CN117421431B - Digital courseware generation method and device based on big data and storage medium - Google Patents
Digital courseware generation method and device based on big data and storage medium Download PDFInfo
- Publication number
- CN117421431B CN117421431B CN202311422906.8A CN202311422906A CN117421431B CN 117421431 B CN117421431 B CN 117421431B CN 202311422906 A CN202311422906 A CN 202311422906A CN 117421431 B CN117421431 B CN 117421431B
- Authority
- CN
- China
- Prior art keywords
- courseware
- digital
- knowledge point
- questions
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 74
- 238000012937 correction Methods 0.000 claims description 28
- 238000004590 computer program Methods 0.000 claims description 8
- 230000003993 interaction Effects 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 description 4
- 238000013499 data model Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
- G06F16/438—Presentation of query results
- G06F16/4387—Presentation of query results by the use of playlists
- G06F16/4393—Multimedia presentations, e.g. slide shows, multimedia albums
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/166—Editing, e.g. inserting or deleting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- General Engineering & Computer Science (AREA)
- Educational Technology (AREA)
- Educational Administration (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of intelligent class, in particular to a digital courseware generation method, device and storage medium based on big data, which comprises the following steps of S1, selecting a corresponding blank template from a database; s2, dividing the blank template into a plurality of knowledge point areas; step S3, after the duration of courseware content is set for the single knowledge point area, selecting corresponding materials and example questions in a database to arrange so as to complete digital courseware for the single knowledge point; step S4, the central control module judges whether the digital courseware aiming at the single knowledge point meets a preset standard according to the evaluation value of the knowledge point courseware; and S5, outputting the digital courseware meeting the preset standard. Therefore, the materials and the example questions in the digital courseware are correspondingly adjusted according to the history learning condition of the students, and the high-quality digital courseware is output.
Description
Technical Field
The invention relates to the technical field of intelligent class, in particular to a digital courseware generation method and device based on big data and a storage medium.
Background
The intelligent class refers to the integration, integration and comprehensive digitization of all information resources related to students' study, teaching, scientific research, management and life services by using computer technology, network technology, communication technology and scientific and normative management. The student learning system provides more learning materials for students, and teachers can know the learning condition of the students on line. Along with the development of intelligent classroom technology, the current courseware of teachers adopts a digital courseware mode more and more, how to prepare the courseware of courses by using the digital mode, so that high-quality teaching plans, learning plans and courseware which are helpful for students to understand and improve the learning efficiency of the students are obtained, and the teaching plans, the learning plans and the courseware gradually become the concern of the education industry.
Chinese patent publication No.: CN105868289B discloses a multimedia courseware generation method, which comprises the following steps: the method comprises the steps that a question stem corresponding to each question type and a background picture corresponding to each question type are stored in a database in advance; synthesizing the stem and the background map corresponding to each question type into a background stem synthesized map, and respectively storing the background stem synthesized map in the resource package corresponding to each question type; respectively generating parameter configuration tables of all the question types for preset attribute configuration parameters of all the question types; reading and analyzing parameters in the parameter configuration table of each question type to respectively obtain a data model corresponding to each question type; when receiving a multimedia courseware generation instruction of any appointed question type, directly calling a data model corresponding to the appointed question type, and reading a resource package corresponding to the appointed question type to generate the multimedia courseware. According to the technical scheme, materials and example questions forming the lecture knowledge points in the teaching plan cannot be correspondingly adjusted according to the history learning condition of students, so that the quality of digital courseware and the teaching quality of teachers are affected.
Disclosure of Invention
Therefore, the invention provides a digital courseware generating method, device and storage medium based on big data, which are used for solving the problem that materials and example questions forming a lecture knowledge point in the digital courseware cannot be correspondingly regulated according to the history learning condition of students in the prior art, so that high-quality digital courseware is output, and the teaching quality of teachers is improved.
On one hand, the invention provides a digital courseware generation method based on big data, which comprises the following steps:
Step S1, selecting a corresponding blank template from a database based on teaching duration of a digital courseware;
s2, dividing the blank template into a plurality of knowledge point areas;
step S3, after the duration of courseware content is set for the single knowledge point area, selecting corresponding materials and example questions in a database to arrange so as to complete digital courseware for the single knowledge point;
Step S4, the central control module judges whether the digital courseware aiming at the single knowledge point meets a preset standard according to the evaluation value of the digital courseware of the knowledge point, and increases the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increases the material quantity of the digital courseware of the knowledge point to the corresponding value, or judges whether the digital courseware aiming at the matching example problem of the single knowledge point meets the preset standard based on the matching evaluation value of the example problem;
and S5, outputting the digital courseware meeting the preset standard.
