Qun Jin

Ranking Metrics and Search Guidance for Learning Object Repository

In line with the popularity of the Internet and the development of search engine, users request information through web-based services. Although general-purpose searching such as one provided by Google is powerful, searching mechanism for specific purposes could rely on metadata. In distance learning (or e-learning), SCORM provides an efficient metadata definition for learning objects to be searched and shared. To facilitate searching in a federated repository, CORDRA provides a common architecture for discovering and sharing Learning Objects. We followed SCORM and CORDRA specifications to develop a registry system, called the MINE Registry, for storing and sharing 20,738 Learning Objects created in the past five years. As a contribution, we propose the concept of “Reusability Tree” to represent the relationships among relevant Learning Objects and enhance CORDRA. We further collect relevant information, while users are utilizing Learning Objects, such as citations and time period persisted. The feedbacks from the user community are also considered as critical elements for evaluating significance degree of Learning Objects. Through theses factors, we propose a mechanism to weight and rank Learning Objects in the MINE Registry, in addition to other external learning objects repositories. As a practical contribution, we provide a tool called “Search Guider” to assist users in finding relevant information in Learning Objects based on individual requirements.

A framework of social interaction support for ubiquitous learning

This paper describes the computer supported ubiquitous learning system and the social interaction between learners. We define the ubiquitous learning with five prime attributes, and present a generalized social interaction support model for the ubiquitous learning. Moreover, in order to support learners with increasing social skill, a solution for constructing social interaction in ubiquitous learning environment is designed, which includes three major functions; encounter, communication and collaboration support functions.

Survey on mobile social networking in proximity (MSNP): approaches, challenges and architecture

AbstractRecently, mobile social networks (MSN) have gained tremendous attention, which free users from face-to-monitor life, while still can share information and stay in touch with their friends on the go. However most MSN applications regard mobile terminals just as entry points to existing social networks, in which centralized servers (for storage and processing of all application/context data) and continual Internet connectivity are prerequisites for mobile users to exploit MSN services, even though they are within proximity area (like campus, event spot, and community, etc.), and can directly exchange data through various wireless technologies (e.g., Bluetooth, WiFi Direct, etc.). In this paper, we focus on mobile social networking in proximity (MSNP), which is explicitly defined in our paper as: MSNP is wireless peer-to-peer (P2P) network of spontaneously and opportunistically connected nodes, and uses geo-proximity as the primary filter in determining who is discoverable on the social network. In this paper, first, primary support approaches related to MSNP available in literature, are summarized and compared, including MSN, mobile P2P and opportunistic networks. And then, we offer the special characteristics of MSNP, open issues and potential solutions. A networking technologies and platform independent architecture is proposed for developing MSNP applications, and proof-of-concept implementation of WiFi direct based MSNP application is also provided. Our primary goal is to identify the characteristics, technical challenges and potential solutions for future MSNP applications, capable to flexibly adapt to different application domains and deployment requirements.

A survey of distance education challenges and technologies

Distance education, e-learning, and virtual university are similar terms for a trend of modern education. It is an integration of information technologies, computer hardware systems, and communication tools to support educational professionals in remote teaching. This chapter presents an overview of distance education from the perspective of policy, people, and technology. A number of questions frequently asked in distance learning panel discussions are presented, with the suggested answers from the authors. The survey presented in this chapter includes communication, intelligent, and educational technologies of distance education. Readers of this 2 Shih, Hung, Ma, and Jin Copyright

Design of a virtual community based interactive learning environment

In this paper, we propose a conceptual framework for every-citizen learning communities based on a recently widespread Internet tool known as Multi-user dimension Object-Oriented (MOO). We discuss the design and development of a prototype system of the virtual community based interactive learning environment, which supports human-human communication in addition to human-computer communication, with emphasis on social interaction.

