This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…

The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers…

Substantial shares of eligible students forgo or lose access to tuition-free college benefits, in part due to limited access to information on eligibility and other requirements. Given students' dependence on the Internet for information on how to pay for college, we examine the availability and digital accessibility of critical program…

Despite the increasing interest in data analytics applications within postgraduate education research, there remains a significant gap in research dedicated to exploring mixed methods research for such investigations. This study undertook to bridge this gap by exploring the application and use of mixed methods research to examine data analytics…

Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and…

External audit is undergoing rapid changes where more and more routine tasks are automated with analytics and artificial intelligence (AI) instruments. The paper addresses a research problem of mapping data analytics to audit tasks and develops a framework aligning audit phases and AI and using data analytics in teaching audit with AI. The paper…

With the growing availability of large-scale text-based data sets, there is an increasing need for an accessible and systematic way to analyze qualitative texts. This article introduces and details the contextual text coding (CTC) method as a mixed-methods approach to large-scale qualitative data analysis. The method is particularly useful for…

Although multiple organizational-level databases are frequently combined into one data set, there is no overview of the matching methods (MMs) that are utilized because the vast majority of studies does not report how this was done. Furthermore, it is unclear what the differences are between the utilized methods, and it is unclear whether research…

The COVID-19 pandemic uniquely affected nearly all the subject matter in a typical principles of macroeconomics class. Fluctuations in the basic macroeconomic data in the COVID era were staggering and offer new teaching opportunities. In addition, because the recession was primarily driven by supply side shocks, the entire episode offers a unique…

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…

Data mining has been successfully and widely utilized in educational information systems, and an important research field has been formed, which is educational data mining. Process mining inherits the characteristics of data mining which can not only use historical data in the system to analyze learning behavior and predict academic performance,…

The purpose of this study is to investigate the current state of graduate level business analytics education in the United States. The goal of this research is twofold. The first goal is to understand how higher education institutions are addressing the growing demand for analysts and data-savvy managers in the job market. To achieve this aim, the…

In this essay, we explored the feasibility of utilizing artificial intelligence (AI) for qualitative data analysis in equity-focused research. Specifically, we compare thematic analyses of interview transcripts conducted by human coders with those performed by GPT-3 using a zero-shot chain-of-thought prompting strategy. Our results suggest that…

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…