Key result
Active learning-based Random Forest achieves up to ~83% accuracy in predicting student anxiety.
Why the study?
The study was conducted to explore applying active and machine learning techniques to understand and predict student anxiety levels.
Machine learning models, particularly Random Forest, combined with active learning techniques can predict student anxiety levels with up to 83% accuracy.
Answers are grounded in this paper and cite their sources.
May support student anxiety screening tools; hypothesis-generating and requires prospective validation before clinical adoption.
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.
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Almadhor et al. (2024) studied Student anxiety (n=787). Active learning-based Random Forest model vs. Other machine learning models (KNN, LR, NB, XGBoost, DT) was evaluated on Model accuracy in predicting student anxiety. Active learning-based Random Forest was the most effective model for predicting student anxiety, achieving an accuracy of 0.60 on the primary dataset and 0.83 on the secondary dataset.
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