CN116957872A - Intelligent classroom lesson preparation method based on big data - Google Patents

Abstract

The invention relates to the technical field of intelligent class, in particular to an intelligent class lesson preparation method based on big data, which comprises the following steps of S1, dividing a single lesson preparation teaching plan into a pre-learning teaching plan, a teaching plan and a problem teaching plan; step S2, writing a pre-learning teaching plan, sending the teaching plan to students to be taught, and feeding back pre-learning information after the students finish pre-learning; step S3, writing a teaching plan for teaching; step S4, the central control module controls and judges whether the lessons prepared for the single knowledge points in the teaching plan meet preset standards according to the accuracy of the historical examination questions corresponding to the single knowledge points; step S5, writing the problem teaching plan, and judging whether the lesson taking notes of the problem teaching plan meets the preset standard according to the evaluation value of the teaching plan; and S6, teaching the students by using the written teaching plan and arranging the problems after the problems are arranged by using the written teaching plan. Thereby ensuring the quality of lesson preparation and teacher teaching.

Description

Intelligent classroom lesson preparation method based on big data

Technical Field

The invention relates to the technical field of intelligent class, in particular to an intelligent class preparation method based on big data.

Background

The lesson preparation is that a teacher selects the most appropriate expression method and sequence according to the requirements of the subject course standard and the characteristics of the subject course and the specific conditions of students so as to ensure the effective study of the students. The lesson preparation is the starting point and the basis of teaching, and is the starting point and the basis for determining the teaching quality of a classroom. At present, the intelligent technology is applied to the teaching process, so that the teaching quality is improved, and the intelligent technology has become a popular trend of the technology of various schools and training institutions.

Chinese patent publication No.: CN107885714a discloses a method for online lesson preparation based on big data, comprising: step S101, collecting resource materials, logging in a cloud server through a client, collecting learning resource materials through a plurality of administrators and users, and storing the learning resource materials in a cloud database; step S102, searching resource materials, and searching needed course resource learning and teaching plan reference by using a cloud server; step S103, importing a teaching plan or a teaching plan template, importing a lesson-taking teaching plan or a teaching plan template, and editing the teaching plan; step S104, dividing knowledge points, and dividing knowledge points and editing personal explanation for courses or chapters; step S105, making problems, searching and referencing related problems from a cloud database through a cloud server according to knowledge points, and arranging problems or making new problems; and step S106, storing the teaching plan, and storing the content in a cloud database for use after the teaching plan is manufactured. Therefore, the scheme does not have a pre-learning link, and the materials and the post-class problems of the teaching plan cannot be correspondingly adjusted according to the pre-learning and history learning conditions of students, so that the quality of the teaching contents and the teaching quality of teachers are affected.

Disclosure of Invention

Therefore, the invention provides a big data-based intelligent class preparation method, which is used for solving the problem that in the prior art, the materials and post-class problems of a teaching plan cannot be correspondingly adjusted according to the pre-study and history learning conditions of students, so that the quality of the class preparation content and the teaching quality of teachers are affected.

In order to achieve the above object, the present invention provides a smart class preparation method based on big data, comprising:

step S1, dividing a single-section lesson-preparing teaching plan into a pre-learning teaching plan, a teaching plan and a problem teaching plan;

step S2, writing a pre-learning teaching plan, sending the teaching plan to students to be taught, and feeding back pre-learning information after the students finish pre-learning; the pre-learning information comprises difficulty coefficients for marking all knowledge points in the pre-learning teaching plan;

step S3, writing a teaching plan, namely aiming at a single knowledge point in the teaching plan, selecting word materials and non-word materials from a database by a central control module to write the teaching plan of the single knowledge point;

step S4, the central control module controls the detection module to detect the accuracy of the historical examination questions corresponding to the single knowledge points stored in the database, judges whether the lessons of the single knowledge points in the teaching plan meet the preset standard according to the detected accuracy, and determines that the material of the lessons of the knowledge points does not meet the preset standard due to the fact that the material of the lessons of the knowledge points does not meet the preset standard or the teaching duration of the lessons of the knowledge points does not meet the preset standard when the material of the lessons of the knowledge points does not meet the preset standard;

Step S5, writing the problem teaching plan, judging whether the teaching course meets the preset standard according to the evaluation value of the teaching plan, controlling the detection module to detect the history correct rate of the problem after the teaching in the problem teaching plan stored in the database when the teaching course does not meet the preset standard, and determining a secondary judging mode of judging whether the teaching course meets the preset standard or not according to the history correct rate or increasing the number of the problem after the teaching in the problem teaching plan to a corresponding value by the central control module;

and S6, teaching the students by using the written teaching plan and arranging the problems after the problems are arranged by using the written teaching plan.

Further, in the step S4, the central control module controls the detection module to detect the accuracy of the historical examination questions corresponding to the single knowledge points stored in the database, and determines, according to the detected accuracy, whether the lessons for the single knowledge points in the teaching plan meet the preset standard or not, where,

the first lesson preparation judging mode is a judging mode that the central control module judges that lessons of single knowledge points in the teaching plan do not meet preset standards and materials of the lessons of the knowledge points do not meet the preset standards because the lessons of the single knowledge points do not meet the preset standards, the central control module controls the detection module to collect student pre-learning feedback information, obtains pre-learning effect evaluation values of the single knowledge points according to the pre-learning feedback information, and determines whether the materials of the lessons of the single knowledge points meet the preset standards or not according to the pre-learning effect evaluation values; the first lesson preparation judging mode meets the condition that the accuracy is smaller than a first preset accuracy;

The second lesson preparation judging mode is that the central control module judges that the lesson preparation of a single knowledge point in the teaching plan does not accord with a preset standard and the lesson preparation time of the knowledge point does not accord with the preset standard, and the adjusting module increases the lesson preparation time of the knowledge point to a corresponding value according to the difference value between the correct rate and the first preset correct rate; the second lesson preparation judging mode meets the condition that the accuracy rate is larger than or equal to the first preset accuracy rate and smaller than a second preset accuracy rate;

the third lesson preparation judging mode is that the central control module judges that lessons of single knowledge points in the teaching plan meet preset standards, and the lessons of the knowledge points are finished according to the current lessons; the third lesson preparation judging mode meets the condition that the accuracy rate is larger than or equal to the second preset accuracy rate.

