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.…

Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures…

Research students in social science disciplines frequently struggle to master statistical analysis. A contributing factor may be the statistical software that is used, as the design of such software may not address the needs of non-statisticians or non-computer programming students. Hence, decisions about which statistical software tools are most…

To fully appreciate genetics, one must understand the link between genotype (DNA sequence) and phenotype (observable characteristics). Advances in high-throughput genomic sequencing technologies and applications, so-called "-omics," have made genetic sequencing readily available across fields in biology from applications in…

The use of Consensual Qualitative Research (CQR) within counseling literature, specifically counselor education, has increased since 2013; however, the application of the method varies across counseling studies. Therefore, we used a modified content analysis to code 58 CQR counseling studies from 2003-2017 to examine whether and how each study…

Digital sensors allow people to collect a large quantity of data in chemistry experiments. Using infrared thermography as an example, we show that this kind of data, in conjunction with videos that stream the chemical phenomena under observation from a vantage point, can be used to construct digital twins of experiments to support science…

While computational chemistry continues to play a growing role in chemical research, issues with access and the timing of when these tools are introduced to chemistry students remain as barriers to wider interest and use. The accessibility of free software for chemical modeling has increased in recent years, promoting the inclusion of…

Collecting data on learners' performance in different chemistry contents and analysing them to identify their knowledge and understanding in related content areas is a major task of Chemistry Education Research. The data collection process on the learners' content knowledge and understanding of content knowledge requires a standard measuring tool.…

The Chicago Hardship Index is a proposed starting point for introducing students to structural urban inequities. ASA's mission statement to use statistics to enhance human welfare serves as a motivation for social justice projects. This article contains an application of ASA's ethical guidelines to such projects, background information about the…

The Postsecondary Value Commission, in its exploration of return on investment and value of a higher education, desired to better understand the current state of earnings data for states and institutions so that they could effectively measure and understand post-college employment and earnings. To that end, this brief includes a review of research…

Predicting academic performance using trace data from learning management systems is a primary research topic in educational data mining. An important application is the identification of students at risk of failing the course or dropping out. However, most approaches utilise past grades, which are not always available and capture little of the…

This study has three primary objectives. First to provide insight into possibilities for moderation/mediation analysis in education research by discussing analytical methods based on the counterfactual and developed in other disciplines. Second is to highlight specific issues related to mediation and the estimation of effects when the outcome is…

In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities.…