Data preparation (also called "wrangling" or "cleaning") -- the evaluation and manipulation of data prior to formal analysis -- is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular…

Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim…

Joint efforts by the Government of Nepal, development partners and key stakeholders to achieve SDG 4 by 2030 have improved education access, participation and retention. However, learning outcomes in Nepal remain stagnant. What resources and contextual factors are associated with good school performance in Nepal? By merging and analyzing existing…

Since 2013, DaSy has tracked the status of Part C and Part B 619 state data systems to understand states' technical assistance needs, provide stakeholders with a national picture of what capacities state data systems have, and to track how those capacities are changing over time. Most recently, in spring of 2021, DaSy collected comprehensive…

Teachers in the United States are increasingly tasked with using data to inform their classroom instruction both through federal policies, such as the Every Student Succeeds Act (ESSA, 2016), and state policies requiring the use of teacher-determined data-driven goals for performance evaluations (MA 603 CMR 35.07). Many teachers, however, report…

Measurement-based care (MBC) research and practice, including clinical workflows and systems to support MBC, are grounded in adult-serving mental health systems. MBC research evidence is building in child and adolescent services, but MBC practice is inherently more complex due to identified client age, the family system and the need to involve…

Instructional leadership is a complex dimension, which requires that principals possess expertise in goal setting, leading the instructional program, and creating the conditions for a successful school environment. Effective instructional leaders manage the instructional program by planning, coordinating, and evaluating the work of teachers and…

In this article, we adopt culturally relevant perspectives on developmental science that acknowledge and value the diversity of backgrounds and experiences of young children and their families to identify opportunities to advance the measurement of early childhood development. We focus on direct child assessments that can drive more equitable…

This article explores utilizing a post-graduation success lens to help community college leaders frame the challenges of achieving equitable improvement for their students. Specifically, it posits that providing and exploring customized labor market data presented in an accessible format can help institutional leaders provide a "true…

This quantitative based applied research study examined data collected fromstudents who have withdrawnfromor completed aneducator preparation program (EPP) ina small rural public community college in WestVirginia. This study compared studentretention rates with Frontier andRemote (FAR) designation by home zip code. These data informedthe research…

This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes…

Wichita State University (WSU) began the SEM process in 2015 with a steering committee co-chaired by the university registrar and the chief data officer (CDO). Work leading up to and undertaken throughout that process and the implementation of the SEM plan it produced helped to form an extraordinary partnership between these two professionals.…

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student…

Hiring academic staff into departments and supporting them remains the single costliest activity that most institutions of higher learning engage in and requires careful, long-term, data-driven planning. This study identifies widely available (but seldom actually used) variables needed for this process: available instructional workload units and…

While teachers' core responsibility is to provide high-quality instruction, they are also expected to engage in data-based decision-making (DBDM), e.g., to analyse and use data to improve instruction. We developed a relevance intervention to promote student teachers' self-determined motivation and application intentions for DBDM and implemented it…