Further, in the step S4, the central control module determines whether the digital courseware for the single knowledge point meets a preset standard based on the knowledge point courseware evaluation value C, and increases the duration of the content of the digital courseware of the knowledge point to a corresponding value when it is determined that the digital courseware does not meet the preset standard, or increases the number of materials of the digital courseware of the knowledge point to a corresponding value, or determines whether the digital courseware for the single knowledge point matching example problem meets the preset standard based on the example problem matching evaluation value, and setsWherein α is a first evaluation coefficient, α=0.55, β is a second evaluation coefficient, β=0.34, T 1 is a historical accuracy of questions for a single knowledge point stored in the database, T 2 is a historical accuracy of post-class exercises for a single knowledge point stored in the database, and T 3 is a historical teacher-student interaction duration ratio for a single knowledge point teaching stored in the database.
Further, a plurality of adjustment modes for the material quantity of the digital courseware of the single knowledge point are arranged in the central control module, and the adjustment amplitude of each adjustment mode to the material quantity is different.
Further, the central control module is provided with a plurality of correction modes aiming at the material quantity of the digital courseware of the single knowledge point under a first preset condition, the correction amplitudes of the correction modes on the material quantity are different, and the first preset condition is that the teaching duration of the single knowledge point is longer than the preset teaching duration after the material quantity of the digital courseware of the single knowledge point is increased.
Further, the central control module determines whether the digital courseware for the single knowledge point matching example questions meets a preset standard based on the example question matching evaluation value L, and increases the number of the knowledge point matching example questions to a corresponding value when the digital courseware does not meet the preset standard, or determines whether the level for the knowledge point matching example questions meets the preset standard based on the difference value between the example question matching evaluation value and the first preset example question matching evaluation value, and setsAnd setting epsilon=0.82, t is the duration of teaching content of the knowledge point, and n is the number of materials of the digital courseware of the knowledge point.
Further, a plurality of adjustment modes for the number of the matching example questions of the knowledge points are arranged in the central control module, and the adjustment amplitudes of the adjustment modes on the number of the matching example questions are different.
Further, the central control module determines whether the level of the example question matched for the knowledge point meets a preset standard based on the difference value of the example question matching evaluation value and the first preset example question matching evaluation value, and when the level of the example question matched for the knowledge point does not meet the preset standard, the single example question is replaced with the example question of the higher level or the example question of the lower level.
Further, the central control module determines a level for a single example question based on the historical accuracy rate of the examination of the single example question, wherein the level of the example question comprises one-level example questions to five-level example questions.
On the other hand, the invention also provides a device for generating the digital courseware based on the big data, which comprises:
the database module is internally stored with data for generating the digital courseware, wherein the data comprises a plurality of blank templates, materials for forming knowledge points of the digital courseware, example questions for forming the knowledge points of the digital courseware, the historical accuracy of examination questions of the knowledge points, the historical accuracy of post-class exercises of the knowledge points, the historical teacher-student interaction time ratio during teaching of the knowledge points and the historical accuracy of examination of each example question;
The central control module is connected with the database module and is used for judging whether the digital courseware aiming at a single knowledge point meets a preset standard according to the evaluation value of the digital courseware of the knowledge point, and increasing the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increasing the material quantity of the digital courseware of the knowledge point to the corresponding value, or judging whether the digital courseware aiming at the single knowledge point matching example problem meets the preset standard based on the example problem matching evaluation value;
The adjusting module is connected with the central control module and used for adjusting the duration of the teaching content of the knowledge point, the number of materials of the digital courseware of the knowledge point, the number of the knowledge point matching questions and the grade of the knowledge point matching questions according to the judging result of the central control module.
On the other hand, the invention also provides a storage medium based on big data for generating the digital courseware, which is a computer readable storage medium and stores a computer program, and the computer program is executed to realize the method for generating the digital courseware based on big data.
