AttentiveLearner:基于内隐认知状态推断的自适应移动MOOC学习

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI:10.1145/2818346.2823297

Xiang Xiao, Phuong Pham, Jingtao Wang

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引用次数: 7

摘要

本演示展示了AttentiveLearner，这是一款针对大规模开放在线课程(MOOCs)和翻转课堂中消费讲座视频进行了优化的移动学习系统。AttentiveLearner使用镜头上的手指手势进行视频控制，并通过未修改的手机上的隐式心率跟踪来捕捉学习者的生理状态。通过迄今为止的三个用户研究，我们发现AttentiveLearner易于学习，使用直观。AttentiveLearner捕捉到的心跳波形可以用来推断学习者的认知状态和注意力。AttentiveLearner可以作为一个有前途的补充反馈渠道，与今天的学习分析技术正交。

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AttentiveLearner: Adaptive Mobile MOOC Learning via Implicit Cognitive States Inference

This demo presents AttentiveLearner, a mobile learning system optimized for consuming lecture videos in Massive Open Online Courses (MOOCs) and flipped classrooms. AttentiveLearner uses on-lens finger gestures for video control and captures learners' physiological states through implicit heart rate tracking on unmodified mobile phones. Through three user studies to date, we found AttentiveLearner easy to learn, and intuitive to use. The heart beat waveforms captured by AttentiveLearner can be used to infer learners' cognitive states and attention. AttentiveLearner may serve as a promising supplemental feedback channel orthogonal to today's learning analytics technologies.

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Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

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