With teacher shortages, high staff turnover, and an influx of new and underprepared teachers, highly capable data-informed teaching teams are needed now more than ever. "Instructional Innovation+" provides educators with a systematic action research approach to achieve instructional innovation and guide teaching teams to success through…

This paper offers a creative methodology for co-creating and analysing data when conducting research with children, families and practitioners in the early years. Instead of relying heavily on either adult observation or language when, for example, interviewing children, we propose the method of choreographed visual montage. We show the novelty…

In this paper, we report findings from a multiple case study that examined how instructors used shared data when teaching qualitative data analysis. More specifically, we explored both instructor and student experiences in two graduate-level qualitative methods courses located at U.S. universities. Drawing upon thematic analysis and the theory of…

The standard methodology for many large-scale assessments in education involves regressing latent variables on numerous contextual variables to estimate proficiency distributions. To reduce the number of contextual variables used in the regression and improve estimation, we propose and evaluate principal component analysis on the covariance matrix…

This article details an exploratory data analysis project using the Common Online Data Analysis Platform (CODAP) based on the "Guidelines for Assessment and Instruction in Statistics Education" (GAISE) four-part statistical problem-solving model. The project goal was to answer what similarities and differences exist within the school…

Data mining in education predictions are made about other groups based on the big data of education. However, the extent to which training data can be accessed is relative. A group that can be described as missing data is children living or working on the street. For this reason this study aimed to deal with children working or living on the…

In this paper, we propose that informal aspects of data science could be introduced in primary school. The International Data Science in Schools Project (IDSSP) framework for data science curriculum provides a guide for a data science curriculum aimed at upper secondary level. We analyzed synergies between the IDSSP framework and the current…

We have updated a version of the coordination geometry table of the d-block elements and their ions that Moore and co-workers presented in 1997. This table is constructed by searching and analyzing the data in the Cambridge Structural Database. In this updated version, we have also analyzed impact of ligands on the coordination geometry and…

This article introduces the concept of the carrying capacity of data (CCD), defined as an integrated, evaluative judgment of the credibility of specific data-based inferences, informed by quantitative and qualitative analyses, leavened by experience. The sequential process of evaluating the CCD is represented schematically by a framework that can…

The use of text mining tools can help librarians improve the precision of searches, increase search sensitivity, and translate search strategies across multiple research databases. When combined with the intuitive approaches that librarians commonly use, text mining tools help reduce biases by improving the objectivity, transparency, and…

Over the past few years, art educators (Cooper et al., 2018; Grodoski, 2018; Taylor, 2017) have begun to embrace data visualization for curricular purposes. In this article, the authors make a case for the inclusion of data visualizations in K-12 art curricula and illustrate a curriculum that focused on introductory data visualization concepts and…

Data literacy skills are becoming critical in today's world as the quantity of data grows exponentially and becomes the 'currency' of power. In spring 2020, a team of two librarians piloted a new one-credit course in data literacy and data visualisation. This report explains the rationale behind the project and discusses the place of data literacy…

Respondent-driven sampling (RDS) is a methodology for sampling and analysing survey data from rare and elusive populations that has gained increasing attention in migration research in recent years. Since its introduction in 1997, it has been applied in numerous surveys in the field of epidemiology (mainly for the estimation of HIV/AIDS prevalence…

Research studies in Educational Data Mining (EDM) often involve several variables related to student learning activities. As such, it may be necessary to run multiple statistical tests simultaneously, thereby leading to the problem of multiple comparisons. The Benjamini-Hochberg (BH) procedure is commonly used in EDM research to address this…

We propose a new method for estimating school-level characteristics from publicly available census data. We use a school's location to impute its catchment area by aggregating the nearest "n" census block groups such that the number of school-aged children in those "n" block groups is just over the number of students enrolled…