Multi-mode learning process state information compression recording method

Abstract

The invention relates to a method for acquisition and compression recording of learning process state information of learners during online teaching, and aims to provide a method for multi-mode learning process state information acquisition and cloud recording based on an event trigger mode. The method adopts an information (facial expressions, gestures, eye conditions, sight, sound and the like) acquisition device, an information processing device and an abnormal information cloud recording device. Through processing of multiple information, characteristic data such as facial expressions, gestures, eye conditions and voice of a learner can be obtained, a learning state of the learner can be determined through time sequence judgment of single-mode information, and through mutual correction of multi-mode information combinations, data cloud recording is realized. The method for on-line learner self-adaption learning process recording and learning state determination-based trigger recording under the learning condition has a wide application prospects in improvement of the learning process detection effect on a network teaching system and intelligence.

Description

technical field [0001] The invention relates to the field of intelligent online learning information recording methods, in particular to a data compression recording method for multi-modal learning process state information. Background technique [0002] Modality refers to the dynamic data collected. Multimodality refers to various dynamic data collected. Online learning is a new way of learning, and it is also an inevitable product of the information age. Compared with traditional teaching, the advantage of online learning is that it uses computers and networks to learn electronic teaching resources, which greatly reduces the pressure of teaching and teaching for teachers. At the same time, online teaching resources are more abundant, and learners can make a wide range of choices according to their own needs. However, when learners participate in online learning, the separation of time and space between teachers and students hinders teachers from understanding the learni...

AI Technical Summary

Problems solved by technology

However, the information collected by existing methods is simplistic, one-sided, and limited, and cannot fully and accurately reflect the entire state of the learner in the learning process.

For example, some learners sit upright, but have unnatural expressions and abnormal facial movements. If only through posture recognition, it is easy to misjudge that the learners are in a good learning state; some learners have serious expressions but close their eyes for a long time. It is also very easy to misjudge that the learner is in a good learning state

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