This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to…

This original research is directed towards the information literacy of international master's students, focusing on the evaluation of their information-seeking behaviour during competitive and business intelligence processes, together with the subsequent verification methods they use once the desired information is found. During this research, 207…

Addressing societal issues in civil and environmental engineering increasingly requires skills in data science and programming. To date, there is not much known about the extent students are learning these skills in current civil and environmental engineering curricula. We conducted a survey of accredited civil and environmental engineering…

The study explored the social perceptions of young children's use of smart devices in South Korea using big data methodologies. Big data methodologies allowed to uncover underlying thoughts and feelings about young children's use of smart devices that had not been discovered in existing studies. The study extracted raw data from three different…

Over the past 20 years, root cause analysis (RCA) has become one of the most widely used retrospective methods for detecting safety hazards in medicine and healthcare. Despite its wide use in management practice and growing popularity in academic research, there is currently a dearth of coverage of RCA in popular healthcare management textbooks…

This paper analyses the effect that the fulfilment of the European Higher Education Area (EHEA) has got among engineers in the information and communication technologies (ICT) sector. The focus is on Spain, and the comparison of the technical and managerial knowledge and skills most demanded by the sector in 2008 and 2018, coinciding with the…

The purpose of this article is to illustrate the dynamic process involved in developing and utilizing a theoretical model in a mixed methods study. Specifically, I illustrate how the theoretical model can serve as the starting point in framing the study, as a lens for guiding the data collection and analysis, and as the end point in explaining the…

Modernizing mathematics refers to the idea that teachers should rethink how mathematics has traditionally been taught in schools by making rich tasks and collaboration the focus of instruction and promoting opportunities for active learning. "Modern Math Tasks to Provoke Transformational Thinking" presents carefully crafted tasks that…

There has been increasing interest in applying integrative data analysis (IDA) to analyze data across multiple studies to increase sample size and statistical power. Measures of a construct are frequently not consistent across studies. This article provides a tutorial on the complex decisions that occur when conducting harmonization of measures…

As Canadian post-secondary institutions emerge from the pandemic restrictions, they are in a historically unique position to assess how online education has both facilitated and hindered learning, and how the effects might be greater for some. In this study, open-ended comments from the Canadian Digital Learning Research Association 2022 Spring…

The expansion of big data and analytics has led to the diffusion of institutional research functions across campus departments. In this new environment, IR professionals expect to lead and coordinate various institutional analyses. This article presents a four-quadrant framework to facilitate the dynamics between data analysis and decision-making.

Education researchers are increasingly interested in applying data mining approaches, but to date, there has been no overarching exposition of their methodological advantages and disadvantages to the field. This is partly because the use of data mining in education research is relatively new, so its value and consequences are not yet well…

Teaching introductory statistics is a service requirement for many mathematics faculty, and the audience is often anxious about the subject and not always excited about the material. Making class fun, relevant, and visually stimulating is key to keeping students engaged in the material and also in promoting student learning. Collecting data from…

A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history,…

This study investigates our role as early years researchers in qualitative data analysis. We draw on our doctoral studies to address how the co-construction, co-performance and co-reflection of narratives elicit deeper and new understandings of early years workers in England, and how our life stories are co-produced through narrative inquiry.…