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Leadership for data-based decision-making: Collaborative data teams

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... Data use has been described as a cyclical process, in which phases of discussing, interpreting and diagnosing data and taking actions follow each other (Verhaeghe et al., 2010). During this process, interactions among team members are considered to be essential for fruitful data use (Copland, 2003;Hubbard, Datnow, & Pruyn, 2014;Wayman, Midgley, & Stringfield, 2006). Problems that are e at times e attributed to the individual capacity of data users might be overcome by interacting with colleagues (Hubbard et al., 2014;Wayman et al., 2006). ...
... During this process, interactions among team members are considered to be essential for fruitful data use (Copland, 2003;Hubbard, Datnow, & Pruyn, 2014;Wayman, Midgley, & Stringfield, 2006). Problems that are e at times e attributed to the individual capacity of data users might be overcome by interacting with colleagues (Hubbard et al., 2014;Wayman et al., 2006). Researchers expect that teachers' interactions with colleagues on data use provide valuable opportunities for teachers to learn, so that data use has the potential to serve as a rich environment for teachers' professional learning (Katz & Dack, 2014;Vanhoof & Schildkamp, 2014). ...
... First, there is insufficient evidence on the nature of teachers' interactions on the subject of pupil learning outcomes. Although researchers into data use have attempted to study various forms of collaboration, such as team work or communities (Bertrand & Marsh, 2015;Hubbard et al., 2014;Wayman et al., 2006), little is known about the learning activities undertaken by teachers during these interactions. Given the potential contribution of data use for teacher learning, more insight into teachers' learning activities with regard to discussing data, interpreting data, diagnosing data and taking actions upon data is needed. ...
Article
Collaboration on data use is expected to provide valuable opportunities for teachers to learn. Therefore, the goals of this qualitative study are to provide insight both into teachers’ learning activities (storytelling, helping, sharing, joint work) with regard to collaborative use of pupil learning outcome data, as well as into teachers’ professional learning (new or confirmed ideas, changed ideas of the self, consciousness, intention to change behavioural practice, turn new or confirmed ideas into practice) from these activities. We find that teachers mainly undertake storytelling and helping activities in terms of data use and that professional learning resulting from these activities is limited.
... These notions will cause teachers to utilize or ignore various features, even the system itself. Recognizing this research—and research showing teachers' negativity when data use conflicts with their personal or professional values (Ingram et al., 2004; Valli & Buese, 2007; Wills & Sandholtz, 2009)—leaders looking to support a data system such as SB938 may be well served to continuously engage educators in activities designed to share ideas about teaching, learning, and data (Farrell & Marsh, 2016; Wayman, Midgley, & Stringfield, 2006 ). Examples of such activities include discussion protocols for collaborative meetings, sharing data projects in a " science fair " format, and working on common problems important to the school that highlight features of the data system (City, Kagle, & Teoh, 2013; Johnson & Avelar La Salle, 2010; Wayman, 2014; Wayman et al., 2006). ...
... Recognizing this research—and research showing teachers' negativity when data use conflicts with their personal or professional values (Ingram et al., 2004; Valli & Buese, 2007; Wills & Sandholtz, 2009)—leaders looking to support a data system such as SB938 may be well served to continuously engage educators in activities designed to share ideas about teaching, learning, and data (Farrell & Marsh, 2016; Wayman, Midgley, & Stringfield, 2006 ). Examples of such activities include discussion protocols for collaborative meetings, sharing data projects in a " science fair " format, and working on common problems important to the school that highlight features of the data system (City, Kagle, & Teoh, 2013; Johnson & Avelar La Salle, 2010; Wayman, 2014; Wayman et al., 2006). Our thinking is that teachers who have articulated and honed their ideas about data would possess more capacity to see how various data system features may support their practice. ...
Article
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Does data use make a difference in student achievement? Despite the field’s optimism on this matter, relatively few studies have attempted to quantify the effects of data use. These studies have often used the presence of a data use intervention (e.g., a data system or data coaching) as a proxy for use, as opposed to tracking teachers’ direct interactions with data, via data system click logs, for example. Accordingly, the present study sought to address this methodological gap by exploring the 2-year effects of data use through a multilevel cross-classified model of teachers’ system interactions and student achievement. A significant relationship was found between system use and elementary reading, but no significant relationships were found for elementary math, junior high math, or junior high reading. The implications of this study on how to conceptualize and measure use, as well as how to support practitioners, are discussed.
... Data include qualitative and quantitative student information, ranging from behavioral data (e.g., attendance, visits to the principal), to assessment data (e.g., teacher-made tests, high-stakes tests), and to teacher observations (e.g., student mood, distractions). Research has repeatedly supported the use of DDDM to positively impact student learning and achievement in the US (Carlson, Borman, & Robinson, 2011;Evans, 2009;Scheurich & Skrla, 2003;Wayman, Midgley, & Stringfield, 2006) and around the globe (Brown et al., 2011;Remesal, 2011;Schildkamp & Ehren, 2013;Schildkamp et al., 2014Schildkamp et al., , 2017. DDDM helps provide a more accurate representation of knowledge deficits and student needs (Herppic, Wittwer, Nuckles, & Renkl, 2014), making data "crucial in enabling teachers to judge students' progress toward [students'] goals and in helping [teachers] to adapt their instruction to the individual needs of their students" (Voss, Kunter, & Baumert, 2011, p. 953). ...
... This indicates that it will be important in our future efforts teaching about DDDM to maximize the importance of teacher data collection skills with an emphasis on identifying and collecting qualitative data. This will ensure future teachers receive the DDDM reform message more positively, making them more likely to systematically or deeply process the information thus resulting in a true conceptual change and adoption of DDDM as recommended policy, research, and field leaders (e.g., CAEP, 2014;Carlson et al., 2011;CCSSO, 2013;Wayman et al., 2006). ...
Article
The purpose of this qualitative study was to explore how pre-service teachers perceive, understand, and feel about Data-driven decision making (DDDM), as well as the impact of targeted, persuasive instruction on those constructs. According to conceptual change theory, learners sometimes hold knowledge, feelings, and beliefs that may be counterproductive to acquiring new knowledge, as a result addressing those variables is crucial to the learning process. Research on teachers suggests they often hold views that make them reluctant to learn more about or engage in DDDM (e.g.), but by tailoring instruction to address teacher concerns, they may become more open to DDDM (Airola & Dunn, 2011). Our qualitative analysis and findings indicated our sample of preservice teachers did overwhelmingly hold initially negative views of DDDM, but our persuasive instructional unit created a cognitive shift away from this negativity and towards openness to and interest in DDDM.
