Using data to improve educational practice in schools has become a popular reform strategy that has grown as a result of the No Child Left Behind Act of 2001. Districts and schools across the United States are under a great deal of pressure to collect and analyze data in hopes of identifying student weaknesses to implement corrective action plans that will lead to overall student achievement in the classroom. Technology tools such as computer-based assessment and reporting systems have provided schools with immediate access to student-level data. The problem is the lack of direction in how to use the information to make instructional changes in the classroom. A review of literature provided an overview of research-based strategies that support data-driven decision making (DDDM) in the classroom. Three case studies in Louisiana were examined to build a conceptual understanding about how districts and schools use data to make informed decisions. Three research questions guided the investigation and focused on the tools used to assess, store, and retrieve student data, evidence that connects the data and improvements in teaching, and recommendations for other districts and schools. Educational practices were documented through a collection of documents, interview/questionnaire data, and physical artifacts. Results were reported in a question and answer format for three case studies. School administrators reported using data to plan, evaluate, and provide feedback to teachers. In contrast, teachers and instructional specialists revealed that data were used to assess and measure student's weekly performance. All schools utilized at least two computer-based assessment and/or reporting systems to manage student-level data within the district and/or school. Instructional coaches provided direct support to teachers. Data analysis revealed that teachers collaborated and supported each other through data team meetings and working sessions. Principals and teachers monitored student behavior through use of data management and reporting tools. Schools showed promising and positive attitudes about making changes and building a data-driven culture. Findings were supported through current research on DDDM.

In this thesis, I describe research on teachers' experiences with learner-centered technology. Specifically, this research investigated teachers' experiences with adoption of the learner-centered tools available from Imagine Learning, an online elementary school literacy program. This thesis includes an extended literature review describing learner-centered classrooms, technology integration, and models of technology adoption, followed by a journal-ready article that describes teachers' experiences throughout the process of adopting Imagine Learning. Finally, I provide a description my experiences throughout this project as well as a proposal for future areas of study.

Educators across the country are expected to be data literate. They must be able to systemically collect and analyze student data to make informed instructional decisions. However, many school leaders lack the knowledge about how to transform mountains of data on student achievement into an action plan that will improve instruction and increase student learning (Boudett, et al., 2007). In addition, time constraints make it difficult for educators to effectively and efficiently collaborate around student data consistently. Most of the research on data use describes the importance for educators to use data to improve student achievement. However, limited research has been documented on the role the campus leader employs when creating a culture of data-driven decision-making as it relates to student achievement. Furthermore, the research on data use in Title 1 schools is also limited. Therefore, it is imperative to examine and describe how a Title 1 middle school principal implemented Data Teams on a campus. Consequently, the goal of this research was to determine how school leaders improve student learning through teacher data teams. The four primary questions this research addressed in this single case study were: 1. What is the role of the principal in implementing successful Data Teams? 2. What campus structures foster the Data Team process? 3. What are the perceptions of teachers regarding the effectiveness of the Data Team? 4. What practices contribute to the sustainability of Data Teams? Data was gathered through semi-structured interviews, direct observations, and document reviews which informed the findings. This research study revealed that the principal played a key leadership role in creating a culture of collaboration and data inquiry by implementing teacher Data Teams. Such leadership role is enacted by: communicating a vision for Data Teams, providing for job-embedded professional development, and offering differentiated support. Structured time, structured meetings, student data system, and structured assessments are structures employed by the school. Student-focused collaboration, enhanced teacher trust, and increased student achievement illustrate evidence of Data Team effectiveness. Shared accountability, building school culture, and focused interventions serve to sustain Data Teams. In conclusion, it can be affirmed the principal has the most influence on what will be supported on a campus. Therefore, the leadership role performed by the principal when guiding a faculty through the implementation of Data Teams must be deliberate and thoughtful. The principal should include key stakeholders in the decision-making process and build capacity among teachers to ensure the sustainability of Data Teams. Furthermore, targeted professional development and structures that allow time for teachers to collaborate are necessary. Because the ultimate goal for schools is student learning, it is important that everyone within the school organization understand their role in the Data Team process. / text

