Numerous prior literature pointed out the disconnection of knowledge and skills between the classroom and the workplace, especially regarding data analytic skills in the big data era. However, the studies on integrating data analytics, including the current IS/IT environment, into the accounting curriculum are minimal. This study provides an…

A version of the classic rotationally resolved infrared (IR) spectrum of a diatomic molecule experiment has been developed using the POGIL framework to more fully engage students in the collection, modeling, analysis, and interpretation of the data. An analysis of the experimental protocol reveals that the POGIL approach actively engages students…

Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…

Although culturally responsive mathematics teaching is important, post-secondary education for preservice teachers (PTs) does not typically lead to learning opportunities for them to use mathematics to recognize the roles of social agents. To address this issue, we created a culturally responsive mathematical modeling task in which we invited PTs…

Quantitative reasoning (QR) is a key skill for undergraduate biology education. Despite this, many students struggle with QR. Here, we use the theoretical framework of student noticing to investigate why some students struggle with QR in introductory biology labs. Under this framework, what students notice when given new information and data…

In many developed countries, disagreement on important policy issues between groups with different social identities ('ideological polarisation') is increasing. In professional settings, these disagreements undermine cooperation and trust between employees, which negatively affects work relationships and managerial decision-making. We investigate…

One potentially fruitful application for human capital analytics is to support policies and practices that might reduce undesirable teacher turnover. Teacher turnover can be harmful to student achievement and faculty cohesiveness and can exacerbate teacher shortages. This article describes an attempt to build a human capital analytics tool to help…

The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center,…

Sustainable entrepreneurship training is becoming increasingly important in the age of digitalization, as it provides future entrepreneurs with the necessary skills to develop responsible and innovative business models. The analysis shows that effective training in this area must involve digital technologies to convey sustainable practices in a…

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…