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The complicated micro structures, macro emergences and dynamic evolutions in a supply chain network pose challenges to decision making for solving operational problems for the network's performance improvement. Most of these problems are complicated since various factors and their complicated relationships are involved. Success of this decision making relies on efficient business analytics based on the comprehensive and multi-dimensional data related to the static attributes and dynamic operations of the network. To confront the challenges, this paper proposes to explore a methodology of data-driven decision making for supply chain networks. In this methodology, a data-granularity model of a supply chain network is developed to standardize the data form for decision making. A four-dimensional-flow model of a supply chain network is proposed to satisfy the data requirements for decision making that are defined in the data-granularity model. Agent-based computational experiment is employed to support the generation of a comprehensive operational dataset of a supply chain network and to verify the solutions generated in decision making. Integrating these models, a data-driven decision-making framework for supply chain networks is proposed. In the framework, a new decision-making mode of “problem definition - business analytics - solution verification - parameter adjustment” is proposed. Oriented towards domain knowledge in supply chain networks, two approaches of business analytics—mapping analysis and correlation analysis—are presented. Finally, a case of a five-echelon manufacturing supply chain network is studied with the methodology. The findings indicate that the proposed methodology, models and framework are effective in supporting the data-centric decision making for solving complicated operational problems in supply chain networks and provide the networks’ managers or member enterprises with an effective tool to generate unbiased and efficient decisions for the networks’ performance improvement. [ABSTRACT FROM AUTHOR]

Copyright of Chinese University Education Journal is the property of Chinese University of Hong Kong and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current preservice teacher education practice related to data use has been deemed inadequate, in that it is unevenly distributed and often superficial. In response, this article describes a course-based classroom assessment data-literacy experience for preservice elementary teachers. Grounded in extant theory and research concerning data literacy and data use and interventions to promote data use processes among educators, the 6-hour experience involves preservice teacher scoring of teacher-developed assessments, and analyzing, interpreting, and making decisions based on these data. The pedagogical strategy’s design (objectives, materials, and activities) and implementation process are illustrated for the reader. It is hoped that members of the teacher education community will adopt or adapt the strategy for implementation with preservice teachers within other programs and institutions. In doing so, teacher educators can provide preservice teachers with rich, in-depth experiences by which to equip them for this salient but difficult facet of their professional practice. [ABSTRACT FROM PUBLISHER]

Sensors are gaining a ubiquitous status over many application domains with regard to enabling data‐driven decision making and smart functionality. Integration of sensors with cloud‐based data storage and analysis has the potential to significantly enhance the efficiencies, resilience and adaptability of managing smart infrastructure management. In this context, a standout application is smart grid, which provides an electricity delivery service with the ability to monitor, protect and optimize various operations of its connected elements from service provider to consumer. An ability to read and manage smart grid measurements remotely using wireless sensors is an important advantage that allows the grid operators to balance loads effectively and enable on‐demand services for various entities. However, the adoption of cloud infrastructure to manage sensor data in a smart grid poses significant risks to data security and consumers privacy. The data maybe exposed to malicious or unwarranted parties, with the potential for various security attacks that may impact data integrity, availability and accountability. We propose a secure and practical billing model using homomorphic encryption within a cloud‐based data processing framework. Moving billing management into the cloud securely, with on‐demand data retrieval and statistical computations is a major strength of the proposed framework. [ABSTRACT FROM AUTHOR]

Despite an increasing focus on issues of accountability, assessment, and data-driven decision making (DDDM) within the postsecondary context, assumptions regarding their value remain largely untested. The current study uses empirical data from 114 senior administrators and 8,847 students at 57 institutions in five states to examine the extent to which institutional assessment and data-driven decision making shape the experiences of first-year students. Nearly all these schools regularly collect some form of assessment data, and more than half report using assessment data to inform decision making. However, the institutional adoption of policies related to the collection of assessment data or the application of data-driven decision making appears to have no relationship with student experiences or outcomes in the first year of college. Thus, findings from the current study are consistent with the small, but growing, body of literature questioning the effectiveness of accountability and assessment policies in higher education. [ABSTRACT FROM AUTHOR]

