Administrators at a Southeastern elementary school eliminated single-sex instructional grouping in 5th-grade classes without a proper analysis of all available data and later reflected upon whether this instructional model should be revived. Because data-based decisions may positively improve teaching and learning for all stakeholders, the purpose of this qualitative case study was to explore all available data leading to this decision, inform stakeholders about the decision-making processes in the local school, and provide data to inform future decisions. Conceptually framed with Mandinach's data-driven decision making (DDDM) model, the guiding question for the study focused on perceptions of teacher, administrator, and leadership team member about the DDDM process related to single-sex instructional grouping in the local venue. The data were collected using 8 interviews with administrators, teachers, and school leadership team members involved in the instructional decision. Data from interview were transcribed, analyzed, and coded for emergent themes, types of data and decisions, decision making processes, and stakeholder perceptions. The findings showed a gap in DDDM practice and affirmed the value of data for informed decision making. The findings guided recommendations for a professional development series created to increase data literacy and DDDM best practices. Improving DDDM for teaching and learning may promote positive social change by developing educational stakeholder skill sets for all decision-making as well as providing targeted, data-driven instruction for learners whether in multi- or single-sex instructional grouping.

Section 16 A (2) (d), (e) and (f) of the South African Schools Act, Act 84 of 1996 assumes that a school principal has specialised knowledge in interpreting legislation, dealing with disciplinary matters pertaining to learners, educators and support staff, and making disciplinary decisions. The legal framework of the Promotion of Administrative Justice Act, Act 3 of 2000, as well as section 33 of the Constitution of the Republic of South Africa, Act 108 of 1996, affects disciplinary decision making in education. The need to understand how legislation affects disciplinary decision making is important, because s ection 16 A of the South African Schools Act, Act 84 of 1996 assumes that education managers have the requisite knowledge and understanding of the law when dealing with disciplinary decision making. Disciplinary decisions taken by education managers fall in the domain of administrative law. The Promotion of Administrative Justice Act, Act 3 of 2000, forms the foundation for administrative action that is lawful, reasonable and fair. Since this Act is relatively new, and education managers have a lack of education law knowledge in general, it can be argued that principals might struggle to take disciplinary decisions that are lawful, reasonable and fair. Thus, there is a need to answer the following question: What are the legal requirements that should be considered in taking disciplinary decisions that are lawful, reasonable and fair and how can these disciplinary decisions be made more effectively? The purpose of the study was to understand the context and content of Section 33 of the Constitution of the Republic of South Africa, Act 108 of 1996, the Promotion of Administrative Justice Act, Act 3 of 2000, and Section 16A of the Schools Act , Act 84 of 1996 and how they would positively influence disciplinary decision making in South African education. The main research question was: What are the legal requirements that should be considered in taking disciplinary decisions that are lawful, reasonable and fair and how can these disciplinary decisions be made more effectively? Chapter 2 answered the research question of which decision-making processes could assist the education manager to take disciplinary decisions that are lawful, reasonable and fair. It was established that principals make frequent use of the rational model for decision making. However, the more comprehensive data-driven decision-making model was proposed. This not only focuses on a single disciplinary decision, but on the cause and trends of all transgressions that exist in a school. This model enables a principal to draw up a plan of action to deal with the cause of the problem. After analysing the applicable legal framework, the concepts of lawful, reasonable, and fair were defined and interpreted in Chapter 3. An administrative action is lawful when an administrator is duly authorised by law to exercise power. Reasonableness has two elements, namely rationality and proportionality. Rationality means that evidence and information should support a decision an administrator takes, while the purpose of rationality is to avoid an imbalance between the adverse and beneficial effects. The approach to fairness has changed since the pre-democratic era. The main components that are linked to procedural fairness are the common-law principles of audi alteram partem, and nemo iudex in sua causa. The qualitative approach was followed in this study to shed light on the perceptions of the participants on the meaning of the legal concepts of lawful, reasonable, and fair in disciplinary decision making, and their understanding of the legal framework of this study. Furthermore, this study sought answers to which decision-making processes could assist the education manager, as well as to the advantages of having a disciplinary coordinator to assist education managers in making lawful, reasonable and fair disciplinary decisions. Convenience and purposeful sampling was used because the schools were conveniently located. Four secondary school principals in Cape Town were chosen, as well as two officials from the Western Cape Department of Education. The reason for purposive sampling was that two of the four schools that were selected had to have a discipline coordinator. Semi-structured interviews were held with the abovementioned principals and officials to answer the main research question. The following information emerged from the semi-structured interviews which were incorporated in the data-driven, decision-making model of school improvement. Some of the findings were: i. Animosity exists between some school principals and the Western Cape Education Department (WCED). There is a lack of communication between the WCED and principals, as well as a lack of training on disciplinary decision making. ii. It was also established that principals made common mistakes related to the interpretation of legislation or applicable regulations. iii. A good practice emanating from the study is a paper trail of all interventions kept by schools. iv. Principals tend to use only the South African Schools Act as a legal framework for disciplinary decision making. v. Principals need to focus on strategies to address the link between bad behaviour and poor academic performance. vi. A discipline coordinator can assist the principal in maintaining discipline, investigating transgressions, organising disciplinary hearings, and in disciplinary decision making. Decision making, lawfulness, reasonableness, and fairness were combined in this research to establish the legal requirements that should be considered in taking disciplinary decisions that are lawful, reasonable and fair, and how these disciplinary decisions can be more effective for the sole purpose of school improvement. / Thesis (PhD)--University of Pretoria, 2014. / tm2015 / Education Management and Policy Studies / PhD / Unrestricted

