Organizational scenarios for the use of learning objects

Organizational scenarios presents the following three scenarios for working with learning objects in Dutch higher education and institutions: Scenario 1: Self-regulation (community scenario) Scenario 2: Institutional regulation Scenario 3: Network organization This document is aimed at policy makers and educational technology consultants.Digitale Universitei

Organisatiescenario’s voor het gebruik van leerobjecten

Organisatiescenario’s presenteert de volgende drie scenario’s voor het werken met leerobjecten in het Nederlandse hoger onderwijs en instellingen: Scenario 1: Zelfregulatie via communities Scenario 2: Regulatie vanuit de onderwijsinstelling Scenario 3: Netwerkorganisatie biedt services aan Doelgroep van dit document zijn beleidsmakers en ICTO-ers.Digitale Universitei

Investigation to determine the effects of long-term bed rest on G-tolerance and on psychomotor performance Final report

Prolonged bed rest effects on gravity tolerance and psychomotor performance of human

Method and system for training a neural network with adaptive weight updating and adaptive pruning in principal components space

A method and apparatus for assessing the visibility of differences between two input image sequences. The apparatus comprises a visual discrimination measure having a retinal sampling section, a plurality of temporal filters and a spatial discrimination section. The retinal sampling section applies a plurality of transformations to the input image sequences for simulating the image-processing properties of human vision. The temporal filters separate the sequences of retinal images into two temporal channels producing a lowpass temporal response and a bandpass temporal response. The spatial discrimination section applies spatial processing to the temporal responses to produce an image metric which is used to assess the visibility of differences between the two input image sequences. Also published as: WO9737325 (A1) JP2002503360 (A) EP0898764 (A1) EP0898764 (A4) EP0898764 (B1) DE69726567 (T2

Health intelligence: Discovering the process model using process mining by constructing Start-to-End patient journeys

Archived with the publisher's permission. Copyright © 2014, Australian Computer Society, Inc. This paper appeared at the Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2014), Auckland, New Zealand. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 153. J. Warren and K. Gray, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.Australian Public Hospitals are continually engaged in various process improvement activities to improve patient care and to improve hospital efficiency as the demand for service intensifies. As a consequence there are many initiatives within the health sector focusing on gaining insight into the underlying health processes which are assessed for compliance with specified Key Performance Indicators (KPIs). Process Mining is classified as a Business Intelligence (BI) tool. The aim of process mining activities is to gain insight into the underlying process or processes. The fundamental element needed for process mining is a historical event log of a process. Generally, these event logs are easily sourced from Process Aware Information Systems (PAIS). Simulation is widely used by hospitals as a tool to study the complex hospital setting and for prediction. Generally, simulation models are constructed by ‘hand’. This paper presents a novel way of deriving event logs for health data in the absence of PAIS. The constructed event log is then used as an input for process mining activities taking advantage of existing process mining algorithms aiding the discovery of knowledge of the underlying processes which leads to Health Intelligence (HI). One such output of process mining activity, presented in this paper, is the discovery of process model for simulation using the derived event log as an input for process mining by constructing start-to-end patient journey. The study was undertaken using data from Flinders Medical Centre to gain insight into patient journeys from the point of admission to the Emergency Department (ED) until the patient is discharged from the hospital.