Further, the central control module calculates the difference between the correct rate and the first preset correct rate in the second lesson preparation judging mode, marks the difference as a correct rate difference, and the adjusting module determines an adjusting mode of the lesson giving time of the lesson preparation aiming at the knowledge point according to the correct rate difference, wherein,

The first teaching duration adjusting mode is that the adjusting module uses a first preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the first teaching duration adjusting mode meets the condition that the accuracy difference is smaller than a first preset accuracy difference;

the second teaching duration adjusting mode is that the adjusting module uses a second preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the second teaching duration adjustment mode meets the condition that the correct rate difference value is larger than or equal to the first preset correct rate difference value and smaller than a second preset correct rate difference value;

the third teaching duration adjusting mode is that the adjusting module uses a third preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the third teaching duration adjusting mode meets the condition that the accuracy rate difference value is larger than or equal to the second preset accuracy rate difference value.

Further, the central control module controls the detection module to collect student pre-learning feedback information in the first pre-learning judgment mode, and obtains a pre-learning effect evaluation value E of a single knowledge point according to the pre-learning feedback information, wherein the pre-learning effect evaluation value E is set, hf is a first evaluation coefficient, hf is a difficulty coefficient of the single knowledge point of the pre-learning feedback, F=1 … u, u is the total number of students learning the pre-learning teaching plan, the central control module determines whether the material of the pre-learning for the single knowledge point accords with the judgment mode of a preset standard according to the pre-learning effect evaluation value of the single knowledge point, wherein,

The first material judging mode is that the central control module judges that the material of the lessons of the knowledge points accords with a preset standard, and the lessons of the knowledge points are finished according to the current material; the first material judgment mode meets the condition that the pre-learning effect evaluation value is smaller than a first preset pre-learning effect evaluation value;

the second material judging mode is that the central control module judges that the materials of the lessons of the knowledge points do not meet a preset standard, and the number of the materials of the lessons of the knowledge points is increased to a corresponding value according to the difference value between the pre-learning effect evaluation value and the first pre-learning effect evaluation value; the second material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the first pre-learning effect evaluation value and smaller than a second pre-learning effect evaluation value;

the third material judging mode is that the central control module judges that the material of the lessons of the single knowledge point does not accord with a preset standard, and reduces the duty ratio of the literal material in the material of the lessons of the knowledge point to a corresponding value according to the difference value between the pre-learning effect evaluation value and the second pre-learning effect evaluation value; the third material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the second pre-learning effect evaluation value.

Further, the central control module calculates the difference between the pre-learning effect evaluation value and the second pre-learning effect evaluation value in the third material judgment mode, marks the difference as a material difference, and the adjustment module determines an adjustment mode for the duty ratio of the literal material in the material according to the material difference, wherein,

the first duty ratio adjusting mode is that the adjusting module uses a first preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; the first duty ratio adjustment mode meets the condition that the material difference value is smaller than a first preset material difference value;

the second duty ratio adjusting mode is that the adjusting module uses a second preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; the second duty ratio adjusting mode meets the condition that the material difference value is larger than or equal to the first preset material difference value and smaller than a second preset material difference value;

the third duty ratio adjusting mode is that the adjusting module uses a third preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; and the third duty ratio adjusting mode meets the condition that the material difference value is greater than or equal to the second preset material difference value.

Further, the central control module calculates the difference between the preset material quantity of the lessons of the knowledge points and the adjusted material quantity under the first preset condition, and marks the difference as a correction difference, the adjustment module determines a correction mode aiming at the duty ratio of the literal materials in the materials according to the correction difference, wherein,

the first correction mode is that the adjusting module uses a first preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the first correction mode meets the condition that the correction difference value is smaller than a first preset correction difference value;

the second correction mode is that the adjusting module uses a second preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the second correction mode meets the condition that the correction difference value is larger than or equal to the first preset correction difference value and smaller than a second preset correction difference value;

the third correction mode is that the adjusting module uses a third preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the third correction mode meets the condition that the correction difference value is larger than or equal to the second preset correction difference value;

the first preset condition meets the condition that the adjustment module completes adjustment of the duty ratio of the literal materials in the materials of the knowledge points, and the number of the materials of the knowledge points after adjustment is smaller than the preset number of the materials.

Further, in the step S5, the central control module determines, according to the teaching plan evaluation value C, whether the teaching of the problem teaching plan meets a preset standard, and sets, where Tj is a duration of a single knowledge point in the teaching plan and Kj is a correct rate of a history examination question corresponding to the single knowledge point in the teaching plan, where Tj is a second evaluation coefficient; j= … m, m is the total number of knowledge points in the teaching plan, where,

the first problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the first problem judging mode meets the condition that the teaching plan evaluation value is smaller than a first preset teaching plan evaluation value;

the second problem judging mode is a secondary judging mode that the central control module judges that the lessons of the problem teaching cases do not accord with the preset standard, the central control module controls the detection module to detect the history correct rate of the problems after the lessons in the problem teaching cases stored in the database, and the central control module determines whether the lessons of the problem teaching cases accord with the preset standard or not according to the history correct rate; the second problem judging mode meets the condition that the teaching plan evaluation value is larger than or equal to the first preset teaching plan evaluation value and smaller than a second preset teaching plan evaluation value;

The third problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan does not accord with a preset standard, and the adjusting module increases the number of problems after class in the problem teaching plan to a corresponding value according to the difference value between the teaching plan evaluation value and the second preset teaching plan evaluation value; the third problem judging mode meets the condition that the teaching plan evaluation value is greater than or equal to the second preset teaching plan evaluation value.

Further, the central control module controls the detection module to detect the history accuracy of the post-lesson problems in the problem teaching plan stored in the database in the second problem judging mode, wherein ri is the accuracy of a single post-lesson problem stored in the database, i= … n, n is the total number of post-lesson problems in the problem teaching plan, the central control module determines a secondary judging mode for judging whether the lesson of the problem teaching plan meets a preset standard according to the obtained history accuracy,

the first secondary judgment mode is that the central control module judges that the lessons of the problem teaching plan do not meet the preset standard, and the adjusting module increases the duty ratio of the number of the basic problems after the lessons in the problem teaching plan to a corresponding value according to the difference value between the preset correct rate and the correct rate; the first secondary judgment mode meets the condition that the accuracy rate is smaller than the preset accuracy rate;

The second secondary judgment mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the second secondary judgment mode meets the condition that the accuracy rate is larger than or equal to the preset accuracy rate.

Further, the central control module calculates the difference between the preset correct rate and the correct rate in the first secondary judgment mode and marks the difference as a correct rate difference, and the adjusting module determines an adjusting mode aiming at the duty ratio of the number of problems after basic class in the problem teaching plan according to the correct rate difference, wherein,

the first basic duty ratio adjusting mode is that the adjusting module uses a first preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the first basic duty ratio adjustment mode meets the condition that the correct rate difference value is smaller than a first preset correct rate difference value;

the second basic duty ratio adjusting mode is that the adjusting module uses a second preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the second basic duty ratio adjustment mode meets the condition that the correct rate difference value is larger than or equal to the first preset correct rate difference value and smaller than a second preset correct rate difference value;

The third basic duty ratio adjusting mode is that the adjusting module uses a third preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the third basic duty ratio adjustment mode meets the condition that the correct rate difference value is larger than or equal to the second preset correct rate difference value.

Further, the central control module calculates the difference between the teaching plan evaluation value and the second preset teaching plan evaluation value in the third problem judgment mode, and marks the difference as a number difference value, the adjusting module determines an adjusting mode for the number of post-class problems in the problem teaching plan according to the number difference value, wherein,

the first quantity adjusting mode is that the adjusting module uses a first preset quantity adjusting coefficient to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the first quantity adjusting mode meets the condition that the quantity difference value is smaller than a first preset quantity difference value;

the second number adjusting mode is that the adjusting module uses a second preset number adjusting coefficient to increase the number of the problems after the class in the problem teaching plan to a corresponding value; the second quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the first preset quantity difference value and smaller than a second preset quantity difference value;

The third quantity adjusting mode is that the adjusting module uses a third preset quantity adjusting coefficient to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the third quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the second preset quantity difference value.

Compared with the prior art, the invention has the beneficial effects that: according to the invention, the single teaching plan for teaching is divided into the pre-learning teaching plan, the teaching plan and the problem teaching plan, the pre-learning teaching plan is sent to the students to be taught, the students feed back the pre-learning information after finishing pre-learning, the teaching plan and the problem teaching plan are organized by combining the information fed back by the students and the historical data of the database, and the problems that the materials and the post-teaching problems of the teaching plan cannot be correspondingly regulated according to the pre-learning and the historical learning conditions of the students, so that the quality of teaching contents and the teaching quality of teachers are affected are solved.

Further, the method and the system for judging whether the single knowledge point of the teaching plan accords with the standard or not can be realized by detecting the accuracy of the historical examination questions corresponding to the single knowledge point stored in the database, judging whether the teaching of the single knowledge point accords with the preset standard or not according to the accuracy, and preliminarily judging that the material of the teaching of the knowledge point does not accord with the preset standard or the teaching duration of the teaching of the knowledge point does not accord with the preset standard when the material does not accord with the preset standard, so that the judgment method for judging whether the single knowledge point of the teaching plan accords with the standard or not is solved.

Further, the central control module judges that the lesson preparation of a single knowledge point in the teaching plan does not meet the preset standard, and the lesson preparation time of the knowledge point does not meet the preset standard, so that the lesson preparation time of the knowledge point is increased, the full lesson preparation content of the knowledge point is ensured, and students can digest the knowledge point fully.

Furthermore, the invention also sets a preset teaching plan, and obtains the pre-learning effect evaluation value of a single knowledge point by collecting the pre-learning feedback information of the students, thereby quantifying the pre-learning effect of the students, and scientifically and reasonably evaluating whether the organization of the materials of the teaching plan of each knowledge point is in accordance with the standard.

Further, when the material of the lesson preparation of a single knowledge point does not meet the preset standard, the ratio of the literal material is too large to enable students to understand the knowledge point sufficiently, the central control module reduces the selection ratio of the literal material and improves the ratio of the non-literal material, namely, the material of the graphics, the audio and the video is increased, so that the content of the lesson preparation is enriched.

Further, when the adjustment module completes adjustment of the duty ratio of the literal material in the material of the knowledge point, and the number of the material of the knowledge point after adjustment is smaller than the preset number of materials, the adjustment module corrects the duty ratio of the literal material, so that accurate adjustment of the duty ratio of the literal material is realized.

Further, after the teaching of the teaching plan is finished, the teaching plan evaluation value is obtained by detecting the time length of a single knowledge point in the teaching plan, the accuracy rate of the corresponding historical examination questions and other information, and the teaching of the problem teaching plan is judged by the teaching plan evaluation value, so that the practice problems of students after the teaching are pertinently adjusted, and the effectiveness of the practice is ensured.

