Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.

基于机器学习的建模技术最近已成为开发用于解释，预测和描述系统/人类行为的模型的强大技术和工具。在开发智能教育系统或技术时，一些研究集中于应用独特的机器学习算法来为特定教育系统构建临时学生模型。但是，从先前的教育经验中收集的教育数据中系统地开发数据驱动的学生模型仍然是一个挑战。我们提出了一种系统的，基于机器学习的综合建模方法，以从历史教育数据中开发出高性能的预测学生模型，以解决这一问题。该方法论解决了从数据处理到建模再到模型部署的基本建模问题。所述方法可以帮助开发用于智能教育系统的学生模型。在对所提出的基于机器学习的方法进行详细描述之后，我们将其应用于智能导航辅导系统。利用在智能导航辅导系统中收集的历史数据，我们进行了大规模的实验，以建立用于培训系统的学生模型。初步结果证明，所提出的方法对于开发各种智能教育系统的高性能模型是有用且可行的。利用在智能导航辅导系统中收集的历史数据，我们进行了大规模的实验，以建立用于培训系统的学生模型。初步结果证明，所提出的方法对于开发各种智能教育系统的高性能模型是有用且可行的。利用在智能导航辅导系统中收集的历史数据，我们进行了大规模的实验，以建立用于培训系统的学生模型。初步结果证明，所提出的方法对于开发各种智能教育系统的高性能模型是有用且可行的。