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发现并消除学习障碍

Discovering and removing barriers to learning
课程网址: http://videolectures.net/nipsworkshops2013_koedinger_removing_bar...  
主讲教师: Kenneth R. Koedinger
开课单位: 卡内基梅隆大学
开课时间: 2014-10-06
课程语种: 英语
中文简介:
我们开发了分析方法,从教育技术使用的数据中发现学生学习的障碍(见learnlab.org)。这些发现可以指导教学的重新设计,我们的在线实验表明,学习效果得到了提高。我们的分析方法涵盖了学生技能习得、元认知和动机等问题。首先,我将说明如何通过将学习的替代认知模型转换为统计模型并预测学习曲线数据进行模型比较来评估学习的替代认知模型。我们已经用机器学习的两种方法来生成可选的认知模型,一种更实用,另一种更前沿。第二个涉及学生学习的计算模型SimStudent,该模型通过使用智能教学系统与学生一样学习。SimStudent获得的认知模型已被证明产生了经经验验证的发现,而这些发现在智能教学系统背后的人类设计的认知模型中并不存在。换句话说,使用SimStudent不仅可以创建没有人工智能编程的智能教学系统,还可以生成比人工构建的教学系统更有效的系统。
课程简介: We have developed analytic methods to discover barriers to student learning from data for educational technology use (see learnlab.org). Such discoveries can guide the redesign of instruction and our online experiments demonstrate enhanced learning outcomes. Our analytic methods span issues of student skill acquisition, metacognition, and motivation. Focusing on the first, I will illustrate how alternative cognitive models of learning can be evaluated by translating them to statistical models and predicting learning curve data for model comparison. We have used machine learning in a couple of ways to generate alternative cognitive models, one more practical and other more cutting edge. The second involves a computational model of student learning, SimStudent, that learns as students do by using an intelligent tutoring system. The cognitive models SimStudent acquires have been demonstrated to yield empirically-verified discoveries not present in the human-designed cognitive models behind the intelligent tutoring systems. In other words, with SimStudent is the potential to not only create intelligent tutoring systems without AI programming, but to also produce systems that are pedagogically more effective than human-built tutoring systems.
关 键 词: 教育技术; 替代认知模型; 智能教学系统; 人工构建的教学系统
课程来源: 视频讲座网
数据采集: 2022-02-20:zkj
最后编审: 2022-02-20:zkj
阅读次数: 51