A Grade Prediction Method Based on Learning Community Dialogue Flow

A learning community and prediction method technology, applied in the field of learning analysis, can solve problems that affect the analysis of students' learning situation and prediction of academic performance, poor system adaptability, and neglect of dialogue factors, etc.
CN108228779BActive Publication Date: 2021-03-23HUAZHONG NORMAL UNIV

Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
HUAZHONG NORMAL UNIV
Publication Date
2021-03-23

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Abstract

The invention belongs to the field of learning analysis, and provides a method for predicting achievement on the basis of conversation stream in learning communities. Achievement grades of learners inlearning groups are outputted for conversation stream files of the learning groups in inputted courses, the method is divided into a training phase and a predicting phase, achievement prediction models are obtained at the training phase, and the achievement can be predicted by the aid of the models at the predicting phase. The method has the advantages that the conversation stream in the online learning communities can be analyzed by the aid of conversation stream partitioning algorithms, conversation state matrix generating algorithms and prediction model generating algorithms on the basis that conversation stream data in the online learning communities are acquired, accordingly, learning effects of a certain learning group can be automatically evaluated, the achievement grades of students in the group can be predicted, and teachers can predict the individual students and can intervene in the individual students.
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Description

technical field

[0001] The invention belongs to the field of learning analysis, and in particular relates to a performance prediction method based on dialogue flow in a learning community. Background technique

[0002] Currently, typical works on dialogue flow analysis in learning communities are as follows: First, Scholand combines linguistic analysis-based speaker relationship assessment with a social network processing framework to predict underlying structural relationships and describe interaction patterns between groups. By selectively extracting, combining, and manipulating the psychological, social, and emotional language markers of different speakers, rich mapping relationships within and between groups can be established, making difficult tasks such as managing organizational change, organizational design, and inter-organizational relationships easier. easy. Second, Dowell used language and dialogue as tools to study the link between academic performance and socia...

Claims

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