二十一世紀在國際評比的推波助瀾下，學生學習成效再度成為各國教改的重要議題，而資料驅動決定（data-driven decision making）在原本講求績效責任的教育脈絡中，更成為協助學校謀求改進發展以提升學生學習的重要作法。惟國內對此尚屬陌生，故為拓展國內資料運用（data use）之學校實務與學術探究，本研究試圖透過大學與高中之協作計畫，探討資料運用在一所個案高中現場之操作情形。在採用觀察、訪談與文件分析方法蒐集本個案研究所需資料後，發現個案學校經由不同資料之結合，解讀與診斷學校問題，進而研訂改進之行動方案。其中也呈顯了學校成員從懷疑到覺得受用的塑義（sensemaking）過程。而校長領導、成員的時間、能力與認同度是影響學校資料運用之關鍵性條件。 Propelled by the international comparison of student achievement in the twenty first century, student learning caught the world’s attention and became critical to the agenda of education reform. Data-driven decision making plays a crucial role in assisting school improvement and enhancing student achievement; however, data use remains a relatively new concept for audiences in Taiwan. To enrich school practices and academic data use, this case study explored how a high school used data through collaboration with a university. Observation, interview, and document analysis were employed to collect data. The results indicated that the school had integrated different data to diagnose its weaknesses and problems. An improvement action plan was developed based on the data. It was also observed that school members who were initially suspicious gradually changed their attitude, finding the use of data to be meaningful in their sensemaking process. Effective principal leadership, as well as the time, capacity, and willingness of school members, were critical conditions for the successful use of data within schools.

To meet the constantly changing needs of schools and diverse learners, educators must frequently monitor student learning, revise curricula, and improve instruction. Consequently, it is critical that careful analyses of student performance data are ongoing components of curriculum decision-making processes. The primary purpose of this study is to demonstrate the application of panel study longitudinal analysis techniques to inform curricula and instructional improvement efforts using actual data retrieved from state accountability reports of a Texas school district. [ABSTRACT FROM AUTHOR]

Purpose: New teacher effectiveness measures have the potential to influence how principals hire teachers as they provide new and richer information about candidates to a traditionally information-poor process. This article examines how the hiring process is changing as a result of teacher evaluation reforms. Research Methods: Data come from interviews with more than 100 central office personnel and 76 principals in six urban school districts and two charter management organizations. These sites were systematically sampled based on the amount of time and resources devoted to creating data systems and implementing processes that allow principals access to teacher effectiveness data. In addition to the fieldwork, we also surveyed all principals in six of the eight systems. A total of 795 principals responded to the survey, with an overall response rate of 85%. Findings: The findings suggest that while teacher effectiveness data can be used to inform hiring decisions there is variation in how and the extent to which principals use these measures in hiring. This variation is explained by central office practices as they mediated how principals approached teacher effectiveness data in the hiring process, as well as individual principal characteristics such as principal knowledge and skills, perceived validity of data, and social capital. Implications for Research and Practice: Our results demonstrate ways in which school systems and principals are incorporating teacher effectiveness data into the hiring process. Both principal preparation programs and school systems should focus on ensuring that principals have the skills and resources to use data for human capital decisions. [ABSTRACT FROM AUTHOR]

Education systems internationally are implementing data use initiatives with the aim of improving student learning. As teachers are key actors in the implementation of such initiatives, this study explored pre-service teachers' ( N = 142) opportunities to learn how to use data during pre-service education, specifically student teaching. The study also examined how student teacher data use varies by school level and relevant coursework and in-school learning experiences. Findings suggest that student teaching might plausibly serve as a source of experiential learning relative to data use, especially at the elementary level, and regardless of pre-service teachers' prior or concurrent teacher education experiences. [ABSTRACT FROM AUTHOR]

This article examines school-level responses to subgroup accountability pressure through an ethnographic case study of a school cited for failing to make adequate yearly progress for student subgroups. Concerns about the calculations and measures used to derive the citation and reservations about acting on accountability data delegitimized the citation and rendered the identified subgroups irrelevant to daily practice. Under district guidance, compliance with subgroup accountability was independent of the school's internal efforts to promote equity. [ABSTRACT FROM AUTHOR]