Compared with the prior art, the invention has the beneficial effects that: the method starts from a single knowledge point area, selects corresponding materials and example questions from a database to complete digital courseware aiming at the single knowledge point, combines the history examination and teaching data of the knowledge point, and correspondingly adjusts the corresponding material data and example questions of the teaching plan, thereby improving the quality of the digital courseware, integrating the digital courseware required by the intelligent classroom by utilizing a big data mode, and really realizing personalized record and teaching by virtue of promoting the innovation of the intelligent teaching mode of subjects.
Furthermore, the invention sets the evaluation value C of the digital courseware for generating the single knowledge point of the digital courseware, so that the evaluation mode of the digital courseware is summarized in the data of the history teaching and examination, thereby scientifically and accurately evaluating whether the single knowledge point meets the preset standard or not, and making corresponding adjustment when the single knowledge point does not meet the preset standard, thereby further ensuring the rationality and accuracy of the evaluation of the digital courseware in the intelligent classroom.
Further, when the number of the materials is insufficient and the record of a single knowledge point does not meet the preset standard, the adjusting module uses different number adjusting coefficients to increase the number of the corresponding materials to the corresponding values, so that the problem of insufficient quality of the digital courseware due to insufficient materials is solved.
Further, when the adjustment module completes the increase of the material quantity of the digital courseware of the single knowledge point and the teaching duration of the adjusted single knowledge point is longer than the preset teaching duration, the central control module is further provided with a plurality of correction modes aiming at the material quantity of the digital courseware of the single knowledge point, and each correction is different in correction size for the decrease of the material quantity of the digital courseware of the knowledge point, so that the accurate adjustment of the material quantity is realized, and the teaching duration of the knowledge point is ensured.
Further, the central control module determines whether the digital courseware of the single knowledge point matching example questions accords with a judging mode of a preset standard according to the example question matching evaluation value, and increases the number of the knowledge point matching example questions to a corresponding value or determines whether the level of the knowledge point matching example questions accords with the judging mode of the preset standard when the number of the knowledge point matching example questions does not accord with the preset standard, so that whether the example questions of the single knowledge point matching example questions meet the standard is accurately determined, the number and the level of the example questions of the lecture of each knowledge point digital courseware are further guaranteed to be matched, and understanding of students on the knowledge points is better promoted.
Further, when the number of the single knowledge point matching example questions is insufficient, and the number of the digital courseware of the knowledge point is insufficient, the adjusting module increases the number of the single knowledge point matching example questions to the corresponding value, so that students can digest the content of the knowledge point through the example questions fully when teaching using the digital courseware.
Further, when the level of the matching example questions of the knowledge points is higher or higher, and the matching example questions are not matched with the teaching contents, the central control module further adjusts the level of the example questions, so that the matching property is ensured, and the students can understand the knowledge contents of courseware step by step.
Further, the central control module classifies the example questions into 1-5 grades according to the historical accuracy of the examination of the individual example questions, and the corresponding example questions are conveniently distributed in the digital courseware generation process.
The invention further provides a device for generating the digital courseware based on the big data, which comprises a database module, a central control module and an adjusting module, wherein the database module of the device stores the data of the digital courseware, the central control module generates the digital courseware through the stored data, and the adjusting module can adjust the digital courseware, so that the high-quality digital courseware is output.
Further, the invention also provides a storage medium based on big data, which is a computer readable storage medium and stores a computer program, and the computer program is executed to realize the big data based digital courseware generation method.
Drawings
FIG. 1 is a flow chart of a digital courseware generation method based on big data according to an embodiment of the invention;
FIG. 2 is a flowchart for determining whether a digital courseware 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 the number of materials according to an embodiment of the present invention;
FIG. 4 is a flowchart of determining whether a digital courseware of a knowledge point matching example question meets a preset standard according to an embodiment of the present 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 three-month historical data and the corresponding historical results through comprehensive analysis and evaluation. According to the material forming the knowledge points of 3221 digital courseware accumulated in the first three months before the generation of the current courseware, the central control module is used for forming the example questions of the knowledge points, the history accuracy of the examination questions of the knowledge points, the history accuracy of the post-class problems of the knowledge points, the history teacher-student interaction time length ratio during teaching of the knowledge points and the history accuracy of the examination of each example question, and determining the numerical value of each preset parameter standard generated for the current digital courseware. 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.