In general, at smaller companies, decisions are based on the intuition of their experts within their respective areas. The decision processes are dependent on several aspects, such as assumptions and context, and some on data. Over the last year, the increase in data flow has enabled SMEs to make a decision in a systematic and planned process referred to as data-driven decision-making(DDM). Small-medium enterprises (SME) companies have been affected by enabling aspects. However, research shows challenges for SMEs trying to develop their DDM. To address these challenges, this thesis aims to propose a process to assess and develop data-driven decision-making in an SME within the manufacturing industry. The study has been made with a qualitative approach. In addition, a case study of an SME within the manufacturing industry has been done to study the phenomenon in a real-life situation. The data collection was conducted by a literature review, interviews, and planned and unplanned observations. The literature review showed that different aspects affect the development of DDM. The aspects discussed were the decision-making process, technology and organisational factors such as general change, organisational culture, resistance to change, management and the last aspect, Data quality. A maturity assessment model was discussed to introduce the ability to assess a company's current state. The empirical data discussed two main aspects: the current state and the desired future state. The empirical findings showed that there were three main levels of decision-making in the current state: Operator level, Production level, and Management level. The desired state discusses data expectations, which provides a view of the company's perception of what data is and how it is used. In the analysis, there were two main challenging aspects identified from the empirical and theoretical data, and these were organisational and technological factors. The challenges related to technological factors were identified, such as digital adaptation, technological uncertainties and data quality. The challenges related to Organisational factors were the decision-making process, adaptation to change, organisational culture and data quality. Based on these challenges and the evaluation of the maturity model and application process, a different proposed application process was created to help organisations develop their DDM. Some of the challenges identified within the SME company connect to the challenges found in theory, and they bring future support that these challenges are present in real-life situations. An aspect that was identified as both a technological factor and an organization is the need for data quality and evaluation of it within the organisation. It shows that this is a critical aspect that must be considered when developing DDM.Keywords: Data-driven decision-making, Techno

Thesis advisor: Larry H. Ludlow / Teachers in the United States are increasingly tasked with using data to inform their classroom instruction both through federal policies, such as the Every Student Succeeds Act (ESSA, 2016), and state policies requiring the use of teacher-determined data-driven goals for performance evaluations (MA 603 CMR 35.07). Many teachers, however, report that they feel underprepared to engage in this type of work (Dunn et al., 2013), also called Data-Driven Decision Making (DDDM). In addition, there is currently a limited set of instruments to measure the construct of using data to inform classroom instruction and the instruments that currently exist measure this construct using a typical Classical Test Theory design.This work developed an instrument called the Using Data to Inform classroom Instruction (UDII) scale to measure teachers’ use of data to inform classroom instruction. It used the Rasch/Guttman Scenario (RGS) methodology, an approach that develops scenarios that reflect the rich lived experiences of individuals (Antipkina & Ludlow, 2020; Ludlow et al., 2014). The RGS approach utilizes the Rasch model, part of the family of Item Response Theory models, which conceptualizes a construct as a hierarchical continuum. Scenario items and people are plotted on the same variable map, which allows for the development of rich descriptions of individuals at particular raw score locations on the continuum. An interpretative variable map is included to help schools and districts use the results of the survey. This work adds to the growing body of literature utilizing the RGS approach, as well as the literature focused on the use of data to inform classroom instruction (or DDDM). The UDII scale can be utilized by schools and districts who are engaged in the work of using data to inform classroom instruction to identify the current skillsets of teachers and/or teams of teachers to provide differentiated support, or it can be used before and after an intervention focused on using data to inform classroom instruction to measure change. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

I samband med att garantin om tidiga stödinsatser infördes 2019 blev det obligatoriskt för lärare att använda ett kartläggningsmaterial för förskoleklassen vid namn Hitta språket. Denna studie syftar till att undersöka hur lärare använder resultatet av kartläggningsmaterialet. Vidare är syftet att få syn på i vilken mån kartläggningsresultaten bidrar till att utforma undervisningen. Därtill undersöks i vilken mån kartläggningsmaterialet bidrar till att elever i behov av stöd får stödinsatser i form av extra anpassningar eller särskilt stöd. Studien är kvalitativ och bygger på åtta semistrukturerade intervjuer med lärare verksamma i förskoleklass. Samtliga lärare fick göra sina röster hörda om hur de tolkar kartläggningsresultatet från Hitta språket. Studien utgår från de teoretiska ramverken Data-Driven Decision Making och The simple view of reading. I resultatet framgår att lärarna uttrycker att de i de flesta fall använder resultaten från Hitta språket till att utforma den ordinarie helklassundervisningen. Hitta språkets resultat verkar främst påverka undervisningsinnehållet gällande avkodningsfärdigheter. När det gäller stödinsatser i form av extra anpassningar och särskilt stöd för elever i riskzonen för språk-, läs och skrivsvårigheter, tolkar lärarna att kartläggningsmaterialet i mycket begränsad utsträckning bidrar till att stödja deras färdigheter. Sammantaget tolkar lärare att Hitta språket inte uppfyller sin tänkta funktion, vilket ger anvisningar om att kartläggningsmaterialets upplägg och riktlinjerna kring hur det ska användas behöver tydliggöras.

Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.

Background: Data-driven decision-making (DDDM) has emerged as a primary approach to decision-making in many organizations. It uses data and analytics to guide decision-making processes and can lead to better business outcomes. Prior research has focused on DDDM in large corporations operating in large economies, and therefore this thesis will examine DDDM in small and medium enterprises in Sweden.  Purpose: The purpose of this research study is to examine the effect DDDM has on the financial performance of Swedish SMEs to investigate if the utilization of DDDM benefits companies financially and to understand the effect of managerial experience, technical skills, information quality, and firm size on the data-driven decision-making process. Method: This study is based on the positivism paradigm, following deductive reasoning and a quantitative approach of gathering data through digital surveys. The sample consisted of 55 Swedish SMEs gathered through simple random sampling. Further, the data was analyzed using Pearson correlation, Spearman rank correlation, and regression analysis to test hypotheses. Findings: The literature review identified a research gap on DDDM, factors that effect DDDM, and Financial Performance. Four hypotheses were developed to answer the research questions. The OLS regression found that DDDM had no significant effect on Financial Performance, the first hypothesis was not supported. The Information Quality variable had a significant positive effect on DDDM resulting in support for the second hypothesis. However, Managerial Experience and Technical Skills did not have a significant effect in the main regression model, hypotheses three and four were not supported. Conclusion: The thesis showed that DDDM did not have a significant effect on financial performance in Swedish SMEs. Additionally, managerial expertise and technical skills did not have an effect on DDDM. However, Information quality did have an effect on the DDDM process and was correlated with technical skills, which is in line with the theories used in the study: Organizational Information Processing Theory (OIPT) and Absorptive Capacity. This further supports that information quality is vital for the DDDM process and can explain why DDDM might not always lead to improvements in financial performance for Swedish SMEs.

No description available.

The use and analysis of data has become a keystone in national policy for educational improvement and a foundational condition in the award of federal grant monies (U.S. Department of Education, 2008, 2009a, 2009b, 2010). Principals are expected to lead their schools in the use of data and are accountable for adequate yearly progress (AYP) for the No Child Left Behind Act (NCLB). Effective use of data can move educators toward student centric learning plans and interventions which improve achievement. While current literature emphasizes the importance of assessment data used to guide sound instructional decisions, gathering scores and generating reports by grade and level does little at individual schools unless there is strong site-based leadership to guide faculty and staff in targeting areas of improvement, implementing a plan, monitoring progress, and adjusting actions. This qualitative case study describes how the principal's leadership guided a journey of data-focused decision making at one middle school. This dissertation describes use of data in decision-making processes to promote student learning from the perspective of a school which has been implementing data-focused decision making for several years and was selected for its established use of student assessment data. This research focused on the processes individuals and groups use to better understand and use data within a school context and the role of school leaders in supporting these actions. The intent of this case study is to describe and understand how school leaders make the use of data an integral part of the operation within a middle school in a large suburban mid-Atlantic school district. By looking at how principals embed data analysis and interpretation in the decision-making processes of the school and engage teachers in the use of data to promote student learning, findings could be useful as a guide to other educational leaders as they implement site based actions and related professional development for school-based leaders and teachers. / Ed. D.

