大数据环境下的学情分析与教学质量评价机制研究

Study on the Mechanism of Student Situation Analysis and Teaching Quality Evaluation in the Big Data Environment

大数据环境为高校教育治理提出了新的挑战,信息技术与高等教育不断融合,教学内容开放化,教师角色复杂化,学习形式个性化和灵活化,教育数据多元化和密集化等,这些因素对学情分析与教学质量评价提出了新的要求。将学情分析与教学质量评价有效结合,构建大数据环境下具有智能预测的多元化、智能化、个性化和数据化的学情分析与教学质量评价体系模型,分为特征数据提取层、学情分析机制层、策略调整反馈层等三个层次,能够促进教学改革,有效提升教学水平,增强学校核心竞争力。

The big data environment has posed new challenges for university education governance, such as the continuous integration of information technology and higher education, the openness of teaching content, the complexity of teachers’ roles,the individualization and flexibility of learning forms, the diversification and denseness of educational data, etc. These factors have put forward new requirements for the analysis of learning conditions and the evaluation of teaching quality. Effective combination of learning situation analysis and teaching quality evaluation can construct a multi-dimensional, intellectualized,personalized and data-based model of learning situation analysis and teaching quality evaluation system under large data environment, which can be divided into three levels: feature data extraction level, learning situation analysis mechanism level and strategy adjustment feedback level. It can promote teaching reform, effectively improve teaching level and enhance learning, and strengthen school core competitiveness.