PREDICTING STUDENTS PERFORMANCE THROUGH INTERNET USAGE BY ARTIFICIAL NEURAL NETWORK
| dc.contributor.author | RABEYA KHATUN RINKY | |
| dc.date.accessioned | 2025-11-23T05:38:54Z | |
| dc.date.available | 2025-11-23T05:38:54Z | |
| dc.date.issued | 2021-01-30 | |
| dc.description.abstract | Students academic performance primarily depends on their internet usage for various activity purpose. Predicting students academic success is critical for educational institutions. Because strategic programs can be planned in improving or maintaining students’ performance during the period of their studies. The estimation in this study is done by a multilayer perceptron neural network model. 18 parameters which contain students internet usage data captured by a questionnaire chosen as input layer parameters. Hidden nodes will be determined experimentally. The output level values which define success of the students. The Back-Propagation algorithm is used for training of ANN. The mean squares of the errors are used as a performance (error) function with its goal set to zero. In conclusion, the application done with comparison with other ML algorithms. It is recommended that a research with same parameters would be better results with higher participation. In this paper we perform our experiment for school, college and university students which helps us to indicate them separately. | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/126 | |
| dc.title | PREDICTING STUDENTS PERFORMANCE THROUGH INTERNET USAGE BY ARTIFICIAL NEURAL NETWORK |
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