Personalized test question recommendation method based on student portrait

A recommendation method, a student's technology, applied in complex mathematical operations, forecasting, resources, etc., can solve problems such as lack, evaluation of recommended objects, and lack of practical ability, and achieve low average absolute error, low root mean square error, and high accuracy Effect

Pending Publication Date: 2021-06-04
LIAONING UNIVERSITY
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the development of big data, especially the application of artificial intelligence and big data in the field of education, personalized education has received more and more attention, but some problems have been exposed in the development process: first, the data mining of students is not enough , mainly depends on students' academic performance, and lacks data that can reflect their practical ability. These data can more accurately describe students and play a more important role in the process of personalized education; secondly, students' participation in innovation and entrepreneurship education activities It is difficult to judge the learning situation; finally, there is a lack of personalized recommendations for students' learning process, such as not being able to recommend more suitable topics to students
Both the DINA model and the DINO model are widely used cognitive diagnostic models, but both are typical discrete cognitive diagnostic models and cannot accurately diagnose students' learning status
[0004] Although the current recommendation technology has been widely studied, it is relatively lack of evaluation of recommended objects from the perspective of utility, which leads to the need for users to further select the recommended results

Method used

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  • Personalized test question recommendation method based on student portrait
  • Personalized test question recommendation method based on student portrait
  • Personalized test question recommendation method based on student portrait

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0109] 1 Experimental scene setup

[0110] This experimental data set is some student answer records and experimental records collected in the big data platform. Among them, the number of students is 193, the number of knowledge points is 40, and the number of test questions is 50. The approximate correspondence between test questions and knowledge points in the data set is as follows: image 3 As shown, black means that the knowledge points are investigated in the test questions, and white means that the knowledge points are not investigated.

[0111] 2 Contrast Algorithms

[0112] This patent uses the DINA model and the DINO model as comparison methods. The DINA model is a non-compensated model whose parameters are defined at the item level, and only needs to estimate the error parameter and the guessing parameter for each test item. It believes that according to the actual situation of the test, students who have mastered all attributes have a higher probability of gues...

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Abstract

A personalized test question recommendation method based on student portraits aims at the objective current situation that students are easy to get lost in the current learning process and the learning state is difficult to accurately judge, a campus big data platform is constructed by integrating an existing system and developing a new system, and then the accurate portraits of the students are described. On the basis, a fuzzy cognitive diagnosis model is provided to reasonably judge the learning state attribute of the student, and the question answering condition of the student is predicted in combination with the requirements of the test questions on the learning state of the student. Based on the prediction information, a utility-based test question recommendation method is designed, and test questions with high answering utility are recommended to students. Compared with a traditional DINA method and a DINO method, the FDINA method provided by the invention not only achieves a lower root-mean-square error and a lower mean absolute error in the aspect of test question answering prediction results, but also obtains higher accuracy, a higher recall rate and a higher F1 value in the aspect of recommendation results, and in addition, the FDINA method can also effectively improve the earnings of students in answering test questions.

Description

technical field [0001] The invention relates to a method for recommending test questions, in particular to a method for recommending personalized test questions based on student portraits. Background technique [0002] With the development of big data, especially the application of artificial intelligence and big data in the field of education, personalized education has received more and more attention, but some problems have been exposed in the development process: first, the data mining of students is not enough , mainly depends on students' academic performance, and lacks data that can reflect their practical ability. These data can more accurately describe students and play a more important role in the process of personalized education; secondly, students' participation in innovation and entrepreneurship education activities It is difficult to judge the learning situation; finally, there is a lack of personalized recommendations for the students' learning process, such ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/335G06F17/16G06Q10/04G06Q10/06G06Q50/20
CPCG06F16/335G06F17/16G06Q10/04G06Q10/06393G06Q50/205
Inventor 曲大鹏张蕊吕国鑫王芮吴松林
Owner LIAONING UNIVERSITY
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