Despite the implementation of various strategies to improve outcomes, the pass rates for the National Council Licensure Exam for Registered Nurses (NCLEX-RN) for an associate degree nursing (ADN) program continue to decrease. This study examined the use of a supplemental computer-assisted formative assessment (SCAFA) as a strategy for NCLEX-RN success. A qualitative case study with a theoretical framework based on constructivism was designed to investigate nurse educators' perspectives of this particular strategy for successful outcomes. To explore these perspectives, data were collected from face-to-face interviews with nurse educators and from program documents from 1 ADN program in the southeastern United States. Guiding research questions explored nurse educators' perceptions of SCAFA and determined if and how data from these assessments were utilized. The data were analyzed using lean coding to determine emerging themes. The findings showed that a lack of consistency in the use of this tool diminishes the effectiveness of this supplemental strategy. Additional themes that emerged: educator and student attitudes, orientation and SCAFA process, resource allocation, training and preparation, and data-driven decision making. These findings were used to design a professional development project focused on the effective use of SCAFA throughout the nursing program. The study and project are expected to promote positive social change by contributing to the body of evidence on computer-assisted formative assessment, bolstering student and nurse educator learning, increasing the number of nursing students who are prepared to successfully pass the NCLEX-RN, improving program outcomes, and contributing to the professional nursing workforce.

This qualitative case study addressed the persistent achievement gaps in annual measurable objectives (AMO) data at a public rural elementary school in the Mideast United States. Response to intervention (RTI) data teams from 2010 did not produce expected student gains after 5 years of implementation in the school under study. Based on Mandinach and Jackson's data-driven decision making conceptual framework, the purpose of this study was to examine the work of the RTI data teams as they attempted to improve student learning and close achievement gaps. A purposeful sample of 13 staff members involved in the RTI implementation process was interviewed. In addition, the RTI data team and student documentation were content analyzed for process and outcomes. Open coping and thematic data analysis of the interview transcripts revealed themes of fidelity, consistency, professional development, and data use in isolation. Findings suggested that the RTI teams lack sufficient time, professional development, and the capacity to address student learning gaps adequately. As an outcome, a guiding model for designing, implementing, and evaluating ongoing blended professional development was proposed. The intent of the project is to eliminate implementation barriers and establish effective data-driven decision making practices that improve instructional practice and student learning. This study has could assist educators in their efforts to implement RTI and build organizational capacity for data-driven decision making to address persistent achievement gaps effectively.

As a result of digitalization, previously analog systems in the manufacturing industry have become digitalized, including the decision-making processes. Companies are, therefore,becoming more dependent on data for strategic decisions. However, because of the rapid development of digitalization, companies are left blindfolded in the path towards smarter manufacturing which often leads to unsuccessful technological implementations. Therefore, the thesis will explore this problem by asking: What are the required initiatives for successfully implementing digital data-driven decision-making to improve efficiency and sustainability by Swedish manufacturing companies? To answer the research questions, an exploratory multiple case study approach was conducted, where interviews with informants from the industry as well as researchers within the context of smarter manufacturing were made. The findings were then used to derive propositions which worked as the foundation of a conceptual model which functionality would be to illuminate the results in the form of a strategy map. Findings suggest that it is not always necessary for companies to implement technologies linked to large investments to enable digital data-driven decision-making. However, for those that do, there needs to be a clear organizational plan and agenda before executing theprojects since they otherwise often lead to insufficient results. That means, the technological aspects are often not the culprit in failed digital data-driven decision-makingprojects. Additional findings suggest that there are synergies connected to digital data-driven decision-making such as data-sharing possibilities that have the potential of becoming a major aspect within the context of sustainability and efficiency. / Som ett resultat av ökad digitalisering har analoga system i tillverkningsindustrin blivit digitaliserade, vilket inkluderar beslutsfattandet. Företag har därför börjat förlita sig alltmer på data för sina strategiska beslut. Men på grund av den snabba utveckling av digitalisering har tillverkningsföretagen lämnats utan klara riktlinjer för hur de bör gå tillväga för att implementera digitalt datadrivet beslutsfattande på ett effektivt men hållbart sätt. Avhandlingen kommer därför att undersöka detta problem genom att fråga: Vilka är de initiativ som krävs för att framgångsrikt implementera digital datadrivet beslutsfattande med målet att förbättra effektiviteten och hållbarheten hos svenska tillverkningsföretag? För att svara på forskningsfrågorna användes en undersökande metod med flerafallstudier, där intervjuer gjordes med informanter från industrin såväl som forskare inom ramen för smartare tillverkning. Resultaten användes sedan för att härleda förslag som därefter användes till konstruktionen av en konceptuell model vars huvuduppgift var att illustrera resultaten i form av en strategikarta. Slutsatserna pekar på att det inte alltid är nödvändigt för företag att implementera teknik kopplad till stora investeringar för att möjliggöra digitalt datadrivet beslutsfattande. Men för de som valt att implementera sådana system behövs en tydlig organisationsplan innan projekten genomförs eftersom de annars ofta leder till ofördelaktiga resultat. Detta tyder på att de tekniska aspekterna oftast inte är vad som orsakar misslyckade datadrivna beslutsprojekt. Dessutom tyder resultaten på att det finns synergier kopplade till digitalt datadrivet beslutsfattande, till exempel möjligheter att dela data som har potential att bli en viktig aspekt inom hållbarhet och effektivitet.

