FLoRA Facilitating Self-Regulated Learning with Personalized Scaffolds on Student’s own Regulation Activities

FLoRA 通过个性化支架促进学生自我调节活动的自我调节学习

• 批准号: 396890455
• 负责人: Professorin Dr. Maria Bannert
• 金额: --
• 依托单位: Radboud Universiteit Nijmegen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/396890455?language=en
• 关键词: FLoRA; Facilitating; Self; Regulated; Learning

The focus of education is increasingly set on students’ ability to regulate their own learning within technology-enhanced learning environments (TELs). Prior research has shown that self-regulated learning (SRL) leads to better learning performance but students often experience difficulties to adequately self-regulate their learning. Instructional scaffolds are a successful method to help learners and consequently improve learning outcomes. However, scaffolds are often standardized and do not adapt to the individual learning process. Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process. This research collaboration will research how to advance support given to students by i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student’s SRL by providing personalized scaffolds. We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). Our joint expertise in the fields of self-regulated learning and learning analytics provide superior opportunities to develop and test more powerful adaptive educational technologies.

教育的重点越来越多地放在学生在技术增强的学习环境（TEL）中调节自己学习的能力上。先前的研究表明，自我调节学习（SRL）导致更好的学习成绩，但学生往往遇到困难，充分自我调节他们的学习。教学支架是一种成功的方法，以帮助学习者，从而提高学习效果。然而，支架往往是标准化的，不适应个人的学习过程。学习分析和机器学习提供了一种在学习过程中更好地理解SRL过程的方法。然而，目前的方法缺乏有效性或需要在学习过程后进行广泛的分析。这项研究合作将研究如何通过i）改进不引人注目的数据收集和机器学习技术来提高对学生的支持，以更好地测量和理解SRL过程，ii）使用这些新的见解通过提供个性化的支架来促进学生的SRL。我们将通过调查和改进探索性研究（探索性研究1和研究2）中的跟踪数据来实现这一目标，并利用从这些研究中获得的洞察力来开发和测试基于实验室（实验研究3和研究4）和随后的现场研究（现场研究5）中的个人学习过程的个性化支架。我们在自主学习和学习分析领域的联合专业知识为开发和测试更强大的自适应教育技术提供了上级机会。