Program accreditation is important for determining whether or not a program or institution meets quality standards. It helps employers to evaluate the programs and qualiƒcations 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 inŽuences 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: classiƒcation to ƒnd the similarities between PEOs, crisp association rules to ƒnd the crisp rules for the PEO-SO map, and rough set association rules to ƒnd 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 (classiƒcation, 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 ƒfty specialists (from the academic engineering ƒeld) 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.