Understanding the processes in education, such as the student learning behavior within a specific course, is a key to continuous course improvement. In online learning systems, students' learning can be tracked and examined based on data collected by the systems themselves. However, it is non-trivial to decide how to extract the desired students'…

While many campuses have access to and publish equity-focused data, it can be difficult to share that data with the campus community in a way that engages multiple offices. This article outlines the Equity Liaison Initiative at the University of Wisconsin--La Crosse, which engages individuals from most offices on campus with equity-focused data to…

Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on "fixing" and "filling" academic gaps, we must envision and rebuild the system from the "student" up--with classrooms, schools and systems…

There are a number of ways that student investment can be measured, including full cost of attendance (COA, also known as sticker price), net price (COA minus grant aid), and opportunity costs in the form of forgone earnings. This paper explores those options and makes recommendations for how student investment should be measured with ideal data…

Developing a solid foundational knowledge of data literacy is integral to building role-specific knowledge and building capacity of educators to use and apply these skills appropriately. However, the definition of data literacy is inconsistent and often too narrowly applied within the field of education. National experts and technical assistance…

This case discusses how scenario-based interviews can be used to develop research into policy and practice. Based on data collected from U.K. further education management settings, it shows how and why scenario-based interviews were used. It also explains how the data collected were then used to illustrate and analyze the implementation of a…

Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical…

Transparency is once again a central issue of debate across types of qualitative research. Work on how to conduct qualitative data analysis, on the other hand, walks us through the step-by-step process on how to code and understand the data we've collected. Although there are a few exceptions, less focus is on transparency regarding…

School exclusion reduction in Scotland--and especially in the city of Glasgow--has received substantial media and policy attention in recent years. In London in particular, multiple governmental agencies have explicitly expressed a desire to replicate the exclusion reduction which recently occurred in Glasgow, often citing the connection between…

The use of large-scale assessments (LSAs) in education has grown in the past decade though analysis of LSAs using multilevel models (MLMs) using R has been limited. A reason for its limited use may be due to the complexity of incorporating both plausible values and weighted analyses in the multilevel analyses of LSA data. We provide additional…

This paper scrutinizes the increasing trend of using international large-scale assessment (ILSA) data for causal inferences in educational research, arguing that such inferences are often tenuous. We explore the complexities of causality within ILSAs, highlighting the methodological constraints that challenge the validity of causal claims derived…

Social network research often depends on the willingness of respondents to provide personal information about themselves and alters. Survey design strategies that increase willingness to share this information are necessary for social network research to be feasible, especially when name generators are used for sampling because rosters are…

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data…

Research on classroom motivational climates and microclimates--students' shared and idiosyncratic perceptions of motivational classroom features--demonstrates their importance for fostering adaptive motivational and achievement-related outcomes. However, a lack of coherent theoretical guidance about the nature of students' classroom climate…

We present an interactive spreadsheet that supports teaching essential concepts in classification using the logistic regression (LoR) model for binary classification. The interactive spreadsheet demonstrates the capabilities of LoR by integrating computation with visualization. Students will reinforce concepts like probabilities, maximum…