Online distance learning is highly learner-centred, requiring different skills and competences from learners, as well as alternative approaches for instructional design, student support, and provision of resources. Learner autonomy and self-regulated learning (SRL) in online learning settings are considered key success factors that predict student performance. SRL comprises processes of planning, monitoring, action and reflection according to Zimmerman. And typically focuses on three key features of learners: (1) use of SRL strategies, (2) responsiveness to self-oriented feedback about learning effectiveness, and (3) motivational processes. SRL has been identified as having a direct correlation with students success, including improvements in grades and the development of relevant skills and strategies. Such skills and strategies are needed to become a successful lifelong learner. This chapter introduces a Mobile Multimodal Learning Analytics approach (MOLAM). I argue that the development of student Self-Regulated Learning would benefit from the adoption of this approach, and that its use would allow continuous measurement and provision of in-time support of student SRL in online learning contexts.
翻译:在线远程学习高度以学习者为中心,需要学习者提供不同的技能和能力,以及教学设计、学生支持和提供资源的替代方法。在线学习环境中的学习者自主和自律学习被认为是预测学生成绩的关键成功因素。学习者自主和自律学习(SRL)包括Zimmerman认为规划、监测、行动和反思过程。通常侧重于学习者的三个关键特征:(1) 使用SRL战略,(2) 对学习效果的自取反馈作出反应,(3) 激励性进程。SRL已被确定为与学生的成功有直接关系,包括提高年级和发展相关技能和战略。这些技能和战略是成功终身学习者的必要条件。本章引入了流动多模式学习分析方法(MOLAM) 。我认为,学生自我调节学习的发展将受益于采用这一方法,而且使用这一方法将允许持续测量和在网上学习环境中提供实时支持学生SRL。