This study examined how higher performing middle schools in the Rio Grande Valley use data to drive instructional decisions. Three research questions guided this study: (a) to what extent do higher performing, Title-1, middle schools in the Rio Grande Valley utilize data to make schoolwide instructional decisions; (b) how does the principal support data use for instructional decision-making; and (c) what do teachers perceive to be the processes that have led to the current level of data use in instructional decision making? A mixed-methods multiple-case study included middle schools that were drawn from a list of higher performing schools according to Just for the Kids and the National Center for Educational Achievement. To be included in the study, schools had to be located in the Rio Grande Valley, Texas, specifically in the counties of Starr, Cameron or Hidalgo. Additionally, the schools needed to be designated a Title-1 school, according to federal criteria. Data for the study was collected using a survey, followed by one-on-one interviews. Descriptive analyses was then conducted using the survey data. The interview data was analyzed using first-level coding followed by the use of cross case analysis to determine themes common to all cases. The findings from this research revealed that data is used extensively in the schools studied; primarily to determine the instructional scope of what is taught. It was found that while data use was extensive, the source and purpose of data use was limited to that which was directly tied to the state-administered assessment (TAKS). The second major finding was that principals create the necessary conditions for data use that becomes an embedded practice, where teachers can take risks with their colleagues in reviewing and using data. This study concludes that more principals can lead their schools to greater levels of data use by creating the necessary conditions for change. At the same time, the findings suggest that there is a need for leaders at all levels to examine and mitigate the unintended consequences of data use that is derived from a single-source and for a single purpose—that is, performance on the state exam (TAKS). / text

This study examined the impact of principals' data-driven decision-making practices on student achievement using the theoretical frame of Dervin's sense-making theory. This study is a quantitative cross-sectional research design where principals' perceptions about data were quantitatively captured at a single point in time. The participants for this study were 253 rural school principals currently serving in schools across Texas, and included both males and females across all ethnic groups, including white, African American, Hispanic, Asian, Native American and other. A developed survey instrument was administered to principals. The findings from the quantitative SEM analyses indicated that the Principal Uses Data to Improve Student Achievement latent variable (Factor 1) and the Principal and Staff Ability to Analyze Data to Improve Student Achievement latent variable (Factor 2) were significantly and positively associated with student achievement. Higher scores on these two latent variables were associated with better student achievement. There was no statistical association between the Principal Uses Data to Design Teacher Professional Development latent variable (Factor 3) and this target outcome. In total, the three latent variables accounted for 6% of the variance in student achievement (TAKS). When the campus level outcome was considered, no statistically significant associations between any of the latent variables and this outcome were evident. In total, the three latent variables accounted for less than 2% of the variance in campus level.

The research study investigated how organizational routines influenced classroom and intervention instruction in a primary school. Educators have used student data for decades but they continue to struggle with the best way to use data to influence instruction. The historical overview of the research highlighted the context of data use from the Effective Schools movement through the No Child Left Behind Act noting the progression of emphasis placed on student data results. While numerous research studies have focused on the use of data, the National Center for Educational Evaluation and Regional Assistance (2009) reported that existing research on the use of data to make instructional decisions does not yet provide conclusive evidence of what practices work to improve student achievement. A descriptive case study methodology was employed to investigate the educational phenomenon of organizational routines implemented for data-driven decision making to influence classroom and intervention instruction. The case study examined a school that faced the macrolevel pressures of school improvement. The study triangulated data from surveys, interviews, and document analysis in an effort to reveal common themes about organizational routines for data-driven decision making. The study participants identified 14 organizational routines as influencing instruction. The interview questions focused on the common themes of (a) curriculum alignment, (b) common assessments, (c) guided reading levels, (d) professional learning communities, and (e) acceleration plans. The survey respondents and interview participants explained how the organizational routines facilitated the use of data by providing (a) focus and direction, (b) student centered instruction, (c) focus on student growth, (d) collaboration and teamwork, (e), flexible grouping of students, and (f) teacher reflection and ownership of all students. Challenges and unexpected outcomes of the organizational routines for data-driven decision making were also discussed. The challenges with the most references included (a) time, (b) too much data (c) data with conflicting information, (d) the pacing guide, and (e) changing teacher attitudes and practices. Ultimately, a data-driven culture was cultivated within the school that facilitated instructional adjustments resulting in increased academic achievement. / Ed. D.

