Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks-Reference-Cited by-同舟云学术

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Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks

Published:2024 Issue: Volume:12 Page:5882-5898
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Vives Luis¹^ORCID,Cabezas Ivan²^ORCID,Vives Juan Carlos³^ORCID,Reyes Nilton German⁴^ORCID,Aquino Janet⁴^ORCID,Cóndor Jose Bautista⁵^ORCID,Altamirano S. Francisco Segura⁴^ORCID

Affiliation:

1. Faculty of Engineering, Peruvian University of Applied Sciences, Lima, Peru

2. Department of Computing and Smart System, Universidad ICESI, Cali, Colombia

3. Professional School of Electrical Mechanical Engineering, Universidad Señor de Sipan, Pimentel, Peru

4. Universidad Nacional Pedro Ruiz Gallo, Lambayeque, Peru

5. Universidad Nacional de Trujillo, Trujillo, Peru

Funder

Research Directorate of the Peruvian University of Applied Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10381724.pdf?arnumber=10381724

Reference41 articles.

1. Computing Curricula 2020

2. The Management of a Private Peruvian University at Pandemic Times: Assessment of Decisions and Implications on the Key Indicators

3. Failure Rate in University Students: Analysis of its Variation in the Transition from Face-to-Face Education to Virtual Education

4. Failure rates in introductory programming

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