STTR Phase I: Cloud-Based Pluggable Learning Analytics Engine for Educational Games

这个STTR第一期项目将研究和开发基于云的可插拔数据分析引擎，以解决教育游戏市场的问题。S需要对学习进行实时评估。如果能够很好地量化从游戏中学习的内容，那么教育类游戏就会变得更加成功，从而让玩家确信花在游戏上的时间是有价值的。然而，目前的游戏开发者没有资格或资金来提供这种分析所需的统计数据和认知评估。因此，本项目将构建一个商业化的可插拔的第三方引擎原型，实时跟踪学习者的知识增长，不受干扰，并为教育利益相关者提供定制的评估总结和反馈。该原型将在三所代表不同人口群体的高中进行开发和测试，并通过游戏教授数据素养。在商业环境下的测试将与两家成功的教育游戏公司合作。数据密集型评估技术的创新使用将在学习过程中提供简化和准确的信息，从而帮助美国目前陷入困境的STEM教育。该项目还将有助于启动一项新业务，该业务有可能提高教育游戏和数字学习的市场价值。这个STTR第一期项目使用蒙特卡罗贝叶斯知识追踪（MC-BKT）算法。该算法是基于多年来在物理、教育和计算方面的研究而开发的，首次实现了实时的个性化知识追踪。在之前的研究中，事后MC-BKT分析导致识别出多达七种与游戏片段中知识增长相关的不同模式，与基于游戏屏幕和玩家话语的视频分析的人类判断相比，准确率为84%。该项目将进行研究，以测试MC-BKT算法的评估潜力是否可以从最初的研究扩展到涉及不同内容领域的游戏玩家，在更多具有不同人口统计数据的教室中（涉及约600名高中生），并且是实时的。基于研究成果，本项目将围绕MC-BKT算法以基于云的可插拔引擎的形式构建原型商业产品。两个受欢迎的商业教育游戏以及本项目内部的各种游戏将测试连接到引擎，用于实时测试知识跟踪，学习问题检测，并将反馈传递给教师，家长，游戏设计师和学习者。

This STTR Phase I project will carry out research and development on a cloud-based pluggable data analytics engine to address the educational game market?s need of real-time assessment for learning. Educational games will become much more successful if learning from games can be well quantified so that buyers will be assured that the time spent using games is productive. However, currently game makers are not qualified or funded to provide the statistics and cognitive assessment required for such analysis. This project will thus build a prototype of commercial pluggable third-party engine that traces the growth of the learner's knowledge in real time without interference and provides customized assessment summary and feedback to educational stakeholders. The prototype will be developed and tested with games that teach data literacy in three high schools representing diverse demographic groups. The testing in a commercial environment will begin in collaboration with two successful educational game companies. The innovative use of data-intensive assessment technology will aid in currently struggling STEM education in the United States by providing streamlined and accurate information while learning occurs. This project will also help launch a new business that has potential to boost the market value of educational games and digital learning.This STTR Phase I project utilizes the Monte-Carlo Bayesian Knowledge Tracing (MC-BKT) algorithm. This algorithm was recently developed in-house based on techniques distilled through years of research in physics, education, and computation, and makes it possible to perform individualized knowledge tracing in real-time for the first time. In prior research, post hoc MC-BKT analysis led to identification of up to seven distinct patterns associated with knowledge growth during game segments, with 84% accuracy as compared with human judgments based on video analysis of game screens and players' discourse. This project will conduct research to test whether this assessment potential of the MC-BKT algorithm can be extended beyond initial research to players with games involving different content domains, in a greater number of classrooms with diverse demographics (involving around 600 high school students), and in real time. Based on research results, this project will build a prototype commercial product around the MC-BKT algorithm in the form of a cloud-based pluggable engine. Two popular commercial educational games as well as various games internally sourced within this project will be test-connected to the engine for real-time testing of knowledge tracing, learning problem detection, and feedback delivery to teachers, parents, game designers, and learners.