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Intelligent question-grouping method based on item response theory analyzing results

A technology of item response theory and analysis results, which is applied in the field of online learning group questions, can solve problems that cannot be targeted at different users, achieve the effect of reducing resistance and improving learning effect

Inactive Publication Date: 2017-09-26
杭州博世数据网络有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology helps users analyze their likelihood of acquiring relevant knowledge from educational materials more efficiently based on specific factors like teacher training or student performance. It also predict how likely these concepts are going up with each new material provided through projects that aim towards improving understanding and engagement skills. Overall, this system makes it easier for educatories to provide customizable answers tailored specifically to individuals who want them most important things during education sessions.

Problems solved by technology

The technical problem addressed in this patented technology relates how students can learn more efficiently by organizing their answers based solely upon randomly selected ones or groups within them rather than just selecting specific choices that may be relevant only to those who are already familiar enough to answer correctly.

Method used

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  • Intelligent question-grouping method based on item response theory analyzing results
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  • Intelligent question-grouping method based on item response theory analyzing results

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] Embodiment 1, a method for diagnosing knowledge points in online learning, comprising the following steps:

[0041] Step 1. Evaluate the probability that the user has mastered the knowledge point. The calculation formula is P(θ)=1 / (1+e^(b-θ)), where θ represents the ability parameter for evaluating the user, and b represents each topic The degree of difficulty, e is a constant 2.71828;

[0042] Among them, for this knowledge point, b adopts the standard difficulty coefficient, which refers to the unified difficulty coefficient standard determined through quantitative and qualitative research methods for all platform users.

[0043] According to the accumulated practice data of a large number of users on the whole platform, combined with the correct rate of each question (the correct rate of each question adopts the average correct rate of all parts of the country (counties) to reduce the impact of differences in teaching levels in various regions of the country, and the...

Embodiment 2

[0058] The difference between embodiment 2 and embodiment 1 is that further, for each difficulty segment, samples are randomly selected, and the difficulty coefficient is finally determined through expert evaluation on the basis of statistical analysis results to ensure that the difficulty coefficient of the topic is objective and accurate . That is to say, experts first clarify the difficulty coefficient of the most difficult topic based on experience, and then test individual topics based on rich teaching experience.

Embodiment 3

[0059] Embodiment 3 differs from Embodiments 1 and 2 in that the expert assessment results and statistical analysis and assignment results are integrated to determine the final difficulty coefficient b=0.5*cumulative data analysis result+0.5*expert difficulty assignment for each topic.

[0060] After the diagnostic results of students' knowledge points are determined according to the above method, the system can carry out intelligent problem grouping.

[0061] Below in conjunction with specific implementation mode, the method for intelligent group questions is described as follows:

[0062] Step 1. Determine the knowledge points that need to be practiced. There are two methods, one is manual selection, and the other is automatic selection by the system. The teacher can determine the knowledge points that need to be practiced according to the number of people who have mastered each knowledge point in the class. Select knowledge points according to the priority of points, and se...

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Abstract

The invention discloses an intelligent question-grouping method based on item response analyzing results. Intelligent question grouping is carried out according to knowledge-point grasping probabilities of students, the intelligent question grouping includes the following steps of 1, determining knowledge points that the students need to exercise; 2, determining the total number N of exercises done by the students; 3, determining the number of exercises, done by the students, of each knowledge point; 4, determining selected exercises of each knowledge point. According to the item response theory, the knowledge-point grasping probabilities of users are evaluated, targeted intelligent question grouping is conducted according to the knowledge-point grasping probabilities of the students, students' personal knowledge-point grasping conditions, learning abilities, learning wills, answering speeds and other indexes are fully taken into account, regarding different student individuals, the intelligent question-grouping method based on the item response analysis results controls the difficulty and number of the exercises, personalized exercises are set to the students, resistance moods of the students in the studying progress are reduced, and the learning effects of the students are improved.

Description

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Claims

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Application Information

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Owner 杭州博世数据网络有限公司
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