Further, through further detecting the history correct rate of the problems after the lessons, secondary judgment is carried out on the lessons of the problem teaching plan, so that the accuracy of problem arrangement is ensured, and redundant problems after the lessons are optimized.

Further, when the problem arrangement is unreasonable, because the occupation ratio of the basic problem is relatively low, the adjusting module of the invention realizes accurate and reasonable adjustment of the occupation ratio of the basic problem through different adjusting coefficients.

Further, when the problem arrangement is unreasonable, because the problem training quantity is insufficient, effective training can not be carried out on teaching knowledge points again, and then the adjusting module guarantees accurate and full training of problem teaching records for lessons through increasing the problem quantity.

Drawings

FIG. 1 is a flow chart of a smart class lesson preparation method based on big data according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for determining whether a lesson preparation of a single knowledge point meets a preset standard according to an embodiment of the present invention;

FIG. 3 is a flowchart of a method for adjusting teaching duration of a lesson preparation of a knowledge point according to an embodiment of the present invention;

fig. 4 is a flowchart of a determination method for determining whether the material of the lesson preparation of the knowledge point meets the preset standard according to the embodiment of the invention.

Detailed Description

In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

It should be noted that the data in this embodiment are obtained by the central control module according to the comprehensive analysis and evaluation of the half-year historical data and the corresponding historical results. The central control module of the invention prepares lessons according to the knowledge points of 9221 lesson teaching plans accumulated in the first half year before the current lesson preparation, and the post-lesson problems aim at the numerical value of each preset parameter standard of the current lesson preparation. It will be understood by those skilled in the art that the determining manner of the present invention for the parameters mentioned above may be selecting the value with the highest duty ratio as the preset standard parameter according to the data distribution, using weighted summation to take the obtained value as the preset standard parameter, substituting each history data into a specific formula, and taking the value obtained by the formula as the preset standard parameter or other selecting manner, as long as the different specific conditions in the single item determination process can be definitely defined by the obtained value are satisfied.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.

Referring to fig. 1, fig. 2, fig. 3, and fig. 4, flowcharts of a smart class preparation method based on big data according to an embodiment of the invention are shown; a flow chart of a judging mode of judging whether the lessons of the single knowledge points accord with preset standards or not; a flow chart of a method for adjusting teaching duration of the lessons of the knowledge points; and a flow chart of a judging mode of whether the material of the lessons of the single knowledge point accords with a preset standard.

The intelligent class preparation method based on big data provided by the embodiment of the invention comprises the following steps:

step S1, dividing a single-section lesson-preparing teaching plan into a pre-learning teaching plan, a teaching plan and a problem teaching plan;

step S2, writing a pre-learning teaching plan, sending the teaching plan to students to be taught, and feeding back pre-learning information after the students finish pre-learning; the pre-learning information comprises difficulty coefficients for marking all knowledge points in the pre-learning teaching plan; the difficulty coefficient of the knowledge point is 1,2,3,4 and 5, the difficulty coefficient is 1, the difficulty coefficient is 2, the difficulty coefficient is 3, the difficulty coefficient is moderate, the difficulty coefficient is 4, the difficulty is 4;

Step S3, writing a teaching plan, namely aiming at a single knowledge point in the teaching plan, selecting word materials and non-word materials from a database by a central control module to write the teaching plan of the single knowledge point;

step S4, the central control module controls the detection module to detect the accuracy of the historical examination questions corresponding to the single knowledge points stored in the database, judges whether the lessons of the single knowledge points in the teaching plan meet the preset standard according to the detected accuracy, and determines that the material of the lessons of the knowledge points does not meet the preset standard due to the fact that the material of the lessons of the knowledge points does not meet the preset standard or the teaching duration of the lessons of the knowledge points does not meet the preset standard when the material of the lessons of the knowledge points does not meet the preset standard;

step S5, writing the problem teaching plan, wherein the problem teaching plan comprises basic problems after class and problems after class improvement, the central control module judges whether the lessons of the problem teaching plan meet preset standards according to the teaching plan evaluation values, when the lessons do not meet the preset standards, the central control module controls the detection module to detect the historical accuracy of the problems after class in the problem teaching plan stored in the database, and the central control module determines a secondary judgment mode of whether the lessons of the problem teaching plan meet the preset standards according to the historical accuracy, or increases the number of the problems after class in the problem teaching plan to a corresponding value;

And S6, teaching the students by using the written teaching plan and arranging the problems after the problems are arranged by using the written teaching plan.

Specifically, in the step S4, the central control module controls the detection module to detect the accuracy of the historical examination questions corresponding to the single knowledge points stored in the database, and determines, according to the detected accuracy, whether the lessons for the single knowledge points in the teaching plan meet the preset standard or not, where,

the first lesson preparation judging mode is a judging mode that the central control module judges that lessons of single knowledge points in the teaching plan do not meet preset standards and materials of the lessons of the knowledge points do not meet the preset standards because the lessons of the single knowledge points do not meet the preset standards, the central control module controls the detection module to collect student pre-learning feedback information, obtains pre-learning effect evaluation values of the single knowledge points according to the pre-learning feedback information, and determines whether the materials of the lessons of the single knowledge points meet the preset standards or not according to the pre-learning effect evaluation values; the first lesson preparation judging mode meets the condition that the accuracy is smaller than 50% of a first preset accuracy;

the second lesson preparation judging mode is that the central control module judges that the lesson preparation of a single knowledge point in the teaching plan does not accord with a preset standard and the lesson preparation time of the knowledge point does not accord with the preset standard, and the adjusting module increases the lesson preparation time of the knowledge point to a corresponding value according to the difference value between the correct rate and the first preset correct rate; the second lesson preparation judging mode meets the condition that the accuracy rate is more than or equal to the first preset accuracy rate and less than 70% of a second preset accuracy rate;

The third lesson preparation judging mode is that the central control module judges that lessons of single knowledge points in the teaching plan meet preset standards, and the lessons of the knowledge points are finished according to the current lessons; the third lesson preparation judging mode meets the condition that the accuracy rate is larger than or equal to the second preset accuracy rate.

