Learning Outcomes and Structure
C1. Learning Outcomes and Structure
Cluster 1 takes a holistic approach for the entire study program design, learning outcomes, and structure – and our process to engage stakeholders in ensuring that the programs retain relevance in a changing world. There are several initiatives on study program structure and improvement, both locally (Future Tech Studies, NTNU), nationally (UHR), and internationally (e.g. ACM/IEEE, CDIO). Excited builds on and contributes to these. In particular, collaboration within the Nordic countries is of interest. Even if IT work is becoming increasingly globalized, there could be qualities of Nordic culture and work-life, and the Scandinavian perspective on IT systems development, that may also have bearings on what and how students should learn.
Cluster 1 focusses on the following areas:
Study program design: How can practical skills, e.g., problem-solving, tool usage, communication and collaboration skills, etc., be progressed over a series of courses? Excited will facilitate a systematic revision of degree programs to make progression explicit and guaranteed in student learning outcomes. CDIO, which has been successfully applied in many universities46, will be one important source of inspiration.
Competence mapping: Mature academic disciplines have well-developed concept inventories, while CS / IT has less in this respect, though with some exceptions. Using and adapting existing inventories where available, and contributing new ones where needed – including skills – Excited will identify competence coverage, overlaps and progression. This can facilitate collaboration in curricular development locally, nationally, and internationally.
Competence renewal: Changes in technology and society require changes to curricula. Some topics in increasing demand now are security, sustainability, and universal design. Patchy, ad hoc inclusion of new content into IT studies may end up just as window-dressing, while a more holistic approach is needed. Additions to the curriculum faces tough questions on what to subtract, as excessive curriculum burden may hinder deep learning.