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The pressure on schools to improve and to raise achievement continues to be a dominant issue in both school and government policies. School Effectiveness and School Improvement seeks to develop the debate further, providing academics and practitioners alike with • a summary and discussion of research on school effectiveness and school improvement up to the present; • new perspectives on these fields, developed from other traditions of thinking and research; • a consideration of the role of organization theory; • an integrated view of these current perspectives; and • clear, practical imp

TITLE -- COPYRIGHT -- ABOUT THE AUTHORS -- CONTENTS -- INTRODUCTION -- PART I What Is Process Improvement? -- THE CONCEPT IS SIMPLE! -- Why Not Always Use Process Reengineering if the Payoff Is Higher? -- Process versus Task -- HOW PROCESS IMPROVEMENT FITS IN WITH OTHER WAYS TO IMPROVE -- What Value Does Process Improvement Offer? -- Ensuring Successful Process Improvement -- YOUR CHALLENGE IS CLEAR! -- Overcome the Doom Predictors! -- Work Your Problem! -- PART II Identifying the Players -- THE CUSTOMER IS #1 -- Working with External Customers -- GETTING CUSTOMER FEEDBACK -- Sample Questions: -- Expanding the Role of the Internal Customer -- THE NEXT MOST IMPORTANT PERSON -- Work Your Problem! -- PART III Ensuring Success -- FIND PROCESSES THAT NEED IMPROVEMENT -- Measurements and Benchmarking -- Measurements at Work and Play -- REASON #1: Employees fear and mistrust measurements. -- REASON #2: There is a general lack of understanding of what or how to measure. -- REASON #3: The results of measurements are typically not shared in an open manner. -- REASON #4: Most people make the process of measuring harder than it needs to be. -- Measure the Pulse Points -- HOW GOOD IS GOOD? -- Don't Forget Benchmarking -- Knowing What to Expect Really Helps! -- PLAN FOR YOUR SUCCESS -- Pick the Right Process -- Criteria for Picking the Right Process -- Common Mistakes in Process Selection -- PROJECT PLANNING -- Identifying Participants -- Setting Specific and Measurable Goals -- Creating Task Listing and Scheduling -- Getting Project Approval -- Work Your Problem! -- PART IV Understanding What Needs Changing -- WHERE TO BEGIN? -- Begin with Your "As-Is" Process -- Inputs to Your Process -- Tasks Within Your Process -- Work Flow Between Tasks Within Your Process -- Value Created Within your Process -- Outputs of Your Process -- CREATE YOUR OWN PROCESS MAP.

A conceptual definition of combat effectiveness is the overall capability of a force to produce a desiredoutcome from combat against an enemy force. An ability to measure combat effectiveness is critical to strategic andtactical decision making; however, it is a challenging task to develop an operational metric for combat effectivenessdue to the large complexity presented by the rich context of a combat environment. The present paper contendsthat, under a direct fire engagement, combat effectiveness can be reasonably assessed by the prevalence of attack opportunities a given force creates in a combat environment. The paper proposes a method to quantitatively measurecombat effectiveness of a military force in a direct fire engagement environment. The proposed metric is basedon a meta-network representation that captures various aspects of a combat environment. Using a meta-networkrepresentation, two types of basic unit structures of attack opportunity – isolated and networked – are identified,which are then used as a basic element for measuring combat effectiveness. Prevalence of network motifs in anetworked combat environment and availability of attack opportunities are computed as a measure of a militaryforce's combat effectiveness.Defence Science Journal, 2014, 64(2), pp. 115-122. DOI: http://dx.doi.org/10.14429/dsj.64.5534

Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors.

With advances in networked communications, the capabilities of command and control (C2) have come to play an increasingly larger role in battlefield success. Within the past two decades a new military strategy has evolved, known as Network-Centric Operations (NCO), which puts information superiority on the frontline. Moreover, the information advantage that is gained through information superiority is translated into a tactical war-fighting advantage. A research gap has been identified in the investigation of networked combat force configurations in the realm of asymmetric engagements. Specifically, the research question is, how should an information age combat force be networked in order to increase its combat effectiveness in asymmetric engagements with balanced forces? The objective of this research is to identify which performance metrics are best suited in measuring combat effectiveness in the situations of asymmetric engagements with balanced force sizes. In order to reach conclusions on the research objective, a series of experiments have been conducted using a discrete-event simulation based on the Information Age Combat Model (IACM). The experiments investigate all of the possible engagements for balanced configurations in the format of X-Y-X, ranging from 3 ≤ X ≤ 10, and 3 ≤ Y ≤ X, where X represents the number of sensors and influencers, and Y represents the number of deciders in the network. A total of 1,457,801 unique combat engagement simulations were conducted for data collection. The exact combat network configurations and percentage of wins for both sides were collected for use in the data analysis. Several computer programs were written in order to calculate the various performance metrics associated with each combat configuration. These data, in addition to the win percentages, are used in order to conduct both linear and nonlinear regression models, so that the value of the metrics may be evaluated as combat network performance indicators. Results indicate that the actual size of the network is a greater predictor for combat performance than any of the metrics calculated from the network configurations. However, it has been determined that network configuration does still play a vital role in combat performance in the case of asymmetric engagements with balanced forces. Moreover, results show that it is possible to configure a network in order to increase its chances of winning in an asymmetric engagement against a larger force size.