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Case-Based Forecasts Enhancing Decision Support for Capacity-Planning in Higher Education

Academic capacity planning is a knowledgeintensive process that has to be based upon forecasts of course demand. Forecasts have to take into account each student’s current course achievements, prospective future course selections, time constraints as well as a wide range of different rules for graduation. This paper presents an innovative concept for forecasting of course enrolments serving as demand-figures in academic capacity planning processes and fulfilling information needs of decision makers on various levels in German higher education. Adaptability to a wide range of different study programs is ensured by employing a refined case-based reasoning approach. 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. Furthermore the methodology of case-based reasoning is enhanced by including a rule-based reasoning component as well as a web-based component for the adaptation of proposed solutions. The results of the case-based reasoning processes are loaded to a star-schema to support capacity planning by a data-driven decision support system. The concept is evaluated in terms of correctness of retrieval as well as accuracy of forecasts by contrasting its results with those of a simple regression forecast using real student data.