Contributions: Prior studies on education have mostly followed the model of the cross-sectional study, namely, examining the pretest and the posttest scores. This article shows that students’ knowledge throughout the intervention can be estimated by time-series analysis using a hidden Markov model (HMM). Background: Analyzing time series and the interaction between the students and the game data can result in valuable information that cannot be gained by only cross-sectional studies of the exams. Research Questions: Can an HMM be used to analyze the educational games? Can an HMM be used to make a prediction of the students’ performance? Methodology: The study was conducted on ( $N=854$ ) students who played the Save Patch game. Students were divided into class 1 and class 2. Class 1 students are those who scored lower in the posttest than class 2 students. The analysis is done by choosing various features of the game as the observations. Findings: The state trajectories can predict the students’ performance accurately for both classes 1 and 2.

中文翻译：

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使用隐马尔可夫模型预测学生在教育游戏中的表现

贡献：以往的教育研究大多遵循横断面研究的模式，即考察前测和后测的分数。这篇文章表明，学生在整个干预过程中的知识可以通过使用隐马尔可夫模型 (HMM) 的时间序列分析来估计。背景： 分析时间序列以及学生与游戏数据之间的交互可以产生仅通过对考试的横断面研究无法获得的宝贵信息。 研究问题：可以使用 HMM 来分析教育游戏吗？可以使用 HMM 来预测学生的表现吗？方法： 该研究是在（ $N=854$ ) 玩过 Save Patch 游戏的学生。学生分为一班和二班。一班学生是那些在后测中得分低于二班学生的学生。分析是通过选择游戏的各种特征作为观察结果来完成的。发现： 状态轨迹可以准确预测学生在 1 级和 2 级的表现。