Specifically, the central control module calculates the difference between the correct rate and the first preset correct rate in the second lesson preparation judging mode, marks the difference as a correct rate difference, and the adjusting module determines an adjusting mode of the lesson giving time of the lesson preparation aiming at the knowledge point according to the correct rate difference, wherein,

the first teaching duration adjusting mode is that the adjusting module uses a first preset duration adjusting coefficient of 1.05 to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the first teaching duration adjusting mode meets the condition that the correction rate difference is smaller than a first preset correction rate difference by 7%;

the second teaching duration adjusting mode is that the adjusting module uses a second preset duration adjusting coefficient 1.11 to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the second teaching duration adjustment mode meets the condition that the correction rate difference value is more than or equal to the first preset correction rate difference value and less than 15% of a second preset correction rate difference value;

The third teaching duration adjusting mode is that the adjusting module uses a third preset duration adjusting coefficient of 1.18 to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the third teaching duration adjusting mode meets the condition that the accuracy rate difference value is larger than or equal to the second preset accuracy rate difference value.

Specifically, the central control module controls the detection module to collect student pre-learning feedback information in the first lesson preparation judging mode, obtains a pre-learning effect evaluation value E of a single knowledge point according to the pre-learning feedback information, and setsWherein->For the first evaluation coefficient, set +.>Hf is the difficulty coefficient of a single knowledge point fed back by pre-learning, hf epsilon (1, 2,3,4, 5), f= … u, u is the total number of students for learning the pre-learning teaching plan, the central control module determines whether the material of the lesson preparation for the single knowledge point meets the judgment mode of the preset standard according to the pre-learning effect evaluation value of the single knowledge point,

the first material judging mode is that the central control module judges that the material of the lessons of the knowledge points accords with a preset standard, and the lessons of the knowledge points are finished according to the current material; the first material judgment mode satisfies that the pre-learning effect evaluation value is smaller than a first preset pre-learning effect evaluation value 102.45;

The second material judging mode is that the central control module judges that the materials of the lessons of the knowledge points do not meet a preset standard, and the number of the materials of the lessons of the knowledge points is increased to a corresponding value according to the difference value between the pre-learning effect evaluation value and the first pre-learning effect evaluation value; the second material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the first pre-learning effect evaluation value and smaller than a second pre-learning effect evaluation value 175.58;

the third material judging mode is that the central control module judges that the material of the lessons of the single knowledge point does not accord with a preset standard, and reduces the duty ratio of the literal material in the material of the lessons of the knowledge point to a corresponding value according to the difference value between the pre-learning effect evaluation value and the second pre-learning effect evaluation value; the third material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the second pre-learning effect evaluation value.

Specifically, the central control module calculates the difference between the pre-learning effect evaluation value and the second pre-learning effect evaluation value in the third material judgment mode, marks the difference as a material difference, and the adjustment module determines an adjustment mode for the duty ratio of the literal material in the material according to the material difference, wherein,

The first duty ratio adjusting mode is that the adjusting module uses a first preset duty ratio adjusting coefficient of 0.9 to reduce the duty ratio of the literal material in the material to a corresponding value; the first duty ratio adjustment mode meets the condition that the material difference value is smaller than a first preset material difference value 31.14;

the second duty ratio adjusting mode is that the adjusting module uses a second preset duty ratio adjusting coefficient of 0.8 to reduce the duty ratio of the literal material in the material to a corresponding value; the second duty ratio adjustment mode satisfies that the material difference value is greater than or equal to the first preset material difference value and less than a second preset material difference value 68.58;

the third duty ratio adjusting mode is that the adjusting module uses a third preset duty ratio adjusting coefficient of 0.7 to reduce the duty ratio of the literal material in the material to a corresponding value; and the third duty ratio adjusting mode meets the condition that the material difference value is greater than or equal to the second preset material difference value.

Specifically, the central control module calculates the difference between the preset material number 150 of the lessons of the knowledge points and the adjusted material number under the first preset condition, and marks the difference as a correction difference, and the adjustment module determines a correction mode for the duty ratio of the literal materials in the materials according to the correction difference, wherein,

The first correction mode is that the adjusting module uses a first preset correction coefficient 1.01 to increase the duty ratio of the literal material in the material to a corresponding value; the first correction mode satisfies that the correction difference is smaller than a first preset correction difference 12;

the second correction mode is that the adjusting module uses a second preset correction coefficient 1.04 to increase the duty ratio of the literal materials in the materials to a corresponding value; the second correction mode satisfies that the correction difference is greater than or equal to the first preset correction difference and less than a second preset correction difference 28;

the third correction mode is that the adjusting module uses a third preset correction coefficient 1.08 to increase the duty ratio of the literal material in the material to a corresponding value; the third correction mode meets the condition that the correction difference value is larger than or equal to the second preset correction difference value;

the first preset condition meets the condition that the adjustment module completes adjustment of the duty ratio of the literal materials in the materials of the knowledge points, and the number of the materials of the knowledge points after adjustment is smaller than the preset number of the materials.

