Information Analytics System Database for Uniform Approach to Continuous Engineering Program Improvement

The Graduate Attribute Information Analysis System (GAIA) developed at the University of Ottawa to support the use of assessment data analytics for ongoing improvement of students' performance and continuous program development was successfully implemented in the Fall of 2015. Since then GAIA serves three engineering programs - Computer, Electrical and Software Engineering programs at the School of Electrical Engineering and Computer Science. GAIA stores and processes assessment data since 2012 and generates systematic assessment reports at program and departmental levels. In this paper we discuss current system modifications that allow for developing of a uniform approach towards data collection, analysis and reporting for the three programs. We introduce the Extract-Transform-Load (ETL) processes in place for assessing common graduate attributes. A special attention is given to the data transformation processes supporting two special purpose reports: Graduate Attribute Report per Cohort (GAR/C) and Course Progression Report per Cohort (CPR/C). The former shows average graduate attribute assessment data per each attribute, while the latter tracks student achievements as students progress within the program. Both reports are simultaneously generated per program. The fine data granularity allows for ETL interaction among sustainable data marts within the main assessment database. In this context, we outline the different stages of data processing leading to a uniform approach to continuous engineering program improvement. The described approach of assessment data management supports engineering accreditation, informs continuous program development and adds to the historic data trend analysis at program and faculty level. Furthermore, it allows for generation of COOP Progress Reports per cohort (COOPR/C).