【作 者】：
杨 雪， 姜 强， 赵 蔚， 李勇帆， 李 松
【关 键 词 】：
大数据； 学习分析； 在线学习； 学业拖延； 诊断干预
【栏 目】：
网络教育
【中文摘要】：
“大数据+”教育背景下，运用学习分析技术对行为日志数据进行解释与分析，可以帮助教师更准确地诊断学生的拖延行为，给予及时有效的教学决策。基于学习分析对大学生在线学习拖延情况进行描述，运用头脑风暴法、德尔菲法诊断拖延原因。根据聚类分析的拖延结果（积极、中等、消极）对学生进行个性化干预，并采用秩和检验方法和访谈法对干预措施的有效性进行客观验证和主观评价。结果表明，发送电子邮件、学业任务资源推送、电子徽章、弹出窗口及可视化学习过程等干预策略可以有效提高时间管理能力、自我效能与正确认知，维持学习动机，提高自我调节能力，增强同伴影响力，进而有效解决学生拖延问题，突出表现在减少了拖延次数与时间，改善了学习效果。
【英文摘要】：
In the context of "big data +" education, learning analytics technology can be used to explain and analyze students' behavioral data and help teachers diagnose students' procrastination behaviors more accurately and then make timely and effective teaching decisions. This paper describes the procrastination of college students' online learning and diagnoses its reasons through brainstorming and Delphi method. Individualized intervention is implemented for students according to the procrastination results (positive, medium, negative) based on cluster analysis. Moreover, the effectiveness of intervention measures is evaluated objectively and subjectively through rank sum test and interview method. The results show that intervention strategies such as sending e-mails, pushing learning resources, electronic badges, pop-up windows and visual learning process can effectively help students to improve their time management ability, self-efficacy and correct cognition, to maintain their learning motivation, improve their self-regulation ability, and to enhance peer influence. Finally, the problem of procrastination is effectively solved, and the number and time of procrastination are reduced. As a result, students' learning effect is improved.