Academic early warning method, system, device and medium based on multi-channel information feature fusion
A multi-channel, academic technology, applied in the field of machine learning, can solve problems such as low prediction accuracy, achieve the effect of improving accuracy and strengthening the ability to acquire time-series feature information
Active Publication Date: 2022-07-26
杭州远传新业科技股份有限公司
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[0004] The embodiment of the present application provides a multi-channel information feature fusion academic early warning method, system, device and medium to at least solve the problem of low prediction accuracy of existing academic early warning methods in related technologies
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[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0042] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can also be applied to the present application according to these drawings without any creative effort. other similar situations. In addition, it will also be appreciated t...
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The present application relates to an academic early warning method, system, device and medium based on multi-channel information feature fusion, wherein the method includes: pre-encoding the multi-type behavior information of students, and obtaining the vectorization of each type of the multi-type behavior information represent the abstract features extracted by the convolutional neural network, and then obtain the attention mechanism representation of the abstract features; fuse the attention mechanism representations to obtain the fusion features of multiple types of behavior information; set the prediction period, according to the prediction period as an interval The fusion features on the continuous time series can make serialized academic early warning through the long short-term memory network. Through this application, the problem of low prediction accuracy of the existing academic early warning method is solved. The academic pre-warning of multi-channel student behavior information feature fusion is realized, the long-term and short-term memory network is used to strengthen the ability to obtain time-series feature information, and the attention mechanism is used to fuse multi-type features to improve the accuracy of student academic pre-warning.
Description
technical field [0001] The present application relates to the field of machine learning, and in particular, to an academic early warning method, system, device and medium based on multi-channel information feature fusion. Background technique [0002] In the context of the era of big data, educational data mining uses theories and techniques of educational psychology, computer science and statistics to discover and solve various problems in educational research and teaching practice. The collection of data such as work and rest trajectories can predict their future academic situation. Currently, the commonly used methods include machine learning methods such as support vector machines, Bayesian, etc., and integrated learning methods such as xgboost, random forest, LightGBM, etc., in addition to There are neural network methods such as: convolutional neural network, recurrent neural network, etc., but these methods do not take into account the temporal and periodic changes of...
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IPC IPC(8): G06F17/00G06Q10/04G06Q50/20G06K9/62G06N3/04
CPCG06Q10/04G06Q50/205G06N3/044G06N3/045G06F18/253
Inventor 嵇望安毫亿梁青陈默王伟凯
Owner 杭州远传新业科技股份有限公司