Deep auto encoder based on a transient search capsule network for student performance prediction

作     者：Rahul Katarya, Rahul 

作者机构：Delhi Technol Univ Dept Comp Sci & Engn Big Data Analyt & Web Intelligence Lab New Delhi India 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (多媒体工具和应用)

年 卷 期：2023年第82卷第15期

页      面：23427-23451页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Deep learning Student performance prediction OULA dataset Classification Deep autoencoder Capsule network 

摘      要：Prediction of Student performance through a machine predicts a student s future success. It can be considered an essential procedure to determine the students academic excellence and identify them at high risk for academic performance. Prediction of student performance also provides universities with a high reputation and ranking. The evaluation of What students can do with their learning is still a tedious task. There are many challenging factors to solve this problem, mainly owing to the enormous amount of data collected from students. Most of the research works have focused on developing new methodologies for student performance prediction. But all the existing work has some performance limitations. Here, a new model called transient search capsule network based on the deep Autoencoder (TSCNDE) is introduced to detect student performance. The TSCNDE method is implemented with the help of the PYTHON tool. The performance prediction process has been completed with the help of the OULA dataset. The obtained results are assessed on accuracy (99.2%), precision, (99.8%), specificity (98.7%), and sensitivity (98.9%) parameters. The results obtained showed that the TSCNDE method is about 99.2% more accurate than the other related method. Also, the obtained results are compared with some existing deep learning and machine learning methods.