The most widely used technique for psychiatric diagnosis is a contemporary manual-based procedure based on prevailing culture-bound data for the classification of mental disorders. However, it has several inherent faults, including the misdiagnosis of complex patient phenomena and others. A potential mental patient from a minority culture could present with atypical symptoms that would be missed by the standard approach. Using the three-way decisions (3WD) as a framework, we propose a unified model that represents the subjective approach (CSA) of clinicians (psychiatrists and psychologists) consisting of three components: qualitative analysis, quantitative analysis, and evaluation-based analysis. The results of the qualitative and quantitative investigation are a classification list and a set of numerical weights based on malady severity levels according to the clinician's highest level of assumptions. Moreover, we construct a comparative classification of diseases into three categories with varying levels of importance; a three-way evaluation-based model is utilized in this study in order to better comprehend and communicate these results. This proposed method enables clinicians to consider identical data-driven individual behavioral symptoms of patients to be integrated with the current manual-based process as a complementary diagnostic instrument to improve the accuracy of mental disorder diagnosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Sourav, Yang and Zhang.)

The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the 'Europe Regional Chapter of the International Society of Exposure Science' (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data., Competing Interests: Author AP is employed by esqLABS GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zare Jeddi, Galea, Viegas, Fantke, Louro, Theunis, Govarts, Denys, Fillol, Rambaud, Kolossa-Gehring, Santonen, van der Voet, Ghosh, Costa, Teixeira, Verhagen, Duca, Van Nieuwenhuyse, Jones, Sams, Sepai, Tranfo, Bakker, Palmen, van Klaveren, Scheepers, Paini, Canova, von Goetz, Katsonouri, Karakitsios, Sarigiannis, Bessems, Machera, Harrad and Hopf.)

This study examines the perceptions of data-driven decision making (DDDM) activities and culture in organizations driven by a social mission. Analysis of survey information from multiple stakeholders in each of eight social enterprises highlights the wide divergence in views of DDDM. Within an organization, managerial and nonmanagerial staff working for the organization and staff from a prominent funder all expressed different perceptions of the same organization’s DDDM activities and culture. Study findings also provide insights into how to improve an organization’s capacity to build and use performance management systems, which include building a common understanding about what activities are—or are not—being undertaken. Finally, findings provide insights about structuring research on DDDM, which indicate that information from only one respondent in an organization or only one organization might not be reliable or generalizable. [ABSTRACT FROM AUTHOR]

This article provides a synthesis of the articles found in the special issue on data use. The synthetic piece contextualizes how the articles contribute to the knowledge base of how teachers use data. It synthesizes the findings by identifying key common themes. It then describes gaps in the current knowledge base and identifies the requisite steps needed to address those gaps. [ABSTRACT FROM AUTHOR]

This article reports on the evolution of a conceptual framework for a construct called data literacy for teachers. Data use has become an emphasis in education but few educators have received sufficient training or preparation pertaining to data literacy skills. This article lays out the framework, identifying the specific knowledge, skills, and dispositions teachers need to use data effectively and responsibly. It concludes with a call to schools of education and teacher preparation programs to begin to integrate data literacy into curricula and practical experiences. [ABSTRACT FROM AUTHOR]

The multidimensionality of students’ data and the limitations of the currently-used data management tools in higher education institutions (HEIs) have been identified as causes of constrained decision-making process in the domain. This, therefore, necessitates a pre-design study for the HEI students’ data-focused InfoVis. The objectives of this study are to identify the prevailing practices of HEI data management, the data analytics methods that are generally employed by HEI data analysts and the comprehensive dimensions that are related to HEI students and adequate for conveying the domain explicit knowledge preferences. A mixed method approach with survey questionnaire and interview as data collection methods, is used. The contributions of this study, among others, are: (i) identification of the pattern and relationship of the domains explicit knowledge preferences; and (ii) the elicitation and description of students’ data dimensions. These are expected to form the basis for the choice and implementation of the content delivery techniques in designing the domain-focused InfoVis. Our future works therefore entail developing the HEI InfoVis conceptual framework, designing the HEI students’ data-focused InfoVis and conducting its users’ experimental evaluation.

