Structural equation modelling approach in multiplatform e-learning system evaluation

A multiplatform e-learning system is an e-learning system that can deliver learning content to different accessing devices such as PCs, PDAs and mobile phones. The main objective of this paper is to present an evaluation of such a system from the end user (learner) perspective. The evaluation investigates the relationships among the dimensions that influence learner satisfaction while engaged in a multiplatform e-learning system. A total of 36 participants took part in a comparative evaluation. A structural equation model (SEM) was constructed to fit the data. This model indicated that there was a good fit for the resulting model. The SEM identified two groups of factors. Primary factors dominated in the non adaptive e-learning system while secondary factors emerged from the multiplatform e-learning system. The SEM also revealed a suppressor within the system that could possibly negate the contributions from other dimensions

System for improving the efficiency of wireless networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 30-31).Wireless data networks are widespread and growing quickly. As their use increases, many wireless networks are becoming congested. In addition, as wireless data capability moves into ever-smaller devices, power becomes a significant issue. This thesis presents a system that increases network bandwidth and decreases energy use without changing existing network hardware or protocols. We use specialized proxy servers to transparently modify the traffic sent over the mobile link such that the total energy used by the receiver is reduced and the effective bandwidth is increased. Our techniques include optimizing packet size, eliminating unnecessary traffic, and masking wireless packet losses. We design and implement two proxies--one for access points and one for mobile devices--which when used together, achieve up to a 20% decrease in energy and 38% increase in throughput.by Hans Robertson.M.Eng

On Data Management in Pervasive Computing Environments

Abstract—This paper presents a framework to address new data management challenges introduced by data-intensive, pervasive computing environments. These challenges include a spatio-temporal variation of data and data source availability, lack of a global catalog and schema, and no guarantee of reconnection among peers due to the serendipitous nature of the environment. An important aspect of our solution is to treat devices as semiautonomous peers guided in their interactions by profiles and context. The profiles are grounded in a semantically rich language and represent information about users, devices, and data described in terms of “beliefs,” “desires, ” and “intentions. ” We present a prototype implementation of this framework over combined Bluetooth and Ad Hoc 802.11 networks and present experimental and simulation results that validate our approach and measure system performance. Index Terms—Mobile data management, pervasive computing environments, data and knowledge representation, profile-driven caching algorithm, profile driven data management, data-centric routing algorithm. æ

Data integrity for active web intermediaries

Master'sMASTER OF SCIENC