A teaching big data intelligent analysis method

Abstract

The application provides a teaching big data intelligent analysis method. The teaching big data intelligent analysis method comprises the following steps: obtaining teaching history data based on big data technology; obtaining learning characteristic data of each student based on the teaching history data analysis; classifying the students according to a preset classification rule and the learning characteristic data to obtain a student classification result; obtaining student participation data of online teaching and offline teaching of a target teacher; and performing teaching analysis on the target teacher to determine an online teaching and offline teaching evaluation result of the target teacher. The application analyzes the teaching history data of the teacher, considers the whole situation, analyzes the preference of each student for the online teaching and offline teaching, and combines the participation data of the students in the online teaching and offline teaching of the teacher in the actual teaching process to reasonably analyze the teaching of the teacher, so that intuitive teaching feedback information is provided for the teacher, and the teaching method of the teacher is facilitated to be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of teaching analysis, in particular to a teaching big data intelligent analysis method. BACKGROUND

[0002] Teacher teaching is the key to social development, improving teaching quality and perfecting teaching method is an important direction to improve teaching level.

[0003] At present, with the continuous progress of society, the way of teacher teaching is gradually diversified, which includes online teaching and offline teaching. Online teaching and offline teaching have their own advantages and disadvantages, and students can choose according to their own preferences. In this process, the online teaching and offline teaching effects of teachers can be evaluated according to the data of students participating in online teaching and offline teaching, which can provide rich teaching feedback information for teachers and help teachers improve teaching methods. SUMMARY

[0004] In view of the above problems, the present application provides a teaching big data intelligent analysis method, which obtains the teaching history data of teachers based on big data technology, intelligently analyzes the teaching history data, obtains the evaluation result, and evaluates the teaching quality of teachers, so as to facilitate teachers to improve their teaching methods.

[0005] The technical scheme of the present application is as follows: a teaching big data intelligent analysis method, comprising:

[0006] Obtaining teaching history data based on big data technology, the teaching history data including relevant data of students participating in online teaching and offline teaching of each teacher;

[0007] Based on the teaching history data, the learning feature data of each student is analyzed, the students are classified according to the preset classification rule and the learning feature data, and the student classification result is obtained;

[0008] For the target teacher, the student participation data of the target teacher in online teaching and offline teaching is obtained;

[0009] According to the student participation data of the target teacher in online teaching and offline teaching and the student classification result, the target teacher is analyzed, and the evaluation result of the online teaching and offline teaching of the target teacher is determined.

[0010] Further, the learning feature data of each student is analyzed based on the teaching history data, comprising:

[0011] The learning feature data of each student is obtained by determining, according to the teaching history data, a cumulative duration of online teaching and offline teaching of each student in a preset time range, and counting a total duration of online teaching and offline teaching of each student in the preset time range.

[0012] Further, the student classification result is obtained by classifying the students according to the preset classification rule and the learning feature data, including:

[0013] For any student, a proportion parameter of a total duration of online teaching and a total duration of offline teaching of the student is calculated according to the learning feature data, if the proportion parameter is in a preset reference range, the student is recorded as a standard category, if the proportion parameter is greater than a maximum value of the preset reference range, the student is recorded as a first category, and if the proportion parameter is less than a minimum value of the preset reference range, the student is recorded as a second category, thereby completing the classification of each student and obtaining the student classification result.

[0014] Further, the teaching analysis of the target teacher is performed according to the student participation data of online teaching and offline teaching of the target teacher and the student classification result, and an evaluation result of online teaching and offline teaching of the target teacher is determined, including:

[0015] A ratio of a total duration of online teaching and a total duration of offline teaching of each student to the target teacher is calculated according to the student participation data of online teaching and offline teaching of the target teacher, thereby obtaining a reference ratio of each student, and an online teaching evaluation value and an offline teaching evaluation value of the target teacher are determined according to the reference ratio of each student and the student classification result, thereby obtaining the evaluation result of online teaching and offline teaching of the target teacher.

