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The impact of Ubiquitous Computing on Learning is not confined within technical dimension. Besides its technical facilitation, this new computing paradigm also challenges human’s belief on learning, and compels us to rethink on the design of learning resources and environments. The paper explores the concept of Ubiquitous Learning, and proposes a conceptual framework for a Ubiquitous Learning Environment (ULE) design and implementation. A ULE is established on the combination between Real World and Virtual Space, Personal Space and Shared Space. Learning in a ULE is conducted in the interactions among three essential communicative elements: Social Human, Object in real world, and Artifact in virtual space. A learning process is a social transfer process between tacit and explicit knowledge. Context‐Awareness is indispensable to all kinds of interactions in a ULE. In particular, this paper gives a discussion to context‐awareness supported Interoperability and Adaptability in a ULE, and suggests a five‐dimensional (Who, What, How, When, Where) representation approach for modeling context and providing context‐awareness information. In the practical dimension, this paper presents a design framework for a ULE implementation by integrating the applications of present affordable learning devices, such as networked PCs (Personal Computer), PDAs (Personal Digital Assistant), mobile phones, sensors, and RFIDs. A basic learning system architecture in a ULE and a prototype ubiquitous language learning system are also addressed in this paper.

Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

The purpose of this paper is to analyse some effective e-tools and interactive tasks to enhance language acquisition and competence building in today’s English for specific purposes (ESP) students in higher education programmes.

There are countless learning methods, often supported by the access of ubiquitous learning materials, which improve students’ engagement, group interaction and independent learning skills in foreign language learners. More frequently, students become successful, independent and cooperative creative learners by means of participating in multimodal virtual learning platforms that would help them to improve a variety of competences. This study is based on English applied to social work, a compulsory subject offered either in a blended (b-learning) or a distance (d-learning) learning environment as part of the degree in social work. It combines its correspondent course book with digital content and multimedia tasks.

Results suggest that a ubiquitous practice for ESP b/d learners can be innovative and successfully competence-addressed when implementing interactive learning tasks that can combine individual and group work inside and outside the classroom hours.

The challenge of this research is to integrate a ubiquitous learning approach so that students would interact more and, consequently, enhance their communicative language skills, both in b-learning and d-learning environments.

The purpose of this study is to propose a knowledge‐based mobile learning framework that integrates various types of museum‐wide content, and supports ubiquitous, context‐aware, personalized learning for museums.

A unified knowledge base with multi‐layer reusable content structures serves as the kernel component to integrate content from exhibitions for education and collection in a museum. The How‐Net approach is adopted to build a unified natural and cultural ontology. The ontology functions as a common and sharable knowledge concept that denotes each knowledge element in the unified knowledge base, and associates each learner's learning context and usage with a content and usage profile respectively. Data mining algorithms, e.g. association mining and clustering, are applied to discover useful patterns for ubiquitous personalization from these content and usage profiles.

A pilot project based on the proposed framework has been successfully implemented in the Life Science Hall of the National Museum of Natural Science (NMNS), Taiwan, demonstrating the feasibility of this framework.

This study proposes a mobile learning framework that can be replicated in many museums. This framework improves learners' learning experiences with rich related content, and with ubiquitous, proactive and adaptive services. Museums can also benefit from implementing this framework through outreach services for educational, promoting and usability needs from combining mobile and Internet communication technologies and learning services.

Change is the operative word in higher education; as roles shift, classrooms are reinvented, and content becomes increasingly more accessible. At the core of these changes is the pervasiveness of learning technology. This papers aims to propose a model for the selection and adoption of emerging learning technologies to enhance learning within the context of higher education.

Higher education institutions are resorting to the deployment of learning technologies to address the demands of the twenty-first century learners and to ascertain their competitiveness. This paper draws from the literature to provide a model, composed of five key characteristics of learning, to support the selection and adoption of emerging learning technologies.

The model posits that the attainment of each of these five characteristics, personalised, ubiquitous, collaborative, lifelong and authentic needs to be supported by corresponding technologies: adaptive learning technologies, artificial intelligence, mobile technology, social technology, massive open online courses, virtual and augmented reality, gamification and the Internet of Things.

Higher education is progressively being displaced from the traditional classroom, and as it progresses towards online settings, it requires the support of technology to facilitate that transference. In examining the potential of future learning technologies, this paper contributes to a growing body of research that focuses on the benefits of technology within higher education and assists educators in the selection and adoption of the most relevant technologies.

The author proposed a mobile learning model of pervasive animated games which allows college students to learn via games accessed through a smartphone. It can develop the process of field observation and self-reflection to enhance learning effectiveness, and the motivation, and attitude of students towards learning.

The author proposed a model for teaching via pervasive animated games. The author used SPSS software and Pearson's correlation coefficients to explore different mobile learning strategies and their relationship with learning attitudes and achievement. Participants were vocational technology college students, who each experienced animated games in individual and group learning settings.

The results found that the learning performance of students in the individual learning group was better than that of the group learning group. A higher level of digital experience was associated with better learning performance, and a more positive attitude towards using mobile phones was associated with better learning performance.

