Development and Validation of a BI Success Model

We propose and test a BI Success model, based on DeLone and McLean’s IS Success model, that incorporates comprehensive data that is needed for decision-making and computer systems that allow integration and analysis of that data as dimensions of BI success. Our model also includes organizational support structure for BI and the users’ involvement in the ongoing development of BI systems as contributing factors. Data collected from over 300 organizations across the world confirmed 7 of 9 hypothesized relationships. Notably, user involvement and the organizational support factors are seen to be associated with the BI capability factors which, in turn, are positively associated with users’ perception of net benefits and their satisfaction with BI practices. This is one of the first studies that evaluates the success of BI at organizational level and considers user involvement, characterized by on-going configuration / customization / improvement cycle, as a contributing factor in the classic IS Success model

An empirical study on behavioural intention to reuse e-learning systems in rural China

The learner’s acceptance of e-learning systems has received extensive attention in prior studies, but how their experience of using e-learning systems impacts on their behavioural intention to reuse those systems has attracted limited research. As the applications of e-learning are still gaining momentum in developing countries, such as China, it is necessary to examine the relationships between e-learners’ experience and perceptions and their behavioural intention to reuse, because it is argued that system reuse is an important indicator of the system’s success. Therefore, a better understanding of the multiple factors affecting the e-learner’s intention to reuse could help e-learning system researchers and providers to develop more effective and acceptable e-learning systems. Underpinned by the information system success model, technology acceptance model and self-efficacy theory, a theoretical framework was developed to investigate the learner’s behavioural intention to reuse e-learning systems. A total of 280 e-learners were surveyed to validate the measurements and proposed research model. The results demonstrated that e-learning service quality, course quality, perceived usefulness, perceived ease of use and self-efficacy had direct effects on users’ behavioural intention to reuse. System functionality and system response have an indirect effect, but system interactivity had no significant effect. Furthermore, self-efficacy affected perceived ease of use that positively influenced perceived usefulness

Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

Design and validation of chronic research tools for an implantable closed-loop neurostimulator

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 119-120).Neurostimulators today provide high frequency Deep Brain Stimulation (DBS) for therapeutic modulation of diseased neural circuits. These devices are approved for the treatment of Parkinson's Disease, Essential Tremor and Dystonia, and are in clinical evaluations for Epilepsy and Depression. Despite the success of DBS therapy, the current systems are open-loop, where the clinician is the sensor and control algorithm, and hence these are programmed with stimulation parameter settings based on acute clinical observations. The need to move towards an effective closed-loop system drives the research for understanding the dynamics of the neural circuits. This work assessed the feasibility of use of a unique implantable research tool, which has sensing and algorithm technology added to an existing DBS device, for a chronic, in-vivo study of the brain state dynamics in an ovine model and presented preliminary validation of the neural interface. In addition, the sensing technology of this bi-directional neural interface was also validated using data from Brain Machine Interface studies. Finally, the work also involved development of a software tool which is a platform for analyzing neural activity datasets from different studies using machine learning techniques.by Afsah Shafquat.M.Eng