... The organization can support information sharing by establishing structures, mechanisms, and processes that enable knowledge transfer (Brown & Duguid, 1991;Senge, 2006;. Examples include activities that connect educators or schools around specific problems, or websites that enable educators to share practices (Cho & Wayman, 2014;Kerr, Marsh, Ikemoto, Darilek, & Barney, 2006;Wayman, Midgley, & Stringfield, 2006). Information sharing contributes to the organization by disseminating expertise to existing members and by stimulating innovation through the introduction of ideas novel to the collective (Senge, 2006;. ...
Article
In the last few decades, a focus on school accountability at the state and federal levels has created expectations for teachers to attend to data in increasingly structured ways. Although professional learning is often cited as an important facilitator of effective data use, research that focuses on the intersection of professional learning and data use is scarce. Examining teacher perceptions of data use supports, and contrasting assertions of what is desired in data-related professional learning with accounts of the ways in which this professional learning actually happens provide an avenue for exploring these issues and for building a research base that can inform the work of district and campus leaders as well as support providers.
... Based on the data team's discussions, as well as full staff input, the team's administrator and teachers should write a plan that clearly articulates how the school will use data to support school-level goals for 67. Wayman, Cho, and Johnston (2007); Wayman, Midgley, and Stringfield (2006). (2006); Wayman, Cho, and Johnston (2007). ...
Article
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As educators face increasing pressure from federal, state, and local accountability policies to improve student achievement, the use of data has become more central to how many educators evaluate their practices and monitor students’ academic progress (Knapp et al., 2006). Despite this trend, questions about how educators should use data to make instructional decisions remain mostly unanswered. In response, this guide provides a framework for using student achievement data to support instructional decision making. These decisions include, but are not limited to, how to adapt lessons or assignments in response to students’ needs, alter classroom goals or objectives, or modify student-grouping arrangements. The guide also provides recommendations for creating the organizational and technological conditions that foster effective data use. Each recommendation describes action steps for implementation, as well as suggestions for addressing obstacles that may impedeprogress. In adopting this framework, educators will be best served by implementing the recommendations in this guide together rather than individually.
... At this point, the teacher should inform the students about all the criteria to be used for evaluation so that they can continue their studies with knowledge of how they will be evaluated. It has been observed in previous studies that students with knowledge of the evaluation procedure have higher achievement levels (Wayman, Midgley & Stringfield, 2006;Wiliam, Lee, Harrison & Black, 2004). As a result, as Newfields (2006) points out, measurement and evaluation are an integral element of all educational systems, serving as a tool for determining the efficacy of educational programs and also as a mirror for instructors' performance. ...
... Collaboration helps teachers to learn how to use data from each other, and promotes a fertile exchange of ideas and strategies (Wohlstetter, Datnow, & Park, 2008). Teachers' collaboration in data teams where they focus on collective inquiry (based on data) to improve student learning can lead to increased teacher and student learning (Wayman, Midgley, & Stringfield, 2006). Furthermore, having a shared goal, a collective focus on student learning, reflective dialogue, leadership, and structured and guided activities related to practice, are also important conditions for effective professional development. ...
Article
The significance of data use for school improvement is recognized internationally. Several interventions have been developed to support schools in the use of data. However, there is a lack of research into the effects of these interventions, especially regarding student achievement. We developed a data use intervention to support teachers and school leaders in using data for school improvement. We studied the extent to which participating teams of teachers and school leaders have solved the student achievement problem they worked on. Five out of nine teams have been able to increase student achievement. We discuss these results and their implications.
... It follows that the accumulating evidence is that the knowledge and skill bases for school leaders wishing to improve schools on any scale, include an informed academic emphasis including knowledge of relevant research, the time and opportunity to plan and implement, structuring and scaffolding the whole school, leadership and monitoring its effects and detailed emphasis on classroom level instructional variables (Shen and Cooley, 2008;Wayman, et al., 2005Wayman, et al., , 2006Mandinach, et al., 2011;Park and Datnow, 2009 learning and teaching, dealing with meta-cognitive strategies and establishing structured teaching and direct instruction matter most (Witziers et al., 2003;Kirschner et al. 2006;Scheerens et al., 2007;Seidel and Shavelson, 2007;Creemers and Kyriakides, 2008;Louis and Marks, 1998;Marzano, 2003). ...
Article
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Purpose This paper argues that in a well-organised school with strong leadership and vision coupled with a concerted effort to improve the teaching performance of each teacher, student achievement can be enhanced. The purpose of the paper is to demonstrate that while macro-effect sizes such as 'whole of school' metrics are useful for school leaders in their professional development roles, there are important micro-conditions that can be uncovered in a more detailed analysis of student achievement data. Design/methodology/approach Evidence of student achievement in a variety of standardised and non-standardised assessment tasks was subjected to examination in a post-hoc, case study design. The assessment tasks were the South Australian Spelling Test Waddington Reading Test, a school-wide diagnostic writing task, teacher running records and national assessment program for literacy and numeracy (NAPLAN). Performance in selected classrooms was compared on these tests utilising a variety of parametric quantitative statistics. Findings School-based reform initiatives require external criteria on which to base decision-making. Without such criteria based on data and the capacity to interpret it, interactions in the school culture have unanticipated consequences that have the potential to neutralise school improvement strategies. Further, findings suggest that fewer but sharper and quicker data collection tools are more valuable in such teacher decision-making, but these require expertise to produce and interpret them.
... This evidence-based instructional paradigm has repeatedly been shown to help teachers effectively facilitate student learning and achievement (Carlson, Borman, & Robinson, 2011;Evans, 2009;Scheurich & Skrla, 2003;Wayman, Midgley, & Stringfield, 2006). However, school cultures are often resistant to the idea of using data to drive instruction in the US (Dunn et al., 2013a) and across the globe (Brown, Lake, & Matters, 2010;Remesal, 2011;Schildkamp & Kuiper, 2010). ...
