Big data collection and analysis method for student learning activities

Abstract

The present application provides a kind of student learning activity big data acquisition analysis method, comprising the following steps: S1: the modularization division of each knowledge point content in teaching material is carried out to form knowledge point module;S2: student is tested to each knowledge point module, and the average value before knowledge point mastery is formed.The method can unify the market analysis of the teaching length of teacher for different knowledge points and the self-study length of student, and correspond with the length memory of teaching material for this knowledge point, so as to find out which knowledge point is unreasonable in the length of existing teaching material and thus improve, simultaneously, according to the length of each student self-study, the difficulty of each knowledge point for each student to judge the degree of mastery, change teaching strategy in combination with the teaching length of existing teacher for this knowledge point, and according to test results, the overall performance trend of teaching material adjustment is fed back, so that each teaching material adjustment can have a clear effect display, and the effect is better, and the effect is poor, then the original scheme is used.

Description

Technical Field

[0001] This invention specifically relates to a method for collecting and analyzing big data on student learning activities. Background Technology

[0002] With technological advancements, the use of e-textbooks has become increasingly widespread. The biggest advantages of e-textbooks are their convenience for online learning and the savings in printing costs. Furthermore, as online electronic data products, e-textbooks offer a significant cost advantage over traditional paper textbooks in terms of content adjustment based on specific user needs. However, the current adjustments to the length of textbook content, the teaching time for each knowledge point, and the amount of time students spend on self-study are primarily based on teachers' subjective feelings and the existing textbook content. Whether these adjustments are scientifically sound and whether they positively impact final grades are difficult to judge.

[0003] Based on the above, a big data collection and analysis method for student learning activities is proposed to achieve dynamic management of teaching methods and textbook content, and to provide dynamic feedback based on actual teaching effects, so that every adjustment is reasonable, thereby making the teaching system and teaching content more controllable, systematic and scientific. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for collecting and analyzing big data on student learning activities, which can effectively solve the aforementioned problems.

[0005] To achieve the above requirements, the technical solution adopted by the present invention is: to provide a method for collecting and analyzing big data on student learning activities, which includes the following steps:

[0006] S1: Modularize the content of each knowledge point in the textbook to form knowledge point modules;

[0007] S2: Test students for each knowledge module to form the average value before mastering the knowledge points;

[0008] S3: Collect the specific time that teachers spend teaching each knowledge point module to form teaching time data;

[0009] S4: Collect the specific time students spend on self-study for each knowledge module to form self-study time data;

[0010] S5: Compare the teaching time data of each knowledge point module with the self-study time data to form a comparison result. Based on the comparison result, make a pre-adjustment to the specific time for teachers to carry out educational work for each knowledge point module and obtain the teaching adjustment value.

[0011] S6: Test students again for each knowledge point module to form the average value after mastering the knowledge points;

[0012] S7: Subtract the average value after mastering the knowledge points of each knowledge point module from the average value before mastering the knowledge points to obtain the adjustment result;

[0013] S8: Based on the aforementioned adjustment results, the specific time for teachers to conduct educational work for each knowledge point module will be adjusted accordingly.

[0014] S9: Repeat steps S1 to S8 until the adjustment results for each knowledge point module are positive. At this point, the final teaching adjustment value for each knowledge point module is obtained.

[0015] S10: Adjust the textbook content corresponding to each knowledge point module according to the final value of the teaching adjustment for each knowledge point module.

[0016] The advantages of this method for collecting and analyzing big data on student learning activities are as follows:

[0017] This method can conduct a unified market analysis of teachers' teaching time for different knowledge points and students' self-study time, and correlate this with the length of the textbook for that knowledge point. This will help identify which knowledge points are not adequately covered in the existing textbook and make improvements accordingly. Furthermore, the method can assess the difficulty level of each knowledge point for each student based on their self-study time, and adjust teaching strategies based on the existing teaching time for that knowledge point. Additionally, it can provide feedback on the overall performance trend after each textbook adjustment based on test scores, ensuring a clear demonstration of the effects of each textbook adjustment. If the effect is good, the original plan can be retained; if the effect is poor, the original plan can be reverted. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, use the same reference numerals to denote the same or similar parts. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A schematic flowchart illustrating a method for collecting and analyzing big data on student learning activities according to an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments.

