Method for constructing problem difficulty level based on big data as well as device and service equipment

Abstract

The embodiments of the invention relate to the technical field of computers, and disclose a method for constructing a problem difficulty level based on big data as well as a device and service equipment. The method comprises the following steps: collecting answer data for multiple student users to answer a certain target exercise; judging a grade identifier indicating the grade of each student in the multiple student users is same as that in attribute information of the target exercise or not; and when the grade identifier indicating the grade of each student in the multiple student users is same as that in the attribute information of the target exercise, calculating the answering accuracy of the target exercise on the basis of the answer data for the multiple student users to answer the target exercise; and determining the problem difficulty level of the target exercise on the basis of the calculated answering accuracy, and updating the determined problem difficulty level into the attribution information of the target exercise. By implementing the embodiments of the invention, the problem difficulty level of an exercise can be objectively defined, and dynamic and flexible management of the defined problem difficulty level can be achieved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a big data-based method and device for constructing the difficulty of a topic, and service equipment. Background technique [0002] At present, in order to facilitate students and users to find exercises of appropriate difficulty from the exercise bank, teachers usually manually mark the exercises in the exercise bank as "difficult", "medium", and "easy" when building the exercise bank. Definition of difficulty of a topic. However, in practice, it is found that the way that teachers manually define the difficulty of exercises has the following defects: First, relying entirely on the teacher's personal subjective experience, it is difficult for teachers to define objectively due to the different teaching experiences of different teachers. The difficulty of the exercises; Second, the difficulty of the exercises is difficult to achieve dynamic and flexible management. C...

AI Technical Summary

Problems solved by technology

However, in practice, it is found that the way that teachers manually define the difficulty of exercises has the following defects: First, relying entirely on the teacher's personal subjective experience, it is difficult for teachers to define objectively due to the different teaching experiences of different teachers. The difficulty of the exercises; second, the difficulty of the exercises is difficult to achieve dynamic and flexible management

Images