Penerapan Pembelajaran Kooperatif Reciprocal Teaching Berbasis Lesson Study untuk Meningkatkan Kemampuan Metakognitif Mahasiswa IKIP Budi Utomo Malang

The quality of education in Indonesia still needs to be improved both in human resources and components in the learning process . Improving the quality of education is very important because the quality of a nation is determined by the education factor . Education in Indonesia is still emphasis on cognitive learning outcomes , so the ability of other students as metacognitive less empowered . Those problems need to be addressed , either by applying learning strategies Reciprocal Teaching ( RT ) based Lesson Study . Application of Lesson Study based learning is expected to empower metacognitive skills of learners .The purpose of this study is to explain the improvement of the learning process with regard metacognitive skills of students after learning Reciprocal teaching strategies implemented based Lesson Study. This study used two cycles with each cycle has four stages . These stages form a spiral cycle which includes 1 ) planning, 2 ) granting the action , 3 ) observation , and 4 ) reflection . Lesson Study is an improvement of learning undertaken collaboratively by measures planning ( Plan) , implementation of the plan learning in the classroom ( Do) , and reflection activities that discussions about things that happen in the learning process in the classroom ( See) . Lesson Study activities aimed at improving the quality of learning that has three stages , namely Plan, Do and See. Samples used in this research is student of Class C in 2014 as many as 45 people . The results showed learning strategies Reciprocal Teaching ( RT ) based Lesson Study can improve student metacognitive ability IKIP Budi Utomo Malang

Allocating Resources and Creating Incentives to Improve Teaching and Learning

Offers insights from scholarly literature, related theory, and practical activities to inform the efforts of policymakers, researchers and practitioners to allocate resources and create incentives that result in powerful, equitable learning for all

Performance Pressure and Resource Allocation in Washington

Based on interviews with state, district, and school officials, explores how performance pressures have changed resource allocation decisions. Examines reform goals and how Washington's finance system impedes efforts to link resources to student learning

Working Together Toward Better Health Outcomes

Healthcare organizations and community-based organizations (CBOs) that provide human services are partnering in shared pursuit of better health outcomes. The Partnership for Healthy Outcomes – Nonprofit Finance Fund (NFF), the Center for Health Care Strategies (CHCS), and the Alliance for Strong Families and Communities (Alliance), with support from the Robert Wood Johnson Foundation (RWJF) – set out to capture and analyze the lessons emerging in this dynamic space. Information from more than 200 partnerships serving all 50 US states provide important lessons from, and for, partnerships that hope to improve access to care, address health inequities, and make progress on social issues like food, education, and housing

Partition strategies for incremental Mini-Bucket

Los modelos en grafo probabilísticos, tales como los campos aleatorios de Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la representación de conocimiento y el razonamiento en modelos con gran número de variables. Sin embargo, los problemas de inferencia exacta en modelos de grafos son NP-hard en general, lo que ha causado que se produzca bastante interés en métodos de inferencia aproximados. El mini-bucket incremental es un marco de trabajo para inferencia aproximada que produce como resultado límites aproximados inferior y superior de la función de partición exacta, a base de -empezando a partir de un modelo con todos los constraints relajados, es decir, con las regiones más pequeñas posibleincrementalmente añadir regiones más grandes a la aproximación. Los métodos de inferencia aproximada que existen actualmente producen límites superiores ajustados de la función de partición, pero los límites inferiores suelen ser demasiado imprecisos o incluso triviales. El objetivo de este proyecto es investigar estrategias de partición que mejoren los límites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro del marco de trabajo de mini-bucket incremental. Empezamos a partir de la idea de que creemos que debería ser beneficioso razonar conjuntamente con las variables de un modelo que tienen una alta correlación, y desarrollamos una estrategia para la selección de regiones basada en esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra estrategia y los comparamos con varios métodos de referencia. Nuestros resultados indican que nuestra estrategia obtiene límites inferiores más ajustados que nuestros dos métodos de referencia. También consideramos y descartamos dos posibles hipótesis que podrían explicar esta mejora.Els models en graf probabilístics, com bé els camps aleatoris de Markov i les xarxes bayesianes, ofereixen poderosos marcs de treball per la representació del coneixement i el raonament en models amb grans quantitats de variables. Tanmateix, els problemes d’inferència exacta en models de grafs son NP-hard en general, el qual ha provocat que es produeixi bastant d’interès en mètodes d’inferència aproximats. El mini-bucket incremental es un marc de treball per a l’inferència aproximada que produeix com a resultat límits aproximats inferior i superior de la funció de partició exacta que funciona començant a partir d’un model al qual se li han relaxat tots els constraints -és a dir, un model amb les regions més petites possibles- i anar afegint a l’aproximació regions incrementalment més grans. Els mètodes d’inferència aproximada que existeixen actualment produeixen límits superiors ajustats de la funció de partició. Tanmateix, els límits inferiors acostumen a ser massa imprecisos o fins aviat trivials. El objectiu d’aquest projecte es recercar estratègies de partició que millorin els límits inferiors obtinguts amb l’algorisme de mini-bucket, treballant dins del marc de treball del mini-bucket incremental. La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiós per la qualitat de l’aproximació raonar conjuntament amb les variables del model que tenen una alta correlació entre elles, i desenvolupem una estratègia per a la selecció de regions basada en aquesta idea. Posteriorment, implementem la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes de referència. Els nostres resultats indiquen que la nostra estratègia obté límits inferiors més ajustats que els nostres dos mètodes de referència. També considerem i descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks provide powerful frameworks for knowledge representation and reasoning over models with large numbers of variables. Unfortunately, exact inference problems on graphical models are generally NP-hard, which has led to signifi- cant interest in approximate inference algorithms. Incremental mini-bucket is a framework for approximate inference that provides upper and lower bounds on the exact partition function by, starting from a model with completely relaxed constraints, i.e. with the smallest possible regions, incrementally adding larger regions to the approximation. Current approximate inference algorithms provide tight upper bounds on the exact partition function but loose or trivial lower bounds. This project focuses on researching partitioning strategies that improve the lower bounds obtained with mini-bucket elimination, working within the framework of incremental mini-bucket. We start from the idea that variables that are highly correlated should be reasoned about together, and we develop a strategy for region selection based on that idea. We implement the strategy and explore ways to improve it, and finally we measure the results obtained using the strategy and compare them to several baselines. We find that our strategy performs better than both of our baselines. We also rule out several possible explanations for the improvement

