Advancing equity in accountability and organizational cultures of data use

2017 ◽  
Vol 55 (4) ◽  
pp. 361-375 ◽  
Author(s):  
Nora Gannon-Slater ◽  
Priya G. La Londe ◽  
Hope L. Crenshaw ◽  
Margaret E. Evans ◽  
Jennifer C. Greene ◽  
...  

Purpose Data use cultures in schools determine data use practices. Such cultures can be muted by powerful macro accountability and organizational learning cultures. Further, strong equity-oriented data use cultures are challenging to establish. The purpose of this paper is to engage these cultural tensions. Design/methodology/approach The data discourse and decisions of four grade-level teams in two elementary schools in one district were studied through observation of 62 grade-level meetings over the course of a year. The observations focused on “data talk,” defined as the structure and content of team conversations about interim student performance data. Findings Distinct macro cultures of accountability and organizational learning existed in the two schools. The teams’ own data use cultures partly explained the absence of a focus on equity, and none of the teams used student performance data to make instructional decisions in support of the district’s equity aims. Leadership missed opportunities to cultivate an equity-focused data use culture. Practical implications School leaders who advocate that equity importantly guides data use routines, and can anticipate how cultures of accountability or organizational learning “show up” in data use conversations, will be better prepared to redirect teachers’ interpretations of data and clarify expectations of equity reform initiatives. Originality/value This study is novel in its concept of “data talk,” which provided a holistic but nuanced account of data use practices in grade-level meetings.

2019 ◽  
Vol 121 (2) ◽  
pp. 1-40 ◽  
Author(s):  
Margaret Evans ◽  
Rebecca M. Teasdale ◽  
Nora Gannon-Slater ◽  
Priya G. La Londe ◽  
Hope L. Crenshaw ◽  
...  

Context Educators often engage with student performance data to make important instructional decisions, yet limited research has analyzed how educators make sense of student performance data. In addition, scholars suggest that teachers recognize a relationship between their instruction and student performance data, but this is a relatively untested assumption. Focus of Study We investigated if and how teachers referenced instruction as a contributing factor for why students performed in particular ways on assessments. We also studied other explanations that teachers offered for student performance data. Research Design Our research team conducted a qualitative case study of six grade-level teams of teachers who met biweekly to make meaning of student performance data. Using data collected from 44 hours of observation of teacher team meetings, 16 individual interviews, and six group interviews with participating teachers, we analyzed the ways in which and the extent to which teachers referenced instruction as a contributing factor to student performance data. Findings: Teachers connected student performance data to their instruction approximately 15% of the time. Teachers more frequently connected student performance data to student characteristics. Notably, student behavior accounted for 32% of all teacher explanations for student performance. We offer five distinct categories of teachers’ explanations of student performance and the extent to which teachers invoked each category. Conclusions The findings in this study build on research on teachers’ attributions for assessment data. In contrast to other studies, our findings suggest that teachers invoked student characteristics in distinct ways when explaining student performance. At times, teachers were knowledgeable about student characteristics, which offered verifiable insights into the “problem” of low achievement. At other times, teachers voiced negative viewpoints of students that served to blame students for their poor performance. We suggest that the practice of data-driven decision making offers an opportunity to bolster educators’ informed judgment and undermine negative, unverifiable claims about children.


1986 ◽  
Vol 8 (3) ◽  
pp. 5-13 ◽  
Author(s):  
Lynn S. Fuchs ◽  
Douglas Fuchs

This meta-analysis investigated the effects on achievement of type of graphing paper employed in displaying student performance data collected over time. The data source was 15 controlled studies with 16 effect sizes. The average weighted unbiased effect sizes for 6-cycle and equal interval paper, respectively, were .53 and .46. Hedges's analogue to analysis of variance indicated this difference was not statistically reliable. Implications for special education practice are discussed.


2020 ◽  
Vol 6 (2) ◽  
pp. 39-48
Author(s):  
Gadis Retno Apsari ◽  
Mohammad Syaiful Pradana ◽  
Novita Eka Chandra

Students are the most important component in a university, especially private universities especially Universitas Islam Darul ‘ulum (Unisda) Lamongan. One of the most important roles of students for higher education is achievement. This study aims to determine the role of Fuzzy Clustering in classifying student performance data. The data includes GPA (Grade Point Average), ECCU (Extra-Curricular Credit Unit), attendance, and students' willingness to learn. So that groups of students who have the potential to have achievements can be identified. In this case, the grouping of student performance data uses Fuzzy Clustering by applying the Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) algorithms with the help of Matlab. In the FCM algorithm, the membership degree is updated so as to produce a minimum objective function value. Meanwhile, the PCM algorithm uses a T matrix, which shows the peculiarities of the data which are also based on minimizing the objective function.


2019 ◽  
Vol 3 (4) ◽  
pp. 166-176
Author(s):  
Haozhang Deng ◽  
Xuemeng Wang ◽  
Zhiyi Guo ◽  
Ashley Decker ◽  
Xiaojing Duan ◽  
...  

2017 ◽  
Vol 98 (5) ◽  
pp. 67-71
Author(s):  
Michael J. Wasta

Research on educators’ professional learning communities (PLCs) suggest that while they often help teachers to make sense of student performance data, they tend to spend relatively little time studying what teachers actually do in the classroom. Evidence suggests that, given modest amounts of guidance and support, PLCs can collect useful data on teacher practice, and team members can identify specific, actionable ways in which to improve instruction.


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