Education plays a crucial role in the field of analytics. The extent to which future professionals will be successful in applying analytics largely depends on how they have been educated. A formation that focuses only on technical skills might lead to forming practitioners that are able to successfully develop analytic models and correctly interpret outputs. However, the share of graduates that will actually use analytics in their future careers largely depends on how well their education succeeded in engaging them to the art of analytics. Engagement demands seeing value in the use of analytics, something that does not come with merely focusing on methodologies. Involvement is a prerequisite of passion. Passionate instructors start from real-life problems that challenge students to experiment and be creative with analytics rather than focusing on what can be done wrong. Passion for analytics not only leads to developers of new, innovative applications, but also to a new generation of passionate instructors who can pass on their passion to future generations.

This collection consists of 5 sections. The first three sections concern teaching analytics. Section 1 focuses on teaching the practice of analytics. How can we teach analytics such that we form successful practitioners of analytics? We distinguish between basic education and lifelong learning of professionals through certification. Section 2 discusses active learning formats of teaching analytics as alternatives for lectures: cases, games, and field-based teaching through capstone projects. Section 3 relates to remote learning, and the lessons learned from transforming traditional analytics courses to online courses, as forced by the Covid-19 pandemic in 2020.

While teaching analytics is the focus of this collection, analytics can of course also be used to enhance teaching itself. The last two sections address the use of analytics to improve education. Section 4 contains several recent studies that illustrate how analytics is used to increase our knowledge on the effectiveness and impact of teaching. Finally, Section 5 addresses analytics for education, presenting some recent examples of how descriptive and prescriptive analytics are used to optimize the practice of teaching. The difference between Section 4 and 5 is that in Section 4 analytics are used ex-post to evaluate the impact or effectiveness of a certain innovation in teaching, while in Section 5 analytics are actively used to optimize teaching; e.g., prescriptive analytics to optimize scheduling of classes or descriptive analytics to actively monitor student progress. An example of the latter is the use of machine learning techniques to discover hidden patterns or correlations in study results data that could be used to improve teaching approaches.

How to navigate this collection?

If you are completely new to the entire field and just interested in exploring, then first read the introduction carefully and choose something you might want to pursue in detail.

If you have an executive role in education, e.g., a program director or (vice-)dean education, and you are looking for inspiration to motivate colleagues to be open for change, please consult the sources of the introduction section and then continue with more specific sections, focused on your interest. For example, if your program intends to become more practice oriented have a look at the content of Section 1. If you want to know more about the state-of-the-art in using analytics actively to improve education, please consult Section 5.

If you are a new academic looking for ideas on introducing practice or case based teaching, look at Sections 1 and 2.

If you are an experienced in-class teacher but interested in updating skills for distance education, then look at Section 3.

If you are a researcher looking for some examples of state-of-the-art research published in top journals related to education in which analytics plays a major role, either methodologically or topic-wise, please consult Section 4. If your research focus is rather on actively using analytics to improve education by implementing prescriptive or descriptive analytics, please have a look at Section 5.

If you are a professional and interested in modern business cases or other ways in which practice is introduced in teaching analytics, have a look at Sections 1 and 2. If you want to have an idea of the current state-of-the-art in online and hybrid teaching, consult Section 3. If you are an analytics solution provider and interested in knowing more about the current state-of-the-art in analytics for education, please consult Section 5.