الإسهام النسبي لبعض المتغيرات النفسية في التنبؤ بالأداء الأكاديمي لدى طلاب الجامعة : دراسة مقارنة باستخدام الشبكة العصبية الاصطناعية والانحدار الخطي المتعدد
The research aimed to verify the effectiveness of using the artificial neural network model and compare it with the multiple linear regression model by predicting the academic performance of a sample of second-level students at the Faculty of Education, Ain-Shams University (n = 472) in the academic year 2022-2023, considering some psychological variables (emotional intelligence, cognitive test anxiety, and general self-efficacy) in addition to previous academic performance and discipline. The following scales were applied: the emotional intelligence scale, the cognitive test anxiety scale, and the general self-efficacy scale. The results indicated that the artificial neural network model (R 2 = 0.266, RMSE = 0.390) was superior to the multiple linear regression model (R 2 = 0.135, RMSE = 0.423). The relative importance of the predictor variables was calculated according to the artificial neural network model by using the permutation method. (Published abstract)