[0016] Further, the determination of the online teaching evaluation value and the offline teaching evaluation value of the target teacher according to the reference ratio of each student and the student classification result includes:

[0017] For the online teaching evaluation value, the following formula is used for calculation:

[0018] P1=α1R1+α2R2+α3R3;

[0019] In the formula, P1 is the online teaching evaluation value, R1 is the first category reference value, R2 is the second category reference value, R3 is the third category reference value, α1 is the first weight parameter, α2 is the second weight parameter, and α3 is the third weight parameter, and 3(α1+α1+α3)=1.

[0020] For the offline teaching evaluation value, the following formula is used for calculation:

[0021] P2=1-P1;

[0022] In the formula, P2 is the offline teaching evaluation value.

[0023] Further, it also includes:

[0024] For the first category reference value R1:

[0025]

[0026] In the formula, i is the number of standard category students, a is the total number of standard category students, s i is the reference ratio of the i th standard category student.

[0027] Further, it also includes:

[0028] For the second category reference value R2:

[0029]

[0030] In the formula, j is the number of first category students, b is the total number of first category students, s j is the reference ratio of the j th first category student.

[0031] Further, it also includes:

[0032] For the third category parameter R3:

[0033]

[0034] In the formula, k is the number of second category students, c is the total number of second category students, s k is the reference ratio of the second category student.

[0035] The present application has the following advantages:

[0036] The present application analyzes the teaching history data of the teacher, considers from the whole, analyzes the preference of each student itself for online teaching and offline teaching, and combines the participation data of the student for the online teaching and offline teaching of the teacher in the actual teaching process of the teacher to reasonably analyze the teaching of the teacher, provides intuitive teaching feedback information for the teacher, and facilitates the improvement of the teaching method of the teacher. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings shown.

[0038] Figure 1A flowchart of a teaching big data intelligent analysis method in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purposes, technical solutions and advantages of the present application clearer, the following further describes some embodiments of the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. However, those skilled in the art can understand that in the embodiments of the present application, many technical details are proposed in order to make the readers better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed by the present application can be implemented.

[0040] Embodiment 1

[0041] Referring to Figure 1 , the embodiment 1 of the present application provides a teaching big data intelligent analysis method, comprising:

[0042] S1, obtaining teaching history data based on big data technology;

[0043] It should be noted that the teaching history data of each teacher can be obtained by big data technology, wherein the teaching history data specifically includes relevant data of a plurality of students participating in online teaching and offline teaching of each teacher, such as time, place and other information of participating in online teaching and offline teaching of each teacher.

[0044] S2, obtaining learning feature data of each student based on the teaching history data;

[0045] It should be noted that the feature data of the student is specifically the time data of the student participating in online teaching and offline teaching of different teachers within a specific time.

[0046] S3, classifying the students according to a preset classification rule and the learning feature data to obtain a student classification result;

[0047] It should be noted that the preset classification rule is specifically to analyze the preference degree of the student to online teaching and offline teaching according to the ratio of the total duration of the student participating in online teaching and offline teaching of different teachers within a specific time.

[0048] S4, obtaining student participation data of online teaching and offline teaching of a target teacher;

[0049] It should be noted that for any teacher, taking any teacher as an example, denoted as a target teacher, the student participation data in the online teaching process and the student participation data in the offline teaching process of the target teacher can be extracted from the obtained teaching history data of each teacher.

[0050] S5, performing teaching analysis on the target teacher based on the student participation data of the online teaching and offline teaching of the target teacher and the student classification result, and determining the online teaching and offline teaching evaluation result of the target teacher.

[0051] It is worth noting that students of different categories have different preferences for online teaching and offline teaching. Directly counting the student participation data of the online teaching and offline teaching of the target teacher is not comprehensive enough. Based on the classification of students, the student participation data of the online teaching and offline teaching of the target teacher is analyzed, which can make the final analysis of the online teaching and offline teaching evaluation result of the target teacher more comprehensive.