Organ transplantation is one of the most important and effective solutions to save end-stage patients, who have one or more critical organ failures. However, the inadequate organs for transplantation to meet the demands has been the major issue. Even worse, the lack of accurate non-invasive assessment methods wastes 20% of donor organs every year. Currently, the most frequently used organ assessment methods are visual inspections and biopsy. Yet both methods are subjective: the assessment accuracy depends on the evaluator's experience. Moreover, repeating biopsies will potentially damage the organs. To reduce the waste of donor organs, online non-invasive and quantitative organ assessment methods are in great needs. Organ viability assessment is a challenging issue due to four reasons: 1) there are no universally accepted guidelines or procedures for surgeons to quantitatively assess the organ viability; 2) there is no easy-deployed and non-invasive biological in situ data to correlate with organ viability; 3) the organs viability is difficult to model because of heterogeneity among organs; 4) both visual inspection and biopsy can be applied only at present time, and how to forecast the viability of similar-but-non-identical organs at a future time is still in shadow. Motivated by the challenges, the overall objective of this dissertation is to develop online non-invasive and quantitative assessment methods to predict and forecast the organ viability. As a result, four data-driven modeling research tasks are investigated to achieve the overall objective: 1) Quantitative and qualitative models are used to jointly predict the number of dead cells and the liver viability based on features extracted from biopsy images. This method can quantitatively assess the organ viability, which could be used to validate the biopsy results from pathologists to increase the evaluation accuracy. 2) A multitask learning logistic regression model is applied to assess liver viability by using principal component analysis to extract infrared image features to quantify the correlation between liver viability and spatial infrared imaging data. This non-invasive online assessment method can evaluate the organ viability without physical contact to reduce the risk of damaging the organs. 3) A spatial-temporal smooth variable selection method is conducted to improve the liver viability prediction accuracy by considering both spatial and temporal effects from the infrared images without feature engineering. In addition, it provides medical interpretation based on variable selection to highlight the most significant regions on the liver resulting in viability loss. 4) A multitask general path model is implemented to forecast the heterogeneous kidney viability based on limited historical data by learning the viability loss paths of each kidney during preservation. The generality of this method is validated by tissue deformation forecasting in needle biopsy process to potentially improve the biopsy accuracy. In summary, the proposed data-driven methods can predict and forecast the organ viability without damaging the organ. As a result, the increased utilization rate of donor organs will benefit more end-stage patients by dramatically extending their life spans. / Doctor of Philosophy / Organ transplantation is the ultimate solution to save end-stage patients with one or more organ failures. However, the inadequate organs for transplantation to meet the demands has been the major issue. Even worse, the lack of accurate and non-invasive viability assessment methods wastes 20% of donor organs every year. Currently, the most frequently used organ assessment methods are visual inspections and biopsy. Yet both methods are subjective: the assessment accuracy depends on the personal experience of evaluator. Moreover, repeating biopsies will potentially damage the organs. As a result, online non-invasive and quantitative organ assessment methods are in great needs. It is extremely important because such methods will increase the organ utilization rate by saving more discarded organs with transplantation potential. The overall objective of this dissertation is to advance the knowledge on modeling organ viability by developing online non-invasive and quantitative methods to predict and forecast the viability of heterogeneous organs in transplantation. After an introduction in Chapter 1, four research tasks are investigated. In Chapter 2, quantitative and qualitative models jointly predicting porcine liver viability are proposed based on features from biopsy images to validate the biopsy results. In Chapter 3, a multi-task learning logistic regression model is proposed to assess the cross-liver viability by correlating liver viability with spatial infrared data validated by porcine livers. In Chapter 4, a spatial-temporal smooth variable selection is proposed to predict liver viability by considering both spatial and temporal correlations in modeling without feature engineering, which is also validated by porcine livers. In addition, the variable selection results provide medical interpretations by capturing the significant regions on the liver in predicting viability. In Chapter 5, a multitask general path model is proposed to forecast kidney viability validated by porcine kidney. This forecasting method is generalized to apply to needle biopsy tissue deformation case study with the objective to improve the needle insertion accuracy. Finally, I summarize the research contribution and discuss future research directions in Chapter 6. The proposed data-driven methods can predict and forecast organ viability without damaging the organ. As a result, the increased utilization rate of donor organs will benefit more patients by dramatically extending their life spans and bringing them back to normal daily activities.

