This article is presented in two parts: in the first part we discuss the use of R and R-related tools when implementing a data science curriculum in the classroom and direct readers to helpful R resources in education, and in the second part, we demonstrate the use of R in exploring COVID-19 data. In particular, we explore ethnic/racial…

The COVID-19 global pandemic has required everyone to make sense of data about community spread, levels of risk, and vaccine efficacy. Yet research shows that students are underprepared in data literacy. Tanya LaMar and Jo Boaler argue that data science education provides an opportunity to address this problem while providing much needed updates…

PISA is an influential international study of the achievement of 15-year-olds. It has a high profile across the devolved nations of the UK, with the results having a substantial impact upon education policy. Yet many of the technical details underpinning PISA remain poorly understood--particularly amongst non-specialists--including important…

Analyzing the learning analytics from a course provides insights that can impact instructional design decisions. This study used educational data mining techniques, specifically a longitudinal k-means cluster analysis, to identify the strategies students used when completing the online portion of an online flipped spreadsheet course. An analysis…

Since the early 2000s, the proliferation of cameras, whether in mobile phones or CCTV, led to a sharp increase in visual recordings of human behavior. This vast pool of data enables new approaches to analyzing situational dynamics. Application is both qualitative and quantitative and ranges widely in fields such as sociology, psychology,…

The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes…

Data reflects a series of decisions made by people--and those decisions affect the story that data tells, what it captures, and how it can and should be used to inform decision-making. Because of this, mistrust in data is often the result of incomplete information and a lack of context. This resource breaks down what it means to build trust in…

Ethnography offers a holistic approach to qualitative researchers in educational contexts and appeals to scholars who wish seek to reveal rich narratives through their immersion in specific domains. This review paper examines the mobilization of the ethnographic research approach reported in studies from two distinctive learning contexts: an…

In the past, Human Research Development (HRD) professionals have faced barriers of gaining access to the data they need to conduct higher level evaluations. However, recent technological innovations have presented opportunities for them to obtain this data, and consequently, apply new approaches for the training evaluation process. One approach…

Sensors and internet of things (IoTs) are ubiquitous in our modern day-to-day living. The past decade has been marked by the rapid emergence and proliferation of a myriad of small devices. Applications range from smart home devices that control cooking ranges to mobile phones, wearable devices that serve as fitness trackers and personalized…

Analysis of symmetrically predicted endogenous subgroups (ASPES) is an approach to assessing heterogeneity in an ITT effect from a randomized experiment when an intermediate variable (one that is measured after random assignment and before outcomes) is hypothesized to be related to the ITT effect, but is only measured in one group. For example,…

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In…

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The…

Scientific progress depends on the integrity of data and research findings. Intentionally distorting research data and findings constitutes scientific misconduct and introduces falsehoods into the scientific record. Unintentional distortions arising from questionable research practices (QRPs), such as unsystematically deleting outliers, pose…

Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training…