The purpose of this study was to explore the perceptions of employees regarding changes in their workplace experience following the implementation of an electronic performance monitoring (EPM) system. The study's conceptual framework utilized Burke and Litwin's (1992) model of organizational change and Bandura's (1986) social cognitive theory to…

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.…

Social media is a popular method for recruiting research participants and especially during the current pandemic. Researchers are increasingly using platforms like Facebook and Twitter to engage with participant groups, either to collect data directly (by posting links to online surveys) or to recruit participants for further research activity (to…

This case study aims to shed light on the methodological aspects of using a qualitative survey as the main data collection instrument to study prospective teachers' meaning-making process while learning about research methods and conducting their own projects. Particular attention is paid to the design and administration challenges of the…

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

Although the COVID-19 pandemic required schools and districts to quickly pivot to a remote and hybrid learning system of education, some stakeholders benefited in this new model of learning. Through this project, R1CC supported two district teams in self-identifying remote and hybrid learning strategies and integrating those strategies more…

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…

Background: Knowledge brokering plays an important role in the evidence-to-policy system, but little is known about whether and how it occurs within government departments. Aims and objectives: Using empirical evidence from one UK government department, this article analyses how knowledge brokering takes place inside the policy making process and…

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…

Background: Computer programming learning and education play a critical role in preparing a workforce equipped with the necessary skills for diverse fields. ChatGPT and YouTube are technologies that support self-directed programming learning. Objectives: This study aims to examine the sentiments and primary topics discussed in YouTube comments…

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…

Automatic item generation may supply many items instantly and efficiently to assessment and learning environments. Yet, the evaluation of item quality persists to be a bottleneck for deploying generated items in learning and assessment settings. In this study, we investigated the utility of using large-language models, specifically Llama 3-8B, for…

Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…

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…