Learning effect optimization method based on user behavior causal relationship in MOOC log data

A technology of causality and optimization methods, applied in the field of artificial intelligence, can solve problems such as lack of symmetry, conclusion authenticity discount, lack of method tools, etc., to achieve the effect of reducing data scale and improving accuracy

Active Publication Date: 2020-09-29
XI AN JIAOTONG UNIV
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Problems solved by technology

We still lack favorable methodological tools for the study of lack of symmetry
At the same time, most studies only care about the predicted results and ignore causal factors, w

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  • Learning effect optimization method based on user behavior causal relationship in MOOC log data
  • Learning effect optimization method based on user behavior causal relationship in MOOC log data
  • Learning effect optimization method based on user behavior causal relationship in MOOC log data

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Embodiment Construction

[0057] The present invention provides a causal relationship mining method based on user learning behavior and learning effect in MOOC user learning log data, which is specifically a method of selecting effective causal independent variables from user learning logs to generate causal network groups, which are then fused into The method of final causal network, its causal inference network framework such as figure 1 shown.

[0058] The present invention is realized through the following technical solutions:

[0059] Based on the learning effect optimization method of user behavior causality in MOOC log data, the user log data is filled with missing values, the data is discretized, and the user propensity calculation and matching are used to obtain causal independent variables, and then by improving the network model generation algorithm, strive to Improve the efficiency and accuracy of network generation, and obtain the visual causal relationship between user behavior and learn...

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Abstract

The invention discloses a learning effect optimization method based on a user behavior causal relationship in MOOC log data. The method comprises the steps of based on log data of MOOC platform users,by means of user tendency calculation and matching, selecting and generating causal independent variables in a causal network according to a causal reasoning framework of a reverse fact, and obtaining the minimum data scale under different data scales according to the average Markov blanket length of the causal network when the length tends to be stable; obtaining a causal network group; screening edges among the network nodes by using an expert accuracy algorithm; grouping the screened network groups; comprehensively generating a final causal network by using a Bagging voting mechanism; obtaining the causal relationship between the user behavior and the learning effect; based on the causal relationship, reasonably planning the learning path of the user according to the reason variable node and the result variable node, the probability that whether the user completes the course is influenced by changing the operation behavior of the user or the learning time of the user, and the accuracy of relationship judgment between the variables is improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a learning effect optimization method based on user behavior causality in MOOC log data. Background technique [0002] MOOC (Massive Open Online Course, MOOC) is a large-scale open online learning platform. The emergence of MOOC has broken the unbalanced distribution of educational resources, realized the networking of educational resources through the Internet, and made education in universities more open and Sharing and timeliness enable the whole people to have access to high-quality learning resources. Since the MOOC learning platform contains a large number of online courses and the massive data generated during the learning process of learning users, this makes it possible to observe the learning mode and path of each learning user in a timely manner while maintaining the scale, and optimize the learning process for users. , improve the quality ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/20G06N5/04G06N20/00
CPCG06Q10/04G06Q50/205G06N5/04G06N20/00
Inventor 魏笔凡郭敏刘均郑庆华郝子琛卜德蕊邓婷
Owner XI AN JIAOTONG UNIV
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