Can gamification increase motivation in English learning? A machine learning predictive analysis.

Abstract

This study explores how gamification can increase motivation in English learning using predictive analysis based on the Random Forest Regressor model. The results show that gamification elements are the most influential factor in predicting motivation, explaining approximately 83% of the observed variability. Furthermore, demographic variables were found to have moderate importance, while control variables were found to have less influence, although they are still relevant. The model demonstrated good fit and consistency through cross-validation (K=5), with an average R² of 0.8338 and a low standard deviation (0.1092). The analysis also revealed a strong correlation between predicted and actual values, supporting the model's predictive capability. From a pedagogical perspective, these findings suggest that integrating gamified strategies—such as points, badges, challenges, and progression—can significantly improve student engagement and motivation. Platforms such as Duolingo have demonstrated the effectiveness of this approach in real-world contexts. Likewise, the combination of gamification and artificial intelligence allows for personalized learning, improving its relevance and adapting to individual needs. In conclusion, gamification emerges as a powerful tool for transforming English learning into a more interactive, motivating, and effective experience, especially when applied with pedagogical criteria and technological support.