Multi-Class Adaptive Active Learning for Predicting Student Anxiety

This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels. This study utilizes active learning strategies to increase the effectiveness of machin...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.58097-58105
Main Authors: Almadhor, Ahmad, Abbas, Sidra, Sampedro, Gabriel Avelino, Alsubai, Shtwai, Ojo, Stephen, Hejaili, Abdullah Al, Strazovska, Lubomira
Format: Article
Language:English
Subjects:
Active learning
Adaptation models
Anxiety
Anxiety disorders
Data models
Datasets
deep learning
Emotion recognition
inclusive education
Machine learning
Mental health
Model accuracy
Nearest neighbor methods
Prediction algorithms
Prediction models
Predictive models
Stress
student anxiety
student wellbeing
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels. This study utilizes active learning strategies to increase the effectiveness of machine learning models in predicting anxiety levels among students. Additionally, this adaptation emphasizes the usefulness of active learning methodologies in enhancing the precision of machine learning models for student anxiety prediction. This study uses two datasets containing information on student behavior and leverages machine learning methods to construct predictive models for student anxiety. This study uses various machine learning models: K-Nearest Neighbors (KNN), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF). Experiments revealed that active learning-based LR yielded a score of 0.61, and RF performed well with an average accuracy of 0.60 on the first dataset. Similarly, for the second dataset, RF is the most effective model, achieving an accuracy of 0.83. These results provide valuable insights into the models' performance across key metrics. Further, this research highlights the potential of employing machine learning techniques and active learning methodologies to predict and manage student anxiety.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3391418