Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…

Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative…

This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant…

Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep…

In response to the heavy demand for business analysts in various industries, many universities have developed business analyticsrelated courses and programs, which aim to develop a competent labor force that can help companies make sense of business data and generate sustainable competitive advantages. Ensuring high levels of student success in…

Drawing on the scholarship of writing and learning, this article motivates the use of writing assignments in analytics courses and develops a framework for instructional design that advances both writing skills and discipline-specific learning. We translate a best practices set of foundational writing concepts into a matrix of design levers for…

There has been an increasing demand for healthcare analytics skills and competence by healthcare organizations. Although many universities have established programs and courses on healthcare analytics, most of these curricula have been designed for information systems (IS), information technology (IT), or analytics students. It is unclear how…

"Data science" is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at…

As teacher residency programs are housed in universities, charter networks, nonprofit organizations, school districts, and museums, they form a model ripe for analysis. In this study, we conduct a content analysis of a random sample of 20 teacher residency program websites, focusing on each program's stated purpose, structures, and attributes.…

Cyberinfrastructure (CI) empowers the foundational computation resources underlying data analytics, spatial modeling, and many other domains serving the growing knowledge economy in the United States. In every part of these interactions with CI, questions of how to seamlessly integrate CI training into educational programs exist. In this article,…

In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…

Informed by interviews with campus administrators and Northern Arizona University (NAU) students, the "Student Success is the DNA of NAU" case study outlines strategies this public, four-year, Hispanic-Serving Institution (HSI) is employing to build NAU into an engine of opportunity for students and the communities they represent. The…

Accreditation is a process by which programs demonstrate their academic quality; that is, they demonstrate that they are making decisions for programmatic changes based on: (1) a sound theoretical foundation; (2) clearly stated mission, goals, and objectives; (3) a comprehensive self-study and thoughtful use of best practices; and (4) consistent,…

A quantitative research study collects numerical data that must be analyzed to help draw the study's conclusions. Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a…

Many traditional Information Systems (IS) programs are either redesigning current courses to incorporate business/data analytics or expanding curricular offerings to include business /data analytics; our IS program chose the former route to meet the demand (from employers) for business/data analytics. In that transition, a traditional systems…