This non-experimental, census survey included the elementary, middle, and high school principals at the comprehensive schools within a large, suburban school division in Virginia. The focus of this study was the factors that influence building administrators in using data to make instructional decisions. The purpose was to discover if there is a difference in the perceptions of elementary, middle, and high school principals of data use to make instructional decisions within their buildings. McLeod’s (2006) Statewide Data-Driven Readiness Study: Principal Survey was used to assess the principals’ beliefs about the data-driven readiness of their individual schools. Each principal indicated the degree to which they agreed or disagreed with statements about acting upon data, data support systems, and the data school culture. Twenty-two items aligned with four constructs identified by White (2008) in her study of elementary school principals in Florida. These four constructs or factors were used to determine if there was a significant difference in principal beliefs concerning teacher use of data to improve student achievement, principal beliefs regarding a data-driven culture within their building, the existence of systems for supporting data-driven decision-making, and collaboration among teachers to make data-driven decisions. For each of the survey items a majority of the responses (≥62%) were in agreement with the statements, indicating the principals agreed slightly, agreed moderately, or agreed strongly that data-driven decision-making by teachers to improve student achievement was occurring within the building, a data-driven culture and data supporting systems exists, and teachers are collaborating and using data to make decisions. Multiple analyses of variance showed significant differences in the means. Some of these differences in means were based on the principals’ assignment levels. While both groups responded positively to the statement that collaboration among teachers to make data-driven decisions, the elementary principals agreed more strongly than the high school principals. When mediating variables were examined, significance was found in principals’ beliefs concerning teacher use of data to improve student achievement depending on the years of experience as a principal. Principals with six or more years of experience had a mean response for Construct 1 of 4.84 while those with five or less years of experience had a mean of 4.38, suggesting that on average those principals with more experience had a stronger belief that teachers are using data to improve student achievement. There is significance between the means of principals with three or fewer years versus those with more than three years in their current assignment on two of the constructs – a data-driven culture and collaboration among teachers. Principals with less time in their current position report a slightly higher agreement than their less experienced colleagues with statements about the data-driven culture within their school. Significant difference was also found between principals’ beliefs about teacher collaboration to improve student achievement and their beliefs regarding collaboration among teachers using data-driven decision-making and the school’s AYP status for 2008-2009. Principals assigned to schools that had made AYP for 2008-2009 moderately agreed that teachers were collaborating to make data-driven decisions. In comparison, principals assigned to schools that had not made AYP only slightly agreed that this level of collaboration was occurring in their schools.

Thesis advisor: Diana Pullin / The current high stakes testing environment has resulted in intense pressure on schools to become more data-driven. As a result, an increasing number of schools are implementing systems where teachers and school leaders collaboratively analyze assessment data and use the results to inform instructional practice. This study examined how teacher participation in the analysis of assessment data influences instructional outcomes. It also examined how levels of capacity in the areas of data use, professional learning, and leadership interact to influence the ability to respond to data. The method is a qualitative case study of an elementary school in the southeastern United States that has implemented formal structures for analyzing and collaborating around assessment data. Data collection occurred through teacher and administrator interviews, data analysis meeting observations, and through the examination of school and district documents. The school in this study responded to data analysis results through three major actions: large-scale initiatives designed to improve instruction in various content areas, remediation, and individual teacher variations in instructional practices. Findings show that while teachers express support for data analysis and suggest positive benefits for the school, they also indicate that participation in data analysis and the resultant improvement efforts have had minimal to modest impact on their teaching practices. Possibly contributing to this outcome was the finding that the school had uneven capacity in the areas of data use, professional learning, and leadership. The school has a well-developed system for data access and reporting. However, it has been less successful in providing the professional learning experiences that will enable more substantial changes in teacher beliefs and practices. Furthermore, a lack of clarity regarding the instructional purpose of data analysis from multiple levels of district and school leadership and the procedural nature of the data analysis process has reduced the ability of school leaders to effectively leverage data analysis for the purpose of substantive and sustained instructional improvement. / Thesis (PhD) — Boston College, 2011. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Leadership and Higher Education.

Thesis advisor: Henry I. Braun / Data-based instructional programs, including interim assessments, are a common tool for improving teaching and learning. However, few studies have rigorously examined whether they achieve those ends and contributors to their effectiveness. This study conducts a secondary analysis of data from a matched-pair school-randomized evaluation of the Achievement Network (ANet). Year-two teacher surveys (n=616) and interviews from a subset of ANet school leaders and teachers (n=40) are used to examine the impact of ANet on teachers’ data-based instructional practices and the mediating roles of instructional leadership, professional and achievement cultures, and teacher attitudes and confidence. Survey results showed an impact of ANet on the frequency with which teachers’ reviewed and used data, but not their instructional planning or differentiation. Consistent with the program model, ANet had a modest impact on school-mean teacher ratings of their leaders’ instructional leadership abilities and school culture, but no impact on individual teachers’ attitudes toward assessment or confidence with data-based instructional practices. Therefore, it was not surprising that these school and teacher characteristics only partially accounted for ANet’s impact on teachers’ data practices. Interview findings were consistent. Teachers described numerous opportunities to review students’ ANet assessment results and examples of how they used these data (e.g., to pinpoint skills on which their students struggled). However, there were fewer examples of strategies such as differentiated instruction. Interview findings also suggested some ways leadership, culture, and teacher characteristics influenced ANet teachers’ practices. Leaders’ roles seemed as much about holding teachers accountable for implementation as offering instructional support and, while teachers had opportunities to collaborate, a few schools’ implementation efforts were likely hampered by poor collegial trust. Teacher confidence and attitudes varied, but improved over the two years; the latter following from a perceived connection between ANet practices and better student performance. However, some teachers were concerned with the assessments being too difficult for their students or poorly aligned with the curriculum, resulting in data that were not always instructionally useful. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.