Specifically, in the step S5, the central control module determines, according to the teaching plan evaluation value C, whether the teaching of the problem teaching plan meets the determination mode of the preset standard, and sets Wherein->For the second evaluation coefficient +.>Tj is the time length of a single knowledge point in the teaching plan, and Kj is the accuracy of the historical examination questions corresponding to the single knowledge point in the teaching plan; j= … m, m is the total number of knowledge points in the teaching plan, where,

the first problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the first problem judging mode meets the condition that the teaching plan evaluation value is smaller than a first preset teaching plan evaluation value by 1.16;

the second problem determination mode is that the central control module determines that the lesson taking of the problem teaching plan does not accord with the preset standard, and the central control module controls the detection module to detect the history accuracy rate of the post-lesson problems in the problem teaching plan stored in the databaseThe central control module is according to the historical correct rate +.>Determining whether the lesson taking of the problem teaching plan meets a secondary judgment mode of a preset standard or not; the second problem judging mode meets the condition that the teaching plan evaluation value is greater than or equal to the first preset teaching plan evaluation value and smaller than a second preset teaching plan evaluation value by 1.58;

the third problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan does not accord with a preset standard, and the adjusting module increases the number of problems after class in the problem teaching plan to a corresponding value according to the difference value between the teaching plan evaluation value and the second preset teaching plan evaluation value; the third problem judging mode meets the condition that the teaching plan evaluation value is greater than or equal to the second preset teaching plan evaluation value.

Specifically, the central control module controls the detection module to detect the historical accuracy rate of the post-lesson problems in the problem teaching plan stored in the database in the second problem determination modeSetting->Wherein ri is the accuracy of a single post-lesson problem stored in the database, i= … n, n is the total number of post-lesson problems in the problem teaching plan, and the central control module determines the problem teaching plan according to the obtained historical accuracyA secondary judging mode of judging whether the lesson preparation meets a preset standard or not, wherein,

the first secondary judgment mode is that the central control module judges that the lessons of the problem teaching plan do not meet the preset standard, and the adjusting module increases the duty ratio of the number of the basic problems after the lessons in the problem teaching plan to a corresponding value according to the difference value between the preset correct rate and the correct rate; the first secondary judgment mode meets the condition that the accuracy rate is less than 75% of the preset accuracy rate;

the second secondary judgment mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the second secondary judgment mode meets the condition that the accuracy rate is larger than or equal to the preset accuracy rate.

Specifically, the central control module calculates the difference between the preset correct rate and the correct rate in the first secondary judgment mode and marks the difference as a correct rate difference, and the adjusting module determines an adjusting mode aiming at the duty ratio of the number of problems after basic class in the problem teaching plan according to the correct rate difference, wherein,

the first basic duty ratio adjusting mode is that the adjusting module uses a first preset basic duty ratio adjusting coefficient 1.03 to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the first basic duty ratio adjustment mode meets the condition that the correction rate difference is smaller than a first preset correction rate difference by 5%;

the second basic duty ratio adjusting mode is that the adjusting module uses a second preset basic duty ratio adjusting coefficient of 1.08 to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the second basic duty ratio adjustment mode meets the condition that the correct rate difference value is more than or equal to the first preset correct rate difference value and less than 10% of a second preset correct rate difference value;

the third basic duty ratio adjusting mode is that the adjusting module uses a third preset basic duty ratio adjusting coefficient of 1.14 to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the third basic duty ratio adjustment mode meets the condition that the correct rate difference value is larger than or equal to the second preset correct rate difference value.

Specifically, the central control module calculates the difference between the teaching plan evaluation value and the second preset teaching plan evaluation value in the third problem determination mode, and marks the difference as a number difference value, the adjustment module determines an adjustment mode for the number of post-class problems in the problem teaching plan according to the number difference value, wherein,

the first quantity adjusting mode is that the adjusting module uses a first preset quantity adjusting coefficient of 1.08 to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the first quantity adjusting mode meets the condition that the quantity difference value is smaller than a first preset quantity difference value by 0.25;

the second number adjusting mode is that the adjusting module uses a second preset number adjusting coefficient of 1.14 to increase the number of the problems after the class in the problem teaching plan to a corresponding value; the second quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the first preset quantity difference value and smaller than a second preset quantity difference value by 0.87;

the third quantity adjusting mode is that the adjusting module uses a third preset quantity adjusting coefficient 1.22 to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the third quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the second preset quantity difference value.

Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.

The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The intelligent class preparation method based on big data is characterized by comprising the following steps:

step S1, dividing a single-section lesson-preparing teaching plan into a pre-learning teaching plan, a teaching plan and a problem teaching plan;

step S2, writing a pre-learning teaching plan, sending the teaching plan to students to be taught, and feeding back pre-learning information after the students finish pre-learning; the pre-learning information comprises difficulty coefficients for marking all knowledge points in the pre-learning teaching plan;

Step S3, writing a teaching plan, namely aiming at a single knowledge point in the teaching plan, selecting word materials and non-word materials from a database by a central control module to write the teaching plan of the single knowledge point;

step S4, the central control module controls the detection module to detect the accuracy of the historical examination questions corresponding to the single knowledge points stored in the database, judges whether the lessons of the single knowledge points in the teaching plan meet the preset standard according to the detected accuracy, and determines that the material of the lessons of the knowledge points does not meet the preset standard due to the fact that the material of the lessons of the knowledge points does not meet the preset standard or the teaching duration of the lessons of the knowledge points does not meet the preset standard when the material of the lessons of the knowledge points does not meet the preset standard;

step S5, writing the problem teaching plan, judging whether the teaching course meets the preset standard according to the evaluation value of the teaching plan, controlling the detection module to detect the history correct rate of the problem after the teaching in the problem teaching plan stored in the database when the teaching course does not meet the preset standard, and determining a secondary judging mode of judging whether the teaching course meets the preset standard or not according to the history correct rate or increasing the number of the problem after the teaching in the problem teaching plan to a corresponding value by the central control module;

And S6, teaching the students by using the written teaching plan and arranging the problems after the problems are arranged by using the written teaching plan.