[0052] In an optional embodiment, for step S2, the learning feature data of each student is analyzed based on the teaching history data, which specifically includes:

[0053] According to the teaching history data, the cumulative duration of each student participating in the online teaching and offline teaching of each teacher within a preset time range is determined, the total duration of each student participating in the online teaching and offline teaching within the preset time range is counted, and the learning feature data of each student is obtained.

[0054] It is worth noting that the teaching history data records the time-related data of each student participating in the online teaching and offline teaching of different teachers. The preset time range is used as a time limit, so that the reference value of the obtained data is greater. In this embodiment, the time range of a semester is used as the preset time range, so that the cumulative duration of the student participating in the online teaching and offline teaching of each teacher within a semester can be determined preferentially. Then, the total duration of the student participating in the online teaching within a semester and the total duration of the student participating in the offline teaching are calculated according to the cumulative duration of the student participating in the online teaching and offline teaching of each teacher, and the learning feature data of the student is obtained. The feature data of each student is obtained through the above-mentioned manner.

[0055] In an optional embodiment, for step S3, the students are classified according to the preset classification rule and the learning feature data, and the student classification result is obtained, which includes:

[0056] For any student, the proportion parameter of the total duration of the student participating in the online teaching and the total duration of the student participating in the offline teaching is calculated according to the learning feature data;

[0057] It is worth noting that the proportion parameter is specifically the ratio of the total duration of the student participating in the online teaching within a semester of a class to the total duration of the student participating in the offline teaching within the same semester.

[0058] If the proportion parameter is in the preset reference range, the student is recorded as a standard category, if the proportion parameter is greater than the maximum value of the preset reference range, the student is recorded as a first category, if the proportion parameter is less than the minimum value of the preset reference range, the student is recorded as a second category, and the classification of each student is completed to obtain a student classification result.

[0059] It is worth noting that the preset reference range takes into account the fluctuation of the student's choice of teaching mode, and in this embodiment, the preset reference range is 0.4 to 0.6. If the proportion parameter of a certain student is between 0.4 and 0.6, it indicates that the student's preference for online teaching and offline teaching is relatively balanced. If the proportion parameter of a certain student is greater than 0.6, it indicates that the student prefers online teaching. If the proportion parameter of a certain student is less than 0.4, it indicates that the student prefers offline teaching. By classifying each student in the above manner, a student classification result is obtained.

[0060] In an optional embodiment, for step S5, the target teacher is analyzed according to the student participation data of the target teacher's online teaching and offline teaching and the student classification result to determine the online teaching and offline teaching evaluation result of the target teacher, including:

[0061] According to the student participation data of the target teacher's online teaching and offline teaching, the ratio of the total duration of each student participating in the target teacher's online teaching and offline teaching is calculated to obtain a reference ratio of each student, and the online teaching evaluation value and offline teaching evaluation value of the target teacher are determined according to the reference ratio of each student and the student classification result to obtain the online teaching and offline teaching evaluation result of the target teacher.

[0062] It is worth noting that by calculating the ratio of the total duration of each student participating in the target teacher's online teaching and offline teaching, the inclination information of each student participating in the target teacher's teaching to the target teacher's online teaching and offline teaching can be obtained, and the online teaching and offline teaching of the target teacher are analyzed globally in combination with the type of each student.

[0063] Specifically, the online teaching evaluation value and offline teaching evaluation value of the target teacher are determined according to the reference ratio of each student and the student classification result, including:

[0064] For the online teaching evaluation value, the following formula is used for calculation:

[0065] P1=α1R1+α2R2+α3R3;

[0066] P1=α1R1+α2R2+α3R3, 3(α1+α2+α3)=1;

[0067] For the offline teaching evaluation value, it is calculated by the following formula:

[0068] P2=1-P1;

[0069] In the formula, P2 is the offline teaching evaluation value.

