Deep embedded knowledge tracking method based on exercise difficulty and student ability

Abstract

The invention belongs to the field of knowledge tracking, and discloses a deep embedded knowledge tracking method based on exercise difficulty and student ability, which comprises the following steps: S1, constructing a deep knowledge tracking model: integrating exercise difficulty features and student ability features into the deep knowledge tracking model; and S2, predicting student performance: fusing an attention mechanism, and predicting the student performance by using the deep knowledge tracking model constructed in the step 1. Compared with a traditional knowledge tracking method, the method has the advantages that the influence of different exercise difficulty and student ability on the answering condition of the student is considered, the model is enabled to focus more attention on answering records with similar exercise difficulty and student ability through an attention mechanism, and the prediction accuracy is improved. According to the method, the accuracy of knowledge tracking is improved, the method can be used in subsequent exercise recommendation application, and the application effect is improved.

Description

technical field [0001] The invention belongs to the field of knowledge tracking, and in particular relates to a deeply embedded knowledge tracking method based on the difficulty of exercises and the ability of students. Background technique [0002] With the advent of the era of education informatization 2.0 and the rapid development of educational big data, learning analysis, artificial intelligence and other technologies, in recent years, the number of people using network resources for learning has increased rapidly, such as large-scale online open courses (Couresra, EDX, MOOCs etc.) platform provides a wealth of high-quality learning resources for learners from all over the world, so that anyone in the world can learn without obstacles. Through these platforms, a large amount of educational resource data has been accumulated, including user information data, learning data, examination data and user behavior data. These data provide a data basis for the in-depth study of...

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned prior art, only the student's answering sequence and answering results are used, and the influence of the student's ability and exercise difficulty on the student's learning process is ignored, resulting in the inability to distinguish the answering conditions of students with different learning abilities when answering different exercise difficulty levels problem, the purpose of the present invention is to provide a deep embedded knowledge tracking method based on the difficulty of exercises and the ability of students

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