The aim of this research is to examine the postgraduate studies conducted in the field of mathematics education with individuals diagnosed as gifted and talented students in Türkiye. For this purpose, master's and doctoral studies in the database of the National Thesis Center of the Council of Higher Education [CHE] of Türkiye were examined. The…

Non-academic factors such as school climate, grit, and growth mindset are receiving much attention in recent education policy and practice. Within this context, this study (N = 425) describes the distribution of U.S. in-service teachers' access to and use of 10 categories of non-academic data. Findings indicate that in-service teachers vary widely…

State legislators have the opportunity to create and support legislation that positions data as a tool to help decisionmakers at all levels take action to support student success. This checklist can serve as a guide to crafting legislation that addresses education data. It applies specifically to education data legislation which includes bills…

Accuracy and precision are measures of experimental error and are fundamental to most chemical analysis laboratory classes. Assessment of accuracy and precision is often based on the comprehension of the results generated by students rather than on the quality of the data generated. This activity focused on developing a chemical analysis…

"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…

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…