The punctuality of rail traffic in Sweden has not increased in a long period of time and this causes problems for people, companies and the community as it affects everyone in different ways. How the Swedish Transport Administration and SJ work on improving the train traffic and punctuality will be addressed in this study. This study will have focus on how data-driven methods can increase the punctuality of train traffic. The study will show which factors are critical for data-driven methods using literature as well as models with descriptions, as a result, the Swedish Transport Administration and SJ will be in focus for how the development of punctuality of train traffic goes. This is a case study with a literature search as well as qualitative interviews as data collection, the literature search will primarily show which factors are necessary for datadriven methods and it will also help form the interview questions, later on the interviews will show how the Swedish Transport Administration and SJ work today so that comparisons can be drawn and a result can be produced.

Este trabalho apresenta uma extensão à formulação multivariável do método de controle baseado em dados conhecido como o Método da Referência Virtual, ou Virtual Reference Feedback Tuning (VRFT). Ao lidar com processos onde o ruído é significativo, as formulações tradicionais do VRFT, por mínimos quadrados ou variáveis instrumentais, apresentam propriedades estatísticas insatisfatórias, que acabam levando o sistema de controle em malha fechada a desempenhos muito distantes daqueles especificados pelo projetista. Portanto, visando aprimorar a qualidade destas estimativas e, consequentemente, os desempenhos em malha fechada, esta dissertação propõe a adição de regularização no método VRFT para sistemas multivariáveis. Regularização é uma ferramenta que vem sendo amplamente utilizada e desenvolvida nos últimos anos nas comunidades de Identificação de Sistemas e Machine Learning e é indicada para reduzir a alta covariância que existe nas estimativas - problema que ocorre na formulação do VRFT com variáveis instrumentais. Também, como contribuições deste trabalho destacam-se uma análise mais detalhada do problema de identificação com regularização para sistemas multivariáveis, assim como o desenvolvimento da matriz ótima de regularização para este cenário e as propriedades da nova formulação do VRFT. Para demonstrar a eficiência desta nova formulação do VRFT são desenvolvidos exemplos numéricos. / This work proposes a new extension for the multivariable formulation of the datadriven control method known as Virtual Reference Feedback Tuning. When the process to be controlled contains a significant amount of noise, the standard VRFT approach, that uses either the least squares method or the instrumental variable technique, yield estimates with very poor statistical properties, that may lead the control system to undesirible closed loop performances. Aiming to enhance these statistical properties and hence, the system’s closed loop performance, this work proposes the use of regularization on the multivariable formulation of the VRFT method. Regularization is a feature that has been widely used and researched on the System Identification and Machine Learning communities on the last few years, and it is well suited to cope the high variance issue that emerge on the VRFT method with instrumental variable. Also, a more detailed analysis on the use of regularization for identification of multivariable systems, the proof of the optimal regularization matrix and the exposure of the new regularized VRFT properties can be highlighted as novelties of this work.

Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

Closing the achievement gaps in mathematics and English language arts (ELA) is an ongoing challenge for most New York City Public school administrators. One New York school experiencing this problem implemented a broad intervention including (a) the Children First Intensive (CFI) program, which includes using data to inform instructional and organizational decision-making; (b) added baseline and post assessments; and (c) differentiated instruction including student conferences. The effects of the intervention had not been evaluated within the context of implementation. The purpose of this quantitative study was to evaluate the impact of the multifaceted learning gaps' intervention on 6th grade student achievement in math and ELA. The framework used in this study was the Halverson, Grigg, Prichett, and Thomas data-driven instructional systems model. The comparative study design used paired t tests to examine the change in math and ELA achievement scores on a group of 6th grade students (N = 26), before after the intervention. Results indicated significant increases in the test scores of the students, suggesting that students' learning gaps were closed using their assessment results and differentiated instruction within the comprehensive intervention. Results were used to create a professional development handbook on using a multifaceted data-based approach to improve student achievement. Positive social change might occur by providing the local site findings on the outcomes of their approach and additional training on using the approach, which may ultimately improve the academic performance of all students.

In today’s information age, technological advances in virtually every industry allow organizations, both big and small, to create and store more data than ever before. Though data are highly abundant, they are still often underutilized resources with regard to improving organizational performance. The popularity and intrigue around big data specifically has opened up new opportunities to study how organizations embrace evidence and use it to improve their business. Generally, the focus of big data has mainly been on specific technologies, techniques, or its use in everyday life; however, what has been critically missing from the conversation is the consideration of culture and climate to support effective data use in organizations. Currently, many organizations want to develop a data-embracing climate or create changes to make their existing climates more data-informed. The purpose of this project was to develop a scale to assess the current state of data usage in organizations, which can be used to help organizations measure how well they manage, share, and use data to make informed decisions. I defined the phenomena of a data-embracing climate based on reviewing a broad range of business, computer science, and industrial-organizational psychology literature. Using this definition, I developed a scale to measure this newly defined construct by first conducting an exploratory factor analysis, then an item retranslation task, and finally a confirmatory factor analysis. This research provides support for the reliability and validity of the Albrecht Data-Embracing Climate Scale (ADEC); however, the future of this new area of research could benefit by replicating the results of this study and gaining support for the new construct. Implications for science and practice are discussed. I sought to make a valuable contribution to the field of I-O psychology and to make a useful instrument for researchers and practitioners in multiple and diverse fields. I hope others will benefit from this scale to measure how organizations use evidence from data to make informed decisions and gain a competitive advantage beyond intuition alone. Do not cite without express permission from the author.

ABSTRACT Background: Alcohol use disorders (AUD) affect health and wellbeing, and have broad societal costs (Bouchery, Harwood, Sacks, Simon, & Brewer, 2011; Rehm et al., 2009; Sudhinaraset, Wigglesworth, Takeuchi, & Tsuker, 2016). While treatments have existed for decades, they are limited in success and expensive to administer. As such, understanding which factors best predict who will benefit most from treatment remains a laudable goal. Prior attempts to predict factors associated with positive treatment outcome are limited by methodology including statistical methods that lead to poor predictive power in new samples. This study aims to use a data-driven approach to clarify the predictors of AUD treatment success (Objective 1) accompanied by a theory-driven analysis assessing the mediation of treatment outcomes through psychological distress (Objective 2). Methods: One hundred forty-five patients seeking treatment for alcohol use problems at the Day One Intensive Outpatient Treatment Program (part of UVM Medical Center) between June 2011 and June 2012 were examined. Variables were extracted through chart review and were categorized using the Bronfenbrenner Ecological Model. First, 20% of the sample was set-aside for model testing, and the remaining 80% was used in an Elastic Net Regularized linear regression, with 10-fold cross validation. Models were tested on the set-aside sample to yield estimates of out-of-sample prediction and repeated models were compared to ensure generalizability. Next, a theoretical model was tested examining a model of psychological distress mediating the relationship between individual predictors and treatment outcome. Results: The models developed from the Elastic Net Regularization approach demonstrated consistency in model strength (mean=0.32, standard deviation=0.03) with models ranging from 14 to 31 included variables. Across the models, 15 variables occurred in >75% of the models, and an additional 7 variables were included in 25% - 75% of the models. Some of the strongest predictors included treatment non-compliance (β=-0.92), ASI Alcohol Composite (β=0.63), treatment dosage (β =-0.36), and readiness to change (β=-0.95). The results of the theory-driven mediation analysis demonstrated several strong direct predictors of outcome frequency of alcohol use, including readiness to change (β=-0.59), initial frequency of alcohol use (β=0.27), and access to a primary care physician (β=-2.20). The theoretical model found that none of the mediation pathways (testing psychological variables) were significantly different from the direct models. Conclusions: This study used both data-driven and theory-driven methods to examine factors affecting treatment of AUDs. The application of data-driven methods provided several predictors of outcome that can guide treatment efforts within Day One IOP treatment, as well as generalized to other abstinence-based treatment settings. For example, focusing on treatment attendance and using motivational interviewing to enhance readiness to change are methods supported by this study. Demographic variables that have been shown to predict treatment outcome in small studies, without cross-validation were not identified by the elastic net regression (e.g., age and gender). It is suspected that this is due to model overfitting in prior studies supporting the importance of using generalizable statistical methods to understand predictors of treatment outcome. This notion is supported by the results of the theory-driven model, which did not yield a strong model of treatment success. Taken together, the results support the use of strong analytic techniques which will guide theory in the future.

