How Can Schools of Education Help to Build Educators’ Capacity to Use Data? A Systemic View of the Issue

2015 ◽  
Vol 117 (4) ◽  
pp. 1-50
Author(s):  
Ellen B. Mandinach ◽  
Jeremy M. Friedman ◽  
Edith S. Gummer
Keyword(s):  
Decision Making ◽  
Descriptive Analysis ◽  
Data Use ◽  
The United States ◽  
Literacy Skills ◽  
Data Driven ◽  
Assessment Literacy ◽  
Schools Of Education ◽  
Data Driven Decision Making ◽  
Data Literacy

Background With the growing emphasis for educators to use data to inform their practice, little has been done to consider the means by which the educators can acquire the requisite data literacy skills. This article provides a context for why schools of education can and must play an important role in preparing teachers to use data. Purpose This article sought to understand if and how schools of education are preparing teacher candidates to use data effectively or responsibly. The study examined the extent to which schools of education teach stand-alone courses on data-driven decision making or integrate data use concepts into existing courses. It also examined state licensure and certification requirements to determine if and how data use is included in documentation. Population A stratified randomized sample of schools of education was drawn with 208 institutions responding, representing a 25.7% response rate. Research Design The survey portion of the study consisted of a stratified randomized sample of all schools or departments of education in the United States. The syllabus review was a voluntary part of the survey. The licensure review was a descriptive analysis of every state's documentation for teacher licensure and certification. Findings/Results The survey results indicated that a vast majority of the schools of education reported that they offered a stand-alone data course, and even more integrated data use into existing courses. The syllabus review provided a deeper dive into the course offerings and indicated that the courses were more about assessment literacy than data literacy. The licensure review yielded a plethora of skills and knowledge related to data that are included in state requirements. However, there was wide variation across states in their requirements. Conclusions Even though schools of education reported that they are teaching about data-driven decision making in their teacher preparation programs, the results indicate that the content is more about assessment literacy than data literacy. This finding is consistent with the often observed conflation of the two constructs. Licensure requirements include both data literacy and assessment literacy, but the emphasis is more on assessment than data. With the increasing emphasis by policy makers on the importance of educators using data, it is essential that schools of education begin to incorporate data concepts into their curricula and that states make explicit the data-related skills and knowledge required for teachers for licensure and certification.

A Systemic View of Implementing Data Literacy in Educator Preparation

2013 ◽  
Vol 42 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Ellen B. Mandinach ◽  
Edith S. Gummer
Keyword(s):  
Decision Making ◽  
Literacy Development ◽  
Data Use ◽  
Literacy Skills ◽  
Data Driven ◽  
Schools Of Education ◽  
Data Driven Decision Making ◽  
Data Literacy ◽  
Systems Perspective ◽  
Systemic View

Data-driven decision making has become increasingly important in education. Policymakers require educators to use data to inform practice. Although the policy emphasis is growing, what has not increased is attention to building human capacity around data use. Educators need to gain data literacy skills to inform practice. Although some professional development opportunities exist for current educators, fewer formal courses and opportunities for data literacy development in schools of education have been developed and implemented. This article explores issues around the growing need for data-driven decision making in programs in schools of education. The issues are complex and the actors needed to bring about change are multiple. A systems perspective to explore course and programmatic implementation is presented.

Response to Intervention

Education ◽  
2013 ◽  
Author(s):  
Amy Eppolito ◽  
Kathryn White ◽  
Janette Klingner
Keyword(s):  
Decision Making ◽  
Special Education ◽  
Learning Disabilities ◽  
Response To Intervention ◽  
General Education ◽  
Progress Monitoring ◽  
The United States ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Rti Model

Response to intervention (RTI) is a comprehensive, systematic approach to teaching and learning designed to monitor academic and behavioral progress for all students, provide early interventions of increasing intensity to struggling learners, and potentially identify learners with more significant learning disabilities. The model is implemented with multitiered instruction, intervention, and assessment. The key components of the RTI model include (1) high-quality instruction matched to the needs of students, (2) evidence-based interventions of increasing intensity, (3) ongoing progress monitoring, and (4) data-driven decision making. Components of the model, such as data-driven decision making and multitiered instruction, have been studied for the past few decades, but the model as an integrated whole has been developed more recently. One catalyst for increased research and interest in RTI has been a change in federal legislation in the United States. The most recent reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA) in 2004 permits the RTI model to be implemented as an alternative means to identify students with learning disabilities (LDs). These amendments to IDEA stipulate that the RTI process may be used to determine if a child is responding to research-based instruction and intervention as part of the special education evaluation process. Although driven by special education policy, RTI has been lauded as an instructional model that can improve general education overall and for special populations. However, critiques of the model argue that it has been implemented with limited research, resources, and funding and may not be valid for identifying LDs. Some experts question the psychometric validity of the model and promote using multiple forms of assessment, including more traditional standardized psycho-educational tests, in combination with RTI when evaluating students for possible LDs.

Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

2018 ◽  
Vol 120 (4) ◽  
pp. 1-34 ◽  
Author(s):  
Amanda Datnow ◽  
Bailey Choi ◽  
Vicki Park ◽  
Elise ST. John
Keyword(s):  
Decision Making ◽  
Student Achievement ◽  
Student Learning ◽  
Data Use ◽  
Data Driven ◽  
Educational Reforms ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Student Ability ◽  
Teacher Team

Background Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students’ needs. However, most prior research has not examined how the use of data shapes teachers’ dialogue about their students’ ability and achievement. Purpose This study examines how teachers talk about student ability and achievement in the era of data-driven decision making and how their talk is shaped by the use of data within teams, their school contexts, and broader accountability systems. Research Design The study draws on interview and observational data gathered from teacher teams in four elementary schools. In each of these schools, teachers were expected to use data to inform instructional differentiation. Data collection efforts involved regular visits to each school over the course of one year to interview teachers and conduct observations of teacher team meetings. In the process of analysis, interview transcripts and field notes were coded, and themes were extracted within and across codes. Findings Across schools, teachers used common labels (e.g., “low,” “middle,” “GATE”) to describe students of different achievement levels and the programs they were involved in. The use of labels and student categories was relational and comparative and influenced by the accountability and policy contexts in which teachers worked. At the same time, regular meetings in which teachers jointly examined data on student learning provided a space for teachers to examine students’ strengths and weaknesses on a variety of measures and talk in terms of student growth. Teachers questioned whether assessment data provided an accurate picture of student achievement and acknowledged the role of student effort, behavior, and family circumstances as important factors that were not easily measured. These discussions opened up deeper inquiry into the factors that supported or hindered student learning. The implementation of the Common Core State Standards also led some teachers to question prior categorizations of student ability. Conclusions/Recommendations The findings from this study suggest that educational reforms and policies regarding data use influence educators’ conceptions of student achievement and ability. On the one hand, accountability policies can narrow the dialogue about students. On the other hand, educational reforms and policies could also lead to new ways of thinking about student learning and to an examination of a broader range of data, and provide opportunities for professional learning.

Critical data-driven decision making: A conceptual model of data use for equity

2021 ◽  
Vol 99 ◽  
pp. 103272
Author(s):  
Stephanie L. Dodman ◽  
Katy Swalwell ◽  
Elizabeth K. DeMulder ◽  
Jenice L. View ◽  
Stacia M. Stribling
Keyword(s):  
Decision Making ◽  
Conceptual Model ◽  
Data Use ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Critical Data

scholarly journals An examination of organizational learning and data driven decision making within two university enrollment management programs

2019 ◽  
Author(s):  
◽  
J. D. Gragg
Keyword(s):  
United States ◽  
Decision Making ◽  
Organizational Learning ◽  
Enrollment Management ◽  
Effective Means ◽  
The United States ◽  
Data Driven ◽  
United States Department ◽  
Data Driven Decision Making ◽  
Feasible Option

As the number of United States high school graduates continue to decline, research into the most effective ways to recruit and retain college students becomes increasingly valuable. According to the United States Department of Education (Hussar and Bailey, 2016), students graduating between now and 2028 will decrease by approximately 4.5 %. Historically, when focusing on enrollment numbers, college admission offices have recruited students in the door and then let other departments at the university worry about retaining them. In today's competitive market, this is no longer a feasible option. The focus of this research was to address the declining number of students, and the effective strategies universities can use to recruit students who will progress and graduate. Researchers Kretchmar and Memory (2010) and Seeman and O'Hara, (2006) concurred that to retain students is to attempt to understand students beyond the numbers, looking at students as more than a GPA or test score, and understand what the students are desiring in a college and be prepared to sell the student on that experience. From this research, it appeared that data-driven decision-making can be an effective means to create a successful enrollment management office. Through a practice of data-driven decision-making, organizational learning can happen, which will allow an organization to maintain success and build a culture that sustains that success. Three key themes emerged from the research that will help to inform best practices for other colleges and universities to implement ideas from this research. The three themes are: an organization must have good, usable data and the resources needed to analyze that data; an organization should strive to create a culture of openness, caring, communication, and teamwork to sustain success, and enrollment management is a mix of art and science; an organization needs a successful enrollment manager that is a person who can blend the use of data with creating a well-rounded, meaningful, and impactful student recruitment experience.

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