A Grade Prediction Method Based on Learning Community Dialogue Flow

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.

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...

AI Technical Summary

Problems solved by technology

[0005] 1. The current work on dialogue flow analysis in learning communities is mainly to combine dialogue analysis technology and social network technology to carry out various reasoning applications, but this research and application has not yet used dialogue analysis to directly conduct student (group) The effect evaluation and performance prediction of

[0006] 2. Due to the complexity of the analysis of student dialogue, the current research and application of student performance prediction ignores the dialogue factor or only considers it as a non-important factor. This treatment will seriously affect the analysis of student learning and the prediction of academic performance.

[0007] 3. The existing grade prediction system is relatively successful in specific applications, but this type of system is also relatively fragile. Once the environment changes, this type of system will show the problem of poor adaptability, which will cause a lot of modification in the program

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