An adaptive navigation support system for conducting context-aware ubiquitous learning in museums

In context-aware ubiquitous learning, students are guided to learn in the real world with personalized supports from the learning system. As the learning resources are realistic objects in the real world, certain physical constraints, such as the limitation of stream of people who visit the same learning object, the time for moving from one object to another, and the environmental parameters, need to be taken into account. Moreover, the values of these context-dependent parameters are likely to change swiftly during the learning process, which makes it a challenging and important issue to find a navigation support mechanism for suggesting learning paths for individual students in real time. In this paper, the navigation support problem for context-aware ubiquitous learning is formulated and two navigation support algorithms are proposed by taking learning efficacy and navigation efficiency into consideration. From the simulation results of learning in a butterfly museum setting, it is concluded that the innovative approach is helpful to the students to more effectively and efficiently utilize the learning resources and achieve better learning efficacy.     

A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning

In a context-aware ubiquitous learning environment, learning systems can detect students’ learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that models the objectives and criteria for determining personalized context-aware ubiquitous learning paths to maximize the learning efficacy for individual students is formulated by taking the meaningfulness of the learning paths and the number of simultaneous visitors to each learning object into account. Moreover, a Heuristic Algorithm is proposed to find a quality solution. Experimental results from the learning activities conducted in a natural science butterfly-ecology course of an elementary school are also given to depict the benefits of the innovative approach.     

A two-tier test approach to developing location-aware mobile learning systems for natural science courses

The advancement of wireless and mobile technologies has enabled students to learn in an environment that combines learning resources from both the real world and the digital world. Although such an approach has been recognized as being innovative and important, several problems have been revealed in practical learning activities. One major problem is owing to the lack of proper learning strategies or tools for assisting the students to acquire knowledge in such a complex learning scenario. Students might feel excited or engaged when using the mobile devices to learn in the real context; nevertheless, their learning achievements could be disappointing. To deal with this problem, this study presents a mobile learning system that employs Radio Frequency Identification (RFID) technology to detect and examine real-world learning behaviors of students. This study also utilizes each student’s responses from a two-tier test (i.e., multiple-choice questions in a two-level format) to provide personalized learning guidance (called two-tier test guiding, T³G). The experimental results from a natural science course of an elementary school show that this innovative approach is able to improve the learning achievements of students as well as enhance their learning motivation.     

A context-aware ubiquitous learning environment for language listening and speaking

This paper reported the results of a study that aimed to construct a sensor and handheld augmented reality (AR)-supported ubiquitous learning (u-learning) environment called the Handheld English Language Learning Organization (HELLO), which is geared towards enhancing students' language learning. The HELLO integrates sensors, AR, ubiquitous computing and information technologies. It is composed of two subsystems: an English learning management system and a u-learning tool. In order to evaluate the effects of the proposed learning environment on the learning performance of students, a case study on English learning was conducted on a school campus. The participants included high school teachers and students. A learning course entitled 'My Campus' was conducted in the class; it included three activities, namely 'Campus Environment', 'Campus Life' and 'Campus Story'. The evaluation results showed that the proposed HELLO and the learning activities could improve the students' English listening and speaking skills.     

Learning in a u-Museum: Developing a context-aware ubiquitous learning environment

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the digital world. Although learning outside of the traditional classroom is an innovative teaching approach, the two main problems are the lack of proper learning strategies and the capacity to acquire knowledge on subjects effectively. To manage these problems, this study proposes a context-aware ubiquitous learning system (CAULS) based on radio-frequency identification (RFID), wireless network, embedded handheld device, and database technologies to detect and examine real-world learning behaviors of students. A case study of an aboriginal education course was conducted in classrooms and at the Atayal u-Museum in Taiwan. Participants included elementary school teachers and students. We also designed and used a questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory to measure the willingness for adoption or usage of the proposed system. The experimental results demonstrated that this innovative approach can enhance their learning intention. Furthermore, the results of a posttest survey revealed that most students' testing scores improved significantly, further indicating the effectiveness of the CAULS.     

EXEHDA-HM: A compositional approach to explore contextual information on hybrid models

The current proposals of hybrid context modeling bring new challenges, an important one is how applications can access and process data stored on these models. Thinking about that, this paper proposes a solution to deal with this challenge through a compositional approach that explores the context information on hybrid models, called EXEHDA-HM. The proposed approach stands out by the design of a repository that supports three database models and by the compositional processing strategy based on rules. In our proposal, the applications can combine data stored on different bases in a single rule, which could enhance the identification of contextual situations. For the evaluation we designed and implemented some case studies on information security area, exploring the hybrid repository composed of relational, non-relational, and triple storage models. Our results demonstrate that was possible to identify richer situations with the data composition across more than one model and there are situations that can only be found through this composition.     