[0021] In the following description, references to "an embodiment," "an embodiment," "an example," "example," etc., indicate that the described embodiment or example may include a particular feature, structure, characteristic, property, element, or limitation, but not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Furthermore, the repeated use of the phrase "an embodiment according to this application," while possibly referring to the same embodiment, does not necessarily refer to the same embodiment.

[0022] For simplicity, certain technical features known to those skilled in the art are omitted in the following description.

[0023] According to one embodiment of this application, a method for collecting and analyzing big data on student learning activities is provided, such as... Figure 1 As shown, it includes the following steps:

[0024] S1: Modularize the content of each knowledge point in the textbook to form knowledge point modules;

[0025] S2: Test students for each knowledge module to form the average value before mastering the knowledge points;

[0026] S3: Collect the specific time that teachers spend teaching each knowledge point module to form teaching time data;

[0027] S4: Collect the specific time students spend on self-study for each knowledge module to form self-study time data;

[0028] S5: Compare the teaching time data of each knowledge point module with the self-study time data to form a comparison result. Based on the comparison result, make a pre-adjustment to the specific time for teachers to carry out educational work for each knowledge point module and obtain the teaching adjustment value.

[0029] S6: Test students again for each knowledge point module to form the average value after mastering the knowledge points;

[0030] S7: Subtract the average value after mastering the knowledge points of each knowledge point module from the average value before mastering the knowledge points to obtain the adjustment result;

[0031] S8: Based on the aforementioned adjustment results, the specific time for teachers to conduct educational work for each knowledge point module will be adjusted accordingly.

[0032] S9: Repeat steps S1 to S8 until the adjustment results for each knowledge point module are positive. At this point, the final teaching adjustment value for each knowledge point module is obtained.

[0033] S10: Adjust the textbook content corresponding to each knowledge point module according to the final value of the teaching adjustment for each knowledge point module.

[0034] According to an embodiment of this application, the specific steps of step S1 of the student learning activity big data collection and analysis method for modularizing the content of each knowledge point in the teaching materials into knowledge point modules are as follows:

[0035] The textbook is divided into different knowledge point modules according to the content of each section;

[0036] The relevance of the three most frequent words on each page of the textbook to each knowledge module is calculated using the following formula:

[0037]

[0038] in:

[0039] T(A,B) represents the relevance of word A in section B of the textbook.

[0040] P(A,B) represents the word frequency of word A in section B of the textbook.

[0041] D represents the entire textbook text dataset;

[0042] │D│ represents the total number of texts in the entire textbook text dataset;

[0043] The relevance results of the three most frequent words on each page are summed to obtain the final result of the relevance between the content of each page and each section, and the content of each page is divided into the three textbook sections with the highest relevance.

[0044] According to one embodiment of this application, step S3 of the big data collection and analysis method for student learning activities, which involves collecting the specific time teachers spend teaching each knowledge point module, specifically includes: collecting the specific time of at least twenty teachers spending teaching each knowledge point module, and then taking the average of the specific time spent teaching each knowledge point module to determine the final result.

[0045] According to one embodiment of this application, step S4 of the student learning activity big data collection and analysis method collects the specific time for students to conduct self-study of each knowledge point module to form self-study time data: the specific time for at least twenty students to conduct self-study of each knowledge point module is collected, and then the average of the specific time for self-study of each knowledge point module is taken as the final result.

[0046] According to one embodiment of this application, step S5 of the student learning activity big data collection and analysis method compares the teaching time data of each knowledge point module with the self-study time data to form a comparison result. Based on the comparison result, the specific time for teachers to conduct educational work for each knowledge point module is pre-adjusted, and the teaching adjustment value is obtained. The specific steps are as follows:

[0047] The average total time spent on self-study was collected from at least twenty students, and the self-study time parameter for each knowledge point module was obtained by dividing the time spent by each student on each knowledge point module by the average total time spent on self-study.