As city-level modal splits are outcomes of city functions, it is essential to understand whether and how city attributes affect modal splits to derive a modal shift toward low-emission travel modes and sustainable mobility in cities. This study elucidates this relationship between modal splits and city attributes in 46 cities worldwide, proposing a two-step data mining framework. First, using the K-Means method, we classify cities into private-vehicle-, public-transit-, and bicycle-dominant groups based on their modal splits. Second, we categorize city attributes into environmental, socio-demographic, and transportation planning factors and quantify their interlocked impacts on cities' modal splits via the decision tree method. We observe that the socio-demographic factor has the highest impact on determining the cities' modal splits. In addition, high population density and employment rate are positively associated with low-emission travel modes. High gasoline tax and low public transit and taxi fares often make people reconsider possessing private vehicles. On the other hand, extreme weather conditions (e.g., hot temperatures) can prevent bicycle usage. Our contribution expands the impact of introduced city planning and policies for modal shifts toward a real-world paradigm and we present implications of the proposed framework in developing practical modal shift strategies. • Propose a novel data-mining framework to explain the city-level modal split. • Investigate environmental, socio-demographic, and planning variables for 46 cities. • Socio-demographic variables are the most important in determining the modal split. • Infrastructures and policies for a specific mode can affect demand for other modes. • Observe a nonlinearity of the influence of elderly rate on modal split. [ABSTRACT FROM AUTHOR]

This is a three-part essay featuring six teacher educators and one classroom teacher researcher. Part one describes faculty efforts to build curriculum for teacher research, scaffold the research process, and analyze outcomes. Part two shares one teacher researcher's experience using an equity audit tool in several contexts: her teaching practice, in collegial decision making, and as a guide for her teacher research projects. In part three, the authors reflect on the value of the equity audit. [ABSTRACT FROM PUBLISHER]

Large-scale efforts to improve student achievement have increasingly encouraged principals to use student data to inform their decision-making and change initiatives in schools. However, research on the nature and best practices of such data use remains very thin. The aim of this study was to develop an incipient theoretical model to understand school leaders' practices in the effective use of data to lead schools. The study, conducted in the state of Alabama in the USA, provides evidence to show that data-driven school leadership (DDSL) is an important feature of today's principalship. Scale analysis indicates a high reliability of the measure of our model. Confirmatory factor analysis was used to refine the model until a good fit was achieved. The refined model can be used to better understand and measure school leaders' DDSL and can be used to inform leadership preparation in this regard. [ABSTRACT FROM AUTHOR]

Copyright of School Administrators is the property of School Administration Research Association, R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Supporting teachers’ use of data has become a large part of educational leaders’ instructional leadership. Drawing on sensemaking theory, we explore how features of data and teachers’ perceptions of them may matter when teachers consider student learning data. Design: The article draws on a 1-year, comparative case study of five low-income, high needs middle schools in three districts. Data sources include interviews with district leaders (n = 13); school administrators, coaches, and case study teachers (n = 73); focus groups (n = 6) with non–case study teachers (n = 24); observations (n = 20); web-based activity logs; and document review. Findings: Different forms of data lead to a wide range of instructional responses. State assessments, used largely at the beginning of the school year, aided teachers in grouping their students but did little to promote change in instructional delivery. District benchmark data, which teachers did not generally find useful, were associated with reteaching and retesting content, creating small groups, and having students reflect on their data, usually without a shift in pedagogy. Data from common grade assessments—valued for their closeness to instruction as well as their predictive information for future success on state assessments—were most often tied to regrouping or reteaching standards not yet mastered by students, but there was some promise of change in pedagogy. Classroom assessment results and student work, identified by teachers as very useful, were proportionately most often linked to changes in instructional delivery. Implications: We offer implications for leadership preparation and practice, education policy, and future research on data use. [ABSTRACT FROM AUTHOR]

Professional learning communities (PLCs) have become a popular strategy in various forms (e.g., data teams, grade-level teams) and with various champions (e.g., district leaders, university researchers, teacher advocates). Although well-implemented PLCs have been shown to distribute leadership, the tension between democratic inquiry processes and the demands of accountability remain understudied. This study asks how teacher inquiry is situated within conflicting school priorities and the impact of competing power structures on PLCs through a case study of a teacher inquiry initiative at 6 urban elementary and middle schools in the US. Data were collected over 3 years of PLC implementation. District officials, principals, reform coaches, and teachers at each setting participated in more than 300 interviews, regular site visits, and annual teacher surveys. The influences of power structures – such as district mandates, accountability pressures, and principal leadership – emerged as strong themes in teacher narratives of what it means to “do” inquiry in their PLCs. [ABSTRACT FROM AUTHOR]

