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

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Abstract

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.

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  1. Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science & Engineering, Delhi Technological University, New Delhi, India

    Rahul & Rahul Katarya

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  1. Rahul
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Rahul, Katarya, R. Deep auto encoder based on a transient search capsule network for student performance prediction. Multimed Tools Appl 82, 23427–23451 (2023). https://doi.org/10.1007/s11042-022-14083-5

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