A refined case-based reasoning approach to academic capacity planning

Abstract

Academic capacity planning is a knowledge-intensive process that has to be based upon predicted course demand. Planners have to take into account students' current course achievements, prospective future course selections, time constraints as well as a wide range of different rules for graduation. The research project proposes a refined case-based reasoning (CBR) approach for anticipating students' future course selection as a means of long-term demand forecasting. The case-base is dynamically interpreted with regard to stored cases' problem descriptions and solutions. Moreover the structure of cases is heterogeneous depending on the students' course achievements. The retain phase of the traditional case-based reasoning cycle is replaced by an adjustment phase that ensures retaining up-to-date, real-world cases only. The results of the case-based reasoning processes are aggregated to support capacity planning.