The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…

The COVID-19 pandemic upended the U.S. education system and the economy in ways that dramatically affected the jobs of K-12 educators. However, data limitations have led to considerable uncertainty and conflicting reports about the nature of staffing challenges in schools. We draw on education employment data from the Bureau of Labor Statistics…

Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented…

Although there are many education information management systems (EMISs) that currently apply administrative development, they contain limited powers, thus leading to the emergence of problems--the most important and damaging of which being the loss of data quality and ensuring how to verify the validity the education and training information…

Because learning from failures is just as important as learning from successes, we used qualitative case study data gathered in the Netherlands and the United States to examine instances in which data teams struggle to contribute to school improvement. Similar factors in both the Dutch and U.S. case hindered the work of the data teams, such as…

The purpose of this research was to compare the performances of the Fixed Effect Model (FEM) and the Random Effects Model (REM) in the meta-analysis studies conducted through 5, 10, 20 and 40 studies with an outlier and 4, 9, 19 and 39 studies without an outlier in terms of estimated common effect size, confidence interval coverage rate and…

In this paper, we introduce Notice, Wonder, Feel, Act, and Reimagine (NWFAR) to promote social justice in data science (DS) education. NWFAR draws on intersectional feminist DS to scaffold critical perspectives towards systems of power and oppression and attend to students' experiences in designs for learning. NWFAR adds three practices that are…

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis…

The author of this article describes a new undergraduate course where students use Python programming for macroeconomic data analysis and modeling. Students develop basic familiarity with dynamic optimization and simulating linear dynamic models, basic stochastic processes, real business cycle models, and New Keynesian business cycle models.…

In the last decade education has experienced a shift from privatization to commercialization. This paper argues that the commercialization of education has evolved more recently as a result of artificially intelligent corporate players, enabling forms of insights sales called 'Dark Advertising'. It unpacks how Dark Advertising are profiting from…

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S.…

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced…

This brief provides district PBIS [positive behavioral interventions and supports] leadership teams a framework to examine school-level fidelity and self-assessment data to guide resource, professional development, and technical assistance decision making.

This guidance is for program and agency staff who have identified the need to develop a new data system or make major enhancements to an existing system. Key considerations are presented as questions to help guide staff through the initiation and planning process. The system initiation phase requires a broad team with the expertise and knowledge…

The problem addressed in this quantitative casual-comparative study was that the teacher attrition rate among K-8 Title I schools continues to be significantly higher when compared to non-Title I schools. There was a need to assess whether there are significant differences in teachers' perceptions of school climate between Title I schools and…