2. The intelligent class lesson preparation method based on big data as claimed in claim 1, wherein in the step S4, the central control module controls the detection module to detect the correctness of the history examination questions corresponding to the single knowledge points stored in the database, and the central control module determines whether the lesson preparation for the single knowledge points in the lesson teaching plan meets the preset standard according to the detected correctness,

the first lesson preparation judging mode is a judging mode that the central control module judges that lessons of single knowledge points in the teaching plan do not meet preset standards and materials of the lessons of the knowledge points do not meet the preset standards because the lessons of the single knowledge points do not meet the preset standards, the central control module controls the detection module to collect student pre-learning feedback information, obtains pre-learning effect evaluation values of the single knowledge points according to the pre-learning feedback information, and determines whether the materials of the lessons of the single knowledge points meet the preset standards or not according to the pre-learning effect evaluation values; the first lesson preparation judging mode meets the condition that the accuracy is smaller than a first preset accuracy;

The second lesson preparation judging mode is that the central control module judges that the lesson preparation of a single knowledge point in the teaching plan does not accord with a preset standard and the lesson preparation time of the knowledge point does not accord with the preset standard, and the adjusting module increases the lesson preparation time of the knowledge point to a corresponding value according to the difference value between the correct rate and the first preset correct rate; the second lesson preparation judging mode meets the condition that the accuracy rate is larger than or equal to the first preset accuracy rate and smaller than a second preset accuracy rate;

the third lesson preparation judging mode is that the central control module judges that lessons of single knowledge points in the teaching plan meet preset standards, and the lessons of the knowledge points are finished according to the current lessons; the third lesson preparation judging mode meets the condition that the accuracy rate is larger than or equal to the second preset accuracy rate.

3. The intelligent class lesson preparation method based on big data according to claim 2, wherein the central control module calculates a difference between the correct rate and the first preset correct rate in the second lesson preparation decision mode and marks the difference as a correct rate difference, and the adjustment module determines an adjustment mode of a lesson giving duration of lessons for the knowledge point according to the correct rate difference, wherein,

The first teaching duration adjusting mode is that the adjusting module uses a first preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the first teaching duration adjusting mode meets the condition that the accuracy difference is smaller than a first preset accuracy difference;

the second teaching duration adjusting mode is that the adjusting module uses a second preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the second teaching duration adjustment mode meets the condition that the correct rate difference value is larger than or equal to the first preset correct rate difference value and smaller than a second preset correct rate difference value;

the third teaching duration adjusting mode is that the adjusting module uses a third preset duration adjusting coefficient to increase the teaching duration of the lessons of the knowledge points to a corresponding value; the third teaching duration adjusting mode meets the condition that the accuracy rate difference value is larger than or equal to the second preset accuracy rate difference value.

4. The intelligent class preparation method based on big data according to claim 3, wherein the central control module controls the detection module to collect student pre-learning feedback information in the first class preparation judging mode, obtains a pre-learning effect evaluation value E of a single knowledge point according to the pre-learning feedback information, and sets Wherein->For the first evaluation coefficient, set +.>Hf is the difficulty coefficient of a single knowledge point fed back by pre-learning, hf epsilon (1, 2,3,4, 5), f= … u, u is the total number of students for learning the pre-learning teaching plan, and the central control module determines whether the material of the lesson preparation for the single knowledge point meets the judgment of the preset standard according to the pre-learning effect evaluation value of the single knowledge pointIn a fixed manner, wherein,

the first material judging mode is that the central control module judges that the material of the lessons of the knowledge points accords with a preset standard, and the lessons of the knowledge points are finished according to the current material; the first material judgment mode meets the condition that the pre-learning effect evaluation value is smaller than a first preset pre-learning effect evaluation value;

the second material judging mode is that the central control module judges that the materials of the lessons of the knowledge points do not meet a preset standard, and the number of the materials of the lessons of the knowledge points is increased to a corresponding value according to the difference value between the pre-learning effect evaluation value and the first pre-learning effect evaluation value; the second material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the first pre-learning effect evaluation value and smaller than a second pre-learning effect evaluation value;

The third material judging mode is that the central control module judges that the material of the lessons of the single knowledge point does not accord with a preset standard, and reduces the duty ratio of the literal material in the material of the lessons of the knowledge point to a corresponding value according to the difference value between the pre-learning effect evaluation value and the second pre-learning effect evaluation value; the third material judgment mode meets the condition that the pre-learning effect evaluation value is larger than or equal to the second pre-learning effect evaluation value.

5. The intelligent class lesson preparation method based on big data as claimed in claim 4, wherein the central control module calculates the difference between the pre-learning effect evaluation value and the second pre-learning effect evaluation value in the third material judgment mode and marks the difference as a material difference, the adjustment module determines an adjustment mode for the duty ratio of the literal material in the material according to the material difference, wherein,

the first duty ratio adjusting mode is that the adjusting module uses a first preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; the first duty ratio adjustment mode meets the condition that the material difference value is smaller than a first preset material difference value;

The second duty ratio adjusting mode is that the adjusting module uses a second preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; the second duty ratio adjusting mode meets the condition that the material difference value is larger than or equal to the first preset material difference value and smaller than a second preset material difference value;

the third duty ratio adjusting mode is that the adjusting module uses a third preset duty ratio adjusting coefficient to reduce the duty ratio of the literal materials in the materials to a corresponding value; and the third duty ratio adjusting mode meets the condition that the material difference value is greater than or equal to the second preset material difference value.