[0048] The average total teaching time of at least twenty teachers was collected, and the teaching time parameter for each knowledge point module was obtained by dividing the teaching time of each teacher for each knowledge point module by the average total teaching time.

[0049] Subtract the self-study time parameter from the teaching time parameter for each knowledge point module;

[0050] If positive, the teaching time for that knowledge point module will be reduced by X.

[0051] If the value is negative, the teaching time for that knowledge point module will be increased, and the value will be decreased by X.

[0052] The calculation method for X is as follows:

[0053] X=T*|bi-xi| / [Σ(xi–μ / N)2 / N]

[0054] in:

[0055] T represents the total teaching time data for all existing knowledge point modules;

[0056] bi represents the self-study time for the i-th knowledge point module;

[0057] N represents the total number of knowledge point modules;

[0058] μ represents the average total teaching time;

[0059] xi represents the teaching time for the i-th knowledge module.

[0060] According to one embodiment of this application, step S2 of the student learning activity big data collection and analysis method, which tests students for each knowledge point module to form the average value before mastering the knowledge points, is specifically implemented as follows:

[0061] Test at least twenty students for each knowledge module and obtain scores, then average all scores.

[0062] Step S6 involves testing students again for each knowledge point module to determine the average level of knowledge mastery. The specific method for this is as follows:

[0063] Test at least twenty students for each knowledge module and obtain scores, then average all scores.

[0064] Step S8 involves adjusting the specific time allotted for teachers to conduct educational work for each knowledge point module based on the adjustment results. The specific method for this adjustment is as follows:

[0065] If the adjustment result is positive, no adjustment is performed;

[0066] If the adjustment result is negative, and the teaching time for this knowledge point module has been increased, then the teaching time for this knowledge point module will be reduced by 1 / e times the increase in teaching time.

[0067] If the adjustment result is negative, and the teaching time for that knowledge point module has been reduced, then the teaching time for that knowledge point module will be increased by 1 / e times the reduction in teaching time.

[0068] Where e is the natural constant.

[0069] According to one embodiment of this application, the method for collecting and analyzing big data on student learning activities further includes the following steps:

[0070] Calculate the length of each knowledge point module in the original textbook, as follows:

[0071]

[0072] W represents the length of the knowledge point module within the textbook;

[0073] x represents the page number of the textbook corresponding to each knowledge point module;

[0074] y represents the number of words on the textbook page corresponding to each knowledge point module;

[0075] e is the natural constant;

[0076] γ is the Euler-Mascheroni constant.

[0077] According to one embodiment of this application, the method for collecting and analyzing big data on student learning activities further includes the following steps:

[0078] The length difference value for each knowledge point module is calculated as follows:

[0079] The total length of each knowledge point module is calculated by summing the length values ​​of each module. The length coefficient of each module is calculated by dividing the length value of each module by the total length value. The total teaching time of each module is calculated by summing the teaching time data of each module. The teaching time coefficient of each module is calculated by dividing the teaching time of each module by the total teaching time value. The length difference of each module is calculated by subtracting the length coefficient from the teaching time coefficient.

[0080] According to one embodiment of this application, in step S10 of the student learning activity big data collection and analysis method, the textbook content corresponding to each knowledge point module is adjusted based on the final teaching adjustment value of each knowledge point module, as follows:

[0081] If the final value of the teaching adjustment is positive, the word count and page count for that knowledge point module will be adjusted as follows:

[0082] Q / [L*log(e 2 )]≥H≥Q / (L*log e);

[0083] e 2 Q / [L*log(e)]≥G≥e 2 Q / (L*log e);

[0084] L represents the difference in the length of the text for that knowledge point;

[0085] G adds the word count to this knowledge point module;

[0086] H represents adding page numbers to this knowledge point module;

[0087] Q represents the length of the original textbook for this knowledge point module;

[0088] e is a natural constant.