This thesis explores the impacts of applying participatory design (PD) to data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a noticeable research gap in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The findings contribute to the development of best practices and guidelines for organizations seeking to optimise their decision-making processes by incorporating participatory design principles into their data-driven decision-making strategies. The research also considers the ethical implications of data-driven decision-making. Ultimately, this thesis advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.

Syftet med denna studie var att ta fram en process för hur modern teknik kan användas för att organisationer ska lyckas med strategisk kompetensförsörjning. Begreppet datadrivna beslut används när så kallade klassificeringsalgoritmer kan hjälpa oss att upptäcka en ’önskad kompetens som saknas’ eller ’föreslå ett område som vi behöver utveckla’. Metoden utgår från tjänstedesign och denna studie använde sig av en empati karta, som skapades från en enkät studie på det sociala yrkesnätverket LinkedIn med virtuell snöbollsmetod (jmf. respondentdriven sampling). Den utgår från kvalitativa data som beskriver insikter utifrån användarnas upplevelser och drivkrafter. Sedan var det viktig att definiera vilka aktörer som berörs av utmaningen för att kunna beskriva stegen i en användarresa och ta fram en designskiss. Design processen inkluderade även intervjuer med huvudaktörerna för att kunna undersöka rotorsaker och sålla idéer med hjälp av klusteranalys. Slutligen testades en digital prototyp och för att utvärdera vad som fungerade och titta på förbättringar skapades feedback matris. Underlaget för att undersöka problemet kommer från behovet inom användargruppen och perspektiv från aktörer, som sedan validerats genom att använda flera olika verktyg hämtade från tjänstedesign. Slutsatsen var att datadrivet beslutsfattande går ut på att använda mätbara indikatorer och data för att fatta beslut som är i linje med strategiska mål inom kompetensförsörjning. Detta redovisas som en användarresa som består av stegen ”Initiera & kartlägga”, ”Genomföra & uppföljning” och ”Utvärdera & reflektera”. / The purpose of this study was to develop a process for how modern technology can be used for organizations to succeed in strategic competence provision. The concept of data-driven decisions is used when so-called classification algorithms can help us discover a 'desired competence that is missing' or 'suggest an area that we need to develop'.  The method is based on service design and this study used an empathy map, which was created from a survey on the professional social network LinkedIn using the virtual snowball method (cf. respondent-driven sampling). It is based on qualitative data that describes insights based on the users' experiences and driving forces. Then it was important to define which stakeholders that are affected by the challenge in order to be able to describe the steps in a journey map and produce a design sketch. The design process also included interviews with the main stakeholders in order to investigate root causes and sorting ideas using cluster analysis. Finally, a digital prototype was tested and to evaluate what worked and look for improvements, a feedback matrix was created. The basis for investigating the problem comes from the need within the user group and perspectives from stakeholders, which are then validated by using several different tools taken from service design. The conclusion was that data-driven decision-making involves define measurable indicators and data to make decisions that are in line with strategic goals in competence provision. This is reported as a user journey consisting of the steps "Initiate & map out", "Implement & follow up" and "Evaluate & reflect".

No description available.

A paradigm shift is taking place in the manufacturing industry, where companies strive for adopting digital tools to be able to compete against their competitors. The endeavor of becoming digitized is taking place simultaneously as the global awareness of sustainability increases. For the reasons that current literature is experiencing a knowledge gap that links data-driven processes, sustainability, and supply chain operations, there is a need for further exploration within this area. Therefore, the aim of this report is to investigate the business opportunities and challenges of data-driven decision-making, and how it relates to more sustainable supply chain operations within the manufacturing industry. To investigate the area within data-driven decision-making and its impact on manufacturing supply chain operations, a literature review was initially conducted and was followed by interview sessions with case companies and experts. In total, 14 interviews were conducted within the area of sustainability, supply chain operations, and data-driven decision-making. The interviews were conducted to follow the designed framework and thus provide knowledge for the challenges, advantages, applications, and value capture in relation to data-driven decision-making and supply chain operations. Comparing the empirical data with previous literature it was noted that data-driven decision-making entails both multiple challenges and advantages when it comes to improving manufacturers' sustainable performance. The main challenges include establishing efficient information sharing, standardized systems, and obtaining data that shows both reliability and validity. Consequently, by solving these challenges the sustainable benefits can be fulfilled, including a mitigated bullwhip-effect, improved planning, and reduced CO2 emissions. These benefits are driven by the transparency, automatization, and optimization that is incorporated with data-driven decision-making. In conclusion, realizing data-driven decision-making within the manufacturing industry entails several challenges, but if companies overcome the challenges the potential benefits will be unlimited. / Ett paradigmskifte pågår för närvarande i tillverkningsindustrin, där företag strävar efter att använda digitala verktyg för att kunna konkurrera mot sina konkurrenter. Strävan efter att bli digitaliserad sker samtidigt som den globala medvetenheten om hållbarhet ökar. Av anledningarna till att den aktuella litteraturen upplever ett tomrum av kunskap som länkar datadrivna processer, hållbarhet och leveranskedjedrift, så finns det ett behov av ytterligare forskning inom detta område. Målet med denna rapport är därför att undersöka affärsmöjligheterna och utmaningarna med datadrivet beslutsfattande, och hur det relaterar till mer hållbara försörjningskedjor inom tillverkningsindustrin. För att undersöka området inom datadrivet beslutsfattande och dess inverkan på leveranskedjedriften och tillverkningsindustrin så genomfördes först en litteraturundersökning som följdes av intervjussessioner med utvalda företag och experter inom området. Sammanlagt intervjuades nio företag och sex experter som valdes ut efter deras kompetenser inom hållbarhet, leveranskedjedrift och datadrivet beslutsfattande. Intervjuerna genomfördes med hjälp av en intervjuguide och därmed ge kunskap om kopplingarna mellan data, aktuella affärsverksamheter och förbättrad ekonomisk, social och miljöprestanda. Detta inkluderar att utforska utmaningar, fördelar, applikationer och värdefångst i kontext till datadrivet beslutsfattande och leveranskedjedrift. Vid analysen av EMPIRISK data och jämförelse med aktuell litteratur noterades det att datadrivet beslutsfattande medför flera olika utmaningar och fördelar när det gäller att förbättra tillverkningsföretagens hållbara prestanda. De viktigaste utmaningarna är att etablera effektiv informationsdelning, standardiserade system och att erhålla data som visar både tillförlitlighet och giltighet. Genom att hantera dessa utmaningar kan de hållbara fördelarna uppnås, vilket inkluderar en minskad bullwhip-effekt, koldioxidutsläpp och förbättrad planering. Dessa fördelar drivs vidare av transparens, automatisering och optimering som ett datadrivet beslutsfattande medför. Sammanfattningsvis innebär förverkligandet av att använda datadrivet beslutsfattande inom tillverkningsindustrin flera utmaningar, men om företag övervinner utmaningarna kommer de potentiella fördelarna att vara obegränsade.

In the modern era of high-stakes accountability, test data have become much more than a simple comparison (Schmoker, 2006; Payne & Miller, 2009). The information provided in modern data reports has become an invaluable tool to drive instruction in classrooms. However, there is a lack of good training for educators to evaluate data and translate findings into solid practices that can improve student learning (Blair, 2006; Dynarski, 2008; Light, Wexler, & Heinze, 2005; Payne & Miller, 2009). Some schools are good at collecting data, but often fall short at what to do next. It is the role of the principal to serve as an instructional leader and guide teachers to the answer the reoccurring question of "now what?" The purpose of this study was to investigate ways in which principals build successful data-driven instructional systems within their schools using a qualitative multi-site case study method. This research utilized a triangulation approach with structured interviews, on-site visits, and document reviews from various middle school supervisors, principals, and teachers. The findings are presented in four common themes and patterns identified as essential components administrators used to implement data-driven instructional systems to improve student achievement. The themes are 1) administrators must clearly define the vision and set the expectation of using data to improve student achievement, 2) administrators must take an active role in the data-driven process, 3) data must be easily accessible to stakeholders, and 4) stakeholders must devote time on a regular basis to the data-driven process. The four themes led to the conclusion of ten common steps administrators can use to acculturate their school or school division with the data-driven instruction process. / Ed. D.

The dissertation "Enhancing Automotive Manufacturing Quality and Reducing Variability Through Six Sigma Principles" provides a thorough analysis of the ways in which Six Sigma techniques can be applied to the automotive manufacturing sector to improve quality control, reduce variability, and boost operational efficiency. Utilizing a diverse of secondary data sources, such as industry reports, case studies, academic research articles, and one-on-one consultations, this study seeks to offer important insights into the implementation and efficacy of Six Sigma principles in the context of automotive manufacturing. By stressing the fundamental ideas of Six Sigma outlined by Deming and Juran and scrutinizing influential works in quality management, the literature study builds a solid theoretical basis. The study's goals and research questions centre on comprehending how Six Sigma improves quality and lowers variability in automobile production processes. This research finds important insights on how Six Sigma may improve quality control, lower process variability, and increase operational efficiency in the automobile manufacturing industry via thorough secondary data analysis. The research offers useful insights into using Six Sigma approaches, emphasizing the significance of staff involvement, data-driven decision-making, and leadership commitment in guaranteeing the success of Six Sigma projects. The thesis ends with suggestions for further research, such as investigating primary data gathering techniques, contrasting this methodology with other approaches to quality management, and using longitudinal analysis to monitor the long-term effects of Six Sigma projects. In summary, this dissertation advances our knowledge of how Six Sigma concepts may be used to promote operational excellence and continuous improvement in the automobile manufacturing sector. It also provides practitioners and stakeholders in the industry with insightful information

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-07-12T16:11:01Z No. of bitstreams: 1 Mauricius Munhoz de Medeiros_.pdf: 3956183 bytes, checksum: b542e87eedac0be0bf8f79f48709672f (MD5) / Made available in DSpace on 2018-07-12T16:11:01Z (GMT). No. of bitstreams: 1 Mauricius Munhoz de Medeiros_.pdf: 3956183 bytes, checksum: b542e87eedac0be0bf8f79f48709672f (MD5) Previous issue date: 2018-02-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este estudo investigou o impacto das capacidades de inteligência analítica de negócios na expansão das capacidades cognitivas gerenciais, orientando a tomada de decisões (com base nos dados), de modo ágil (dinâmico), para a melhoria da gestão do desempenho organizacional. Explicou-se o fenômeno sob a perspectiva teórica das capacidades dinâmicas. Para a definição dos construtos, foram revisados, também, os elementos teóricos a respeito das capacidades de inteligência analítica de negócios e tomada de decisões. Executou-se uma pesquisa de métodos mistos, desenhada em duas etapas. A primeira, exploratória, realizada através de entrevistas com 10 gestores, permitiu o mapeamento dos relacionamentos e a identificação das variáveis, oportunizando o desenvolvimento do instrumento quantitativo. A segunda, confirmatória, realizada através de uma survey com 366 respondentes, cujos resultados foram analisados para validar o instrumento de pesquisa e mensurar o impacto por meio da modelagem de uma equação estrutural, confirmando-se 5 das 7 hipóteses definidas no modelo conceitual. O cerne da discussão está na explicação do impacto das capacidades de inteligência analítica de negócios na tomada decisões, onde os achados evidenciam impacto significativo das capacidades de inteligência analítica gerencial, governança e processamento de grandes dados, e analítica avançada de negócios. A pesquisa contribui para a teoria, por ter explicado as capacidades de inteligência analítica de negócios como capacidades dinâmicas, bem como pelo desenvolvimento e validação de um instrumento para a mensuração integrada dessas capacidades. Para o campo gerencial, o estudo aponta direcionamentos e recomendações ao indicar potencialidades e limitações para o desenvolvimento dessas capacidades. / This study investigated the impact of business analytical intelligence capabilities on the expansion of managerial cognitive capabilities, orienting decision making (based on data) in an agile (dynamic) way, to improve organizational performance management. The phenomenon was explained according to the theoretical perspective of dynamic capabilities. For the definition of the constructs, the theoretical elements regarding business analytical intelligence capabilities and decision making were also reviewed. A mixed-method research was carried out in two stages. The first, which was exploratory, was conducted through interviews with 10 managers and allowed the mapping of relationships and identification of variables, allowing the development of the quantitative instrument. The second, which was confirmatory, was performed through a survey with 366 interviewees, which results were analyzed to validate the research instrument and measure the impact through the modeling of a structural equation, confirming 5 of the 7 hypotheses defined in the conceptual model. The heart of the discussion lies in the explanation of the impact of business analytical intelligence capabilities on decision making, in which the findings evidence significant impact of managerial analytical intelligence capabilities, governance and the processing of big data, and advanced business analytics. This research contributes to the theory by explaining business analytical intelligence capabilities as dynamic capabilities, as well as by developing and validating an instrument for the integrated measurement of these capabilities. For the managerial field, this study points out directions and recommendations when indicating potentialities and limitations for the development of these capabilities.