Article
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Data-driven decision-making (DDDM) is a difficult topic to cover, but typically required, in the applied educational psychology other courses required for teacher licensure in the United States. While a growing body of literature indicates in-service teachers are resistant to DDDM and underprepared to engage in it, little has been done to understand pre-service teachers while they are still in the ideal arena in which to address resistance and subsequently build DDDM skills. The purpose of this study was to examine pre-service teachers’ affective response to the classroom-level DDDM via their concerns profile (n = 78). Participants’ concerns profile revealed that much like in-service teacher literature suggests, this sample of pre-service teachers were resistant to learning more about DDDM, believed they knew of better innovations for use, and are unlikely to use DDDM in their future classrooms. The findings provide important insight for those of us tasked with covering this topic in our educational psychology courses.
... Research on continuous improvement recognizes frequent monitoring at multiple levels of education services (Rummler & Brache, 1990). These include formative assessment of student performance; instruction delivery and treatment integrity; scrutiny of support systems to ensure the availability of resources; and data tracking of key indicators (input, process, and outcomes) (Wayman, Midgley, & Stringfield, 2006). ...
Technical Report
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Multitiered system of support (MTSS) is a framework for organizing service delivery. At the core of MTSS is the adoption and implementation of a continuum of evidence-based interventions that result in improved academic and behavioral outcomes for all students. MTSS is a data-based decision making approach based on the frequent screening of progress for all students and intervention for students who are not making adequate progress. When employed effectively, a multi-tiered approach prevents problems, and allows earlier identification and intervention when there are issues in a more cost efficient manner than traditional approaches. Most models of MTSS have three tiers. The first tier is designed around a core curriculum that addresses and meets the needs of all students. The second tier provides additional instruction for those students needing supplementary support. The third tier offers intensive and individualized services for the students for whom less intensive support has not worked. Many educational initiatives, such as Positive Behavior Interventions and Supports, Response to Intervention, Continuous Improvement Model, and Lesson Study, Differentiated Accountability, have incorporated the core elements of MTSS into their programs.
... Despite the benefits of data use, most teachers do not use data to the best effect or do not use data at all (Schildkamp and Teddlie, 2008). One promising way to increase the use of data is to set up data teams within schools (Wayman et al., 2006). Data teams can be considered as professional learning communities. ...
Article
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Purpose – In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the process of knowledge creation in data teams. These teams are composed of school leaders and teachers, who work together to improve the quality of education. They collaboratively create knowledge related to data use and to an educational problem they are studying. The paper aims to discuss these issues. Design/methodology/approach – A qualitative micro-process case study was conducted for two data teams. The modes, transitions and content of the knowledge creation process were analyzed for all data team meetings over a two-year period. In addition, all team members were interviewed twice to triangulate the findings. Findings – Results show that the knowledge creation process was cyclical across meetings, but more iterative within meetings. Furthermore, engagement in the socialization and internalization mode provided added value in this process. Finally, the SECI model clearly differentiated between team members’ processes. Team members who engaged more often in the socialization and internalization modes and displayed more personal engagement in those modes gained greater and deeper knowledge. Research limitations/implications – The SECI model is valuable for understanding how teams gain new knowledge and why they differ in those gains. Practical implications – Stimulation of active personal engagement in the socialization and internalization mode is needed. Originality/value – This is one of the first attempts to concretely observe the process of knowledge creation. It provides essential insights into what educators do in professional development contexts, and how support can best be provided.
... Vooralsnog is weinig geweten over het informatiegebruik in Vlaamse scholen en de bijdrage die dit informatiegebruik levert tot zowel het lesgeven en het leren op school als tot het schoolbeleid. Er zijn indicaties dat sommige scholen actief op zoek gaan naar evidentie om hun onderwijspraktijk en -beleid te informeren (Schildkamp & Kuyper, 2010;Wayman, 2005;Wayman, Midgley, & Stringfield, 2007). Vaak blijkt echter dat men binnen scholen niet goed weet hoe men met bepaalde vormen van informatie moet omgaan. ...
... In the absence of proper technological support, even the best-trained team will encounter barriers that continually impact the improvement of students' educational outcomes (Wayman et al., 2006). It is suggested that schools use their existing database management system, assuming it is reliable and of high quality (e.g., efficient, accessible, and comprehensive). ...
Chapter
This article describes a prevention approach designed to produce system improvement by training school-based teams to gather and respond to data to prevent antisocial behavior and delinquency. This comprehensive approach includes: (1) ensuring that the school has supportive administrative leadership, along with commitment and buy-in from faculty and staff; (2) providing high-quality needs-based professional development; (3) using academic and behavioral screening to identify at-risk youth; (4) using data systems and data-based decision-making practices; (5) using evidence-based strategies such as response to intervention or positive-behavior support; (6) integrating programs and planning into regular school operations through professional learning communities; and (7) implementing continuous program evaluation and modification. This approach combines research on best practices in delinquency prevention with the goal of increasing the capacity of school personnel to provide sustainable benefits to the organization and its students.
... During the last two decades, collaboration was identified as one of the key elements for the professional development of teachers (e.g., Avalos, 2011;Binkhorst, Handelzalts, Poortman, & van Joolingen, 2015;Borko, 2004;Van Veen, Zwart, & Meirink, 2012). Several studies show that collaboration in teams is essential for professional development with regard to data use (e.g., Datnow et al., 2013;Wayman, Midgley, & Stringfield, 2006). reported that professional development in the form of data teams shows promise. ...
Article
Data use is increasingly considered to be important for school improvement. One promising strategy for implementing data use in schools is the data team intervention. Data teams consist of teachers and members of the school leadership team, who collaboratively analyze and use data to solve an education-related problem at the school. This mixed-methods study aims at measuring the effects of working in a data team on the application of data use in ten secondary schools by using questionnaires and case study interviews. The results show that at the end of the intervention period, educators on the data teams did not apply data use more often for accountability actions, but seemed to be more aware of data use for school development and instruction. Furthermore, it seemed that the teachers made a start at applying data use for instructional actions.
... And we believe everyone in the district should have a voice in how these definitions are built. In a district such as Gibson, there is promise in intentionally engaging in various activities centered on calibration (Wayman et al., 2007; Wayman, Midgley, & Stringfield, 2006). Similar to how a mechanic might calibrate the numerous working parts of an engine to create synchronous efficiency, so can a district commit to activities that help them build shared mental models (Senge, 2006) about how data can inform their practice. ...
