What question did this study set out to answer?

The aim is to enhance spoken proficiency in English instruction using digital media and machine learning (ML).

June 14, 2026Open Access

Advancements in English Teaching: Integrating Digital Media and Machine Learning for Interactive Learning

Key Points

The aim is to enhance spoken proficiency in English instruction using digital media and machine learning (ML).
Developed an ML-based pronunciation model using Intelligent Honeybee Mating tuned FSVM (IHM-FSVM)
Utilized multimedia instruction including apps, videos, and gamified exercises
Preprocessed speech data with Wiener filtering and extracted features using MFCC
IHM-FSVM achieved 97.9% accuracy in pronunciation assessment
The model recorded 95.9% recall and 96.2% precision
The F1-score for the model was 96.5%, outperforming conventional methods.

Abstract

Integrating digital media and interactive learning has transformed English instruction, enhancing teaching and learner engagement. Limited resources hinder spoken proficiency, but advances in digital media and ML provide solutions. This study proposes an ML-based pronunciation model using Intelligent Honeybee Mating tuned FSVM (IHM-FSVM) in an online interactive framework. Students used multimedia instruction via apps, videos, and gamified exercises. Speech data were preprocessed with Wiener filtering and features extracted with MFCC. IHM-FSVM outperformed conventional methods, achieving 97.9% accuracy, 95.9% recall, 96.2% precision, and 96.5% F1-score, demonstrating its effectiveness in improving interactive English learning and pronunciation.

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