"Leading Thoughtful Conversations about School-by-School Spending Data" is a product of a federally-funded study to support the US Department of Education and the field more broadly to understand what data visualizations work to fuel thoughtful conversation among district and school leaders on financial strategy and management. This…

Quantitative decision making (management science, business statistics) textbooks rarely address data cleansing issues, rather, these textbooks come with neat, clean, well-formatted data sets for the student to perform analysis on. However, with a majority of the data analyst's time spent on gathering, cleaning, and pre-conditioning data, students…

Generating personalized learning pathways for e-learners is a critical issue in the field of e-learning as it plays a pivotal role in guiding learners towards the successful achievement of their learning objectives. The existing literature has proposed various methods from different perspectives to address this issue, including learner-based,…

This study explores the use of generative AI, specifically ChatGPT, in statistical data analysis and its implications for statistics education at universities of applied sciences. This paper begins with first discussing the future division of labor between humans and machines in the context of statistical data analyses following the widespread…

The challenge: This paper examines the state of knowledge and evaluation in prison higher education. Little is known about its efforts, outcomes, and impact or about the students enrolled in such efforts. Potential consequences: Incarcerated college students are a disenfranchised population with restricted autonomy. Without understanding prison…

This study explores the association between having a transit stop within walking distance of campus and Pell Grant recipient completion rates at US commuter institutions, using a novel dataset on transit stop locations and institutional level data. The findings indicate that there is a positive association between transit access and Pell…

An efficient assessment cycle is a necessary component of early literacy instruction. To support student growth, educators must screen, diagnose, and monitor student progress. Diagnostic assessments provide critical information that can empower educators to address student needs. Using information from diagnostic assessments requires educators to…

The concept of statistical sense provides an understanding of the goals of statistics education and helps to clarify the design of activities that promote the development of statistical literacy, reasoning and thinking. The new approaches to statistics in schools mean special attention must be paid to teacher training. This training should enable…

Single-case design (SCD) is frequently utilized in applied contexts, such as schools or clinics, due to its utility in evaluating individual intervention effects of students. Data collected in SCD are often displayed in a linear graph, which can vary drastically in their construction leading to inconsistencies in rater interpretation. This has led…

Dependability (also known as consistency) is one of four criteria in rigor and trustworthiness in qualitative research. In this article, the process of establishing consistency is discussed through the lenses of constructivism and interpretivism, as the observed social reality is viewed as epistemologically counter-intuitive. Two strategies were…

With three school years touched by the pandemic so far, the extent of the damage to this generation of students is coming into focus. Three concerns are top of mind for state and district leaders: making up for disrupted learning, ensuring that schools have enough quality teachers and staff to lead this work, and building a diverse teacher…

Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible.…

Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable…

A critical issue in using fine-grained gameplay data to measure learning processes is the development of indicators and the algorithms used to derive such indicators. Successful development--that is, developing traceable, interpretable, and sensitive-to-learning indicators--requires understanding the underlying theory, how the theory is…

Purpose: This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational…