<![CDATA[This study reviews techniques and algorithm models often used in the analysis of educational data mining. The review in this study is based on previous studies to provide researchers knowledge about trends and challenges analysis Educational data mining in game design meaningful. However, there is a lot of games design developed without analysis Educational data mining which then will not answer the student problem. The analysis needed periodic data and developing the game required actual student conditions, this is a combination inseparable. Determine Research questions, Search Terms, and filtering for the selection and analysis of the article review. There are some student problems on analysis review, namely prediction student performance, student behavior, student at-riks, and student dropout. The number of Articles in the study was 33 with 21 Articles of research and 12 of Article review. The number of studies 8 with percent 38% used techniques Confusion matric with 33% percent used algorithms Decision Tree in 7 of studies. The section in this study consists of techniques evaluation, model selection, outcome, subject, and algorithm method. Which are recommended techniques and algorithms for analysis Educational data mining and in ideal game design to further research.]]>
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