Choice question error reason analysis method suitable for adaptive teaching

Abstract

The invention discloses a choice question error reason analysis method suitable for adaptive teaching, and relates to the field of education and teaching. The method comprises the steps of setting oneor more options of a choice question as the correct options; setting the remaining options as error options; according to the question stem of the choice question, determining the names of the knowledge points associated with the question stem, according to the question stem corresponding to the error option, obtaining the knowledge point names associated with the question stems, according to thecontent of the error options, obtaining the number of a choice question inputted by a user, judging whether the options corresponding to the numbers are correct options or not; if not, according to the question corresponding to the options, obtaining the knowledge point names, the ultra-nano knowledge point names and the ultra-nano knowledge point contents associated with the question stems, anddisplaying the numbers, the ultra-nano knowledge point names and the ultra-nano knowledge point contents; and carrying out the semantic analysis on the ultra-nano knowledge point names and the ultra-nano knowledge point contents by utilizing a general language model, so that a large amount of manpower is saved, and the training effect on the students is improved.

Description

technical field [0001] The invention relates to the field of education and teaching, in particular to a multiple-choice error cause analysis method suitable for self-adaptive teaching. Background technique [0002] In today's multiple-choice learning, due to the general commonality of correct options and wrong options, a simple distinction between correct options and wrong options can be achieved. However, the error characteristics reflected by the different error options of each multiple-choice question are not the same, which corresponds to the different individual needs of each student. Due to the limited number of teachers, when a large number of students are training and studying a large number of exercises, the teacher is unable to identify and judge the cause of the wrong option for each multiple-choice question of each student. With the expansion of the number of students and the number of topics, this demand is completely and effectively solved by the current educa...

AI Technical Summary

Problems solved by technology

Due to the limited number of teachers, when a large number of students are training and learning a large number of exercises, the teacher is unable to identify and judge the cause of the wrong option for each multiple-choice question of each student

With the expansion of the number of students and the number of topics, this demand is completely and effectively solved by the current education and teaching.

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