Referring to fig. 1, fig. 2, fig. 3, and fig. 4, which are flowcharts of a digital courseware generation method based on big data according to an embodiment of the invention; the embodiment of the invention judges whether the digital courseware of the single knowledge point accords with a flow chart of a preset standard; the flow chart of the adjusting mode of the material quantity in the embodiment of the invention; the embodiment of the invention judges whether the digital courseware of the knowledge point matching example questions accords with a flow chart of a preset standard.
The embodiment of the invention comprises the following steps:
On one hand, the invention provides a digital courseware generation method based on big data, which comprises the following steps:
Step S1, selecting a corresponding blank template from a database based on teaching duration of a digital courseware;
s2, dividing the blank template into a plurality of knowledge point areas;
step S3, after the duration of courseware content is set for the single knowledge point area, selecting corresponding materials and example questions in a database to arrange so as to complete digital courseware for the single knowledge point;
Step S4, the central control module judges whether the digital courseware aiming at the single knowledge point meets a preset standard according to the evaluation value of the digital courseware of the knowledge point, and increases the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increases the material quantity of the digital courseware of the knowledge point to the corresponding value, or judges whether the digital courseware aiming at the matching example problem of the single knowledge point meets the preset standard based on the matching evaluation value of the example problem;
and S5, outputting the digital courseware meeting the preset standard.
Specifically, in the step S4, the central control module determines, according to the knowledge point courseware evaluation value C, whether the digital courseware for the knowledge point meets the determination mode of the preset standard, and setsWherein alpha is a first evaluation coefficient, alpha=0.55, beta is a second evaluation coefficient, beta=0.34, T 1 is a historical correct rate of questions for a single knowledge point stored in the database, T 2 is a historical correct rate of post-class exercises for the single knowledge point stored in the database, T 3 is a historical teacher-student interaction time period ratio for teaching of the single knowledge point stored in the database,
The first judging mode is that the central control module judges that the digital courseware of the single knowledge point does not accord with a preset standard, and increases the duration of the content of the digital courseware of the knowledge point to a corresponding value according to the difference value between the first preset knowledge point courseware evaluation value and the knowledge point courseware evaluation value; the first judgment mode meets the condition that the knowledge point courseware evaluation value is smaller than a first preset knowledge point courseware evaluation value by 1.88;
The second judging mode is that the central control module judges that the digital courseware of the single knowledge point does not accord with a preset standard, and increases the material quantity of the digital courseware of the knowledge point to a corresponding value according to the difference value of the evaluation value of the knowledge point courseware and the evaluation value of the first preset knowledge point courseware; the second judgment mode meets the condition that the knowledge point courseware evaluation value is more than or equal to the first preset knowledge point courseware evaluation value and less than a second preset knowledge point courseware evaluation value by 2.01;
The third judging mode is that the central control module judges that the digital courseware of the single knowledge point does not accord with the preset standard and the reason that the digital courseware of the knowledge point does not accord with the preset standard is that the example problem matched with the knowledge point does not accord with the preset standard, and the central control module determines whether the digital courseware of the single knowledge point matched example problem accords with the judging mode of the preset standard according to the example problem matching evaluation value; the third judging mode meets the condition that the knowledge point courseware evaluation value is more than or equal to the second preset knowledge point courseware evaluation value and less than a third preset knowledge point courseware evaluation value by 2.21;
The fourth judging mode is that the central control module judges that the digital courseware of the single knowledge point accords with a preset standard, and outputs the digital courseware of the knowledge point according to the current parameters; and the fourth judgment mode meets the condition that the knowledge point courseware evaluation value is greater than or equal to the third preset knowledge point courseware evaluation value.
Specifically, the central control module calculates the difference value between the knowledge point courseware evaluation value and the first preset knowledge point courseware evaluation value in a second judgment mode, marks the difference value as a material difference value, and the adjustment module determines an adjustment mode of the material quantity of the digital courseware aiming at a single knowledge point according to the material difference value, wherein,
The first quantity adjusting mode is that the adjusting module uses a first preset quantity adjusting coefficient 1.03 to increase the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded up; the first quantity adjusting mode meets the condition that the material difference value is smaller than a first preset material difference value by 0.03;
the second quantity adjusting mode is that the adjusting module uses a second preset quantity adjusting coefficient 1.08 to increase the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded upwards; the second quantity 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 by 0.08;
The third quantity adjusting mode is that the adjusting module uses a third preset quantity adjusting coefficient of 1.15 to increase the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded up; the second quantity adjusting mode meets the condition that the material difference value is larger than or equal to the second preset material difference value.
