Abstract
In outcome-based academic programs, Program Educational Objectives (PEOs) and Student Outcomes (SOs) are two cores around which all programs' components and processes revolve. Needless to say, the PEOs-SOs mapping is very critical for program success and, therefore, a deep understanding of PEOs, SOs, and their intra/inter-correlations is very important for effective program's decisions making. In this context, this paper proposes a data mining-based approach to discover hidden knowledge of PEOs, SOs, and their mapping and correlations in engineering programs. More specifically, the proposed approach employs association rule mining techniques to generate association rules, among and between PEOs and SOs, from PEOs-SOs mapping data of a set of engineering programs, which can be then filtered and manipulated to discover the desired knowledge. To this end, a set of 152 self-study reports of engineering programs, accredited by American Board for Engineering and Technology-Engineering Accreditation Commission (ABET-EAC), are collected and the mapping data between their PEOs and ABET-EAC SOs are extracted. The dataset is processed and transformed into a representation suitable for association rules mining. This involves developing a set of PEOs labels, annotating data with PEOs labels, and extracting three target datasets. Apriori algorithm is then applied to each dataset to generate three sets of association rules. The generated association rules are then filtered and manipulated to discover the knowledge of PEOs, ABET-EAC SOs, and their mapping and correlations. Finally, a discussion on the informativeness of the discovered knowledge for the decisions making in engineering education is given.