The use of artificial intelligence (AI) in higher education has mostly focused on issues associated with teaching and assessment. In this paper I used AI to support the analysis of data which consisted of public comments on a newspaper article. This small, low risk research was chosen to demonstrate the potential use of AI and how it may support…

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data--both genetic and behavioral--that are collected as part of scientific studies or a part of treatment can provide a deeper,…

Generative AI tools (GenAI) are increasingly used for academic tasks, including qualitative data analysis for the Scholarship of Teaching and Learning (SoTL). In our practice as academic developers, we are frequently asked for advice on whether this use for GenAI is reliable, valid, and ethical. Since this is a new field, we have not been able to…

Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12…

With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular…

This brief explores data science methods that workforce programs can use to predict participant success. With access to vast amounts of data on their programs, workforce training providers can leverage their management information systems (MIS) to understand and improve their programs' outcomes. By predicting which participants are at greater risk…

Background: For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require each document first be manually coded for constructs of interest by trained human raters. These hand-coded scores are then used as a measured outcome for an impact analysis, with the average scores of the treatment group…

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in…

Background: Despite the usefulness of systematic reviews and meta-analyses, they are time-consuming and labor-intensive (Michelson & Reuter, 2019). The technological advancements in recent years have led to the development of tools aimed at streamlining the processes of systematic reviews and meta-analyses. Innovations such as Paperfetcher…

Constant changes to COVID-19 restrictions have required adaptability from social scientists including responding to new challenges such as infiltration by bots. This research note presents unexpected encounters of bot infiltration and recruitment during survey data collection under pandemic conditions. The note draws from a household survey on a…

Although educational research and evaluation generally occur in multilevel settings, many analyses ignore cluster effects. Neglecting the nature of data from educational settings, especially in non-randomized experiments, can result in biased estimates with long-term consequences. Our manuscript improves the availability and understanding of…

The arc of quantitative educational research should not be etched in stone but should adapt and change over time. In this article, I argue that it is time for a reshaping by offering my personal view of the past, present and future of our field. Educational research--and research in the social and life sciences--is at a crossroads. There are many…

This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of…

This article introduces researchers in the science concerned with developing and studying research methods, measurement, and evaluation (RMME) to the educational data mining (EDM) community. It assumes that the audience is familiar with traditional priorities of statistical analyses, such as accurately estimating model parameters and inferences…

This paper presents the best practices used by institutional review boards (IRBs) and human research protections programs (HRPPs) to prepare online graduate student investigators for human research protections specific to research within online graduate degree programs or where research supervisors are not proximal to graduate student…