Article
The effective use of student data has gained increasing attention in the past 10 years. Although district leaders would like to support data use and improvement, exactly how to go about such work systemically is often unclear. Accordingly, the aim of this chapter is to illuminate the inner workings of data use throughout a mid-sized school district. In doing so, we highlight issues in how data were used and supported, and provide discussion about how districts such as this one may improve data use throughout the district.
... This external facilitator visits the data team's school every 2-3 weeks for a meeting to work on the steps for a period of 2 years. Collaboration around the use of data brings focus to the conversations and a sense of purpose, helps teachers to learn from each other how to use data, and allows for a fertile exchange of ideas and strategies (Datnow et al., 2013;Wayman, Midgley, & Stringfield, 2006;Wohlstetter, Datnow, & Park, 2008). Also, collaborative data use is more likely to contribute to teacher and student learning than individual data use (Chen, Heritage, & Lee, 2005;Means, Chen, DeBarger, & Padilla, 2011;Means, Padilla, & Gallagher, 2010;. ...
Article
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 highquality 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.
... Hence, the amount of research on data use has recently expanded. Significant differences have been found in how practitioners use data to inform their policy and practice and in the extent to which data use serves as an accelerant for educational reform and school improvement (Wayman, 2005;Wayman et al., 2007;Schildkamp & Kuyper, 2010;Verhaeghe et al., 2010). ...
Article
The contribution of data use in schools has been proven via visible changes in policy and practice in schools (instrumental effects), changes in practitioners learning or cognition (conceptual effects) and changes in opinions or attitudes regarding teaching or policy-making (symbolic effects). Nevertheless, limited research is available on the extent to which data use in schools results in the aforementioned effects and how they can be explained by data use expectations and collaboration. This paper addresses both issues by describing and explaining data use effects via a large-scale study in Flanders. Data collected from 1472 teachers indicate that, although teachers are moderately positive about the extent to which data use results in different types of effects, data use effects cannot be taken for granted. Structural Equation Modelling (SEM) shows that explicating data use expectations and data use collaboration are essential in order to facilitate data use effects in schools.
... Hence, the amount of research on data use has recently expanded. Significant differences have been found in how practitioners use data to inform their policy and practice and in the extent to which data use serves as an accelerant for educational reform and school improvement (Wayman, 2005;Wayman et al., 2007;Schildkamp & Kuyper, 2010;Verhaeghe et al., 2010). ...
Article
In recent decades, the belief has originated that data use contributes to more thought-out decisions in schools. The literature has suggested that fruitful data use is often the result of interactions among team members. However, up until now, most of the available research on data use has used ‘collaboration’ as an umbrella concept to describe very different types of interactions, without specifying the nature of collaboration nor the degree of interdependency that takes place in interactions. Therefore, the current study investigates and describes Flemish teachers’ individual, cooperative and collaborative data use. In doing so, the level of interdependency of teachers’ interactive activities (storytelling, helping,sharing, joint work) is taken into account. The results of a qualitative study with semi-structured interviews show that teachers’ data use is predominantly of individual nature and that felt interdependencies among teachers are few. The study enhances knowledge and opens the conceptual debate about teachers’ interactions in the context of data use.
... Teachers should be supported in using data effectively. Data teams have proven to be a promising way to enhance the effectiveness of data use (Earl & Katz, 2006;Schildkamp, Poortman, & Handelzalts, 2015;Wayman, Midgley, & Stringfield, 2007). This study showed that such interventions should take the impact of perceived control and instrumental attitude into account. ...
Article
Data-based decision-making has the potential to increase student achievement results. Data-based decision-making can be defined as teachers’ systematic analysis of data sources in order to study and adapt their educational practices for the purpose of maximizing learning results. Teachers must apply the findings from their data use to their personal teaching activities. Therefore, data-based decision-making may be influenced by individual teachers' psychological characteristics. The present study aimed to explore which psychological factors contribute to teachers’ data use in a Dutch primary school context. A questionnaire-based quantitative methodology was employed. We included the following psychological constructs: affective and instrumental attitudes, perceived control, social norms, self-efficacy, collective efficacy, and intentions regarding data use. Results of the path analysis showed that perceived control, instrumental attitude, and intention regarding data use all significantly influenced data use. Additionally, intention was found to be a mediator of the relation between affective attitude and data use. Interventions aimed at data-based decision-making should take these psychological factors into account to increase teachers’ implementation of data-based decision-making for instruction and, consequently, educational quality.
... Another understanding of education personnel is human resources tasked with carrying out administration, management, development, supervision, and technical services to support the delivery of educational processes in education or research units [8]. Education personnel include school principals, education unit supervisors, administrative staff, library staff, laboratory personnel, technicians, study group managers, study tutors, and cleaning staff [9]. ...
Article
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Education personnel are Human Resources who have an important role to support the performance of academics, who are obliged to provide academic services. In carrying out its duties, can not be separated from the existence of interdependence and even the relationship between one unit to another unit. This paper aims to find out the level of understanding of integrity and professionalism level of education personnel. The method in this study is exploratory study. The subjects taken were the head of subdivision at Universitas Negeri Surabaya (UNESA). Data collection techniques used are kusioner. The result of this research is that the integrity and professionalism of the education personnel at UNESA has been functionally good, but structurally there still needs to be improvement. So it is necessary to have various training courses to support the quality of the performance of all parties and it is considered important.
... Data Teams are '…groups of educators that can work and learn together as they engage in the process of using student data to examine and improve their craft' (Wayman, Midgley and Stringfi eld, 2006). They are professional learning communities using a standard data-driven procedure (Hubers et al., 2016) that facilitates community conversations and collaborations to identify and undertake the best moral formation action in a specifi c situations through an eight stages process: problem defi nition, formulation of hypotheses, data collection, data quality check, data analysis, interpretation and conclusions, and evaluation . ...
Article
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Character development requires not only high-quality curriculums, but also educators who are able to adapt programs to learners’ needs and context and staff development strategies. Big data and learning analytics strategies may improve youth character development especially in developing countries facilitating educators’ development and practical wisdom, as well as curriculum implementation’s effectiveness in countries with less knowhow in the issue. This study presents a systematic mapping literature review on the models and methods of learning analytics applied in the improvement of youth character education. Based on the literature review results, the research provides insights for future research and implementation of character education programs, and proposes a revised participatory knowledge management data-driven procedure that may facilitate educators to identify and undertake the best character formation actions in specific situations.
