Proposed object-based e-learning framework embracing cloud computing

E-learning is continually evolved in the adaptation of emerging technologies and pedagogies, so does the development of learning objects. Object-based learning approach has been widely adopted in e-learning.The principle concern in the conventional e-learning models is the inflexibility of the e-learning content to assist the use and management of the learning sources which are highly distributed.Rigidity of e-learning content limits its reusability and shareability.Possessing the ability to deliver strong computing power and secure data storage as services, Cloud computing is a promising computing model to promote innovative changes and add notable values to e-learning landscape.The flexibility and on-demand access to a centralized shared pool of computing resources provided by Cloud computing enables high re-usability and share-ability, hence overcomes the principle concern in e-learning due to the rigidity of e-learning content.This paper describes the design of an object-based e-learning framework embracing Cloud computing.The proposed object-based e learning framework can be used to form a new education domain that shares the Cloud characteristics of elasticity, flexibility, efficiency and reliability.Principal to the framework design is the development of Cloud-based e-learning objects where learners have the flexibility to access, personalize and deploy them in e-learning environment.Cloud-based e-learning objects are highly adaptable, reusable and easy changeable, hence allowing them to be used dynamically with greater customizability and flexibility in e-learning.Adapting Cloud computing into object-based e-learning is believed to be able to create a breakthrough in future dynamic -e-learning development

Prediction Of User Acceptance And Adoption Of Smart Phone For Learning With Technology Acceptance Model

This study examines how the Technology Acceptance Model (TAM) can be used as a practical tool for early user acceptance and adoption of testing mobile communication devices for learning by evaluating the relationships among perceived usefulness, perceived ease of use, attitude towards using, behavioural intentions to use and actual use. In the study, 60 potential users were presented with an introductory demonstration of smart phone for a digital systems course. Following the demonstration, data on user perceptions and attitudes about smart phones were gathered based on this initial exposure. Subjects with prior experience using the smart phones were eliminated from further analysis resulting in a final sample of 40 users. Hierarchical multiple regression was used to assess the overall model and influence of each variable of interest in determining actual use of smart phone. The analysis showed that both the user perceptions and attitudes have significant positive effects on behavioural intention and actual use of smart phone. Implications of these findings for practice and research are examined

Fall Detection and Motion Analysis Using Visual Approaches

Falls are considered one of the most ubiquitous problems leading to morbidity and disability in the elderly. This paper presents a vision-based approach toward the care and rehabilitation of the elderly by examining the important body symmetry features in falls and activities of daily living (ADL). The proposed method carries out human skeleton estimation and detection on image datasets for feature extraction to predict falls and to analyze gait motion. The extracted skeletal information is further evaluated and analyzed for the fall risk factors in order to predict a fall event. Four critical risk factors are found to be highly correlated to falls, including 2D motion (gait speed), gait pose, 3D trunk angle or body orientation, and body shape (width-to-height ratio). Different variants of deep architectures, including 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Gated Recurrent Units (GRU) model, and attention-based mechanism, are investigated with several fusion techniques to predict the fall based on human body balance study. A given test gait sequence will be classified into one of the three phases: non-fall, pre-impact fall, and fall. With the attention-based GRU architecture, an accuracy of 96.2% can be achieved for predicting a falling event

Outsourcing performance implication of supplier manufacturer relationships and the perceived impact of environmental dynamism in Malaysia electrical and electronic industry

This study used the partial least squares (PLS) and structural equation modeling (SEM) tool to examine the implication of supplier manufacturer relationships and the perceived impact of environmental dynamism of outsourcing performance in Malaysian electrical and electronic industry.This study also examined the perceived impact of environmental dynamism as moderating variables.The result indicated that the two dimension of the environmental dynamism which are customer demand and market competition do not moderate the relationship between relational oriented exchange (ROE) and outsourcing success.However, customer demand is supported as an independent variable to represent the outsourcing performance implication of supplier manufacturer relationships.The details of theoretical and practical implications of the findings are discussed

Empirical Study Of Students' Acceptance Of Learning Objects

This study examined the underlying factors and causal relationships that determine learners' behavioural intentions to use learning objects in the context of higher education. Based on the literature review of several theories and models in Information Science (IS) discipline, which can serve as a framework to formulate the preliminary Learning Object Acceptance Model (LOAM) for evaluating the acceptance of learning objects among higher education students have been identified

An investigation of user perceptions and attitudes towards learning objects

This study empirically evaluates the technology acceptance model drawn from Information Systems (IS) literature to investigate how user beliefs and attitudes influence learning-object use among higher education learners by evaluating the relationships between perceived usefulness, perceived ease of use, attitude, behavioural intentions and actual use. In the study, 601 potential learning-object users were presented with an introductory demonstration of learning objects for a Digital Systems course. Following the demonstration and practice, data on user beliefs, attitudes and intention to use learning objects were gathered, while data on actual use of learning objects was collected at the end of the semester. Subjects with prior experience using the learning objects were eliminated from further analysis, resulting in a final sample of 481 users. structural equation modelling was employed to test the hypothesised study model. The analysis showed that both the user beliefs and attitudes have significant positive relationships with behavioural intention and that behavioural intention accurately predicted the actual use of learning objects. The results extend the validity of the TAM into a learning object context and clearly pointed out that it can be used to predict users' future behaviour

An Empirical Study of Learning Object Acceptance in Multimedia Learning Environment

The purpose of this study was to develop Learning Object Acceptance Model (LOAM) to examine the underlying factors and causal relationships that determine learners' behavioral intention to use multimedia learning objects in higher education. This study called for the 342 respondents to progress through two phases of learning objects participation: Introduction and Direct-use experience. Structural Equation Modeling (SEM) was used to evaluate data from the resultant surveys with the AMOS 4.0. The results showed that the study model produced measurement and structural models with adequate model fits. Learners' perceived of usefulness and ease of use a fully mediated the relationship between learning object characteristics and behavioral intention. However, individual characteristics were found to have no statistically significance on behavioral intention in this context

Understanding the user acceptance of gesture-based human-computer interactions

This study empirically evaluates how user perceptions and attitudes influence behavioral intention use of multi-touch gestures on a large multi-touch screen by using Technology Acceptance Model (TAM). In the study the researchers presented 20 novice users with an introduction and hands-on of multi-touch gestures on window, object and image manipulations. Following the hand-on session, data on user perceptions and attitudes about multi-touch gestures were gathered. The hierarchical multiple regression analyses were employed to test the hypothesized study model. The analysis results showed that both the user perceptions and attitudes have significant positive effects on user behavioral intention to use multi-touch gestures on computer applications. Suggestions for future research and conclusions for both researchers and practitioners are offered

Review on Generic Components of the MMORPGs

The massively multiplayer online role-playing games (MMORPGs) have grown to be an interesting emergence since 1980s and rapidly mature into an essential cultural element of the civilization today. The MMORPGs are cross-generational with players coming from kids to adults into the age of 70s. They are cross-cultural with players coming from different societies, different languages and different cultural traditions. They are also cross-genres with titles ranging from science fiction fantasy, mythology, sword and sorcery, crime fiction, romance to serious professional practices drawing inspiration from comics, novels, occults etc. The extraordinarily good reception of these games prompted for this study on what makes these games so addictive, influential and even corruptive. More interestingly, given that all these games come with complicated structures, the study attempts to discover the factors that make these difficult knowledge transfer process so appealing to the gamers that they are able to scale up the steep learning curve to finally completing these difficult games successfully, and loving it

Automatic multiple faces tracking and detection using improved edge detector algorithm

The automatic face tracking and detection has been one of the fastest developing areas due to its wide range of application, security and surveillance application in particular. It has been one of the most interest subjects, which suppose but yet to be wholly explored in various research areas due to various distinctive factors: varying ethnic groups, sizes, orientations, poses, occlusions and lighting conditions. The focus of this paper is to propose an improve algorithm to speed up the face tracking and detection process with the simple and efficient proposed novel edge detector to reject the non-face-likes regions, hence reduce the false detection rate in an automatic face tracking and detection in still images with multiple faces for facial expression system. The correct rates of 95.9% on the Haar face detection and proposed novel edge detector, which is higher 6.1% than the primitive integration of Haar and canny edge detector