Specifically, the central control module calculates the difference value between the teaching duration of the adjusted single knowledge point and the preset duration of 25min under the first preset condition, marks the difference value as a correction difference value, and the adjustment module determines a correction mode of the material quantity of the digital courseware aiming at the single knowledge point according to the correction difference value, wherein,
The first correction mode is that the adjusting module uses a first preset correction coefficient of 0.99 to reduce the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded downwards; the first correction mode meets the condition that the correction difference is smaller than a first preset correction difference by 2.5;
the second correction mode is that the adjusting module uses a second preset correction coefficient of 0.98 to reduce the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded downwards; the second correction mode meets the condition that the correction difference is larger than or equal to the first preset correction difference and smaller than the second preset correction difference by 5.3;
The third correction mode is that the adjusting module uses a third preset correction coefficient of 0.97 to reduce the material quantity of the digital courseware of the single knowledge point to a corresponding value, and if the material quantity is not an integer, the material quantity is rounded downwards; 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 is that the adjusting module completes the increase of the material quantity of the digital courseware of the single knowledge point, and the teaching duration of the adjusted single knowledge point is longer than the preset teaching duration.
Specifically, the central control module determines, according to the example problem matching evaluation value L, a determination mode for determining whether the digital courseware of the knowledge point matching example problem meets a preset standard or not in the third determination mode, and setsWherein epsilon is a third evaluation coefficient, epsilon=0.82 is set, t is the duration of the teaching content of the single knowledge point, n is the number of materials of the digital courseware of the single knowledge point,
The first example judgment mode is that the central control module judges that the digital courseware of the single knowledge point matching example questions does not accord with a preset standard, and increases the number of the knowledge point matching example questions to a corresponding value according to the difference value between a first preset example question matching evaluation value and the example question matching evaluation value; the first example question judging mode meets the condition that the example question matching evaluation value is smaller than a first preset example question matching evaluation value 21.85;
the second example question judging mode is a judging mode that the central control module judges that the digital courseware of the single knowledge point matching example question does not accord with a preset standard, and determines whether the grade of the knowledge point matching example question accords with the preset standard according to the difference value of the example question matching evaluation value and the first preset example question matching evaluation value; the second example question judging mode meets the condition that the example question matching evaluation value is greater than or equal to the first preset example question matching evaluation value and smaller than a second preset example question matching evaluation value by 45.28;
The third example question judging mode is that the central control module judges that the digital courseware of the single knowledge point matching example question accords with a preset standard, and generates the digital courseware for the single knowledge point according to the current matching example question; and the third example question judging mode meets the condition that the example question matching evaluation value is greater than or equal to the second preset example question matching evaluation value.
Specifically, the central control module calculates the difference value between the first preset example question matching evaluation value and the example question matching evaluation value in the first example question judging mode, marks the difference value as a quantity difference value, and the adjusting module determines an adjusting mode for the quantity of the single knowledge point matching example questions according to the quantity difference value, wherein,
The first problem number adjusting mode is that the adjusting module uses a first preset problem number adjusting coefficient 1.03 to increase the number of the knowledge point matching problem to a corresponding value; the first example quantity adjustment mode satisfies that the quantity difference is less than a first preset quantity difference of 5.33;
The second problem number adjusting mode is that the adjusting module uses a second preset problem number adjusting coefficient 1.08 to increase the number of the knowledge point matching problem to a corresponding value; the second example problem 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 15.58;
The third problem number adjusting mode is that the adjusting module uses a third preset problem number adjusting coefficient 1.15 to increase the number of the knowledge point matching problem to a corresponding value; the third example question number adjusting mode meets the condition that the number difference value is larger than or equal to the second preset number difference value.