A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning

Recent developments in computing and mobile technologies have enabled the mobile and ubiquitous learning approach, which situates students in an environment that combines real-world and digital-world learning resources. Although such an approach seems to be innovative and interesting, several problems have been revealed when applying it to practical learning activities. One major problem is owing to the lack of proper learning strategies or tools that can guide or assist the students to learn in such a complex learning scenario. Students might feel excited or interested when using the mobile devices to learn in the real world; however, their learning achievements could be disappointing. To cope with this problem, in this study, a knowledge engineering approach is proposed to develop Mindtools for such innovative learning scenarios. Experimental results from a natural science course of an elementary school show that this innovative approach not only enhances learning motivation, but also improves the learning achievements of the students.     

An application of a context-aware file system

Ubiquitous computing environments stretch the requirements of traditional infrastructures used to facilitate the development of applications. Activities are often supported by collections of applications, some of which are automatically launched with little or no human intervention. This task-driven environment challenges existing application construction and data management techniques. In this paper, we describe a file system that organises application data based on contextual information, imports user data based on its physical presence, and supports format conversions to accommodate device context. We describe several applications that we have developed within our ubiquitous computing infrastructure and show how they leverage the novel features of our file system to simplify their complexity.

Modular framework support for context-aware mobile cinema

In this paper we describe a novel approach for interactive cinema based on context-aware narration using handheld computers. The paper describes both the artistic approach and the ubiquitous computing framework developed to realize the scenario. This framework has been used in various projects, including the described video production course at the ISNM, where five interactive cinema concepts have been developed and shown during a public demonstration. In our approach, a new type of user experience has been established by placing the viewer inside the movie’s physical locations during playback. Moreover, the developed ubiquitous computing framework provides a foundation for future work in the area of ad-hoc, service-oriented Ubicomp scenarios.     

A semantic service discovery approach for ubiquitous computing

Services in the ubiquitous computing are heterogeneous in nature. To be pervasive, these services should be defined in terms of their functionality and capabilities rather than the meaningless Universally Unique IDentifiers (UUIDs) or types of services. With that, clients can access the proper service based on semantic requests, rather then a pre-configured profile. In this paper, we study the requirements for semantic query to be feasible in service discovery processes. Current discovery protocols and the concept of semantics are brought together to construct a framework to realize the semantic service discovery for ubiquitous computing. Many issues are discussed in relation to service discovery topologies, ontology languages, and semantic query languages.     

A context-aware sensing strategy with deep reinforcement learning for smart healthcare

Health sensing system (HSS), offering a variety of health services, has attracted considerable research attention in the area of smart healthcare. However, continuous sensing inevitably brings dramatic energy consumption of mobile sensing devices. On the other hand, the reduction of sensing time duration causes excessive delay in sensing a user state change and the missing of critical physiologic signal. Thus, the trade-off between energy consumption and delay constitutes a primary challenge in the design of HSS. In this paper, we propose an adaptive sensing strategy to intelligently determine the trigger time for sensing physiological parameters at a HSS. Furthermore, human context recognition (HCR) is adopted to design context-aware sensing strategy, where the health condition, sensing requirements, and dependence on physiological data are considered simultaneously. To devise the sensing strategy, we first generate a dynamic observation model. Next, we propose a sort retention double-DQN based sensing strategy. In comparison to traditional double-DQN, the proposed approach can effectively enhance learning stability and sample efficiency. With SRD-DQN, we can obtain the optimized solution for the schedule of the successive window according to the current state. We implement blood pressure and heart rate monitoring simulations to evaluate the performance of the proposed sensing strategy. Simulation results reveal that the sensing strategy can effectively restrain energy consumption and delay, and SRD-DQN converges faster than traditional DQN.     

Building ubiquitous QoC-aware applications through model-driven software engineering

As every-day mobile devices can easily be equipped with multiple sensing capabilities, ubiquitous applications are expected to exploit the richness of the context information that can be collected by these devices in order to provide the service that is the most appropriate to the situation of the user. However, the design and implementation of such context-aware ubiquitous appplications remain challenging as there exist very few models and tools to guide application designers and developers in mastering the complexity of context information. This becomes even more crucial as context is by nature imperfect. One way to address this issue is to associate to context information meta-data representing its quality. We propose a generic and extensible design process for context-aware applications taking into account the quality of context (QoC). We demonstrate its use on a prototype application for sending flash sale offers to mobile users. We present extensive performance results in terms of memory and processing time of both elementary context management operations and the whole context policy implementing the Flash sale application. The cost of adding QoC management is also measured and appears to be limited to a few milliseconds. We show that a context policy with 120 QoC-aware nodes can be processed in less than 100 ms on a mobile phone. Moreover, a policy of almost 3000 nodes can be instantiated before exhausting the resources of the phone. This enables very rich application scenarios enhancing the user experience and will favor the development of new ubiquitous applications.     

Automated context aggregation and file annotation for PAN-based computing

This paper presents a method for automatically annotating files created on portable devices with contextual metadata. We achieve this through the combination of two system components. One is a context dissemination mechanism which allows devices in a personal area network (PAN) to maintain a shared aggregate contextual perception. The other is a storage management system that uses such context information to automatically decorate files created on personal devices with annotations. As a result, the user is able to flexibly browse and lookup files that were generated on the move, based on the contextual situation at the time of their creation. What is equally important is that the user is relieved from the cumbersome task of having to manually provide annotations in an explicit fashion. This is especially valuable when generating files on the move, using U/I-restricted portable devices.

Implicit human computer interaction through context

In this paper the term implicit human-computer interaction is defined. It is discussed how the availability of processing power and advanced sensing technology can enable a shift in HCI from explicit interaction, such as direct manipulation GUIs, towards a more implicit interaction based on situational context. In the paper, an algorithm is given based on a number of questions to identify applications that can facilitate implicit interaction. An XML-based language to describe implicit HCI is proposed. The language uses contextual variables that can be grouped using different types of semantics as well as actions that are called by triggers. The term of perception is discussed and four basic approaches are identified that are useful when building context-aware applications. Two examples, a wearable context awareness component and a sensor-board, show how sensor-based perception can be implemented. It is also discussed how situational context can be exploited to improve input and output of mobile devices.     

面向普适环境的上下文感知中间件研究

针对普适环境下上下文感知计算需求,引入广义模型化理论,建立了一种面向通用环境资源的上下文信息数据模型;在此基础上,提出了上下文感知中间件体系框架,并详细阐述了其构件化的实施方案。该中间件平台的上下文获取层能够封装各类感知器捕获的资源信息,中间处理层负责信息的管理、推理和聚合,基于门面模式的上下文访问层提供同步和异步相结合的上下文信息统一访问入口。通过实验测试了平台的时间损耗,表明该中间件可提供通用的上下文感知服务且具有较好的系统性能。     

Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies

Mobile and ubiquitous learning are increasingly attracting academic and public interest, especially in relation to their application in higher education settings.The systematic analysis of 36 empirical papers supports the view that knowledge gains from instructionist learning designs are facilitated by distributed and more frequent learning activities enabled by push mechanisms. They also lend themselves to the activation of learners during classroom lectures. In addition, and as a particular advantage of mobile technology, “hybrid” designs, where learners create multimodal representations outside the classroom and then discussed their substantiated experiences with peers and educators, helped to connect learning in formal and more informal and personalized learning environments.Generally, empirical evidence that would favour the broad application of mobile and ubiquitous learning in higher education settings is limited and because mobile learning projects predominantly take instructionist approaches, they are non-transformatory in nature. However, by harnessing the increasing access to digital mobile media, a number of unprecedented educational affordances can be operationalised to enrich and extend more traditional forms of higher education.     

Granular best match algorithm for context-aware computing systems

In order to be context-aware, a system or application should adapt its behaviour according to current context, acquired by various context provision mechanisms. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA – Context-Aware Personal Reminder Agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the user’s intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method.     

Access control management for ubiquitous computing

The purpose of ubiquitous computing is anywhere and anytime access to information within computing infrastructures that is blended into a background and no longer be reminded. This ubiquitous computing poses new security challenges while the information can be accessed at anywhere and anytime because it may be applied by criminal users. Additionally, the information may contain private information that cannot be shared by all user communities. Several approaches are developed to protect information for pervasive environments against malicious users. However, ad hoc mechanisms or protocols are typically added in the approaches by compromising disorganized policies or additional components to protect from unauthorized access.In this paper, we present a usage control model to protect services and devices in ubiquitous computing environments, which allows the access restrictions directly on services and object documents. The model not only supports complex constraints for pervasive computing, such as services, devices and data types but also provides a mechanism to build rich reuse relationships between models and objects. Finally, comparisons with related works are analysed.