Mandates have been issued for educators to collaborate and improve student achievement, requiring a change in instructional practices through teacher talk. Teachers have struggled to make the transitional conversion from team planning to observed changes in instructional practices with evidence of improvement. The purpose of this qualitative study was to examine how teachers collaborated while following the Ohio Improvement Process. The purpose was then to make data-driven changes regarding instructional practices in the continuous improvement cycle. The conceptual framework was constructed from the teachers' dialogic stances towards talk of instruction, along with the intellectual and emotional attitudes teachers have about making changes. The guiding research question examined the ways teachers have been influenced by each other to make changes in instructional practices. The case study design observed a sample of 10 teachers from two teacher-based teams, with five of those teachers being interviewed. Observational data were examined for dialogic stance toward talk of instructional practices, whereas interview data were analyzed looking for evidence of the cognitive restructuring. Statements were categorized as motivations and influences. The analysis revealed that the teachers are changing their thinking through motivations and influences from collaboration. Literature has supported the findings that teachers could benefit from a gradual implementation process leading to the continuous improvement cycle. By developing a policy recommendation paper with a focus on teacher learning, positive social change may include preparing and empowering teachers for the changes that occur through collaboration.

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems. / Dissertation (MIT (Information Systems))--University of Pretoria, 2021. / Informatics / MIT (Information Systems) / Unrestricted

The world’s data sets are growing exponentially every day due to the large number of devices generating data residue across the multitude of global data centers. What to do with the massive data stores, how to manage them and defining who are performing these tasks has not been adequately defined and agreed upon by academics and practitioners. Data science is a cross disciplinary, amalgam of skills, techniques and tools which allow business organizations to identify trends and build assumptions which lead to key decisions. It is in an evolutionary state as new technologies with capabilities are still being developed and deployed. The data science tasks and the data scientist skills needed in order to be successful with the analytics across the data stores are defined in this document. The research conducted across twenty-two academic articles, one book, eleven interviews and seventy-eight surveys are combined to articulate the convergence on the terms data science. In addition, the research identified that there are five key skill categories (themes) which have fifty-five competencies that are used globally by data scientists to successfully perform the art and science activities of data science. Unspecified portions of statistics, technology programming, development of models and calculations are combined to determine outcomes which lead global organizations to make strategic decisions every day. This research is intended to provide a constructive summary about the topics data science and data scientist in order to spark the dialogue for us to formally finalize the definitions and ultimately change the world by establishing set guidelines on how data science is performed and measured.

As data availability increases so do the opportunities within businesses. Companies need to explore technologies that are able to exploit and capitalise on this vast amount of data in order to stay relevant in today’s competitive market. Artificial intelligence and simulation are two promising technologies that are able to manage and utilise these large amounts of data. This paper explores the opportunities and challenges that exist of combining artificial intelligence with simulation in order to achieve data-driven decisions within industries. Although these two technologies are well researched in isolation, their combination and synergetic effects remain largely unexplored. The aim of this study is to survey this existing vacuum by performing a literature review and producing a digitised AI and simulation ecosystem that encapsulates the opportunities and challenges enabled by these two technologies. This research explored this ecosystem by applying and developing it on a real case study of an automotive parts supplier’s production process. It was concluded that this modularised digitised ecosystem could act as an alternative to expensive and generic software solutions due to its high customisation, simple integration and cost-efficiency, especially for SMEs. The study also concluded that adding additional AI and simulation models to the ecosystem reduces the modules’ unit costs since they can share some high cost structures such as: databases, servers and user-interfaces; this idea was encapsulated in the term digitised economies of scale.

After implementing a statewide standardized testing program in 1998, the Virginia Department of Education realized that some schools were making great gains in student achievement while other schools continued to struggle. The Department conducted a study to identify the practices used by schools showing improvement. Six effective practice domains were identified. The current study was a follow-up to the research conducted by the Virginia Department of Education. A questionnaire measuring the six effective practice domains: (a) curriculum alignment, (b) time and scheduling, (c) use of data, (d) professional development, (e) school culture, and (f) leadership was administered to teachers in 148 schools in Virginia; 80 schools participated. Two questions guided the study: (1) How frequently do schools use the Virginia Department of Education effective practices, and (2) what is the relationship between the use of the effective practices and school pass rates on the 3rd grade 2005 Standards of Learning (SOL) reading test? Descriptive statistics, linear regression, and discriminant function analysis were applied to explore the relationships between the predictor variables (percentage of students receiving free or reduced-price lunch and the use of the effective practices) and the criterion variable (school pass rate on the 2005 SOL 3rd grade reading test). Academic culture and the percentage of students receiving free or reduced-price lunch accounted for significant amounts of the variance in school pass rates. The remaining five effective practice measures were not related to school pass rates. The measures may have affected the results. In most cases, one person was used as the proxy for the school, and this person may have provided a biased assessment of what was happening in the school. / Ed. D.

Risks are something that occur daily for all individuals and can arise from a variety of activities. If we don't take these risks into account, we will likely eventually be affected by them. By identifying and analyzing risks, the most appropriate decision can be made to mitigate or avoid the risk completely. The purpose of this study is to investigate which factors should be included in a risk analysis, and how a risk analysis can be designed for contract prioritization.  The work is carried out using an abductive scientific approach as the basis of the study. Data collection is done qualitatively through interviews. In order to achieve good scientific quality with high validity and reliability, the work was done in a systematic way with good planning.  The authors conclude that the company has a correct way of defining risks but lacks a clearly structured way of identifying new potential risks, which theory shows is important for a successful risk analysis. The result describes the financial risks as the cost critical and credit risk as the most important factor. The result describes that the company doesn't have a specific model, and the authors recommend FDFMEA as a model for risk analysis in contract prioritization, which is a developed and customized version of the traditional FMEA. The case company has a well functioning approach to how risks are handled, for example using insurance to avoid specific risks. With regards to data driven decisions, theory underlines the importance of the model presenting data in a simple and transparent way. This is something that the case company currently lacks but plans to improve. / Risker är något som förekommer dagligen för alla människor och kan uppstå ur en mängdaktiviteter, tar vi inte hänsyn till dessa risker bör vi sannolikt förr eller senare drabbas avrisken. Genom att identifiera och analysera riskerna kan det mest lämpliga beslutet fattasför att mildra eller undvika risken. Målet med denna studie är att undersöka vilka faktorersom bör ingå i en riskanalys samt hur ett riskarbete kan genomföras i en beslutsprocessför säljkontrakt. Arbetet genomförs med ett abduktivt arbetssätt som grund för studien ochdatainsamlingen sker kvalitativt genom intervjuer. För att uppnå en god vetenskapligkvalitet med hög validitet och reliabilitet utfördes arbetet på ett systematiskt arbetssättmed god planering. Författarna drar slutsatsen att företaget har ett korrekt sätt att se på risker, men saknar etttydligt strukturerat sätt att identifiera nya potentiella risker, något som teori påvisarvikten av för en lyckad riskanalys. Resultatet beskriver de finansiella riskerna som mestkritiska och kreditrisk som den viktigaste faktorn. Vidare beskriver resultatet att företagetinte har en specifik beräkningsmodell, författarna rekommenderar FDFMEA som modellför riskanalys, en utvecklad och anpassad version av traditionell FMEA analys.Fallföretaget har ett väl fungerande arbetssätt för hur risker behandlas där man tillexempel använder försäkringar för att undvika risker. I avseende på datadrivna beslutpåvisar teori vikten av att modellen presenterar data på ett enkelt och transparent sätt,något fallföretaget brister i för tillfället men planerar förbättringar.