Question pushing method and device, electronic equipment and storage medium
A topic push and topic technology, applied in the field of education and learning systems, can solve the problems of ignoring the relevance of topics and not considering the degree of mastery of knowledge points.
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Embodiment 1
[0059] Embodiment 1 provides a topic push method, aiming at classifying the topic data containing the same knowledge points and then inputting them into the cyclic neural network model in time order to obtain the correlation characteristics between topics, based on the correlation characteristics and preset The current answer characteristics of the question bank data, and obtain the question status s of the question bank data t , according to the topic state s t With the Markov decision process, the queuing sequence of the questions is obtained, and the target topic is selected from the queuing sequence, so as to effectively push the target topic to the students. This way of pushing questions, considering that students’ mastery of the knowledge points of the questions will change dynamically over time, according to the characteristics of students’ current answering questions and the changing characteristics of historical answering questions, pushing questions according to diff...
Embodiment 2
[0074] Embodiment 2 is an improvement made on the basis of Embodiment 1. The answer time and answer results of the question data set are extracted, and the relevance features of the question data set are obtained through the cyclic neural network model.
[0075] Extract each topic data set Q i (i=1,...,N) corresponds to the answer time and answer results of historical question data, N is the number of knowledge points, the knowledge points of the question data set, the answer time and answer results Input the preset cyclic neural network model in time order to obtain the correlation feature H of each topic data set i4 =[H i1 ,...,H i4 ], i=1, .
[0076] The correlation feature can also include the answer time change feature, then the correlation feature is H i5 =[H i1 ,...,H i5], i=1,...,N. Furthermore, the relevance feature can also be other description features of knowledge point mastery obtained through certain technical means through probability calculation. For exa...
Embodiment 3
[0078] Embodiment 3 is an improvement on the basis of Embodiment 1. The current answer features and correlation features of the acquired preset question bank data are input into the recurrent neural network model to obtain the question status s of the question bank data. t .
[0079] Get the current answer feature X of the question push object practice preset question bank data t =[X i1 ,...,X im ], i=1,...,M, M is the number of questions in the question bank data, m is the number of features contained in the current answer feature, X i3 =[X i1 ,...,X i3 ], i=1,...,N, the current answer feature includes three features: answer knowledge points, current answer time and current answer result.
[0080] The current answer feature X t With the associativity feature H in (i=1,...,N, N is the number of knowledge points, and n is the number of related features) input to the cyclic neural network model to obtain the item features of the item bank data, and the item status s is fo...
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