Esta tesis investiga el problema de gestión de tesorería desde un punto de vista multidimensional. La gestión de tesorería trata de equilibrar la cantidad que se mantiene en efectivo y la que se dedica a inversiones a corto plazo. Normalmente, los tesoreros toman decisiones basándose en el nivel óptimo de tesorería por motivos operativos y de precaución. En esta tesis exploramos las oportunidades para mejorar la toma decisiones derivadas de modelar la incertidumbre presente en los flujos de caja con la ayuda de procedimientos basados en datos en un entorno multiobjetivo. Por un lado, los tesoreros pueden conseguir ahorros a través de la previsión de tesorería. Para ello, realizamos un estudio empírico con el objetivo de aprovechar las más recientes técnicas de aprendizaje automático como paso clave para conectar el análisis de los datos disponibles con los procesos de optimización en la gestión de tesorería. Por otro lado, los tesoreros pueden estar interesados no solo en el coste sino también en al riesgo asociado a sus decisiones. Por esta razón, tratamos el problema de gestión de tesorería desde una perspectiva multiobjetivo, considerando tanto el coste como el riesgo. Además, debido a la cambiante situación financiera actual, exploramos la selección de modelos de gestión de tesorería en función de diferentes condiciones operativas y de su robustez. También demostramos la utilidad de las previsiones a través de un nuevo modelo de gestión de tesorería que mejora el estado del arte al garantizar soluciones óptimas. Como la mayoría de las empresas trabaja con sistemas de tesorería con múltiples cuentas bancarias, desarrollamos un marco para la formulación y solución del problema de gestión de tesorería con múltiples cuentas bancarias. Finalmente, en un intento de acercar teoría y práctica, también ofrecemos una librería de software en Python para usuarios interesados en la construcción de sistemas de ayuda a la toma de decisiones en gestión de tesorería. / This thesis investigates the cash management problem from a multidimensional perspective. Cash management focuses on finding the balance between cash holdings and short-term investments. Typically, cash managers make decisions based usually on a firm's optimal cash balance for operational and precautionary purposes. We here explore the opportunities for improved decision-making derived from modeling cash flow uncertainty with the help of data-driven procedures within a multiobjective context. On the one hand, cash managers may achieve cost savings by forecasting future cash flows. To this end, we perform an empirical analysis of daily cash flow time-series to take advantage of modern machine learning techniques as a key step to connect data analysis and optimization methods in cash management. On the other hand, cash managers may be interested not only in the cost but also in the risk associated to decision-making. Thus, we address the cash management problem from a multiobjective perspective focusing on both cost and risk. In addition, under the current situation of time-varying financial circumstances, the selection of cash management models according to operating conditions and its robustness are worth considering questions. We also show the utility of forecasts through a new cash management model which outperforms the state-of-the-art by guaranteeing optimal solutions. Since most firms usually deal with cash management systems with multiple accounts, we develop a framework to formulate and solve the multiple bank accounts cash management problem. Finally, in an attempt to fill the gap between theory and practice, we also provide a software library in Python for practitioners interested in building decision support systems for cash management. / Esta tesi investiga el problema de gestió de tresoreria des d'un punt de vista multidimensional. La gestió de tresoreria tracta d'equilibrar la quantitat que es manté en efectiu i la que es dedica a inversions a curt termini. Normalment, el tresorers prenen decisions basant-se en el nivell òptim de tresoreria per motius operatius i de precaució. En aquesta tesi explorem les oportunitats per millorar la presa de decisions derivades de modelitzar la incertesa present en els fluxos de caixa amb l'ajuda de procediments basats en dades. Per un costat, els tresorers poden aconseguir estalvis de costos mitjançant la previsió de tresoreria. Per tal d'aconseguir-ho, realitzem d'un estudi empíric amb l'objectiu d'aprofitar les més recents tècniques d'aprenentatge automàtic per connectar l'anàlisi de les dades disponbiles amb els procesos d'optimització en la gestió de tresoreria. Per altra banda, els tresorers poden estar interessats no sols en el cost sinó també en el risc associat a les seues decisions. Per tant, tractem el problema de gestió de tresoreria des d'un punt de vista multiobjectiu, fixant-se tant en el cost com en el risc. A més a més, degut a la canviant situació financera actual, explorem la selecció de models de gestió de tresoreria en funció de diferents condicions operatives i de la seua robustesa. També demostrem la utilitat de les previsions mitjançant un nou model de tresoreria que millora l'estat de l'art al garantir solucions òptimes. Com que la majoria d'empreses treballa amb sistemes de tresoreria amb múltiples comptes bancaris, desenvolupem un marc per a la formulació i solució del problema de gestió de tresoreria amb múltiples comptes bancaris. Finalment, en un intent d'apropar teoria i pràctica, també oferim un llibreria en Python per a usuaris interessats en la construcció de sistemes d'ajuda a la presa de decisions en la gestió de tresoreria. / Salas Molina, F. (2017). DATA-DRIVEN DECISION-MAKING AND ITS APPLICATION TO THE CORPORATE CASH MANAGEMENT PROBLEM [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/95408