基于大数据的精准教学评价和学习行为预测理论和方法研究

Precision teaching evaluation and learning behavior prediction are the keys to improving the quality of education and teaching. With the advent of information technology acquisition of educational data has never been easier. The widely available educational data is helpful to assess the students in systematic, real-time way and to predict the learning behavior in precision way, as well as poses multiple challenges for Precision teaching evaluation and learning behavior prediction are the keys to improving the quality of education and teaching. With the advent of information technology acquisition of educational data has never been easier. The widely available educational data is helpful to assess the students in systematic, real-time way and to predict the learning behavior in precision way, as well as poses multiple challenges for data analysis, and consequently often necessitates the use of powerful computing facilities and efficient methods. Due to the characteristics of multi-dimensional, multi-level, and multi-model, understanding the educational data remains a difficult and, until recently, a much overlooked task. In this project, we construct teaching evaluation model and predict learning behaviors with increasing levels of precision. We explicitly attempt to study the characteristics of implicit and social learning behaviors, also attempt to detangle the dynamic complexity of learning behavior. We attempt to discover the impact and interactions of student performance and leaning behavior based on integrating multi-dimensional educational data. This project will provide a more comprehensive view of learning behaviors, bring us to a fuller understanding of the students and thus to improving students’ learning quality.

精准教学评价及学习行为预测对于教育教学质量的提升有着重要意义。大数据技术的发展给基于学习过程系统、科学、精准地评价教学及对学习者学习行为进行精确预测带来了可能性，同时对信息科学与技术也提出了巨大的挑战，对多维度、多层次、多模态教育大数据进行深入、有效的分析和挖掘，揭示这些数据所蕴含的潜在知识，需要解决一系列的信息学理论和方法问题，亟待开展深入、系统的研究。本项目旨在通过对教育问题和学习行为数据特点的深入研究，基于数据融合观点集成多源异构学习行为数据，构建多维度、科学、完整的教学评价体系，提出精准的学习行为预测方法。主要包括：线下学习行为预测、在线学习行为预测、协同学习行为预测、基于教育大数据的多维教学评价体系构建。本项目的研究对于揭示教育过程中的认知规律、完善数据挖掘、模式识别等相关领域的基础理论具有积极的推动作用。

精准教学评价及学习行为预测对于教育教学质量的提升有着重要意义。大数据技术的发展给基于学习过程系统、科学、精准地评价教学及对学习者学习行为进行精确预测带来了可能性，同时对信息科学与技术也提出了巨大的挑战，对多维度、多层次、多模态教育大数据进行深入、有效的分析和挖掘，揭示这些数据所蕴含的潜在知识，需要解决一系列的信息学理论和方法问题，亟待开展深入、系统的研究。本项目旨在通过对教育问题和学习行为数据特点的深入研究，基于数据融合观点集成多源异构学习行为数据，构建多维度、科学、完整的教学评价体系，提出精准的学习行为预测方法。主要包括：线下学习行为预测、在线学习行为预测、协同学习行为预测、基于教育大数据的多维教学评价体系构建。本项目的研究对于揭示教育过程中的认知规律、完善数据挖掘、模式识别等相关领域的基础理论具有积极的推动作用。

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