The multidimensionality of students' data and the limitations of the currently-used data management tools in higher education institutions (HEIs) have been identified as causes of constrained decision-making process in the domain. This, therefore, necessitates a pre-design study for the HEI students' datafocused InfoVis. The objectives of this study are to identify the prevailing practices of HEI data management, the data analytics methods that are generally employed by HEI data analysts and the comprehensive dimensions that are related to HEI students and adequate for conveying the domain explicit knowledge preferences. A mixed method approach with survey questionnaire and interview as data collection methods, is used. The contributions of this study, among others, are: (i) identification of the pattern and relationship of the domains explicit knowledge preferences; and (ii) the elicitation and description of students' data dimensions. These are expected to form the basis for the choice and implementation of the content delivery techniques in designing the domain-focused InfoVis. Our future works therefore entail developing the HEI InfoVis conceptual framework, designing the HEI students' data-focused InfoVis and conducting its users' experimental evaluation. [ABSTRACT FROM AUTHOR]

Despite a growing body of research on data use in education, there has been relatively little focus on the role of students. This article begins to fill this gap by exploring teacher and administrator reports on engaging students in data use at six middle schools. Even though teachers expressed a belief that involving students in data use would motivate students, they often enacted potentially demotivating, performance-oriented classroom structures: sharing data publically, comparing results with others, focusing on status, and providing limited feedback/support on how to close gaps in knowledge. School and district conditions and accountability policies shaped these classroom practices. In some cases, these contextual factors pressed teachers to focus on performance; in others, it buffered them, allowing for a greater emphasis on individual student learning. The authors contribute a theoretically driven, motivational perspective on data use and a cautionary tale of the “trickle-down” effects of accountability policy on students. [ABSTRACT FROM AUTHOR]

Data-driven decision making continues to be a growing educational reform initiative across the globe. The effective use of data requires that teachers develop the knowledge and skills to analyze and use data to improve instruction. The purpose of this article is to examine teachers' capacity for and beliefs about data use. These issues are examined through a review of research in the past decade. We find that teachers' beliefs about and capacity for data use are often not connected within the literature or in practice, but we argue they are the heart of the connection between data and instructional change. Teachers' capacity to use data and their beliefs about data use are shaped within their professional communities, in training sessions, and in their interactions with coaches, consultants, and principals. However, efforts to develop teachers' capacity for data use often fall short of their goals. Correspondingly, teachers have varied beliefs about data use, and some feel they lack the ability to use data to inform instruction. In order to be more successful, capacity building should directly address teachers' beliefs, and data use must be decoupled from external accountability demands and involve a variety of information on student learning. [ABSTRACT FROM AUTHOR]

As in international schooling contexts, talk about data-driven practice has become ubiquitous in schooling dialogues in the USA, and with the pending reauthorization of the No Child Left Behind Act (the main driver of increased data use in American schools), educators in the USA should expect even greater calls for formalized data use. Yet, the field lacks readily accessible tools that allow school district leaders and evaluators to examine educator perceptions related to data-informed practice. This paper outlines the process used in the development, testing, and validation of one instrument that district leaders and evaluators may use to learn more about the ways in which data are used and perceived in their respective contexts. Potential applications as well as limitations of the instrument are outlined. [ABSTRACT FROM AUTHOR]

Large-scale efforts to improve student achievement have increasingly encouraged principals to use student data to inform their decision-making and change initiatives at schools. However, research on the nature and best practices of such use remains very thin. This literature review synthesizes research on principals' data-use for the last 14 years and identified 18 school leadership practices of using data to inform their decision-making, which constitutes the incipient, working conceptual data-informed school leadership. This framework provides a much-needed guide to school administrators and policy makers who want to make evidence-based decisions to improve student learning. The framework can be also used to inform principal preparation or training. [ABSTRACT FROM AUTHOR]