos Colaborativos para Salas de Aula do Futuro

Silva

Nova

Vasconcelos

et al. 2017

Anais Dos Workshops Do VI Congresso Brasileiro De Informática Na Educação (CBIE 2017)

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“…SLEs encompass traditional classrooms as well as online and distance education. Taking learning anywhere and everywhere in a consistent fashion requires technologies that move such as the smart phones supported by 3G and 4G networks (Lee et al 2012) as well as learning environments that move such as the flipped classrooms at homes. To provide contextaware learning (Yu et al 2009;Hwang et al 2008), hardware and software sensors are necessary to recognize the context and the learning needs of the user to tailor learning content and activities.…”

Section: Introductionmentioning

confidence: 99%

Competence analytics

et al. 2014

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A smart learning environment is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this article introduces a framework called Smart Competence Analytics in LEarning (SCALE) that tracks finer-level learning experiences and translates them into opportunities for customized feedback, reflection, and regulation. The SCALE framework is implemented in four layers: the sensing layer, the analysis layer, the competence layer, and the visualization layer. The sensing layer provides the datasets to support context-awareness through state-of-the-art sensing technologies. The analysis layer, by the means of powerful code analysis tools, derives performance metrics (e.g., learner coding metrics) which serve as input to the competence layer to identify proficiency levels of learners. Finally, a learning analytics dashboard called MI-DASH (visualization layer) allows interaction with performance and competence metrics. The current SCALE system has been used in a study to track the habits, performances, and competences of novice programmers. Growth of coding competences of first-year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning paths. The article describes the analytics-based approach pursued in the study and highlights key findings.

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A Ubiquitous Smart Learning Platform for the 21st Smart Learners in an Advanced Science and Engineering Education

Cited by 13 publications

References 3 publications

Utilising Mobile-Augmented Reality for Learning Human Anatomy

Utilising Mobile-Augmented Reality for Learning Human Anatomy

PB-SmartClass: Um Modelo Arquitetural Baseado em Projetos Colaborativos para Salas de Aula do Futuro

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