[0089] According to one embodiment of this application, in step S10 of the student learning activity big data collection and analysis method, the textbook content corresponding to each knowledge point module is adjusted based on the final teaching adjustment value of each knowledge point module, as follows:

[0090] If the final value of the teaching adjustment is negative, the word count and page count for that knowledge point module will be adjusted as follows:

[0091] Q / [L*log(e 2 )]≥S≥Q / (L*log e);

[0092] e 2 Q / [L*log(e)]≥Z≥e 2Q / (L*log e);

[0093] L represents the difference in the length of the text for that knowledge point;

[0094] Z represents a reduction in the number of words for this knowledge point;

[0095] S represents reducing the number of pages for this knowledge point;

[0096] Q represents the length of the original textbook for this knowledge point module;

[0097] e is a natural constant.

[0098] According to one embodiment of this application, the student learning activity big data collection and analysis method can conduct a unified market analysis of teachers' teaching time for different knowledge points and students' self-study time, and correlate it with the length of the textbook for that knowledge point. This allows the method to identify which knowledge points are not adequately covered in the existing textbooks and make improvements accordingly. Furthermore, the method can determine the difficulty level of each knowledge point for each student based on their self-study time, and adjust teaching strategies based on the existing teaching time for that knowledge point. Additionally, the method can provide feedback on the overall performance trend after each textbook adjustment based on test scores, ensuring a clear effect demonstration for each textbook adjustment. If the effect is good, the original method is retained; if the effect is poor, the original method is reverted.

[0099] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A student learning activity big data collection and analysis method, characterized in that, Includes the following steps: S1: Modularize the content of each knowledge point in the textbook to form knowledge point modules; S2: Test students for each knowledge module to form the average value before mastering the knowledge points; S3: Collect the specific time that teachers spend teaching each knowledge point module to form teaching time data; S4: Collect the specific time students spend on self-study for each knowledge module to form self-study time data; S5: Compare the teaching time data of each knowledge point module with the self-study time data to form a comparison result. Based on the comparison result, make a pre-adjustment to the specific time for teachers to carry out educational work for each knowledge point module and obtain the teaching adjustment value. S6: Test students again for each knowledge point module to form the average value after mastering the knowledge points; S7: Subtract the average value after mastering the knowledge points of each knowledge point module from the average value before mastering the knowledge points to obtain the adjustment result; S8: Based on the aforementioned adjustment results, the specific time for teachers to conduct educational work for each knowledge point module will be adjusted accordingly. S9: Repeat steps S1 to S8 until the adjustment results for each knowledge point module are positive. At this point, the final teaching adjustment value for each knowledge point module is obtained. S10: Adjust the textbook content corresponding to each knowledge point module according to the final value of the teaching adjustment for each knowledge point module; Step S5 compares the teaching time data for each knowledge point module with the self-study time data to form a comparison result. Based on the comparison result, it adjusts the specific time for teachers to conduct educational work for each knowledge point module and derives the teaching adjustment value. The specific steps are as follows: The average total time spent on self-study was collected from at least twenty students, and the self-study time parameter for each knowledge point module was obtained by dividing the time spent by each student on each knowledge point module by the average total time spent on self-study. The average total teaching time of at least twenty teachers was collected, and the teaching time parameter for each knowledge point module was obtained by dividing the teaching time of each teacher for each knowledge point module by the average total teaching time. Subtract the self-study time parameter from the teaching time parameter for each knowledge point module; If positive, the teaching time for that knowledge point module will be reduced by X. If the value is negative, the teaching time for that knowledge point module will be increased by X. where X is calculated as follows: ; in: T represents the total teaching time data for all existing knowledge point modules; bi represents the self-study time for the i-th knowledge point module; N represents the total number of knowledge point modules; μ represents the average total teaching time; xi represents the teaching time for the i-th knowledge module; Step S2 involves testing students on each knowledge point module to determine the average pre-mastery level of the knowledge points. The specific method for this is as follows: Test at least twenty students for each knowledge module and obtain scores, then average all scores. Step S6 involves testing students again for each knowledge point module to determine the average level of knowledge mastery. The specific method for this is as follows: Test at least twenty students for each knowledge module and obtain scores, then average all scores. Step S8 involves adjusting the specific time allotted for teachers to conduct educational work on each knowledge point module based on the adjustment results. The specific method for this adjustment is as follows: If the adjustment result is positive, no adjustment is performed; If the adjustment result is negative, and the teaching time for this knowledge point module has been increased, then the teaching time for this knowledge point module will be reduced by 1 / e times the increase in teaching time. If the adjustment result is negative, and the teaching time for this knowledge point module has been reduced, then the teaching time for this knowledge point module will be increased by 1 / e times the reduction in teaching time. Where e is the natural constant.