... However, despite the benefits associated with data use, most schools do not use data in their decision-making processes or use it ineffectively (Schildkamp & Kuiper, 2010;Schildkamp & Teddlie, 2008). Therefore, several programs have been developed to support schools in the effective use of data (Coburn & Turner, 2011;Wayman, Midgley, & Stringfield, 2006). The data team intervention ( ) is an example of such a program, and provides the context for the present study. ...
Article
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Background/Context: The data team intervention was designed to support schools in using data while developing a solution to an educational problem. The participating data team members are responsible for building collective capacity within their school for using data and implementing actions related to the improvement plan. This can be challenging, because although they have gained knowledge and experience with data use and the educational problem, their colleagues who were not on the data team have not. As a result, there is heightened risk for discontinuity between the behaviors of such colleagues and of the data team members: colleagues will not automatically use data nor implement the actions for improvement. These discontinuities are referred to as boundaries. To establish common ground with their colleagues, data team members need to act as boundary crossers by brokering their knowledge. Purpose: The present study used a process view to determine how data team members acted as boundary crossers by studying what content they had brokered, the level at which they had addressed that content, and what activities they had used to cross boundaries. Intervention: Data teams consist of six to eight educators who collaboratively learn how to use data to analyze and address an educational problem at their school. They work following a cyclical procedure. Research design: A longitudinal qualitative case study was conducted in four Dutch schools that implemented the data team intervention. Data collection and analysis: Artifacts were collected and all team members were interviewed twice. Log files, minutes of the meetings, and progress reports were used to obtain a complete picture of boundary crossing and to provide background information. A coding scheme was used in order to determine what content was brokered, the level at which this content was addressed, and the activities used to broker this content. Findings/Results: Findings illustrated that team members mainly brokered knowledge about the educational problem and data use as applied to the educational problem, rarely about data use in general. Overall, content was almost exclusively addressed at the level of awareness, indicating that only basic information was brokered. Conclusions/Recommendations: Successful boundary crossing cannot be taken for granted: team members brokered their knowledge in ways less likely to be effective. When they receive additional support for this, they are likely to increase their team’s effectiveness in building school-wide capacity for both data use and the implementation of actions related to the improvement plan.
... In data use, or the discussion and interpretation of data, the diagnosis of problems and the definition and implementation of improvement actions, researchers put a great emphasis on teacher interactions. Peer interactions can provide teachers with the support necessary to acquire the complex knowledge and skills needed to transform data into meaningful decisions and actions (Hubbard et al., 2014;Jimerson, 2014;Wayman et al., 2007). Given the context, in which changes in teaching practices sometimes require prompt action, informal data use interactions may be of particular importance for teachers. ...
Article
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In recent years, the emphasis on interaction in data use has grown because of its potential to support individual teachers. However, in practice, teachers do not appear to interact widely in their use of data, either formally or informally. To gain knowledge of how sustainable data use interactions can be facilitated, this study investigated how formal data use in teams of teachers affects the teachers’ informal interactive data use. A survey provided insight into 72 teachers’ perceptions of data use discussion, interpretation, diagnosis and action at formal team meetings. Subsequently, social network analysis of seven teacher informal data use networks revealed that teachers with more positive perceptions about formal data use become more active in their informal data use network. Within the problem diagnosis phase, this tendency is to generalize across the participating teams. The results of this study imply that, particularly to define problems and formulate actions based on pupil learning outcome data, it is necessary to ensure strong connections between teachers in formal groupings in order to affect their informal interactive behaviour.
... Research has shown that the transformation of data into information and knowledge and the translation of this knowledge into meaningful decisions requires a wide range of knowledge and skills (Mandinach & Gummer, 2016). Knowing that teachers often get stuck in this complex process, the literature has underlined that collaboration is essential in data use (Hubbard, Datnow, & Pruyn, 2014;Jimerson, 2014;Means, Chen, DeBarger, & Padilla, 2011;Wayman, Midgley, & Stringfield, 2007). The expertise of colleagues is seen as essential to succeed in effective data use. ...
Article
Teacher interactions are seen as a source for teachers’ professional development. To better understand this potential, research is needed into who is consulted in data use. Therefore, this study investigates whether Flemish teachers’ popularity in data use discussions can be attributed to formal aspects of the formal school organization, similarity among teachers, proximity and informal bonds between teachers. A multi method study combining social network analysis and interview data was designed. The results reveal that informal bonds between teachers may not be overlooked in how interactions are formed. Because the participants do not seem to choose the colleagues they interact with for data use purposefully, the potential of these interactions for their professional development is questionable. Future research should invest in examining how conscious teachers are of the knowledge and skills of their colleagues in data use and how this knowledge affects the formation of data use interactions.
... Hence, the amount of research on data use has recently expanded. Significant differences have been found in how practitioners use data to inform their policy and practice and in the extent to which data use serves as an accelerant for educational reform and school improvement (Schildkamp & Kuyper, 2010;Verhaeghe, Vanhoof, Valcke & Van Petegem, 2010Wayman, 2005;Wayman, Midgley & Stringfield, 2007). ...
Article
This research tests a hypothesized model that links collaborative data-use professional development experiences and successful use of student data to aid instruction. A serial, regression-based mediation model is tested (from professional development to self-efficacy, to positive affect, to successful use of data). Survey data from over 200 K–12 educators from across the United States were collected; the sample reflected a purposeful quota based on age and gender. The hypothesized model was supported. In all, the analyses demonstrate the importance of collaborative learning in professional development experiences related to use of student data. Professional development providers can use this knowledge to design more effective experiences. Similarly, school leaders and teacher coaches can use this knowledge to guide educators to experiences that are likely to result in the development of data-use skills to aid in classroom instruction.
Book
In an educational context where school and district performance is of increasing focus, it's essential for leaders at all levels of the educational system to focus on improving student performance. This volume zeros in on a promising set of strategies and practices for all leaders to motivate, support, and sustain learning in contemporary schools. Learning-Focused Leadership in Action explores what it means for educational leadership to be “learning-focused,” what this looks like in practice at both the school and district level, and how such leadership changes can be set in motion. Drawing on extensive case study research in schools and districts that are making progress on learning improvement, this volume explores how leaders at all levels of the educational system can productively seek to improve the quality of learning opportunities and student performance, no matter how challenging the circumstances.
