Educational effectiveness and improvement in developing societies. Some experiences from the Primary Education Quality Improvement Project in Indonesia

The improvement of education in developing societies might benefit from theory and research on educational effectiveness. ... The research evidence points at the importance of factors at the classroom level and the relatively small possibilities that the school and the above school level have to influence those factors at the classroom level. This is illustrated by the evaluation of the primary education quality improvement project in Indonesia, a project that aimed at the improvement of education through teacher professional development, provision of textbooks, community participation and management of schools. The results tend to support the general feeling about educational effectiveness. Conclusions stress the importance of the development of knowledge by (inter)national consultants, the content of the intervention - educational effectiveness and improvement and the adaptation of the knowledge to national and local circumstances - and procedural and technical knowledge how to design, implement and evaluate educational interventions. (DIPF/orig.

Conceptualizing school effectiveness

The theoretical status of existing school effectiveness models is analyzed by using perspectives from organizational theory and models of classroom effectiveness. This leads to the formulation of a basic framework for conceptualizing school effectiveness that includes variables at the levels of the school, the context of the school and the classroom, while background variables of pupils are also taken into account. One of the conclusions is that hypothesis construction and empirical research on cross-level relationships within this basic framework are of central importance to enhance our understanding of school effectiveness

Developments in the educational effectiveness research programme

Educational effectiveness as a research programme moved from an input-output paradigm to an input-process-output paradigm and, in view of the fact that so-called contextual school effectiveness is gaining in importance, this might be more properly termed a context-input-process-output-based approach. The aim of this introductory chapter is to put the state of the art of educational effectiveness research into perspective by summarizing the most important developments in output measurement, the identification of relevant input-, process- and contextual conditions and the causal modeling of these categories. Specific consideration is given to the improvement of substantive multi-level models of educational effectiveness and to available theories that could help to reveal the explanatory mechanisms behind these models

Queueing models for appointment-driven systems.

Many service systems are appointment-driven. In such systems, customers make an appointment and join an external queue(also referred to as the “waiting list”). At the appointed date, the customer arrives at the service facility, joins an internal queue and receives service during a service session. After service, the customer leaves the system. Important measures of interest include the size of the waiting list, the waiting time at the service facility and server overtime. These performance measures may support strategic decisionmaking concerning server capacity (e.g. how often, when and for how long should a server be online). We develop an ew model to assess these performance measures. The model is a combination of a vacation queueing system and an appointment system.Queueing system; Appointment system; Vacation model; Overtime; Waiting list;

Healthcare queueing models.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.Operations research; Health care evaluation mechanisms; Organizational efficiency; Management decision support systems; Time management; Queueing theory;

Modeling a healthcare system as a queueing network:The case of a Belgian hospital.

The performance of health care systems in terms of patient flow times and utilization of critical resources can be assessed through queueing and simulation models. We model the orthopaedic department of the Middelheim hospital (Antwerpen, Belgium) focusing on the impact of outages (preemptive and nonpreemptive outages) on the effective utilization of resources and on the flowtime of patients. Several queueing network solution procedures are developed such as the decomposition and Brownian motion approaches. Simulation is used as a validation tool. We present new approaches to model outages. The model offers a valuable tool to study the trade-off between the capacity structure, sources of variability and patient flow times.Belgium; Brownian motion; Capacity management; Decomposition; Health care; Healthcare; Impact; Model; Models; Performance; Performance measurement; Queueing; Queueing theory; Simulation; Stochastic processes; Structure; Studies; Systems; Time; Tool; Validation; Variability;

Scheduling Markovian PERT networks with maximum-NPV objective.

We examine project scheduling with net-present-value objective and exponential activity durations, using a continuous-time Markov decision chain. Based on a judicious partitioning of the state space, we achieve a significant performance improvement compared to the existing algorithms.Project scheduling; Net present value; Stochastic activity durations; Exponential distribution;

Determinants for successful deployment of clinical prediction models : a design science research in the Dutch healthcare sector

Whereas the promises of (predictive) analytics in healthcare are clear and extensively reported, the executive practicalities are not. Mapping the factors that have a hand in the implementation and continuation (i.e. deployment) of such projects improves the execution of prediction models and hence improves diagnostic and prognostic healthcare for patients. This research takes a design science approach to create an artifact aimed at successful deployment of clinical prediction models (CPMs). Through a literature review, various factors that play a role in the deployment of CPMs are categorized. Interviews with an extensive expert panel lead to the development of the CRISP-DM Deployment Extension for CPMs. Next to opinions on the importance of each factor, new in-sights are collected on related topics. A case study at a Dutch hospital allows for the testing of the artifact. A gap analysis is conducted, leading to a practical advice in terms of successful deployment. The research concludes with a proposed deployment strategy and a list of eight recommendations that can be considered the determinants for successful deployment of clinical prediction models

Storage free terrain simulation

Landscape visualisation is the process of recreating a natural environment and displaying it in an interactive graphical simulation. To do this a terrain is displayed together with accompanying plant life and other objects. Present landscape visualisation software is capable in theory of displaying very detailed and large landscapes. The software is also in theory capable of simulating environments with thousands if not millions of individually structured plants. In practice though, the simulation of such landscapes requires such a large amount of storage space that it is not achievable on personal computers. Even storing small landscapes with a moderate amount plant life can be a major development problem. The extent of this problem is such that modem simulators almost always exhibit the following limitations. • When detailed landscapes are stored to the hard disk, the area of terrain covered is usually very small. • When large terrains are stored to the hard disk the detail used is usually low. • When detailed plants are used in a landscape only twenty or so plants are created and used over and over again in the landscape. This work is an original approach to solving the storage space problem that involves not storing any landscape data to the hard disk at all. In this solution, instead of the landscape simulator displaying a landscape that is stored on a hard disk, the landscape simulator displays a landscape that is randomly generated. The landscape is produced on a need-to know basis, the only landscape that exists in the simulator is the landscape that the user of the simulator can see. If the user\u27s position in the landscape alters then the newly visible areas of landscape are created, and the areas no longer visible are removed from the simulator entirely. Areas of landscape being visited for a second time are always re-created the same way as they were originally created