Factors Influencing the Functioning of Data Teams

2015 ◽  
Vol 117 (4) ◽  
pp. 1-42 ◽  
Author(s):  
Kim Schildkamp ◽  
Cindy Poortman
Keyword(s):  
Professional Development ◽  
Decision Making ◽  
Data Use ◽  
Quality Data ◽  
Data Teams ◽  
Use Of Data ◽  
Data Team ◽  
Factors Influencing ◽  
Team Characteristics ◽  
Data Based Decision Making

Background Data-based decision making can lead to increased student achievement; however, schools struggle with the implementation of data-based decision making. Professional development in the use of data is therefore urgently needed. However, professional development is often ineffective in terms of improving the knowledge, skills, and attitude of the receiver. Purpose We need a more fundamental understanding of how we can increase the effectiveness of data-use-related professional development. This study therefore focuses on the factors influencing a professional development intervention for data-based decision making: the data team procedure. Data teams are teams of teachers and school leaders who collaboratively learn how to use data, following a structured approach and guided by a facilitator from the university. Based on an extensive literature review, we developed a data use framework in which the use of data is influenced by data characteristics, school organization characteristics, and user and team characteristics. Research Design We conducted case studies. Data Collection We focused on observing in depth the factors that influence the work of the data teams and interviewing the data team members about these factors. Four data teams of six schools for upper secondary education were followed over a period of 2 years. We observed and analyzed 34 meetings and analyzed 23 interviews, combined with our field notes. Although this pilot study only permits analytical generalization of the findings, the findings provide more in-depth insight into the factors that enable and hinder interventions, focusing on supporting collaborative data use in schools. Findings The results show that several data characteristics (access and availability of high-quality data), school organizational characteristics (a shared goal, leadership, training and support, involvement of relevant stakeholders), and individual and team characteristics (data literacy, pedagogical content knowledge [PCK], organizational knowledge, attitude, and collaboration) influence the use of data in data teams. The results also show that these influencing factors are interrelated. Conclusions Schools need support in all aspects of the use of data (from formulation of a problem definition to taking action based on the data). This study can form a starting point for larger studies into the factors influencing these types of professional development interventions to ensure effective implementation and sustainability.

Leadership to Support and Sustain Data Use in Data Teams

2018 ◽  
pp. 98-118 ◽  
Author(s):  
Kim Schildkamp ◽  
Cindy Louise Poortman
Keyword(s):  
School Improvement ◽  
Leadership Behaviors ◽  
School Leaders ◽  
Data Use ◽  
Intellectual Stimulation ◽  
Data Teams ◽  
Use Of Data ◽  
Data Team ◽  
Different Parts

This chapter focuses on how school leaders can support the use of data in data teams with the data team intervention, a step-by-step systematic approach to school improvement. First, the data team professional development intervention is described and an example of a data team in action is provided. Next, the authors closely examine the role of the school leader in supporting the use of data in data teams. Several leadership behaviors that are important to support data teams are described: developing a vision, norms, and goals for data use; providing individualized support; providing intellectual stimulation; creating a climate for data use; and, networking to connect different parts of the organization. Concrete examples are provided with regard to how these behaviors are demonstrated in data teams. The chapter ends with a checklist and reflection tool, which school leaders can use to reflect on their own leadership behaviors with regard to supporting data use in data teams.

Embracing School Counselors’ Situatedness: Data-Based Decision Making as Fulfillment of a Complex Identity

2021 ◽  
Vol 24 (1_part_3) ◽  
pp. 2156759X2110119
Author(s):  
Brett Zyromski ◽  
Catherine Griffith ◽  
Jihyeon Choi
Keyword(s):  
Decision Making ◽  
School Counselors ◽  
School Counselor ◽  
Use Of Data ◽  
Counselor Identity ◽  
Using Data ◽  
Definition Of ◽  
Support Students ◽  
Students Success ◽  
Data Based Decision Making

Since at least the 1930s, school counselors have used data to inform school counseling programming. However, the evolving complexity of school counselors’ identity calls for an updated understanding of the use of data. We offer an expanded definition of data-based decision making that reflects the purpose of using data in educational settings and an appreciation of the complexity of the school counselor identity. We discuss implications for applying the data-based decision-making process using a multifaceted school counselor identity lens to support students’ success.

Does Training Predict Second-Grade Teachers’ Use of Student Data for Decision-Making in Reading and Mathematics?

