Improvement of relative accreditation methods based on data mining and artificial intelligence for higher education - Tastimur, Canan; Karakose, Mehmet; Akin, Erhan (Institute of Electrical and Electronics Engineers Inc., 2016)
Expansion of higher education has a great significance for development of the country and this case should not be ignored. Quality evaluation at higher education is also an important issue. This process takes shape with prospects and requests placed on higher education institutions. Especially some fields such as engineering began to progress teaching and learning and to set apart resources for quality education curricula. Accreditation concept in higher education is seen as defining of quality of education and it has to be under constantly modify. With expansion of higher education, accreditation system has become necessary. Effective implementation of accreditation purposes will provision proper functioning of the education system. In this study, an IT-based method is proposed which simplifies application of the accreditation system to departures. High efficiency and more accurate results can be obtained by being given results obtained from proposed method as feedback. There are determined whether success accreditation criteria by examining each of accreditation criteria with knowledge extracting and processing techniques. © 2016 IEEE.
15th International Conference on Information Technology Based Higher Education and Training, ITHET 2016 -- 8 September 2016 through 10 September 2016 -- Istanbul -- 125053
Keywords: Accreditation Standards; Artificial intelligence; Data mining; Higher Education; Information extraction