Assessing and Mitigating Foreign Language Anxiety Among English Language Learners Using Hybrid Survey-Analytics Approach

Foreign Language Anxiety (FLA) causes students studying English as a second language to struggle academically
and lack motivation to complete their education. This
psychological barrier manifests itself in this way since it
typically leads to avoidance behaviors, reduced involvement in classroom activities, and worse performance outcomes.
Although language education systems are fighting FLA,
research on the topic has increased. Most of the studies now
accessible lack a robust quantitative assessment technique and do not use predictive modeling to identify at-risk children. This research intends to provide a hybrid approach combining a machine learning categorization model's quantitative analysis with qualitative survey tools such the Foreign Language Classroom Anxiety Scale (FLCAS). Two hundred fifty college students learning English provided a dataset that had to be preprocessed. Characteristics like communication apprehension, test anxiety, and fear of unfavorable evaluation were drawn out and processed through models including Logistic Regression, Random Forest, and Support Vector Machine (SVM) to determine the degrees of anxiety individuals experience. In terms of accuracy, the Random Forest classifier surpassed both the Support Vector Machine and the Logistic Regression with an accuracy of 88.6%.