2020 ◽  
pp. 153450842090252
Author(s):  
Marissa J. Filderman ◽  
Jessica R. Toste ◽  
North Cooc
Keyword(s):  
Professional Development ◽  
Decision Making ◽  
Reading Instruction ◽  
Student Assessment ◽  
Math Instruction ◽  
Use Of Data ◽  
Frequent Use ◽  
Reading And Math ◽  
Early Childhood Longitudinal Survey ◽  
And Mathematics

Although national legislation and policy call for the use of student assessment data to support instruction, evidence suggests that teachers lack the knowledge and skills required to effectively use data. Previous studies have demonstrated the potential of training for increasing immediate teacher outcomes (i.e., knowledge, skills, and beliefs), yet research is still needed that investigates whether these immediate learning outcomes correspond to improved practices in reading and math instruction. Using the Early Childhood Longitudinal Survey: Kindergarten (2011), the present study sought to investigate whether data-focused training predicted teacher use of data for four prevalent decision-making outcomes: monitor progress on specific skills, identify skill deficits, monitor overall progress of students performing below benchmark, and determine placement in instructional tiers. Results indicate that professional development to use data to identify struggling learners and coursework focused on the use of assessment to select interventions and supports significantly predicted teachers’ frequent use of data across key decision-making dimensions in reading instruction. Results for math instruction differ in that more frequent data use was not consistent across outcomes, more training sessions were needed, and professional development to use data to guide instruction significantly predicted use of data to monitor students who performed below benchmark.

Data-Based Decision-Making

2020 ◽  
Author(s):  
Mark Carter ◽  
Jennifer Stephenson ◽  
Sarah Carlon
Keyword(s):  
Decision Making ◽  
Special Education ◽  
Student Outcomes ◽  
Policy And Practice ◽  
Positive Effects ◽  
Use Of Data ◽  
Wide Range ◽  
Meta Analyses ◽  
Classroom Use ◽  
Data Based Decision Making

The term data-based decision-making can refer to a wide range of practices from formative classroom use of monitoring in order to improve instruction to system-wide use of “big” data to guide educational policy. Within the context of special education, a primary focus has been on the formative classroom use of data to guide teachers in improving instruction for individual students. For teachers, this typically involves the capacity to (1) determine what data need to be collected to appropriately monitor the skill being taught, (2) collect that data, (3) interpret the data and make appropriate decisions, and (4) implement changes as needed. A number of approaches to such data-based decision-making have evolved, including precision teaching, curriculum-based assessment, and curriculum-based measurement. Evidence from systematic reviews and meta-analyses indicates instruction incorporating data-based decision-making has positive effects on outcomes for students with special education needs although the size of these effects has been variable. While the extent of the research base is modest, there are indications that some specific factors may be related to this variability. For example, the use of decision-making rules and graphic display of data appears to improve student outcomes and the frequency of data collection may differentially affect improvement. The presence and frequency of support offered to teachers may also be important to student outcomes. There is a need to increase our research base examining data-based decision-making and, more specifically, a need to more clearly define and characterize moderators that contribute to its effectiveness. In addition, there is a case for research on the wider use of data on student outcomes to inform broader policy and practice.

A Meta-Analysis of the Impact of Professional Development on Teachers’ Knowledge, Skill, and Self-Efficacy in Data-Based Decision-Making

2020 ◽  
pp. 002221942097019
Author(s):  
Samantha A. Gesel ◽  
Lauren M. LeJeune ◽  
Jason C. Chow ◽  
Anne C. Sinclair ◽  
Christopher J. Lemons
Keyword(s):  
Professional Development ◽  
Decision Making ◽  
Self Efficacy ◽  
Meta Analysis ◽  
Experimental Studies ◽  
Sensitivity Analyses ◽  
Significant Heterogeneity ◽  
K 12 ◽  
The Impact ◽  
Data Based Decision Making

The purpose of this review was to synthesize research on the effect of professional development (PD) targeting data-based decision-making processes on teachers’ knowledge, skills, and self-efficacy related to curriculum-based measurement (CBM) and data-based decision-making (DBDM). To be eligible for this review, studies had to (a) be published in English, (b) include in-service or pre-service K–12 teachers as participants, (c) use an empirical group design, and (d) include sufficient data to calculate an effect size for teacher outcome variables. The mean effect of DBDM PD on teacher outcomes was g = 0.57 ( p < .001). This effect was not moderated by study quality. These results must be viewed through the lens of significant heterogeneity in effects across included studies, which could not be explained by follow-up sensitivity analyses. In addition, the experimental studies included in this review occurred under ideal, researcher-supported conditions, which impacts the generalizability of the effects of DBDM PD in practice. Implications for research and practice are discussed.

