Method and system for expanding MOOC course concepts

Abstract

The embodiment of the invention provides a method and system for expanding a MOOC course concept, and the method comprises the steps of taking an online interactive game as a training environment, and carrying out the training to obtain a reinforcement learning model; performing concept extension based on an in-class concept set and the reinforcement learning model, obtaining user feedback in the extension process, and the in-class concept set being composed of course knowledge point content needing to be supplemented and explained in the MOOC course; and returning the feedback of the user to the interactive game to be expanded again until a preset target is reached, so as to obtain an expansion result. According to the embodiment of the invention, by using the training method of reinforcement learning, the invention can be applied to newly set courses on a large scale after training on some specific courses, and compared with a traditional method, a large amount of manual annotation is saved, so that the method has relatively high ductility. Meanwhile, due to the multi-level training mode, when courses related to the multidisciplinary field are processed, a high-quality extension result can be kept.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method and system for expanding the concept of MOOC courses. Background technique [0002] With the popularization and commercialization of massive online open course platform (also known as MOOC, Massive Open Online Course, referred to as MOOC), this new type of education has attracted more and more independent learners according to their own needs and needs. I like to study online. In fact, while this model brings convenience, it also lacks some functions: In traditional classrooms, teachers can supplement the knowledge points that need to be explained according to students' performance and questions, but this kind of interaction is difficult in MOOCs Achieved, because MOOC courses are mainly pre-recorded course videos. When students are learning the course videos, they cannot timely feedback the problems in the course learning process to the teacher, and these pro...

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Problems solved by technology

In fact, while this model brings convenience, it also lacks some functions: In traditional classrooms, teachers can supplement the knowledge points that need to be explained according to students' performance and questions, but this kind of interaction is difficult in MOOCs Achieved, because MOOC courses are mainly pre-recorded course videos. When students are learning the course videos, they cannot timely feedback the problems in the course learning process to the teacher, and these problems generally belong to some expanded content and knowledge in the course. Point extension, because the teacher cannot obtain the feedback from the students on the spot, the interaction between the teacher and the students is often very lagging

[0003] However, the existing automatic concept expansion methods are difficult to use in actual MOOCs for two main reasons. First, the concept of a MOOC course is often composed of multiple categories of concepts. Existing methods in When expanding such a set of concepts, the phenomenon of "semantic drift" is likely to occur, which makes the quality of the extended results too low to be used; second, the existing methods are mainly supervised training methods, and MOOC courses are often updated, which makes The performance of these methods in the newly entered courses is not satisfactory, and cannot meet the teaching requirements

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