When students struggle with reading, school-based teams seek more information about why those students are struggling in order to provide instruction that will meet the students' needs. This process occurs for students needing Tier 2 or 3 instruction and students with disabilities who have reading goals. Reading specialists, special education…

The argument-based approach to validity (Kane, 2013) focuses on two steps: (1) making claims about the proposed interpretation and use of test scores as a coherent, interpretive argument; and (2) evaluating those claims based on theoretical and empirical evidence related to test performances and scores. This paper discusses the role of…

The purpose of this commentary is to push the boundaries (real and perceived) of how we think about teacher accountability, education and the purpose of schooling in contemporary times. It takes as a starting point a view that recent changes to the Every Student Succeeds Act does little to shift the underpinning logics of high-stakes teacher…

Posing statistical questions is a fundamental and often overlooked component of statistical inquiry. In this paper, we provide an overview of shared understandings regarding what constitutes a good statistical question. We then describe three approaches--a checklist for improving statistical questions, a three-phase feedback activity, and a…

The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between-trial heterogeneity and including multi-arm…

This study sought to understand the relationship between undergraduate yield and an institution's internet presence. Data were collected from 2018 "U.S. News & World Report" national university rankings, the Integrated Postsecondary Education Database (IPEDS), and summer 2018 (June-August) SEMrush data, a web metrics database housing…

While home visiting services are an important component of the early childhood (EC) landscape, few states include home visiting data in their early childhood integrated data systems (ECIDS). An ECIDS links together data from different early care and education programs to generate data used to support program and policy decisions. One reason for…

In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success.…

The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized…

This study aimed to investigate pre-service pre-school education teachers' data representation activities in terms of their relation to real life, the appropriateness of the data that the children can collect, the conformability of the graphs required for the data representation to the children's level, and the clarity of open-ended questions…

This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory--a prominent theory of students' academic motivation--and utilizes interviews (n = 60) to investigate how students at risk…

Big data analysis involves the examination of large sets of data in order to uncover patterns and trends. Within the social studies or history classroom, such analyses can be effective for gaining insights into historical trends over time or across geographical regions. For instance, using simple technology tools, students can perform word…

Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these…

Educational Data Mining is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students' behavior, interests and results in a better way. In recent years there have been various works related to this specialty and multiple data mining techniques derived from this…

Over the last decade, there has been a steady increase in the number of Indigenous graduate research students in Australia, yet research and pedagogy has not kept pace with changes underway in the sector. From an extensive search of literature published between 2000 and 2017, 15 papers (representing 10 research projects conducted by seven teams or…