Learning behavior dynamic prediction method and apparatus, equipment and storage medium

Abstract

The invention discloses a learning behavior dynamic prediction method and device, equipment and a storage medium. The prediction method comprises the following steps: based on the learning behavior data of a user in a historical period, adopting a long-short-time memory cycle neural network as a prediction model for training to obtain an optimal model; and acquiring learning behavior data of the user in the first time period, and outputting a prediction result based on the optimal model. The invention adopts LSTM-RNN as a prediction model to predict the output results, can improve the accuracyof the prediction, and uses an interactive visual analysis mode to display user information data, facilitates viewing and analysis of user data information at the background of the system, so as to help teachers to timely and intuitively know the results of data analysis, and then achieve the ''closed loop'' of the teaching process. The invention can be widely applied to various learning behaviorprediction systems.

Description

technical field [0001] The invention relates to the field of computer data analysis, in particular to a learning behavior dynamic prediction method, device, equipment and storage medium. Background technique [0002] MOOC: Acronym for massive open online courses, massive open online courses; [0003] LSTM: the abbreviation of Long Short-Term Memory, long and short-term memory; [0004] LSTM-RNN: the abbreviation of Long Short-Term Memory-Recurrent Neural Networks, long and short-term memory recurrent neural network; [0005] Adam algorithm: The name Adam comes from adaptive moment estimation, adaptive moment estimation, which is an algorithm for optimizing a random objective function based on a first-order gradient. [0006] Logistic Sigmoid function: the value range is (0,1), and it is often used as an activation function in deep learning. The function expression is [0007] AUC: Abbreviation for Area Under Curve, the area under the ROC curve; the AUC value is a probab...

AI Technical Summary

Problems solved by technology

[0016] 1. The prediction accuracy is not high;

[0017] 2. Lack of visual solutions, it is inconvenient to manage in the MOCC background

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