This systematic review presents the first synthesis of the scientific literature on estimating school and teacher/class effects on student academic performance using random-effects (RE) models with three or more levels. The review delves into the theoretical framework underpinning the estimation of educational effects, the associated statistical…

Artificial intelligence (AI) has been put forth as a technological innovation which can change the way in which healthcare will be delivered in the near future. AI developers plan to deploy tools that will aid diagnosis, improve therapy, minimize errors, increase safety, and optimize systems and bring down costs. In addition, a paradigmatic shift…

The "Building Your Research Data Management Toolkit" was developed to provide introductory research data management skills training to liaisons in academic libraries. This paper assesses the participants' perceived change in knowledge, behaviors and attitudes as a result of participation in the RoadShow program. Long term changes in…

The application of big data in education sparks debates on its effects. Researchers explore its impact on learning outcomes, with divergent views on neural networks' potential and negative consequences. This study assesses big data's influence on basic literacy skills in music education for 5th graders. Using clustering and k-density analyses,…

Undergraduate research is considered to be a high-impact practice; however, research into the data literacy of students conducting undergraduate research is lacking. In addition, institutionwide assessments of data practices are challenging because of varied disciplinary approaches to data. This study investigates the data practices of…

The benefits of government administrative data are well-established, but research has not examined processes by which these data can be identified, organised and governed in line with ethical considerations. This is an important undertaking given the potential benefits of robust administrative data, especially at a time when the collection of…

Despite the promotion of data-driven or data-informed instructional practices in teacher education and professional development, past research indicates that teachers use a limited number of sources for student data to make short-term adjustments to their teaching in order to address deficiencies in student learning. Science teachers, with a more…

In science classrooms, students usually see and work with data that's intended to tell them right away about the natural world. Students then often treat the data we provide to them as factual, rather than as a source of evidence (Duschl 2008; Sandoval and Millwood 2005; Berland and Reiser 2009; McNeill and Berland 2017; Hancock, Kaput, and…

As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the "what" and "how" of data science in pre-collegiate education have not yet reached consensus. This paper…

The development of school education depends on the quality of the education provided, and it is a key metric for assessing the effectiveness of schools in developing talent. Building specialized, intelligent education quality monitoring (EQM) databases is crucial for speeding EQM progress in the big data era. This article examines the development…

Most school and district leaders have a wealth of information available to them before, during, and after the hiring process, but they might not analyze it regularly or use it to inform their recruitment and retention plans. However, using data strategically is key to positively impacting teacher recruitment and retention. This brief discusses…

Teacher turnover continues to be a significant problem in the United States. Teacher turnover is expensive because it costs money to continue recruiting, hiring, and training new teachers to replace those leaving (Carver-Thomas & Darling-Hammond, 2017). Most important though, teacher turnover hurts student achievement and success (Sorensen…

Data-informed decision-making is no longer an optional or occasional practice, as higher education professionals now routinely respond to calls for accountability by providing data to show how their work impacts students. Institutions are operating with a culture that, at a minimum, includes the use of descriptive and diagnostic analyses to assess…

This case study examines employee attrition, its detrimental effects on businesses, and the potential of data analytics to address this challenge. By employing Latent Dirichlet Allocation (LDA), a sophisticated NLP technique, we delve into the underlying reasons for employee departures. Additionally, we explore using RapidMiner to develop…

Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into…