Curriculum Design
Curriculum design concerns all universities around the world. Traditionally, it is performed manually by academics with years of experience in the design process. Decisions about what content to include in a curriculum and what competences to teach are often made based on highly subjective impressions of individuals. These decisions are very rarely backed with solid quantitative measures. Furthermore, since curricula are designed by academics there is a high risk of bias towards content that might be primarily of academic interest.
Graduate needs and roles, however, are so diverse, so multi-disciplinary, the skills needed are multi-faceted and subtle; there is little clear guidance and there are now so many millions of data points in job ads, skill descriptions, tools needed, etc that it is nearly impossible for a human even if unbiased to filter and digest these to develop a curricula.
Also, whereas curricula in many social science disciplines change only relatively slowly over time, curricula from information technology focused disciplines such as information systems require frequent revisions and updates.
Further, students are becoming increasingly analytic when it comes to evaluating and choosing their education. They need evidence the program is strong and well developed and better than alternatives. The tools we develop help them to see that.
Our approach: Data Science
Data science is said to come with fundamental and disruptive changes not just for businesses but also in the way our society works. Europe is well-aware of this. It has reacted by several initiatives (for more information see e.g. "Making Big Data work for Europe"). In the last few years many companies created data science labs and job advertisements show a growing demand for data scientists. More and more universities are trying to fulfill the market demand for graduates in this area by offering specialized graduate programs in data science. At its core data science is supposed to turn data into business value by using sophisticated analysis methods stemming from mathematics and computer science. Often they originate from sub areas such as statistics, machine learning and visualization. We will make use of Data Science to support you in designing your curriculum!
Data Science for Curriculum Design
There is an abundance of information available, which could inform curriculum design, but it can only be handled through semi-automatic means due to the immense volume of information. Typically, the data format is unstructured or semi-structured text found on webpages, ebooks and leaflets. Dealing with unstructured information in large quantities is challenging but also offers opportunities. We believe that relying on a data driven curriculum design allows to better reflect the needs of our economy and society. For example, by considering regional job advertisements, one might identify different demands that lead to different curricula and specializations, comparing your curriculum with the ones of other higher research and education institutions might point you towards your perfect niche!
The data we are able to retrieve is aggregated and processed iteratively by sophisticated algorithms followed by human analysis. We propose a semi-automatic, iterative process. Despite partial automation the required manual work is considerable, in particular for developing the text mining based methodology. However, after an initial investment in automating the design process, future updates to curricula come with reduced effort compared to purely manual design while incorporating a larger amount of information. The methodology that we develop will substantively facilitate curriculum design. It will foster efficiency and improve quality at the same time, making an important contribution to society, which is increasingly fueled by knowledge and subject to increasing speed of change.
