Method for discriminating homework plagiarism based on big data acquisition and analysis

A big data and homework technology, applied in data processing applications, electronic digital data processing, digital data information retrieval, etc., can solve problems such as operability, feasibility and accuracy, and plagiarism that confuses teachers, so as to overcome tedious labor and effects of uncertainty, resolution of accuracy and authority issues

Pending Publication Date: 2022-05-03
SHAOXING UNIVERSITY
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  • Application Information

AI Technical Summary

Problems solved by technology

Since such coursework is generally dominated by calculation questions and objective questions, it has always been a difficult problem for teachers to manually judge whether any two assignments are suspected of plagiarism, the degree of plagiarism, and who plagiarized whom.
For the plagiarism screening of paper assignments, the traditional method can only rely on the teacher's experience and characteristic mistakes in the assignments. Due to the lack of convincing data support and easy misjudgment, the operability, feasibility and accuracy of this method obviously insufficient

Method used

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  • Method for discriminating homework plagiarism based on big data acquisition and analysis
  • Method for discriminating homework plagiarism based on big data acquisition and analysis
  • Method for discriminating homework plagiarism based on big data acquisition and analysis

Examples

Experimental program
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Effect test

Embodiment 1

[0030] A method for screening homework plagiarism based on big data collection and analysis, comprising the following steps:

[0031] (1) Collect the scores of each knowledge point in students' online and offline task points in real time, and calculate the score rate of each knowledge point in the middle.

[0032] (2) Collect the score values ​​of the corresponding knowledge points in the usual stage test and the final exam, and calculate the score rate of the knowledge points corresponding to the test questions.

[0033] (3) Statistics and summary of the score data of each knowledge point in the homework and examination collected in real time.

[0034] Further, based on the Excel table, the score data of each knowledge point is collected and summarized.

[0035] (4) After the stage test and the final exam, the curve of the score rate of the knowledge points in the test and the corresponding score rate of the corresponding knowledge points in the daily homework is drawn in ti...

Embodiment 2

[0046] In order to illustrate the present invention better, now take the engineering automation major "analog electronic technology" course as an example, as figure 2 As shown, this method is described:

[0047] 1) Collect learning data of online and offline task points: first collect the score data of knowledge points in offline operations, such as figure 1 As shown, 6 offline paper-based after-school homework assignments are arranged. Take 8 calculation questions for each homework as an example. 108 pieces of data such as the score of the small question); secondly, 18 online tests + 2 pass-through exams and 1 online final exam score per semester are collected, and a total of 42 online learning data are generated.

[0048] 2) Organize the knowledge points mastery data in the exam: arrange 2 stage tests and 1 final exam. Taking 12 calculation questions in each exam as an example, a total of 72+6 pieces of data will be generated. According to the knowledge points assessed in...

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Abstract

The invention relates to the technical field of big data analysis and numerical processing, in particular to a method for discriminating homework plagiarism based on big data acquisition and analysis, and aims to realize objective and comprehensive discrimination of homework plagiarism based on big data acquisition and analysis. Through the method, the problem that a teacher can only simply judge whether the homework plagiarism phenomenon exists or not through experience in the past can be avoided, tedious labor, uncertainty and other factors caused by homework plagiarism checking are overcome, the problems of accuracy and authority of homework plagiarism screening are solved, the advantages of big data are fully utilized, and the method is suitable for popularization and application. A teacher can realize data acquisition and analysis only by mastering the use of Excel, and whether the students have homework plagiarism and the plagiarism degree can be accurately identified only by analyzing the difference values of knowledge points in homework and examination.

Description

technical field [0001] The invention relates to the technical field of big data analysis and numerical processing, in particular to a method for identifying plagiarism in a job based on big data collection and analysis. Background technique [0002] Under the background of the popularization of higher education, plagiarism in homework of college students is common, especially in science and engineering majors that are more difficult to study and have higher learning requirements. Since such coursework is generally dominated by calculation questions and objective questions, it has always been a difficult problem for teachers to manually judge whether any two assignments are suspected of plagiarism, the degree of plagiarism, and who plagiarized whom. For the plagiarism screening of paper assignments, the traditional method can only rely on the teacher's experience and characteristic mistakes in the assignments. Due to the lack of convincing data support and easy misjudgment, t...

Claims

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Application Information

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IPC IPC(8): G06F16/2457G06F16/2458G06Q50/20
CPCG06F16/2457G06F16/2462G06Q50/205
Inventor 赵伟强
Owner SHAOXING UNIVERSITY
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