Improving School Improvement

PREFACEIn opening this volume, you might be thinking:Is another book on school improvement really needed?Clearly our answer is yes. Our analyses of prevailing school improvement legislation, planning, and literature indicates fundamental deficiencies, especially with respect to enhancing equity of opportunity and closing the achievement gap.Here is what our work uniquely brings to policy and planning tables:(1) An expanded framework for school improvement – We highlight that moving from a two- to a three-component policy and practice framework is essential for closing the opportunity and achievement gaps. (That is, expanding from focusing primarily on instruction and management/government concerns by establishing a third primary component to improve how schools address barriers to learning and teaching.)(2) An emphasis on integrating a deep understanding of motivation – We underscore that concerns about engagement, management of behavior, school climate, equity of opportunity, and student outcomes require an up-to-date grasp of motivation and especially intrinsic motivation.(3) Clarification of the nature and scope of personalized teaching – We define personalization as the process of matching learner motivation and capabilities and stress that it is the learner's perception that determines whether the match is a good one.(4) A reframing of remediation and special education – We formulate these processes as personalized special assistance that is applied in and out of classrooms and practiced in a sequential and hierarchical manner.(5) A prototype for transforming student and learning supports – We provide a framework for a unified, comprehensive, and equitable system designed to address barriers to learning and teaching and re-engage disconnected students and families.(6) A reworking of the leadership structure for whole school improvement --We outline how the operational infrastructure can and must be realigned in keeping with a three component school improvement framework.(7) A systemic approach to enhancing school-community collaboration – We delineate a leadership role for schools in outreaching to communities in order to work on shared concerns through a formal collaborative operational infrastructure that enables weaving together resources to advance the work.(8) An expanded framework for school accountability – We reframe school accountability to ensure a balanced approach that accounts for a shift to a three component school improvement policy.(9) Guidance for substantive, scalable, and sustainable systemic changes –We frame mechanisms and discuss lessons learned related to facilitating fundamental systemic changes and replicating and sustaining them across a district.The frameworks and practices presented are based on our many years of work in schools and from efforts to enhance school-community collaboration. We incorporate insights from various theories and the large body of relevant research and from lessons learned and shared by many school leaders and staff who strive everyday to do their best for children.Our emphasis on new directions in no way is meant to demean current efforts. We know that the demands placed on those working in schools go well beyond what anyone should be asked to do. Given the current working conditions in many schools, our intent is to help make the hard work generate better results. To this end, we highlight new directions and systemic pathways for improving school outcomes.Some of what we propose is difficult to accomplish. Hopefully, the fact that there are schools, districts, and state agencies already trailblazing the way will engender a sense of hope and encouragement to those committed to innovation.It will be obvious that our work owes much to many. We are especially grateful to those who are pioneering major systemic changes across the country. These leaders and so many in the field have generously offered their insights and wisdom. And, of course, we are indebted to hundreds of scholars whose research and writing is a shared treasure. As always, we take this opportunity to thank Perry Nelson and the host of graduate and undergraduate students at UCLA who contribute so much to our work each day, and to the many young people and their families who continue to teach us all.Respectfully submitted for your consideration,Howard Adelman & Linda Taylo

Being Black Is Not a Risk Factor: A Strengths-Based Look at the State of the Black Child

Including nine essays from experts and five "points of proof" organization case studies, this publication challenges the prevailing discourse about black children and intends to facilitate a conversation around strengths, assets, and resilience. It addresses the needs of policymakers, advocates, principals, teachers, parents, and others

The impact of external market factors on operational practices and performance of companies

The links between operational practices and performance are well studied in the literature, both theoretically and empirically. However, mostly internal factors are inspected more closely as the basis of operational performance, even if the impact of external, environmental factors is often emphasized. Our research fills a part of this existing gap in the literature. We examine how two environmental factors, market dynamism and competition impact the use of some operational practices (such as quality improvement, product development, automation, etc.) and the resulting operations and business performance. The method of path analysis is used. Data were acquired through an international survey (IMSS – International Manufacturing Strategy Survey), which was executed in 2005, in 23 participating countries in so called "innovative" industries (ISIC 28-35) with a sample of 711 firms. Results show that both market dynamism and competition have large impact on business performance, but the indirect effects, through operations practices are rather weak compared to direct ones. The most influential practices are from the area of process and control, and quality management

The relationship between knowledge management and innovation level in Mexican SMEs: Empirical evidence

The transformation of the current society from an industry-based economy to a knowledge management and innovation-based economy is changing the design and implementation of business strategies and the nature of the competition among the organizations which are mainly small and medium-size enterprises (SMEs). They struggle to survive in a market which is more demanding and competitive, so they seeknowledge management as one of the most effective strategies that may help to enable the innovation activities into the businesses. For these reasons, this research paper has as a main goal to analyze the relationship between knowledge management and innovation in Mexican SMEs. The empirical analysis used 125 manufacturing SMEs (each SME having from 20 to 250 employees) as a sample to be carried out. The obtained results indicate that knowledge management has a positive impact in products, process, and management systems innovation