6. The intelligent class lesson preparation method as claimed in claim 5, wherein the central control module calculates a difference between a preset material number of lessons preparation of the knowledge point and an adjusted material number under a first preset condition and marks the difference as a correction difference, the adjustment module determines a correction mode for a duty ratio of the text material in the material according to the correction difference,

the first correction mode is that the adjusting module uses a first preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the first correction mode meets the condition that the correction difference value is smaller than a first preset correction difference value;

The second correction mode is that the adjusting module uses a second preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the second correction mode meets the condition that the correction difference value is larger than or equal to the first preset correction difference value and smaller than a second preset correction difference value;

the third correction mode is that the adjusting module uses a third preset correction coefficient to increase the duty ratio of the literal material in the material to a corresponding value; the third correction mode meets the condition that the correction difference value is larger than or equal to the second preset correction difference value;

the first preset condition meets the condition that the adjustment module completes adjustment of the duty ratio of the literal materials in the materials of the knowledge points, and the number of the materials of the knowledge points after adjustment is smaller than the preset number of the materials.

7. The intelligent class lesson preparation method based on big data as claimed in claim 6, wherein in step S5, the central control module determines whether lesson preparation for the problem teaching plan meets a preset standard according to the teaching plan evaluation value C, and sets upWherein->For the second evaluation coefficient +.>Tj is the time length of a single knowledge point in the teaching plan, and Kj is the accuracy of the historical examination questions corresponding to the single knowledge point in the teaching plan; j= … m, m is the total number of knowledge points in the teaching plan, where,

The first problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the first problem judging mode meets the condition that the teaching plan evaluation value is smaller than a first preset teaching plan evaluation value;

the second problem determination mode is that the central control module determines that the lesson taking of the problem teaching plan does not accord with the preset standard, and the central control module controls the detection module to detect the history accuracy rate of the post-lesson problems in the problem teaching plan stored in the databaseThe central control module is according to the historical correct rate +.>Determining whether the lesson taking of the problem teaching plan meets a secondary judgment mode of a preset standard or not; the second problem judging mode meets the condition that the teaching plan evaluation value is larger than or equal to the first preset teaching plan evaluation value and smaller than a second preset teaching plan evaluation value;

the third problem judging mode is that the central control module judges that the lesson taking of the problem teaching plan does not accord with a preset standard, and the adjusting module increases the number of problems after class in the problem teaching plan to a corresponding value according to the difference value between the teaching plan evaluation value and the second preset teaching plan evaluation value; the third problem judging mode meets the condition that the teaching plan evaluation value is greater than or equal to the second preset teaching plan evaluation value.

8. The intelligent class lesson preparation method based on big data as claimed in claim 7, wherein the central control module controls the detection module to detect the historical accuracy rate of the post-lesson problems in the problem teaching plan stored in the database in the second problem determination modeSetting->Wherein ri is the accuracy of a single post-lesson problem stored in the database, i= … n, n is the total number of post-lesson problems in the problem teaching plan, the central control module determines a secondary judgment mode for whether the lesson of the problem teaching plan meets a preset standard according to the obtained historical accuracy, wherein,

the first secondary judgment mode is that the central control module judges that the lessons of the problem teaching plan do not meet the preset standard, and the adjusting module increases the duty ratio of the number of the basic problems after the lessons in the problem teaching plan to a corresponding value according to the difference value between the preset correct rate and the correct rate; the first secondary judgment mode meets the condition that the accuracy rate is smaller than the preset accuracy rate;

the second secondary judgment mode is that the central control module judges that the lesson taking of the problem teaching plan meets the preset standard, and the current problem teaching plan is used for arranging the problems after the lessons for the students; the second secondary judgment mode meets the condition that the accuracy rate is larger than or equal to the preset accuracy rate.

9. The intelligent class lesson preparation method as claimed in claim 8, wherein the central control module calculates the difference between the preset correct rate and the correct rate in the first secondary decision mode and marks the difference as a correct rate difference, and the adjustment module determines an adjustment mode for the duty ratio of the number of basic post-lessons in the problem teaching plan according to the correct rate difference, wherein,

the first basic duty ratio adjusting mode is that the adjusting module uses a first preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the first basic duty ratio adjustment mode meets the condition that the correct rate difference value is smaller than a first preset correct rate difference value;

the second basic duty ratio adjusting mode is that the adjusting module uses a second preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the second basic duty ratio adjustment mode meets the condition that the correct rate difference value is larger than or equal to the first preset correct rate difference value and smaller than a second preset correct rate difference value;

the third basic duty ratio adjusting mode is that the adjusting module uses a third preset basic duty ratio adjusting coefficient to increase the duty ratio of the number of the problems after basic lessons in the problem teaching plan to a corresponding value; the third basic duty ratio adjustment mode meets the condition that the correct rate difference value is larger than or equal to the second preset correct rate difference value.

10. The intelligent class lesson preparation method as claimed in claim 9, wherein the central control module calculates a difference between the teaching plan evaluation value and the second preset teaching plan evaluation value in the third problem determination mode, and marks the difference as a number difference value, the adjustment module determines an adjustment mode for the number of post-class problems in the problem teaching plan according to the number difference value, wherein,

the first quantity adjusting mode is that the adjusting module uses a first preset quantity adjusting coefficient to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the first quantity adjusting mode meets the condition that the quantity difference value is smaller than a first preset quantity difference value;

the second number adjusting mode is that the adjusting module uses a second preset number adjusting coefficient to increase the number of the problems after the class in the problem teaching plan to a corresponding value; the second quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the first preset quantity difference value and smaller than a second preset quantity difference value;

the third quantity adjusting mode is that the adjusting module uses a third preset quantity adjusting coefficient to increase the quantity of the problems after the class in the problem teaching plan to a corresponding value; the third quantity adjusting mode meets the condition that the quantity difference value is larger than or equal to the second preset quantity difference value.