Specifically, the central control module calculates the difference value between the example question matching evaluation value and the first preset example question matching evaluation value in the second example question judging mode, marks the difference value as a grade difference value, and determines whether the grade of the knowledge point matching example question meets the judging mode of a preset standard according to the grade difference value, wherein,
The first type of judgment mode is that the central control module judges that the grade of the single knowledge point matching example questions does not accord with a preset standard, and the adjustment module changes the single example questions into example questions with a grade higher than one grade, and if the single example questions are of a fifth grade, the example questions are not changed; the first type judgment mode meets the condition that the grade difference value is smaller than a preset grade difference value of 12.05;
The second type of judgment mode is that the central control module judges that the grade of the single knowledge point matching example questions does not accord with a preset standard, and the adjustment module changes the single example questions into example questions with one grade lower than the single example questions, and if the single example questions are the first grade, the example questions are not changed; the second type judgment mode meets the condition that the grade difference value is larger than or equal to a preset grade difference value.
Specifically, the central control module determines a determination mode for the individual example question level according to the history accuracy rate of the examination of the individual example question in the second example question determination mode, wherein,
The first level judgment mode is that the central control module judges that the level of a single example question is a first level example question; the first level judgment mode meets the condition that the accuracy rate is less than 85% of a first preset accuracy rate;
The second level judgment mode is that the central control module judges that the level of a single example question is a second-level example question; the second level judgment mode meets the condition that the accuracy rate is larger than or equal to the first preset accuracy rate and smaller than 65% of a second preset accuracy rate;
the third level judgment mode is that the central control module judges that the level of a single example question is three-level example questions; the third level judgment mode meets the condition that the accuracy rate is more than or equal to the second preset accuracy rate and less than the third preset accuracy rate by 45%;
the fourth grade judging mode is that the central control module judges that the grade of a single example question is a four-grade example question; the fourth level judgment mode meets the condition that the accuracy rate is larger than or equal to the third preset accuracy rate and smaller than the fourth preset accuracy rate by 25%;
the fifth grade judging mode is that the central control module judges that the grade of a single example question is five-grade example questions; the fifth level judgment mode satisfies that the accuracy rate is greater than or equal to the fourth preset accuracy rate.
On the other hand, the invention also provides a device for generating the digital courseware based on the big data, which comprises:
the database module is internally stored with data for generating the digital courseware, wherein the data comprises a plurality of blank templates, materials for forming knowledge points of the digital courseware, example questions for forming the knowledge points of the digital courseware, the historical accuracy of examination questions of the knowledge points, the historical accuracy of post-class exercises of the knowledge points, the historical teacher-student interaction time ratio during teaching of the knowledge points and the historical accuracy of examination of each example question;
The central control module is connected with the database module and is used for judging whether the digital courseware aiming at a single knowledge point meets a preset standard according to the evaluation value of the digital courseware of the knowledge point, and increasing the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increasing the material quantity of the digital courseware of the knowledge point to the corresponding value, or judging whether the digital courseware aiming at the single knowledge point matching example problem meets the preset standard based on the example problem matching evaluation value;
The adjusting module is connected with the central control module and used for adjusting the duration of the teaching content of the knowledge point, the number of materials of the digital courseware of the knowledge point, the number of the knowledge point matching questions and the grade of the knowledge point matching questions according to the judging result of the central control module.
On the other hand, the invention also provides a storage medium based on big data for generating the digital courseware, which is a computer readable storage medium and stores a computer program, and the computer program is executed to realize the method for generating the digital courseware based on big data.
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.
