Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning
CEUR Workshop Proceedings
; 3387:331-343, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20243702
ABSTRACT
The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)
classification; data; dataset; datastore; Extra Trees; feature importance; Gradient Boosting; Logistic Regression; model; Neural Network; Prediction; Random Forest; SMV; Adaptive boosting; Classification (of information); Computer aided instruction; Decision trees; E-learning; Education computing; K-means clustering; Learning systems; Nearest neighbor search; Random forests; Support vector machines; Extra-trees; Logistics regressions; Neural-networks; Students
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Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio observacional
/
Ensayo controlado aleatorizado
Idioma:
Inglés
Revista:
CEUR Workshop Proceedings
Año:
2023
Tipo del documento:
Artículo
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