Article
The use of data for educational decision making has never been more prevalent. However, teachers and school leaders need support in data use. Support can be provided by means of professional development in the form of “data teams”. This study followed the functioning of 4 data teams over a period of 2 years, applying a qualitative case study design. The findings show that data use is not a linear process, and that teams go through different feedback loops to reach higher levels of depth of inquiry. The data team procedure is a promising way of enhancing data-based decision making in schools.
Article
Data Based Decision Making (DBDM), the process of gathering, analyzing, applying, and sharing data in order to promote school improvement, has recently become a prominent process in the quest to assist students in attaining educational success and helping schools meet accountability benchmarks (Wayman, 2005; Poynton & Carey, 2006). This manuscript presents a pilot study undertaken in a Mid-Atlantic state to discern foundational understandings of DBDM by school staff. Results from the study reveal a lack of clarity on the foundational underpinnings of DBDM, as well as a lack of assessment literacy.
Article
This meta-analysis explores training teachers in the use of data, defined as any quantifiable information that helps teachers know more about their students for instructional decision-making. The questions addressed are as follows: (a) What are the features of data literacy training for kindergarten through 12th-grade teachers? (b) What are the effects of data literacy training on kindergarten through 12th-grade teacher outcomes? and (c) Do training characteristics moderate the effects of training? A comprehensive search of research conducted between 1975 and 2019 yielded 33 studies with 163 effect sizes that met inclusion criteria. Using a random effects model, findings demonstrated significant positive effects on knowledge and skills, g = .67, 95% confidence interval (CI) = [0.40, 0.93], and beliefs, g = .48, 95% CI = [0.17, 0.79]. A collaborative training format significantly and positively moderated effects. Implications for teacher trainings and the design of future research are discussed.
Formative feedback systems provide rich opportunities for teachers to reflect on and adjust instruction to learn about and better meet student needs. However, data tools that support teachers' practice in the classroom are often left out of school data system designs. Via a Design-Based Research approach we collaborated with 11 elementary school teachers, leaders, and staff to examine educators' student data collection practices and iteratively design tools to support classroom teaching and learning within the school's and district's Data-Driven Instructional System. This paper illustrates how our hand-held data tool design supports and extends teachers' practice.
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In this study, teacher education students' concerns, sense of efficacy, and anxiety related to the future use of data to drive educational decision-making were explored. In alignment with prior research with practicing teachers, this sample of pre-service teachers reported concerns (thoughts, preoccupations, and feelings) that indicate they are not interested in engaging in data driven decision-making (DDDM). Moreover, they had a low sense of efficacy for DDDM and high levels of anxiety for DDDM; further indicating that they are unlikely to adopt DDDM practices. We explain these results, but we go further and propose a new way of talking about data that may mitigate some of these concerns. Specifically, we propose a new paradigm for evidence-based practice in which teacher experience and intuition are deemed of equal import with data. We propose anchored judgment as an integrated decision-making model in which the intersection of teacher experience, teacher intuition, and classroom data creates the context for optimal instructional decision-making. This model is based on established research about effective decision-making in psychology, medicine, and business, and may help support the international educational mandate for DDDM.
Article
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School development by systematic data use requires schools to be provided with informationrich environments. However, providing school performance feedback does not guarantee a successful use. Limited data literacy competences of the users are one of the main stumbling blocks. Support initiatives were developed and evaluated to overcome this shortcoming. In a randomized field study, the effects of two experimental conditions related to inservice and onservice education and training (INSET and ONSET) are compared with a control group. This study examines the relationship between data-literacy competences, support provisions for data interpretation, actual usage of the feedback, and school improvements effects. The research was based on in-depth interviews involving 18 primary school principals. The results of a case ordered predictor-outcome meta-matrix do not only reveal difficulties in handling the information but also incongruences in attitude towards feedback use between school principals and teachers. The ONSET-condition led to the most optimal results promoting a tailored support approach.
Chapter
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English schools arguably have more sophisticated datasets at their disposal than any other jurisdiction in the world. These datasets gather the academic outcomes and a range of demographic data for all young people in England from age 4 to 16. They are used to provide schools with a wide range of school- and student-level measures related to academic attainment and value-added progress to inform school self-evaluation. This chapter discusses some of the ways these data are utilised in English secondary schools in order to inform school improvement. It presents findings from a nationwide survey of teachers on their self-reported use of, and attitudes toward, student- and school-level attainment and progress data. The purpose of the survey was to investigate the extent to which teachers in schools use these data; whether they are satisfied with their level of understanding of data; and the frequency of training they feel they require to both interpret and utilise it. The survey found that levels of data use in English secondary schools are high and that satisfaction with data use is linked to teachers’ level of responsibility, with Deputy and Assistant School Principals reporting the most extensive data use and greatest satisfaction with their level of data use. The frequency of training in data use appears to be positively linked to teachers’ self-reported level of understanding of data. The results suggest that teachers with no formal leadership roles require training on at least annual basis to significantly improve their understanding of data. Teachers strongly feel that data analysis and interpretation should be delegated to a greater extent throughout the staff in the school. There is also a perception that Heads of Department should take the leading role in data analysis and interpretation, more so than is the case in many schools where analysis and interpretation of data is often the province of more senior members of the school leadership team.
Chapter
Over the past decade, school districts in the United States have wrestled with the implementation of the No Child Left Behind Act (NCLB). This policy mandated improved student achievement for all, in part through a more widespread and sophisticated use of student data. The law placed strong incentives for districts to parse individual and collective student performance and use it to improve instruction. Despite the law’s intention and expectation, districts are still struggling with transforming its data-driven promises into reality. This struggle derives largely from the fact that, although NCLB set high expectations regarding data use, it offered districts little guidance as to how they should actually use data. Furthermore, other than the test scores, disaggregated by race, socioeconomic status, and limited English proficiency, the policy is very vague about what data are to be used to improve instruction. The writers of NCLB intentionally deferred to schools the internal processes of using data to inform instruction, carrying the implicit assumption that once districts were given this imperative, they would have (or could quickly create) the know-how to improve performance. This has proved an ambitious assumption, as research on data use has illuminated the substantial technological, pedagogical, and cultural challenges to educational data use (Datnow et al. 2007; Ingram et al. 2004; Means et al. 2010; Wayman et al. (in 2010a; Young 2006). Consequently, there remains a substantial gap between NCLB policy and its actual practice. In this chapter, we explore the relationship between NCLB and data use. Our examination will be guided by Cohen and Moffitt’s (2009) framework that examined the entire history of the Elementary and Secondary Education Act (ESEA) of 1965 (of which NCLB was itself a reauthorization). Cohen and Moffit’s (2009) framework contains four parts: (1) policy aims and ambiguities, (2) policy instruments, (3) capabilities of policy, and (4) policy environment. Cohen and Moffitt (2009) used their framework to broadly examine the NCLB policy. In this chapter, we will extend that work by using their framework to perform a specific analysis of the relationship between NCLB and the effective use of data. We will do this analysis in two stages: First, we will view current data use research through each of the framework’s four sections to describe the existing relationship between NCLB and school data use. Second, we will use the framework and current research to describe a systemic approach that would enable schools to effectively use data under NCLB. In doing so, we will argue that although NCLB may be an imperfect policy with plenty of room for improvement, there is nothing inherent to the law that would prevent a district from utilizing principles of good data use.