Claims (7)
1. The digital courseware generation method based on big data is characterized by comprising the following steps of:
Step S1, selecting a corresponding blank template from a database based on teaching duration of a digital courseware;
s2, dividing the blank template into a plurality of knowledge point areas;
step S3, after the duration of courseware content is set for the single knowledge point area, selecting corresponding materials and example questions in a database to arrange so as to complete digital courseware for the single knowledge point;
Step S4, the central control module judges whether the digital courseware aiming at the single knowledge point meets a preset standard according to the knowledge point courseware evaluation value C, and increases the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increases the material quantity of the digital courseware of the knowledge point to the corresponding value, or judges whether the digital courseware aiming at the single knowledge point matching example problem meets the preset standard based on the example problem matching evaluation value;
s5, outputting digital courseware meeting preset standards;
Setting up Wherein, the method comprises the steps of, wherein,For the first evaluation coefficient, set，For the second evaluation coefficient, setT 1 is the historical accuracy of examination questions for the single knowledge point stored in the database, T 2 is the historical accuracy of post-class questions for the single knowledge point stored in the database, and T 3 is the historical teacher-student interaction time ratio for teaching for the single knowledge point stored in the database;
the central control module judges whether the digital courseware of the single knowledge point matching example questions accords with a preset standard based on the example question matching evaluation value L, and increases the number of the knowledge point matching example questions to a corresponding value when judging that the digital courseware does not accord with the preset standard, or judges whether the grade of the knowledge point matching example questions accords with the preset standard based on the difference value of the example question matching evaluation value and the first preset example question matching evaluation value, and sets Wherein, the method comprises the steps of, wherein,For the third evaluation coefficient, setT is the duration of the teaching content of the single knowledge point, and n is the material number of the digital courseware of the single knowledge point;
The central control module judges that the grade of the example question matched for the knowledge point does not meet the preset standard based on the difference value of the example question matching evaluation value and the first preset example question matching evaluation value, and the single example question is replaced with the example question with the higher grade or the example question with the lower grade;
The central control module calculates the difference value between the example question matching evaluation value and the first preset example question matching evaluation value, marks the difference value as a grade difference value, and determines whether the grade of the knowledge point matching example question meets the preset standard or not according to the grade difference value,
The first type of judgment mode is that the central control module judges that the grade of the single knowledge point matching example questions does not accord with a preset standard, and the adjustment module changes the single example questions into example questions with one grade higher than the grade, if the single example questions are of a fifth grade, the example questions are not changed; the first type judgment mode meets the condition that the grade difference value is smaller than a preset grade difference value;
The second type of judgment mode is that the central control module judges that the grade of the single knowledge point matching example questions does not accord with a preset standard, and the adjustment module changes the single example questions into example questions with one grade lower than the grade, if the single example questions are the first grade, the example questions are not changed; the second type judgment mode meets the condition that the grade difference value is larger than or equal to a preset grade difference value.
2. The big data-based digital courseware generation method according to claim 1, wherein a plurality of adjustment modes of the number of materials of the digital courseware aiming at a single knowledge point are arranged in the central control module, and the adjustment amplitude of each adjustment mode on the number of materials is different.
3. The big data-based digital courseware generation method according to claim 2, wherein the central control module is provided with a plurality of correction modes aiming at the material quantity of the digital courseware of a single knowledge point under a first preset condition, the correction amplitudes of the correction modes on the material quantity are different, and the first preset condition is that the teaching duration of the single knowledge point is longer than the preset teaching duration after the material quantity of the digital courseware of the single knowledge point is increased.
4. The big data-based digital courseware generation method of claim 3, wherein a plurality of adjustment modes for the number of the matching example questions of a single knowledge point are arranged in the central control module, and the adjustment amplitudes of the adjustment modes for the number of the matching example questions are different.
5. The big data based digital courseware generation method of claim 4, wherein the central control module determines a level for a single of the example questions based on a historical accuracy rate of the examination of the single of the example questions, the level of the example questions comprising a level of example questions to a level of five example questions.
6. An apparatus for using the big data based digital courseware generation method of any one of claims 1-5, comprising:
the database module is internally stored with data for generating the digital courseware, wherein the data comprises a plurality of blank templates, materials for forming knowledge points of the digital courseware, example questions for forming the knowledge points of the digital courseware, the historical accuracy of examination questions of the knowledge points, the historical accuracy of post-class exercises of the knowledge points, the historical teacher-student interaction time ratio during teaching of the knowledge points and the historical accuracy of examination of each example question;
The central control module is connected with the database module and is used for judging whether the digital courseware aiming at a single knowledge point meets a preset standard according to the evaluation value of the digital courseware of the knowledge point, and increasing the duration of the content of the digital courseware of the knowledge point to a corresponding value when judging that the digital courseware does not meet the preset standard, or increasing the material quantity of the digital courseware of the knowledge point to the corresponding value, or judging whether the digital courseware aiming at the single knowledge point matching example problem meets the preset standard based on the example problem matching evaluation value;
The adjusting module is connected with the central control module and used for adjusting the duration of the teaching content of the knowledge point, the number of materials of the digital courseware of the knowledge point, the number of the knowledge point matching questions and the grade of the knowledge point matching questions according to the judging result of the central control module.
