SA-FEM: Combined Feature Selection and Feature Fusion for Students' Performance Prediction.

Ye, Mingtao; Sheng, Xin; Lu, Yanjie; Zhang, Guodao; Chen, Huiling; Jiang, Bo; Zou, Senhao; Dai, Liting

Ye, Mingtao; Sheng, Xin; Lu, Yanjie; Zhang, Guodao; Chen, Huiling; Jiang, Bo; Zou, Senhao; Dai, Liting.

Afiliação

Ye M; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Sheng X; Huannan Subdistrict Office, Dinghai District, Zhoushan 316000, China.
Lu Y; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Zhang G; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Chen H; College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325000, China.
Jiang B; Shanghai Institute of AI Education, East China Normal University, Shanghai 200062, China.
Zou S; College of Computer Science and Technology (College of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China.
Dai L; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel) ; 22(22)2022 Nov 15.

Article em En | MEDLINE | ID: mdl-36433433

Assuntos

COVID-19; Pandemias; Humanos; Algoritmos; Estudantes

Palavras-chave

e-learning behavior classification; feature fusion; feature selection; prediction and analysis; prediction of learning performance

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pandemias / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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