scholarly journals The Nature of Teacher Learning in Collaborative Data Teams

2016 ◽  
Author(s):  
Robert Michaud
Keyword(s):  
Teacher Learning ◽  
Conceptual Models ◽  
Empirical Studies ◽  
Data Use ◽  
Educational Leaders ◽  
Learning Opportunities ◽  
Data Teams ◽  
Key Factors ◽  
Data Team ◽  
Effective Data Use

As data teams have grown in popularity in recent years, they have been increasingly looked to by educational researchers because of the tantalizing prospect of combining teachers’ on the job professional development with increased and effective data use to drive instruction. Data teams have been increasingly implemented within schools by educational leaders attempting to take advantage of what teachers learn from each other in the context of a data team. Many conceptual models of data team function have been proposed, but few empirical studies have examined how teachers learn from collaborating with each other in a data team. This paper explores the nature of teachers’ learning in data teams, uncovering key factors that impact the learning opportunities created by collaborating around student data.

Characterizing Global Activity Schedule Adjustment Behavior by Using a Sequence Alignment Method

2005 ◽  
Vol 1926 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Chang-Hyeon Joh ◽  
John W. Polak ◽  
Tomás Ruiz
Keyword(s):  
Decision Making ◽  
Sequence Alignment ◽  
Future Research ◽  
Alignment Method ◽  
Use Of Data ◽  
Factors Influencing ◽  
Activity Schedule ◽  
Decision Making Processes ◽  
Schedule Modification ◽  
Global Activity

Considerable interest has developed recently in the decision-making processes underlying activity schedule adjustment. This paper suggests a method, based on sequence alignment techniques, to measure schedule adjustment behavior and applies the method to develop a model of the factors influencing schedule adjustment, with the use of data from a recent Internet-administered survey. The results indicate that the amount of schedule modification that occurs is largely determined by characteristics of the planned schedule instead of background socioeconomic or activity-related variables. The implications of this finding for future research in this area are considered.

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.

Does Teacher Data Use Lead to Improved Student Achievement? A Review of the Empirical Evidence

2020 ◽  
Vol 122 (12) ◽  
pp. 1-42
Author(s):  
Jana Grabarek ◽  
Leanne M. Kallemeyn
Keyword(s):  
Professional Development ◽  
Student Achievement ◽  
Research Question ◽  
Data Use ◽  
Future Research ◽  
Promising Practices ◽  
Factors Influencing ◽  
Study Results ◽  
Teacher Data ◽  
Positive Impacts

Background/Context The importance attached to practicing data use is evident in its inclusion in federal law, competitive grant programs, state teaching license requirements, and professional development (PD) workshops around the world. Yet, practitioners and scholars have identified misconceptions clouding data use practice, questioned its utility, and suggested its discontinuation. These tensions are linked to various conceptualizations of data use, which include simple, linear, and complex, contextualized understandings. Prior research on data use as sensemaking, data use intervention components and promising practices, factors influencing data use, and using data to address equity suggest data use is a complex endeavor. Purpose/Objective/Research Question/Focus of Study This study explored the link between teacher data use, in its many forms, and improvements in student achievement. Research Design This study is a systematic review of 39 quantitative, qualitative, and mixed methods studies. Data Collection and Analysis Descriptive details of each study were recorded, including the sample and its demographics; study location, length, design, and measures; school subject foci; type(s) of data used and type(s) of data strategies employed; school levels involved; and findings/results. Data use efforts also were coded for their inclusion of data use intervention components and promising practices; teacher, context, and assessment factors influencing data use; and equity practices and principles. Study results were categorized as positive, mixed, or null based on main effects, and shifts in proportions of study outcomes were noted as results were analyzed through a variety of lenses. Findings/Results Fifteen studies identified positive relationships (38% of studies) between data use and student achievement, 10 pointed to mixed relationships (26%), and 14 shared no (or null) relationships (36%). No differences were evident when considering studies by the school levels, subject areas, and study designs involved. Studies that had positive impacts on student achievement more often than the sample overall incorporated the following elements: ongoing professional development, comprehensive data use interventions targeting multiple leverage points, multiple types of data, and intentions to use data for continuous improvement of all students. Conclusions/Recommendations Findings demonstrate that a comprehensive framework for data use can have positive impacts on student achievement. Implications for future research and practice are provided.

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