Deep knowledge-project joint tracking-based learner portraying method and application thereof

Abstract

The invention discloses a deep knowledge-project joint tracking-based learner portraying method and application thereof. The method comprises the following steps: receiving learning behavior data from a learner, and extracting learner information and item information in the learning behavior data, including a learner identity identifier, an item identifier, an item state, a learner answer result and an original learner portrait; and enabling the extracted information to pass through a deep knowledge-project joint tracking network, and updating the learner portrait of the learner in real time. According to the invention, an integrated framework of knowledge tracking and a conceptual graph is constructed, and inaccurate user portraits caused by local missing of learning data are avoided; besides, the deep knowledge-project joint tracking in the invention overcomes the uninterpretable property of the hidden state of the traditional knowledge tracking algorithm, and integrates the course knowledge point inline state in the model, thereby further improving the accuracy of generating the learner portrait; in addition, the invention can be applied to a learning platform application scene with dynamically increased items.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a learner portrait method on an online education platform and an application thereof. Background technique [0002] User portraits, that is, labeling of user information, abstract a complete picture of users after collecting and analyzing data of main information such as user static attributes, social attributes, and behavioral attributes, which are used to support the basic methods of big data applications such as personalized recommendations. [0003] Online education mainly refers to online learning systems such as MOOC (massive open online courses, referred to as MOOC); in addition, there are some auxiliary teaching platforms, such as puzzle A platform; with the development of online learning systems on the Internet, With vigorous development, more and more learners are willing to learn through the Internet. Constructing excellent user portraits for learners on...

AI Technical Summary

Problems solved by technology

The advantage of this model is that the recurrent neural network is easy to train and it is easy to obtain better model prediction results; although there are many variants of deep knowledge tracking methods that have improved the prediction performance to varying degrees, the model needs to be used in conjunction with The question bank during training remains the same, which cannot be applied to the learning platform with dynamic increase of questions

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