Short Text Mining for Classifying Educational Objectives and Outcomes

Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes program objectives. However, mapping objectives (PEOs) to student (SOs) is a challenging time-consuming task, especially for new which applying ABET-EAC (American Board Engineering Technology American Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs automatically ensure that (classification) reasonable correct. The classification also plays vital role assessment students’ learning. Since PEOs are expressed as short text, they do not contain enough semantic meaning information, consequently suffer from high sparseness, multidimensionality curse dimensionality. this work, novel associative text technique proposed map SOs. datasets extracted 152 self-study reports (SSRs) were produced operational settings an accredited by ABET-EAC. processed transformed into representational form appropriate association rule mining. rules utilized delegate classifiers SOs has shown promising results, can simplify avoid many problems caused enriching external resources related or relevant dataset.