A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs
نویسندگان
چکیده
Abstract Predicting student’s successful completion of academic programs and the features that influence their performance can have a significant effect on improving students’ completion, graduation rates reduce attrition rates. Therefore, identifying students are at risk, courses where improvements in content, delivery mode, pedagogy, assessment activities improve learning experience In this work, we developed prediction explanatory model using adaptive neuro-fuzzy inference system (ANFIS) methodology to predict grade point average (GPA), time, enrolled information technology program Ajman University. The approach adopted uses grades introductory fundamental IT high school (HSGPA) as predictors. Sensitivity analysis was performed quantify relative significance each predictor explaining variations GPA. Our findings indicate HSGPA is most influential factor predicting GPA, with data structures, operating systems, software engineering coming closely second place. On side, found discrete mathematics course causing followed by engineering, security, HSGPA. When ran testing data, 77% predicted values fell within one root mean square error (0.29) actual which has maximum four. We also shown ANFIS better predictive accuracy than commonly used techniques such multilinear regression. recommend other institutions conduct comparable studies shed some light our findings.
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ژورنال
عنوان ژورنال: Education and Information Technologies
سال: 2022
ISSN: ['1573-7608', '1360-2357']
DOI: https://doi.org/10.1007/s10639-022-11205-2