Related papers: A Framework for Infusing Authentic Data Experience…
Several authors, including the American Statistician (ASA), have noted the challenges facing statisticians when attacking large, complex, unstructured problems, as opposed to well-defined textbook problems. Clearly, the standard paradigm of…
This study documents the impact of a summer camp series that introduces high school students to coding, data science, and deep learning. Hosted on-campus, the camps provide an immersive university experience, fostering technical skills,…
It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To…
Cognitive transfer is the ability to apply learned skills and knowledge to new applications and contexts. This investigation evaluates cognitive transfer outcomes for a tertiary-level introductory statistics course using the CATALST…
Case studies are typically used to teach 'ethics', but when the content of a course is focused on formulae and proofs, a case analysis and the knowledge, skills, and abilities they require can be distracting. Moreover, case analyses are…
Modern demands of the statistics profession call for reimagining statistics training. The discipline needs to attract and develop students who are effective as real-world problem solvers, interdisciplinary collaborators, communicators,…
Educators teaching entry-level university engineering modules face the challenge of identifying which topics students find most difficult and how to support diverse student needs effectively. This study demonstrates a rigorous yet…
Graphs are essential representations in the professions and education concerning the science, technology, engineering, and mathematics (STEM) disciplines. Beyond their academic relevance, graphs find extensive utility in everyday scenarios,…
The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary…
How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all courses provide skills that transfer to existing and future…
This paper explores the use of scenario-based visualisation examples as a pedagogical strategy for teaching students the complexities of data insight, representation, and interpretation. Teaching data visualisation often involves explaining…
Computing is increasingly central to innovation across a wide range of disciplinary and interdisciplinary problem domains. Students across noncomputing disciplines need to apply sophisticated computational skills and methods to fields as…
Presents a differentiated teaching proposal that allows the student to be the agent in the construction of knowledge, overcoming the difficulties that Mathematics presents. Aiming to understand how the use of statistical tools can…
We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language…
The vast amount of data produced everyday (so-called 'digital traces') and available nowadays represent a gold mine for the social sciences, especially in a computational context, that allows to fully extract their informational and…
Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data…
As observational datasets become larger and more complex, so too are the questions being asked of these data. Data simulations, i.e., synthetic data with properties (pixelization, noise, PSF, artifacts, etc.) akin to real data, are…
The central role of statistical literacy has been discussed extensively, emphasizing its importance as a learning outcome and in promoting a citizenry capable of interacting with the world in an informed and critical manner. Our work…
As any scientific discipline, the software engineering (SE) research community strives to contribute to the betterment of the target population of our research: software producers and consumers. We will only achieve this betterment if we…
Rapidly evolving technology, data and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills including data literacy is particularly prominent given both the potential of novel…