【摘要】 学习环境的智能化已成为主流趋势,如何合理运用学习分析技术挖掘并分析其中的学生学习行为数据,优化学生学习行为、激发其学习主动性,是在线教育领域值得关注的课题。为此,基于对学习行为文献的分析,从数据采集与存储、行为分析、反馈与提醒、智能化行为优化、智能引擎等五个方面,构建了学习行为优化模型,并将模型运用于湖南S大学《信息技术与课程整合》课程的智慧教学实践中,依托智慧教学平台获取并分析学生学习行为数据,再针对具体情景采取合理的学习行为优化措施。研究结果表明,经过这一优化,不仅有效提升了学生认知维度的学习成绩、强化了互动维度的学习互动;而且提高了时效维度的学习任务完成率、强化了参与维度的学习积极性和参与性。更多还原

【Abstract】 The intelligentization of learning environment has become the mainstream trend. How to use the learning analysis technology to mine and analyze student’s learning behavior data,optimize student’s learning behavior,and stimulate learning initiative is a topic worthy of attention in the field of online education. To this end,based on the analysis of learning behavior literature,a learning behavior optimization model was constructed from five aspects: data collection and storage,behavior analysis,feedback and caution,intelligent behavior optimization,and intelligent engine. The model was applied to the intelligent teaching practice of Hunan S University’s course,"The Integration of Information Technology and Curriculum". Student’s learning behavior data was obtained from the intelligent teaching platform. Different measures were adopted to optimize learning behavior for specific scenarios. The research results show that optimical measures can not only improve the academic records on cognitive dimension effectively and enhance learning effects on interactive dimension,but also increase task-performing efficiency on time dimension and strengthen motivation and participation on participation dimension.更多还原