Chapter
Prompted in large part by federal and state mandates requiring the collection and reporting of a variety of student data, student data systems have become an increasingly important component of educational reform efforts. Recent technological advancements have led to the proliferation of data systems facilitating the organization, access, and querying of large volumes of student data. This article reviews current evidence on the types and features of the student data systems available to educators and discusses key determinants of effective use of data systems in the educational context. Finally, critical themes shaping the future directions of student data systems are discussed. © 2010 J C Wayman, V Cho, and M P Richards Published by null All rights reserved.
Book
This exciting new book is for school leaders who are interested in transforming their school and district practices. Discussing issues that impact students, teachers within their classrooms, and the larger school community, Formative Assessment Leadership explores how leaders can implement effective professional development and positive change in their schools. Breaking down formative assessment into manageable, understandable parts, the authors provide: • An exploration of what formative data-based decision making looks like • Scaffolding that enables school leaders to effectively integrate processes into their own school structure • Discussion of potential barriers to success and how to overcome these challenges • Practical examples that help ground the formative assessment leadership concepts • A range of worksheets and templates to help implement formative assessment leadership in your schools.
Thesis
School performance feedback systems (SPFS) are specifically designed for providing schools with confidential information on their functioning. They follow the trend of data-driven schools improvement by fulfilling the need of schools of accessible information-rich environments. Several local initiatives have been developed and implemented worldwide. However, little is known yet on the impact of these systems on the schools’ functioning and performance. Furthermore, no detailed studies on SFPS user comprehension have been performed. In this dissertation, five studies have been reported and discussed. In the second chapter, a general introduction in characteristics of SPFSs is provided. A framework for characteristics of SPFSs has been applied to five SPFSs worldwide. This descriptive and analytic study illustrates both the wide variety in features but also provides a discussion on the rationales for making choices in feedback design. Following on a framework of SPFS characteristics, Chapter 3 is devoted to a framework for SPFS use. Parts of this framework are used in further studies described in the successive chapters. Based on the Visscher framework, both influencing factors, SPF use and the resulting effects have been analyzed in the context of the School Feedback Project by examining users’ perceptions. Intrigued by the call for research on feedback interpretability, the fourth chapter focuses on the representation and interpretation of central SPF concepts. Alternatives in representation modes of value added and learning gain have been examined, by integration of literature on graphical data representation. Particular attention has been paid to misconceptions and interpretation difficulties. The Chapters 5 and 6 tackle two crucial variables in SPF use: data literacy competences and support in using SPF. By reporting the results of both a quantitative (Chapter 5) and a qualitative (Chapter 6) study, the outcomes of a field experiment with participants of the School Feedback Project results in recommendations for effective support in using SPF. A final chapter enumerates the key finding from all studies by answering the research questions. A complementary overall discussion and general conclusion conclude this dissertation.
Article
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Accountability policies assume that educators will use student data to improve student learning, but data use in practice has turned out to be harder than theorized. The purpose of this paper was to examine how science teachers in grades 5-8 used data in their classrooms. Utilizing sensemaking theory, we found that teachers decided how to use data based on district and school policies and expectations around assessment and data use, balancing those messages with their own understandings of science education. In practice, this led to the privileging of certain kinds of assessment and data use at the expense of others.
Article
The use of data to improve learning, instruction and student achievement has been a popular educational intervention in countries across the globe. Yet, with all the available data generated by standardized tests or through the course of instruction, systematic use of data as a lever for school change still remains elusive. This article is an introduction to a special issue that explores the possibilities of digital media and technology to support data-informed teaching and learning. This paper introduces this topic by highlighting the need for investigating more closely how data are used to support learning in practice and briefly highlights some of the prevailing issues and opportunities related to the potential productive uses of data.
Article
Formative assessment and instruction have turned out to be a remarkably difficult practice to implement in schools. Fundamental to this challenge is the fact that formative assessment is inherently a local, concrete instructional practice, as is the work or transforming assessment data into pedagogically responsive action. This paper explores teachers’ thinking in their uses of a new data analysis tool to enact evidence-based instructional practices. Furthermore, this paper describes the possible relationships between teachers’ existing beliefs, expertise, and routines and their construction of new practices. We show how current theories of assessment do not account for important aspects of formative instruction in practice and discuss the implications for teacher learning.
Chapter
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Mit der Einführung neuer Steuerungsmaßnahmen wird gegenwärtig bundesländerübergreifend der Versuch unternommen, ausgehend von der Ebene der einzelnen Schule, die Qualität von Schulen zu verbessern. So sollen Schulen zunehmend mehr Eigenverantwortung für eine qualitätsorientierte Entwicklung übernehmen. Zugleich richtet die Bildungsverwaltung ihren Blick verstärkt auf das Setzen von Standards und die Ergebniskontrolle. Mit Blick auf die zu erreichenden Standards sind die Schulleitungen gehalten, neue und zunehmend evidenzbasierte Managementaufgaben zu übernehmen, insbesondere in Bezug auf Personalführung wie auch Schul- und Unterrichtsentwicklung. Vor diesem Hintergrund erfolgt mit dem hier vorliegenden Beitrag eine Aufarbeitung der unterschiedlichen Forschungszugänge zum datengestützten Schulleitungshandeln sowie zu den darin berichteten empirischen Befunden. Dies ermöglicht diff erenzierte Betrachtungen von Beziehungs- und Zusammenhangsstrukturen zwischen neuen Steuerungsansätzen einerseits und dem verstärkt eingeforderten datengestützten Schulleitungshandeln andererseits. Abschließend werden weiterführende Überlegungen zur Neujustierung der wissenschaft lichen Betrachtung des Verhältnisses von neuer Steuerung und Schulleitungshandeln angestellt sowie mögliche, daraus resultierende Forschungsdesiderate aufgezeigt.
