The aptness of tangible user interfaces for explaining abstract computer network principles

The technological deployment of Tangible User Interfaces (TUI) with their intrinsic ability to interlink the physical and digital domains, have steadily gained interest within the educational sector. As a concrete example of Reality Based Interaction, such digital manipulatives have been successfully implemented in the past years to introduce scientific and engineering concepts at earlier stages throughout the educational cycle. With difference to literature, this research investigates the suitability and effectiveness of implementing a TUI system to enhance the learning experience in a higher education environment. The proposal targets the understanding of advanced computer networking principles by the deployment of an interactive table-top system. Beyond the mere simulation and modelling of networking topologies, the design presents students the ability to directly interact with and visualise the protocol execution, hence augmenting their ability to understand the abstract nature of such algorithms. Following deployment of the proposed innovate prototype within the delivery of a university undergraduate programme, the quantitative effectiveness of this novel methodology will be assessed from both a teaching and learning perspective on its ability to convey the abstract notions of computer network principles

The aptness of tangible user interfaces for explaining abstract computer network principles

The technological deployment of Tangible User Interfaces (TUI) with their intrinsic ability to interlink the physical and digital domains, have steadily gained interest within the educational sector. As a concrete example of Reality Based Interaction, such digital manipulatives have been successfully implemented in the past years to introduce scientific and engineering concepts at earlier stages throughout the educational cycle. With difference to literature, this research investigates the suitability and effectiveness of implementing a TUI system to enhance the learning experience in a higher education environment. The proposal targets the understanding of advanced computer networking principles by the deployment of an interactive table-top system. Beyond the mere simulation and modelling of networking topologies, the design presents students the ability to directly interact with and visualise the protocol execution, hence augmenting their ability to understand the abstract nature of such algorithms. Following deployment of the proposed innovate prototype within the delivery of a university undergraduate programme, the quantitative effectiveness of this novel methodology will be assessed from both a teaching and learning perspective on its ability to convey the abstract notions of computer network principles

How to make the fourth revolution: Human factors in the adoption of electronic instructional aids

The prospects and problems of getting higher education in the United States (high school and above) to more fully utilize electronic technologies are examined. Sociological, psychological, and political factors are analyzed to determine the feasibility of adopting electronic instructional techniques. Differences in organizations, attitudes, and customs of different kinds of students, teachers, administrators, and publics are crucial factors in innovation

Learning by Gaming: Supply Chain Management

Today\u27s third level students are of a virtual generation, where online interactive multi-player games, virtual reality and simulations are a part of everyday life, making gaming and simulation a very important catalyst in the learning process. Teaching methods have to be more innovative to help students understand the complexity of decisions within dynamic supply chain environment. Interactive simulation games have the potential to be an efficient and enjoyable means of learning. A serious interactive business game, Automobile Supply Chain Management Game (AUSUM), has been introduced in this paper. Using theories learnt in class as a knowledge base, participants have to develop effective supply chain partnership strategy to enhance their supply chain networks. Deploying the game over the web encourages student interaction and group work. Most importantly the game will enable students to fundamentally grasp the impact of strategic decisions on other parts and players of the supply chain network

Pedestrian detection and tracking using stereo vision techniques

Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies

Learning Visual Classifiers From Limited Labeled Images

Recognizing humans and their activities from images and video is one of the key goals of computer vision. While supervised learning algorithms like Support Vector Machines and Boosting have offered robust solutions, they require large amount of labeled data for good performance. It is often difficult to acquire large labeled datasets due to the significant human effort involved in data annotation. However, it is considerably easier to collect unlabeled data due to the availability of inexpensive cameras and large public databases like Flickr and YouTube. In this dissertation, we develop efficient machine learning techniques for visual classification from small amount of labeled training data by utilizing the structure in the testing data, labeled data in a different domain and unlabeled data. This dissertation has three main parts. In the first part of the dissertation, we consider how multiple noisy samples available during testing can be utilized to perform accurate visual classification. Such multiple samples are easily available in video-based recognition problem, which is commonly encountered in visual surveillance. Specifically, we study the problem of unconstrained human recognition from iris images. We develop a Sparse Representation-based selection and recognition scheme, which learns the underlying structure of clean images. This learned structure is utilized to develop a quality measure, and a quality-based fusion scheme is proposed to combine the varying evidence. Furthermore, we extend the method to incorporate privacy, an important requirement inpractical biometric applications, without significantly affecting the recognition performance. In the second part, we analyze the problem of utilizing labeled data in a different domain to aid visual classification. We consider the problem of shifts in acquisition conditions during training and testing, which is very common in iris biometrics. In particular, we study the sensor mismatch problem, where the training samples are acquired using a sensor much older than the one used for testing. We provide one of the first solutions to this problem, a kernel learning framework to adapt iris data collected from one sensor to another. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to considerable improvement in cross sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems. In the last part of the dissertation, we analyze how unlabeled data available during training can assist visual classification applications. Here, we consider still image-based vision applications involving humans, where explicit motion cues are not available. A human pose often conveys not only the configuration of the body parts, but also implicit predictive information about the ensuing motion. We propose a probabilistic framework to infer this dynamic information associated with a human pose, using unlabeled and unsegmented videos available during training. The inference problem is posed as a non-parametric density estimation problem on non-Euclidean manifolds. Since direct modeling is intractable, we develop a data driven approach, estimating the density for the test sample under consideration. Statistical inference on the estimated density provides us with quantities of interest like the most probable future motion of the human and the amount of motion informatio

Use of information and communication technologies in higher education in Kenya

In this study, I investigated how Information and Communication Technologies (ICTs) are applied in higher education in Kenya. Research questions were; 1) What is the level of self-efficacy and ICTs integration into higher education in Kenya? 2) What is the level of awareness and adoption of ICTs in higher education in Kenya? and 3) What factors enable or hinder utilization of ICTs in higher education development? Mixed method research was applied where 81 questionnaires and 8 semi structured interviews were carried out on lecturers and students. Staff and students were competent in regard to ICTs. Access to ICTs resources in private universities was better than in public universities. Most students and faculty were competent with the common software but competence in regard to specialized softwares was poor. ICTs were used for teaching and learning though the adoption was poor mainly in public universities. Barriers to effective utilization of ICTs in higher education included absence of reward systems, lack of policies, poor support and limited financial resources. In conclusion, the demand for higher education in Kenya surpasses the physical resources at the disposal of higher education institutions in the country. The use of ICTs is essential to ensure that quantity and quality of higher education with the limited resources. Universities therefore need to have their top leadership supporting ICTs plans and strategies, have policies regarding the use of ICTs and have support for ICT tools