Learning Unit State Recognition Based on Multi-channel Data Fusion

Understand

Despite recent advances in MOOC, the current e-learning systems have advantages of alleviating barriers by time differences, and geographically spatial separation between teachers and students.

• However, there has been a 'lack of supervision' problem that e-learner's learning unit state(LUS) can't be supervised automatically.
• In this paper, we present a fusion framework considering three channel data sources: 1) videos/images from a camera, 2) eye movement information tracked by a low solution eye tracker and 3) mouse movement.
• Based on these data modalities, we propose a novel approach of multi-channel data fusion to explore the learning unit state recognition.