There is little evidence concerning the impact of professional learning communities (PLCs) at juvenile correctional facilities. This qualitative case study explored the implementation of a PLC at a juvenile correctional facility school that housed students 10 to 19 years of age in southeastern United States. The purpose of this study was to understand the perceptions of teachers and paraprofessionals about how the PLC supported their work as they designed, constructed, and delivered instruction at the correctional facility. The social interactions among engaged educators through collaboration, collective inquiry, reflections, and communication derived from constructivist learning theory. Qualitative methodology included document review and structured face-to-face interviews with 4 teachers and 3 paraprofessionals. Following an inductive model, educators' perceptions were analyzed using an open coding process to derive categories, themes, and meaning. Five themes emerged: professional learning growth and benefits, teacher learning in PLCs, attitude adjustment of the culture, collaboration and sharing, and active engagement of paraprofessionals in PLCs. This study provided 5 recommendations: use allotted time, prioritize concerns, keep an open communication, discuss student-centered questions, and ensure supportive relationships. The findings indicated that the PLC supported teachers and paraprofessionals with strategies and accommodations to promote student achievement. This study has the potential to strengthen teacher collaboration and instruction to empower incarcerated students to succeed academically and become productive citizens.

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.

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.

The primary objective of this research is to model and optimize wastewater treatment process in a wastewater treatment plant (WWTP). As the treatment process is complex, its operations pose challenges. Traditional physics-based and mathematical- models have limitations in predicting the behavior of the wastewater process and optimization of its operations. Automated control and information technology enables continuous collection of data. The collected data contains process information allowing to predict and optimize the process. Although the data offered by the WWTP is plentiful, it has not been fully used to extract meaningful information to improve performance of the plant. A data-driven approach is promising in identifying useful patterns and models using algorithms versed in statistics and computational intelligence. Successful data-mining applications have been reported in business, manufacturing, science, and engineering. The focus of this research is to model and optimize the wastewater treatment process and ultimately improve efficiency of WWTPs. To maintain the effluent quality, the influent flow rate, the influent pollutants including the total suspended solids (TSS) and CBOD, are predicted in short-term and long-term to provide information to efficiently operate the treatment process. To reduce energy consumption and improve energy efficiency, the process of biogas production, activated sludge process and pumping station are modeled and optimized with evolutionary computation algorithms. Modeling and optimization of wastewater treatment processes faces three major challenges. The first one is related to the data. As wastewater treatment includes physical, chemical, and biological processes, and instruments collecting large volumes of data. Many variables in the dataset are strongly coupled. The data is noisy, uncertain, and incomplete. Therefore, several preprocessing algorithms should be used to preprocess the data, reduce its dimensionality, and determine import variables. The second challenge is in the temporal nature of the process. Different data-mining algorithms are used to obtain accurate models. The last challenge is the optimization of the process models. As the models are usually highly nonlinear and dynamic, novel evolutionary computational algorithms are used. This research addresses these three challenges. The major contribution of this research is in modeling and optimizing the wastewater treatment process with a data-driven approach. The process model built is then optimized with evolutionary computational algorithms to find the optimal solutions for improving process efficiency and reducing energy consumption.