Learning Analytics for Adaptive Systems supporting Self-regulated Social Learning

支持自我调节社交学习的自适应系统的学习分析

• 批准号: 356029-2013
• 负责人: Gasevic, Dragan
• 金额: $ 1.09万
• 依托单位: Athabasca University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=549899
• 关键词: Learning; Analytics; Adaptive; Systems; supporting

Opportunities to facilitate learning, in all forms from elementary schools to post-secondary, workplace, and life-long learning and at different levels, on the Internet and with social media are widely recognized today. However, information offered on the Internet and learning activities in social media, even via formal academic courses, are mainly not based on empirically-proven recommendations from learning sciences. Often, learners are left "on their own" to figure out which study tactics best work for them. This is a serious threat given that most learners have weak skills for self-regulated learning, as reported in learning science research. This can have severe effects on their learning success with numerous societal consequences such as high dropout rates and workforce unprepared for career transitions.

今天，人们普遍认识到，在互联网和社交媒体上，从小学到中学后、工作场所和终身学习等各种形式的学习机会都是不同层次的。然而，互联网上提供的信息和社交媒体上的学习活动，即使是通过正式的学术课程，也主要不是基于学习科学的经实践证明的建议。通常，学习者只能“靠自己”来找出最适合他们的学习策略。这是一个严重的威胁，因为大多数学习者自我调节学习的技能很弱，正如学习科学研究所报告的那样。这可能对他们的学习成功产生严重影响，并产生许多社会后果，如辍学率高，劳动力对职业过渡毫无准备。