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The concept of data-informed decision making (DIDM), a term used interchangeably with data-driven decision making (DDDM) and data-based decision making (DBDM), is relatively new to Irish education and the school planning process. This research sought to clarify what data principals use and how they use that information for school improvement considering new school self-evaluation requirements. The paper begins by charting the rise internationally of data use in school planning, decision making and accountability. It proceeds to describe the policy context in this area in Ireland and then reports recent research with school leaders around how data is collected and used in their work. Although the paper focusses on Ireland, it is tentatively suggested that school leaders, teachers and policymakers in other countries, and there are many, which have come late to the expectation that school improvement and accountability should be heavily data-informed may find the efforts of Irish principals in this regard of interest.
Chapter
Schools these days are confronted with a lot of data, which they have to transform into information to be used for school improvement. However, research shows that most teachers do not use data properly, or do not use data at all. In the Netherlands, a data team intervention was developed and piloted to support schools in the use of data. A data team is a team, consisting of 4–6 teachers, a data expert, an (assistant) school leader, and a researcher, who work together to solve a certain educational problem, following a structured approach. This approach involves: defining the problem, coming up with hypotheses concerning what causes the problem, collecting data to test the hypotheses, analyzing and interpreting data, drawing conclusions, and implementing measures to improve education. This study focuses on the following research questions: How do these teams function? Which factors influence the work of these data teams? What are the effects of these data teams? The results show the data team intervention led to an increase in effective data use, changes in classroom instruction, and to school improvement (e.g., a significant increase in mathematic achievement). Due to the small sample of this study, the increase in student achievement cannot directly be linked to the work of the data teams, but it is likely that the use of data contributed to these effects.
Article
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Genel çerçevesini eylem araştırması metodolojisinin oluşturduğu bu çalışmada, öğretmen adaylarının ölçme-değerlendirme hakkındaki düşüncelerinin, tutumlarının ve becerilerinin geliştirilmesi hedeflenmiştir. Ölçme-değerlendirme dersine devam eden üçüncü sınıf fen bilimleri öğretmen adaylarının ölçme-değerlendirmeye ilişkin düşünce, tutum ve becerilerinin mikro-öğretim uygulamaları yoluyla geliştirilmesine odaklanılmıştır. Karma araştırma yöntemi kullanılan çalışmada, hem nicel hem de nitel veri toplama araçları uygulanmıştır. Nicel veri toplama araçları kullanılarak, mikro-öğretim uygulaması yapan fen bilimleri öğretmen adaylarının (deney grubu) ölçme-değerlendirme alanındaki gelişimleri, geleneksel yolla ölçme-değerlendirme dersini almış fen bilimleri öğretmeni adaylarıyla (kontrol grubu) karşılaştırılarak incelenmiştir. Kontrol ve deney grubunda bulunan öğretmen adaylarının ölçme ve değerlendirme okuryazarlık düzeyleri, ölçme-değerlendirmeye ilişkin düşünceleri, ölçme ve değerlendirmeye ilişkin tutumları istatistiksel olarak karşılaştırılmıştır. Nicel verilerin analizinde parametrik istatistiklerden t-testi, ANOVA, MANOVA kullanılmıştır. Çalışmada öğretmen adayları tarafından mikro-öğretim öncesi ve mikroöğretim sonrası hazırlanan ders planı taslağı, ölçme-değerlendirme rubriği, öz-değerlendirme formu, grup değerlendirme formu, açık uçlu sorular formu ve odak grup görüşme gibi çeşitli nitel veri toplama araçları kullanılarak öğretmen adaylarının mikro-öğretim uygulamaları sürecindeki gelişimleri incelenmiştir. Nitel verilerin analizinde ise içerik analizi ve doküman analizi yöntemleri kullanılmıştır. Çalışmanın ortaya koyduğu sonuçlar, mikro-öğretim yoluyla gerçekleştirilen ölçme değerlendirme uygulamalarının, öğretmen adaylarının ölçmedeğerlendirme alanındaki bilgi, beceri ve tutumlarına olumlu yönde bir katkı sağladığına işaret etmiştir.
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Internationally, there has been a policy push for using student data for instruction. Yet, research has noted few examples of actually understanding how this data-use practice takes place. This study presents a case of an instructional data team making sense of student data. The study shares data to show how teachers’ process for using data to inform their instructional choices is an interpretive one. The study also highlights the fact that, to make sense of test data that are often incapable of capturing backstories of students’ work in school, teachers draw upon informal data that they glean from their observations of and interactions with students. The collective data in the heads of teachers emerge through data team discussions. We conclude with implications for policy, professional learning, and teacher development.
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Data usage-related reforms lead schools to different practices in terms of management. The conceptualization of data usage and data-based management will ensure that robust steps are taken for the future in terms of reforms made or to be made in the name of education. In this direction, this study is developed as a qualitative case study to determine the opinions of school administrators for data-based management on a school basis. The working group consists of 12 administrators, seven of whom are women and five of whom are men, who work in private and public schools. Purposive sampling method was used to determine the participants. Data from the working group were collected through the semi-structured interview form and themes were created by analyzing in NVino 11. In line with the established themes, the opinions of the administrators were also evaluated. According to the research findings, administrators mostly use data on "e-Okul" and "MEBBIS" modules. Administrators use the said data in decision-making processes, to meet the demands of senior management units, to work with stakeholders, to increase student success. While the administrators stated that there is not enough support or software products to use the data more effectively in the management process, they stated that there is a need for a series of trainings and that these trainings should be in the form of hands-on workshops. They also stated that there should be auxiliary personnel and support services for data analysis. Administrators who reported their problems with time management in accessing data stated that they were not proficient in data analysis and had problems with the motivation, quality, and workload of the personnel. Introduction Technology-related developments bring with it digitalization. According to Gobble (2018), digitalization is a simple process of converting analogue information to digital.
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