Data Analytics and Machine Learning in Education-Reference-Cited by-同舟云学术

Data Analytics and Machine Learning in Education

Published:2023-01-20 Issue:3 Volume:13 Page:1418
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Gómez-Pulido Juan A.¹^ORCID,Park Young²,Soto Ricardo³^ORCID,Lanza-Gutiérrez José M.⁴^ORCID

Affiliation:

1. Department of Technology of Computers and Communications, Universidad de Extremadura, 10003 Cáceres, Spain

2. Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA

3. School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 4059, Chile

4. Department of Computer Sciences, Universidad de Alcalá, 28805 Alcalá de Henares, Spain

Abstract

The widespread application of information and communication technologies in education, especially in the context of learning management platforms, is generating a large amount of data related to the academic activities in which students and teachers participate [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/3/1418/pdf

Reference14 articles.

1. Chang, W., Ji, X., Liu, Y., Xiao, Y., Chen, B., Liu, H., and Zhou, S. (2020). Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm. Appl. Sci., 10.

2. de la Fuente-Mella, H., Guzmán Gutiérrez, C., Crawford, K., Foschino, G., Crawford, B., Soto, R., León de la Barra, C., Cisternas Caneo, F., Monfroy, E., and Becerra-Rozas, M. (2020). Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques. Appl. Sci., 10.

3. Kabathova, J., and Drlik, M. (2021). Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques. Appl. Sci., 11.

4. Boumi, S., and Vela, A.E. (2021). Quantifying the Impact of Student Enrollment Patterns on Academic Success Using a Hidden Markov Model. Appl. Sci., 11.

5. Falát, L., and Piscová, T. (2022). Predicting GPA of University Students with Supervised Regression Machine Learning Models. Appl. Sci., 12.

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1. Integration of Collaborative Filtering Into Naive Bayes Method to Enhance Student Performance Prediction;International Journal of Information and Communication Technology Education;2024-08-22

2. Enhancing Educational Strategy Through K-Means Clustering: A Study on Academic Departments;2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE);2024-06-19

3. A Review Analysis on Using "AIED" to Improve Student Engagement in Hybrid Education;Bulletin of Business and Economics (BBE);2024-06-01

4. Towards Smart Education in the Industry 5.0 Era: A Mini Review on the Application of Federated Learning;2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech);2023-11-14

5. Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis;Education Sciences;2023-06-21