Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance

Robbiano, Christopher; Maciejewski, Anthony A.; Chong, Edwin K. P.

doi:10.1109/te.2020.2989920

Cited by 1 publication

(1 citation statement)

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“…We analyze and synthesize primary studies, and group them based on nine dimensions. Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance [10], presented a analysis techniques that were used provide novel examples of applications to student data.…”

Section: Related Workmentioning

confidence: 99%

Real time multi feature performance analysis model for efficient prediction of student performance using neural networks

Suresh

Devi

2022

ijhs

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The problem of student performance analysis and prediction has been well studied. There exist number of approaches to handle this issue which would use variety of features like the involvement in sports, education, academic and others. However, they suffer to achieve higher performance in predicting the performance of the student. To handle this issue, an efficient real time multi feature Performance Analysis Model (MFPAM) is presented in this article. The model consider number of features include the number of sessions the student attended, the number of seminars appeared, number of tests cleared, number of webinars accessed, number of assignments submitted, number of queries produced, number of subjects cleared in each time stamp, number of sports played, number of videos visited, number of extracurricular activities appeared and so on. By analyzing each features in different time stamp of the academic carrier, the method predict the performance of the student. The logs of student have been trained with neural network and at the test phase, the neurons perform analysis on each factor to produce support on different performance factors. At the output layer, the neurons generate set of weight measures towards various class of performance.

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Section: Related Workmentioning

confidence: 99%