Open AccessDeep Neural Network Model for Identification of Predictive Variables and Evaluation of Student’s Academic Performance
Author(s) -
Kandula Neha,
Jahangeer Sidiq,
Majid Zaman
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350507
Subject(s) - graduation (instrument) , academic achievement , set (abstract data type) , artificial neural network , class (philosophy) , mathematics education , computer science , predictive modelling , identification (biology) , performance prediction , predictive validity , machine learning , psychology , artificial intelligence , engineering , simulation , mechanical engineering , botany , biology , programming language , clinical psychology
An important concern for students at all levels, from universities to colleges to junior high and high school, is predicting academic achievement and individual performance. Class tests, homework, lab exams, general tests, and final exams all have an impact on a student's academic success or failure. Students' progress can be assessed by looking at their grades in core subjects and electives. The majority of research, on the other hand, says that a student's achievement is best measured by graduation. Researchers set out to develop mathematical models that may be utilized to forecast student academic performance evaluations based on internal and external type predictive indicators. Multiple predictive variables are taken into account for the assessment of student performance while modelling an efficient template for student performance assessment. The proposed model uses Deep Neural Network (DNN) in the process of considering the predictive variables and evaluating student performance using the variables. The proposed model is compared with the traditional models and the results represent that the proposed model accuracy levels are high contrasted to existing models.