Abstract
This study explores the integration of Word2Vec embeddings and machine learning models to analyze and enhance serious game data. Word2Vec captures semantic relationships in textual content, while the Naive Bayes classifier extracts meaningful patterns. The approach improves understanding of linguistic nuances, contributing to the effectiveness of serious3 games in achieving educational objectives. Experimental results demonstrate the model's efficacy in uncovering hidden insights within the game data. This research provides a robust framework for optimizing serious game content and enhancing its educational impact.
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