[0070] Wherein, for the first category reference value R1:

[0071]

[0072] In the formula, i is the item number of the standard category student, a is the total number of the standard category student, s i is the reference ratio of the i-th standard category student.

[0073] For the second category reference value R2:

[0074]

[0075] In the formula, j is the item number of the first category student, b is the total number of the first category student, s j is the reference ratio of the j-th first category student.

[0076] For the third category parameter R3:

[0077]

[0078] In the formula, k is the item number of the second category student, c is the total number of the second category student, s k is the reference ratio of the second category student.

[0079] It is worth noting that for the students taught by the target teacher, the above formula considers the information of the students taught by the target teacher participating in the online teaching and offline teaching of the target teacher, and the selection tendency of the students taught by the target teacher for the online teaching and offline teaching of the target teacher, and evaluates the online teaching and offline teaching of the target teacher, and the online teaching evaluation value and offline teaching evaluation value obtained are in a contrast relationship, and the target teacher can have intuitive understanding of the level of the online teaching and offline teaching of the target teacher according to the online teaching evaluation value and offline teaching evaluation value, which provides rich teaching feedback information for the target teacher, and helps the teacher to improve the two teaching methods.

[0080] It is to be understood that all of the above modifications and alterations can be made to the above-described arrangements and that all such modifications and alterations are intended to be included within the scope of the present application. Those skilled in the art will readily appreciate that other modifications and alterations can be made to the present application without departing from the scope of the application.

Claims

1. A method for intelligent analysis of teaching big data, characterized in that, include: The teaching history data is obtained based on big data technology, including data related to students' participation in online and offline teaching by various teachers. Based on the teaching history data analysis, the learning characteristic data of each student is obtained, including determining the cumulative time each student has participated in online and offline teaching by various teachers within a preset time range according to the teaching history data, and calculating the total time each student has participated in online and offline teaching within the preset time range to obtain the learning characteristic data of each student. Students are classified according to preset classification rules and the learning feature data to obtain student classification results. This includes calculating the ratio of the student's total online teaching time to the total offline teaching time based on the learning feature data for any given student. If the ratio parameter is within a preset reference range, the student is classified as a standard category. If the ratio parameter is greater than the maximum value of the preset reference range, the student is classified as a first category. If the ratio parameter is less than the minimum value of the preset reference range, the student is classified as a second category. This process is repeated for each student to obtain the student classification results. For target teachers, obtain student participation data for both online and offline teaching. Based on the student participation data of the target teacher's online and offline teaching and the student classification results, the teaching analysis of the target teacher is carried out to determine the evaluation results of the target teacher's online and offline teaching. This includes calculating the ratio of each student's total online teaching time to total offline teaching time based on the student participation data of the target teacher's online and offline teaching, obtaining a reference ratio for each student, and determining the online teaching evaluation value and offline teaching evaluation value of the target teacher based on the reference ratio of each student and the student classification results. The determination of the target teacher's online and offline teaching evaluation scores based on each student's reference ratio and the student classification results includes: The online teaching evaluation score is calculated using the following formula: ； In the formula, For online teaching evaluation values, This is the reference value for the first category. This is a reference value for the second category. This is a reference value for the third category. As the first weight parameter, This is the second weighting parameter. The third weighting parameter, ; The offline teaching assessment score is calculated using the following formula: ； In the formula, This is an evaluation value for offline teaching.

2. The intelligent analysis method for teaching big data as described in claim 1, characterized in that, Also includes: For the first category of reference values : ； In the formula, The number of items for standard category students, The total number of students in the standard category, For the first Reference ratios for students in each standard category.

3. The intelligent analysis method for teaching big data as described in claim 2, characterized in that, Also includes: For the second category of reference values : ； In the formula, The number of items for students in the first category. The total number of students in the first category. For the first The reference ratio for students in the first category.

4. The intelligent analysis method for teaching big data as described in claim 3, characterized in that, Also includes: For the third category parameter : ； In the formula, For the number of items for the second category of students, The total number of students in the second category. This is a reference ratio for students in the second category.

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