Analysis of ai algorithms for foreseeing university student’s academic and co-curricular performance - Farhana, Soheli; Lajis, Adidah; Awang Long, Zalizah; Nasir, Haidawati (International Journal of Engineering and Technology, 2019)
Evaluation of studentâ€™s activity turns out to be more difficult because of the huge volume of information in the instructive databases. As of now in the universities, the absence of an existing framework to investigate and screen the understudy advancement and execution isn't being tended to. There are three main reasons why this is going on. To begin with, the examination on existing forecast strategies is as yet lacking to distinguish the most reasonable techniques for foreseeing the intelligent methods in the system. Second is because of the absence of examinations on the elements influencing understudiesâ€™ accomplishments specifically courses inside university system. Third is to unavailability of the correlation between the academic and co-curricular activities. Subsequently, a systematical writing audit on foreseeing understudy execution by utilizing different artificial intelligence (AI) algorithms strategies is proposed to enhance performance accomplishments. This paper is briefly discussed and analyzed of the different AI algorithms to predict the performance analysis of universities student by corelating academic and co-curricular values. Finally, an ideal algorithm is proposed to develop the performance analysis system by comparing the above analysis results. The accuracy of the proposed algorithm is achieved to 95.38% through analysis. It could convey the advantages and effects to understudies, instructors and scholastic foundations.Â Â