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.

Afiliación

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 en En | MEDLINE | ID: mdl-36433433

Asunto(s)

COVID-19; Pandemias; Humanos; Algoritmos; Estudiantes

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Pandemias / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

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