Program accreditation is important for determining whether or not a program or institution meets quality standards. It helps
employers to evaluate the programs and qualications of their graduates as well as to achieve its strategic goals and its continuous
improvement plans. Preparing for accreditation requires extensive e
ort. One of the required documents is the program’s selfstudy
report (SSR), which includes the PEO-SO map (which allocates the program’s educational objectives (PEOs) to student
learning outcomes (SOs)). It inuences program structure design, performance monitoring, assessment, and continuous improvement.
Professionals in each academic engineering program have designed their PEO-SO maps in accordance with their
experiences. e problem with the incorrect design of map design is that the SOs are either missing altogether or cannot be
assigned to the correct PEOs. e objective of this work is to use a hybrid data mining approach to design the correct PEO-SO
map. e proposed hybrid approach utilizes three di
erent data mining techniques: classication to nd the similarities between
PEOs, crisp association rules to nd the crisp rules for the PEO-SO map, and rough set association rules to nd the coarse
association rules for the PEO-SO map. e work collected 200 SSRs of accredited engineering programs by the ABET-EAC. e
paper presents the di
erent phases of the work, such as data collection and preprocessing, building of three data mining models
(classication, crisp association rules, and rough set association rules), and analysis of the results and comparison with related
work. e validation of the obtained results by di
erent fty specialists (from the academic engineering eld) and their recommendations
were also presented. e comparison with other related works proved the success of the proposed approach to
discover the correct PEO-SO maps with higher performance. |