The learning method still has its limitations, the learner's digital information level, learning mode, learning attitudes will have an impact on the student playing teaching pervasive animation games. Therefore, improving student information level is one of the important topics of teaching pervasive animation games and mobile learning.

The author proposed a mobile learning strategy based on pervasive animated games. The result in the strategy of mobile learning shows that the level of students' digital experience and the overall design of animated games are important criteria for successful implementation.

The implementation of the national education and lifelong education should break through the traditional learning mode in the digital era. A seamless learning environment is the intelligent form of deep integration of digital learning environment and physical learning environment. The development and construction of seamless learning space and platform is a new trend of international mobile learning research and practice. So, the purpose of this paper is to build a seamless learning platform of the open education system by expanding and improving the connotation and extension of seamless learning, based on the theories of mobile learning, ubiquitous learning and open education, combining with the characteristics of the big data era.

A seamless learning platform model will be constructed through constructing five modules including resource integration module, requirement module, management module, teacher integration module, and carrier module, using interdisciplinary research methods (combined with economics), functional analysis, model methods and exploratory research methods. Finally, this paper attempts to realize the application of a seamless learning platform in open education through the strategic path of public-private partnership (PPP).

Seamless learning platform model given by this paper can effectively eliminate the problem of information asymmetry between learner-demander and educational manager through the effective management of carrier fusion module. Furthermore, it can effectively integrate the learning resources and teachers of the open education system and social individual education system, and provide high-quality shared learning resources and diverse stratification teachers to students and social workers through PPP project cooperation ways.

There are many papers on the study of open education and resource construction. However, few papers have studied how to effectively integrate and optimize various existing scattered online resources, including various system courses, teachers and platform systems. The paper provides an effective way to solve the above sub-problems and the realization path/model reference for the effective and accurate promotion of lifelong sustainable learning for all.

This study aims to use the novel approach of applying the behavioural reasoning theory (BRT) to understand the relative influence of reason for and reason against the adoption of mobile learning applications (M-learning apps) among information technology (IT) and information technology enabled services (ITeS) employees.

This study surveys 680 employees of IT and ITeS companies in India to examine the adoption of M-learning apps for learning using the BRT and the primary data analysis was done using the partial least squares-structural equation modelling technique.

It is found that the context-specific adoption factors for M-learning apps are hedonic motivation, self-efficacy, learning autonomy, ubiquitous and relative advantage, whereas the reasons against adoption of the M-learning apps are traditional barrier, usage barrier and image barrier. It is also found that values of openness to change positively affect the reasons for adoption and do not significantly affect reasons against adoption of M-learning. Values of openness to change affect the attitude towards M-learning apps and attitude affects the adoption intention of M-learning apps for learning.

This cross-sectional study was conducted only in the Indian IT/ITeS firms and future research can be conducted in other sectors and countries to generalize the results.

This research uniquely highlights the adoption factors both for and against, which should be considered while developing marketing strategies for M-learning apps’ adoption. It is imperative for training managers to consider these factors during the selection of M-learning apps and for designers while designing the M-learning apps.

This study provides new insights towards the use of mobile apps for learning with the employees’ perspective using the BRT theory and it highlights the reason for adoption and reason against adoption of M-learning apps.

This is paper is concerned with the learning outcomes associated with connectivity through online networks, open online exchange and wider changes associated with contemporary information practices. The theme of connectivity is used here to capture both the detailed specificity of relations that define networks of learners and the ambient effect of wide accessibility to resources and people through open, online forums.

The paper follows the idea of a network from the ground up, outlining the social network perspective as a way to consider the foundational bases of learning and networks, as well as the effect of ambient influence. The paper addresses the ways learning may be viewed as a social network relation, an interpersonal relationship and an outcome of interaction and connectivity, and how network connectivity can be used as input for design for learning.

The paper presents a range of perspectives and studies that view learning from a social network and connectivity perspective, emphasizing both the person-to-person connectivity of a learning tie and the impact of contemporary data and information sharing through the dynamics of open contributory practice.

The outcome of connectivity in the service of learning is bound up with digital information practices, including individual practices of search, retrieval, participation, knowledge dissemination, knowledge construction and more. This paper provides a network perspective on learning relations that accommodates analysis in online and offline environments, but incorporates attention to the open, online retrieval and contributory practices that now influence learning practices and which may support design of new learning environments.

This paper offers insight into the way social networks and connectivity combine to show network relations, relationships, outcomes and design input at the actor, network and societal levels.

The purpose of this paper is to analyze the trends and potential research issues involved with library-supported mobile learning.

Publications in the Scopus database from 2007 to 2016 are analyzed on various aspects, such as the adopted research methods, technologies, participants, application domains and research issues.

In addition to the awareness of the increasing number of digital library-associated mobile learning in the past decade, there are several interesting findings in this paper, such as most research mainly focused on investigating the affective domain of participants and assessing the effects of mobile learning. Most of the library-supported mobile learning research was conducted at special libraries, followed by academic libraries.

Potential applications and research directions of library-supported mobile learning are suggested based on the findings of the present paper.

This paper provides insight in relation to library-associated mobile learning. The findings and suggestions can serve as a good reference for researchers and school teachers when conducting mobile learning studies and to develop quality learning activities with access to library facilities using mobile and wireless communication technologies.