The worldwide big data and analytics investment market continues to grow, with projections indicating strong compound annual growth rates over the next decade. To achieve true competitive advantage from business intelligence and data analytics, organizations must control how these investments are being integrated into the culture of the…

In the study the authors show how visualization and analysis of education data could be used in simulation and adjustment of education program curriculum for future evaluation of the formation levels the graduates competences as specified by educational standard. The authors suggest the scenario that demonstrate the possibility of automatic…

This work proposes relevant ingredients to highlight the factors with significant impact on efficiency of research structures in higher education. The ingredients in question include methods and their implementation taking into account the choice and operationalization of factors as well as options of the retained methods. The methods employed in…

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test--retest effects or high attrition over time. Planned missingness designs (PMDs),…

Mass spectrometry is essential for large-scale protein identification in proteomics. By database searching of mass spectrometry data, it is possible to identify proteins without the need to interpret MS/MS spectra (de novo sequencing), which is time-consuming and not feasible as a systematic tool in proteomic analysis. Many authors have reported…

We are living in the Big Data era, and yet we may have serious troubles when dealing with a handful of kinetic data if we are not properly instructed. The aim of this paper, related to enzyme kinetics, is to illustrate how to determine the K[subscript m] and V[subscript max] of a michaelian enzyme avoiding the pitfalls in which we often fall. To…

This article presents a pedagogical strategy that uses Chex Mix™ as a visual and physical means of helping students learn data analysis. The manipulative is used to guide students to a deeper level of analysis by looking "within" and "between" data sources. Specific steps are shared about using Chex Mix™ snacks to teach the…

Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations,…

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…