LONET: An interactive search network for intelligent lecture path generation

Sharing resources and information on the Internet has become an important activity for education. In distance learning, instructors can benefit from resources, also known as Learning Objects (LOs), to create plenteous materials for specific learning purposes. Our repository (called the MINE Registry) has been developed for storing and sharing learning objects, around 22,000 in total, in the past few years. To enhance reusability, one significant concept named Reusability Tree was implemented to trace the process of changes. Also, weighting and ranking metrics have been proposed to enhance the searchability in the repository. Following the successful implementation, this study goes further to investigate the relationships between LOs from a perspective of social networks. The LONET (Learning Object Network), as an extension of Reusability Tree, is newly proposed and constructed to clarify the vague reuse scenario in the past, and to summarize collaborative intelligence through past interactive usage experiences. We define a social structure in our repository based on past usage experiences from instructors, by proposing a set of metrics to evaluate the interdependency such as prerequisites and references. The structure identifies usage experiences and can be graphed in terms of implicit and explicit relations among learning objects. As a practical contribution, an adaptive algorithm is proposed to mine the social structure in our repository. The algorithm generates adaptive routes, based on past usage experiences, by computing possible interactive input, such as search criteria and feedback from instructors, and assists them in generating specific lectures.

Capturing User Contexts: Dynamic Profiling for Information Seeking Tasks

Knowing each userpsilas information needs is important for information systems to better facilitate human information activities. This is especially important in the days of information overload we are experiencing today. However, knowing and correctly applying individual information needs is extremely difficult, often impossible. Yet knowing multiple contexts of user information behavior can give us some conception (or a hint) of conceivable information a user tries to obtain in a particular context. In this paper we focus on capturing such information contexts into dynamically changing profiles which are further used to facilitate the userpsilas information seeking activities.

Modeling and Analyzing Individual's Daily Activities using Lifelog

Lifelog is a data set composed of one or more media forms that record the same individualpsilas daily activities. One of the main challenging issues is how to extract meaningful information from the huge and complex lifelog data which is continuously captured and accumulated from multiple sensors. This study is focused on the activity models and analysis techniques to process lifelog data in order: to find what events/states are interesting or important, to summarize the useful records in some structured and semantic ways for efficient retrievals and presentations of past life experiences, and to use these experiences to further improve the individualpsilas quality of life. We propose an integrated technique to process the lifelog data using the correlations between different kinds of captured data from multiple sensors, instead of dealing with them separately. To use and test the proposed models and the analysis techniques, several prototype systems have been implemented and applied to some domain-specific lifelog data; such as in improving a grouppsilas collaborative efforts in revising a software, in managing kidpsilas outdoor safety care, in providing a runnerpsilas workout assistance, and in structuring lifelog image generation, respectively.

An adaptively emerging mechanism for context-aware service selections regulated by feedback distributions

BackgroundIn the cloud computing environments, numerous ambient services may be created speedily and provided to a variety of users. In such a situation, people may be annoyed by how to make a proper and optimal selection quickly and economically.MethodsIn this study, we propose an Adaptively Emerging Mechanism (AEM) to reduce this selection burden with an interdisciplinary approach. AEM is applied and integrated into the Flowable Service Model (FSM), which has been proposed and developed in our previous study. We consider the user’s feedback information is a pivotal factor for AEM, which contains the user’s satisfaction degree after using the services. At the same time, we assume that these factors, such as the service cost, matching result precision, responding time, personal and social context information, etc., are essential parts of the optimizing process for the selection of ambient services.Results and ConclusionBy analyzing the result of AEM simulation, we reveal that AEM can (1) substantially improve the selection process for LOW feedback users; (2) bring no negative effect on the selection process for MEDIUM or HIGH feedback users; and (3) enhance the rationality for services selection.

QuaCentive: a quality-aware incentive mechanism in mobile crowdsourced sensing (MCS)

Today’s smartphones with a rich set of cheap powerful embedded sensors can offer a variety of novel and efficient ways to opportunistically collect data, and enable numerous mobile crowdsourced sensing (MCS) applications. Basically, incentive is one of fundamental issues in MCS. Through appropriately integrating three popular incentive methods: reverse auction, reputation and gamification, this paper proposes a quality-aware incentive framework for MCS, QuaCentive, which, pertaining to all components in MCS, can motivate crowd to provide high-quality sensed contents, stimulate crowdsourcers to give truthful feedback about quality of sensed contents, and make platform profitable. Specifically, first, we utilize the reverse auction and reputation mechanisms to incentivize crowd to truthfully bid for sensing tasks, and then provide high-quality sensed contents. Second, in to encourage crowdsourcers to provide truthful feedbacks about quality of sensed data, in QuaCentive, the verification of those feedbacks are crowdsourced in gamification way. Finally, we theoretically illustrate that QuaCentive satisfies the following properties: individual rationality, cost-truthfulness for crowd, feedback-truthfulness for crowdsourcers, platform profitability.