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This research seeks to offer insight for game producers and educational institutions to investigate the effectiveness of these motivators for increasing player motivations and thus improving the quality of learning. Sentiment analysis and LDA topic modeling were used to analyze reviews of five selected video games on the Steam platform. The 6\u00a0C\u2019s Learning Motivation Model guided text mining to analyze the motivational factors used in the games and how they contribute to user learning. The effectiveness of these motivational factors was discussed in conjunction with categorized text mining. Results show that the major motivation factors of educational games are \u2018construction meaning\u2019, \u2018challenge\u2019, and \u2018control\u2019 in the 6\u00a0C\u2019s learning motivation model. Among them, users focus on whether the game\u2019s content meets their interests and the construction of the educational meaning of the game. The advantage of control, a high degree of motivational factor in video games, may turn out to be a factor that leads to user churning when the game is not interesting or attractive enough. Previous educational game research seldom involved a large sample size for generalizable findings. In addition, this research extends the application of the 6\u00a0C\u2019s learning motivation model to the digital educational gaming arena, providing a novel player-centric perspective. Based on the results, we provide recommendations and design considerations for educational game developers to enhance players\u2019 experience and motivations.<\/jats:p>","DOI":"10.1007\/s10639-024-12726-8","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T09:01:54Z","timestamp":1715072514000},"page":"22023-22056","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analytics of motivational factors of educational video games: LDA topic modeling and the 6\u00a0C\u2019s learning motivation model"],"prefix":"10.1007","volume":"29","author":[{"given":"Yitong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zerong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7926-9568","authenticated-orcid":false,"given":"Dickson K.W.","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"12726_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compedu.2017.11.002","volume":"118","author":"O Almatrafi","year":"2018","unstructured":"Almatrafi, O., Johri, A., & Rangwala, H. 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