According to decades of research in educational psychology, learning is a social process that is enhanced when it happens in contexts that are familiar and relevant. But because of the skyrocketing popularity of data science, today we often work with students coming from an abundance of academic concentrations, professional, and personal…

The main goal of this study was to evaluate the impact of an animation and visualization of data structures (AVDS) tool on both perceptions and objective test performance. The study involved a rigorous experiment that assessed the usability, acceptability, and effectiveness of the AVDS tool in solving exercises. A total of 78 participants…

This paper focuses on two third-grade students' work on the same statistical question whereby one acts with analogue material and the other with TinkerPlots™. The aim of the research was to find out whether different material influences the actions and, thus, possibly the mathematical interpretations of the learners. To investigate this research…

Educational process mining aims (EPM) to help teachers understand the overall learning process of their students. Although deep learning models have shown promising results in many domains, the event log dataset in many online courses may not be large enough for deep learning models to approximate the probability distribution of students' learning…

Data literacy has become a much sought after skill by organizations as the importance of data driven decision making continues to rise in importance. This study explores the teaching of Tableau as a supplement to Excel to enhance the data literacy skills of students. Twenty-eight students taking an introductory course in Business Analytics and…

Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of…

R and Python are commonly used software languages for data analytics. Using these languages as the course software for the introductory course gives students practical skills for applying statistical concepts to data analysis. However, the reliance upon the command line is perceived by the typical nontechnical introductory student as sufficiently…

This study created an all-English learning environment for teachers and students by integrating the theoretical framework of the University Competence Assessment Network common competency with the pedagogical method of English as a Medium of Instruction (EMI). The study targeted approximately 45 students, ranging from sophomore to senior, taking…

The age of big data drives the need for emerging technologies to enable scalable analytics on massive, rapidly generated, and varied data. It requires "data scientists" with deep knowledge of managing the six Vs of big data: volume, velocity, variety, volatility, veracity, and value. As a result of this trend, new analytical tools are…

Educators are tasked with continually updating course objectives, content, assignments, and assessment to meet model curriculum guidelines. IS2020 proposes program level outcomes for required and elective areas. Two elective areas in IS2020 are Data and Business Analytics and Data and Information Visualization. IS2020 details 14 program level…

In today's age of information, the use of data is very powerful in making informed decisions. Data analytics is a field that is interested in identifying and interpreting trends and patterns within big data to make data-driven decisions. We focus on informal statistical inference and data modeling as a means of developing students' data analytics…

Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…

Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the "quantified self"--in which data about one's own activities are collected and made into objects of study--provides inspiration for one such approach. By…