UNLOCKING EDUCATIONAL INSIGHTS: INTEGRATING WORD2VEC EMBEDDINGS AND NAIVE BAYES CLASSIFIER FOR SERIOUS GAME DATA ANALYSIS AND ENHANCEMENT
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Volume 6 (2), December 2023, Pages 191-198
Anar Mammadli
Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.
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.
Keywords:
Serious Game, Artificial Intelligence, NLP, Text Categorization, Embeddings.
DOI: https://doi.org/10.32010/26166127.2023.6.2.191.198
Reference
Altszyler, E., Sigman, M., Ribeiro, S., & Slezak, D. F. (2016). Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database. arXiv preprint arXiv:1610.01520.
Azam, N., & Yao, J. (2011, June). Incorporating game theory in feature selection for text categorization. In International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (pp. 215-222). Berlin, Heidelberg: Springer Berlin Heidelberg.
Bayes, T. (1968). Naive bayes classifier. Article Sources and Contributors, 1-9.
Dobrovsky, A., Borghoff, U. M., & Hofmann, M. (2017). Applying and augmenting deep reinforcement learning in serious games through interaction. Periodica Polytechnica Electrical Engineering and Computer Science, 61(2), 198-208.
Georgios N.. Yannakakis, & Togelius, J. (2018). Artificial Intelligence and Games. Springer.
Jeerige, A., Bein, D., & Verma, A. (2019, January). Comparison of deep reinforcement learning approaches for intelligent game playing. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0366-0371). IEEE.
Mammadli, A., & Ismayilov, E. A. (2023, August). Application of Deep Learning Technologies in Serious Games. In 2023 5th International Conference on Problems of Cybernetics and Informatics (PCI) (pp. 1-4). IEEE.
Naili, M., Chaibi, A. H., & Ghezala, H. H. B. (2017). Comparative study of word embedding methods in topic segmentation. Procedia computer science, 112, 340-349.
Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Serafim, P. B. S., Nogueira, Y. L. B., Vidal, C., & Cavalcante-Neto, J. (2017, November). On the development of an autonomous agent for a 3d first-person shooter game using deep reinforcement learning. In 2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames) (pp. 155-163). IEEE.
Zagal, J. P., Mateas, M., Fernández-Vara, C., Hochhalter, B., & Lichti, N. (2005, June). Towards an ontological language for game analysis. In DiGRA Conference (pp. 1-13).
Zhang, Y., Jin, R., & Zhou, Z. H. (2010). Understanding bag-of-words model: a statistical framework. International journal of machine learning and cybernetics, 1, 43-52.