2. The method of claim 1, wherein: Step S1 involves modularizing the content of each knowledge point in the textbook into knowledge point modules. The specific steps are as follows: The textbook is divided into different knowledge point modules according to the content of each section; The three highest frequency words in the content of each page of the teaching material are calculated for correlation with each knowledge point module according to the following formula: ; in: T(A,B) represents the relevance of word A in section B of the textbook. P(A,B) represents the word frequency of word A in section B of the textbook. D represents the entire textbook text dataset; │D│ represents the total number of texts in the entire textbook text dataset; The relevance results of the three most frequent words on each page are summed to obtain the final result of the relevance between the content of each page and each section, and the content of each page is divided into the three textbook sections with the highest relevance.

3. The method of claim 1, wherein: Step S3 involves collecting the specific time teachers spend teaching each knowledge point module. Specifically, this includes collecting the specific time spent teaching each knowledge point module by at least twenty teachers, and then taking the average of the specific time spent teaching each knowledge point module to determine the final result.

4. The method of claim 1, wherein: Step S4 collects the specific time students spend on self-study for each knowledge module to form self-study time data: collect the specific time of self-study for each knowledge module from at least twenty students, and then take the average of the specific time of self-study for each knowledge module to determine the final result.

5. The method of claim 1, wherein, It also includes the following steps: The length of each knowledge point module in the original textbook is calculated, and the calculation method is as follows: ; W represents the length of the knowledge point module within the textbook; x represents the page number of the textbook corresponding to each knowledge point module; y represents the number of words on the textbook page corresponding to each knowledge point module; e is the natural constant; γ is the Euler-Mascheroni constant.

6. The method of claim 5, wherein the method further comprises: It also includes the following steps: The length difference value for each knowledge point module is calculated as follows: Add up the length values ​​corresponding to each knowledge point module to get the total length value of the textbook knowledge point module. Divide the length value of each knowledge point module by the total length value to get the length coefficient of each knowledge point module. Add up the teaching time data corresponding to each knowledge point module to get the total teaching time value of the textbook. Divide the teaching time data corresponding to each knowledge point module by the total teaching time value of the textbook to get the teaching time coefficient of each knowledge point module. Subtract the length coefficient of each knowledge point module from the teaching time coefficient of the knowledge point module to get the length difference value of the knowledge point.

7. The method of claim 6, wherein: S10 adjusts the textbook content corresponding to each knowledge point module based on the final teaching adjustment value of each knowledge point module, as follows: If the final value of the teaching adjustment is positive, the number of words and pages of the knowledge point module are adjusted as follows: ; ; L represents the difference in the length of the text for that knowledge point; G adds the word count to this knowledge point module; H represents adding page numbers to this knowledge point module; Q represents the length of the original textbook for this knowledge point module; e is a natural constant.

8. The method of claim 6, wherein: S10 adjusts the textbook content corresponding to each knowledge point module based on the final teaching adjustment value of each knowledge point module, as follows: If the final value of the teaching adjustment is negative, the word count and page count of the knowledge point module are adjusted as follows: ; ; L represents the difference in the length of the text for that knowledge point; Z represents a reduction in the number of words for this knowledge point; S represents reducing the number of pages for this knowledge point; Q represents the length of the original textbook for this knowledge point module; e is a natural constant.

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