7. A storage medium, which is a computer-readable storage medium, storing a computer program, characterized in that the computer program is executed to implement the big data-based digital courseware generation method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311422906.8A CN117421431B (en) | 2023-10-31 | 2023-10-31 | Digital courseware generation method and device based on big data and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311422906.8A CN117421431B (en) | 2023-10-31 | 2023-10-31 | Digital courseware generation method and device based on big data and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117421431A CN117421431A (en) | 2024-01-19 |
| CN117421431B true CN117421431B (en) | 2024-09-20 |
Family
ID=89522708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311422906.8A Active CN117421431B (en) | 2023-10-31 | 2023-10-31 | Digital courseware generation method and device based on big data and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117421431B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105185178A (en) * | 2015-10-21 | 2015-12-23 | 华中师范大学 | Courseware formulating system and method |
| CN116150110A (en) * | 2023-04-20 | 2023-05-23 | 广东省出版集团数字出版有限公司 | Automatic digital teaching material modeling system based on AI deep learning |
| CN116957872A (en) * | 2023-09-20 | 2023-10-27 | 广州宏途数字科技有限公司 | Intelligent classroom lesson preparation method based on big data |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4071687A1 (en) * | 2014-05-30 | 2022-10-12 | Mcrowdsourcing Canada Inc. | Actionable verifiable micro-crowd sourcing |
| US10870791B2 (en) * | 2017-08-14 | 2020-12-22 | PfP Industries LLC | Compositions and methods for cross-linking hydratable polymers using produced water |
| CN109766664A (en) * | 2019-01-31 | 2019-05-17 | 史文华 | A kind of courseware design method, apparatus, storage medium and electronic equipment |
| CN111242816B (en) * | 2020-01-02 | 2024-03-15 | 广州蓝鸽软件有限公司 | Multimedia teaching plan manufacturing method and system based on artificial intelligence |
-
2023
- 2023-10-31 CN CN202311422906.8A patent/CN117421431B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105185178A (en) * | 2015-10-21 | 2015-12-23 | 华中师范大学 | Courseware formulating system and method |
| CN116150110A (en) * | 2023-04-20 | 2023-05-23 | 广东省出版集团数字出版有限公司 | Automatic digital teaching material modeling system based on AI deep learning |
| CN116957872A (en) * | 2023-09-20 | 2023-10-27 | 广州宏途数字科技有限公司 | Intelligent classroom lesson preparation method based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117421431A (en) | 2024-01-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107067849B (en) | Parent end system based on cloud teaching platform | |
| Fuller | Raising School Quality in Developing Countries: What Investments Boost Learning? World Bank Discussion Papers 2. | |
| CN110867103A (en) | Intelligent dynamic grouping learning system based on student personalized learning | |
| CN116150110B (en) | Automatic digital teaching material modeling system based on AI deep learning | |
| CN114066252A (en) | Teaching resource dynamic allocation system based on knowledge coding and LFNN model | |
| CN113870074A (en) | Education interactive system based on artificial intelligence | |
| CN115879701B (en) | Classroom duration self-adaptive adjustment system based on intelligent classroom | |
| CN117421431B (en) | Digital courseware generation method and device based on big data and storage medium | |
| CN116957872B (en) | Intelligent classroom lesson preparation method based on big data | |
| CN110210649A (en) | Class scheduling method and system for campus shift | |
| CN113626695A (en) | Primary and secondary school growth information literacy portrait construction method and system based on situation test | |
| CN109472410B (en) | Dynamic intelligent class scheduling method in shifts | |
| CN113962497A (en) | Teacher resource analysis is accuse system based on internet education | |
| CN112258355A (en) | Teacher data management system based on information platform of primary and middle schools | |
| CN113257059B (en) | Online education training system based on intelligent cloud platform | |
| CN114743418A (en) | Japanese teaching system based on AI intelligence | |
| Liu et al. | Using learning analytics technologies to find learning structures from online examination system | |
| CN113888055A (en) | Dynamic teaching management system | |
| CN112150097A (en) | Learning design generation method and device, electronic equipment and storage medium | |
| CN114582182B (en) | Accurate teaching and learning system for big data of quasi-teaching and wisdom | |
| Sencindiver | Success in Calculus I: Implications of Students' Precalculus Content Knowledge and Their Awareness of that Knowledge | |
| CN111599230B (en) | Language teaching method and device based on big data | |
| THOMAS | Language learning strategies and individual learner characteristics: Situating strategy use in diverse contexts | |
| Han | Mismatches in the labor market for college graduates: Focusing on field-of-study choice | |
| LU506371B1 (en) | Intelligent evaluation system for students' level in advanced algebra teaching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |