A multi-criteria assessment of decision support systems in educational environments

Decision support systems (DSS) are useful business intelligence (BI) tools as
they help managers in large organizations make the best out of many
decisions. Decisions are based on various types of raw data, models,
documents, knowledge, and past experiences. This paper examines numerous
criteria of decision support systems in the educational environment. Two
effective methods were discovered and applied in this research, the analytic
hierarchy process (AHP) and simple multi-attribute rating technique
(SMART). These methods were selected due to their abilities to deal with
complex decisional environments in general and widely used in practice for
the educational environment in specific. The performance of methods is
compared using two datasets called xApi-Education and IPEDS datasets. The
obtained results based on the measurement of space complexity showed the
level of convergence and similarity between these two methods. However,
the experiments show that the simple multi-attribute rating technique
outperformed the analytic hierarchy process in terms of accuracy, deviation,
and time complexity measurement.