Method and system for online teaching, storage medium and processor
By acquiring students' learning behavior characteristics, identifying common and individual difficulties, generating specialized exercise sets, and adjusting teaching methods, the problem of poor teaching effectiveness in online teaching was solved. This enabled dynamic optimization of teaching content and personalized learning paths, thereby improving teaching quality and learning outcomes.
Patent Information
- Application Number
- CN202511182932.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-22
- Publication Date
- 2025-11-28
AI Technical Summary
Existing online teaching methods make it difficult to accurately assess each student's learning progress, making it difficult for instructors to adjust their teaching methods accordingly, resulting in poor learning outcomes.
By acquiring students' learning behavior characteristics during the learning process, identifying common and individual difficulties, generating specialized exercise sets, and adjusting teaching methods through exercise tests, we can achieve dynamic optimization of teaching content and personalized learning paths.
It significantly improved teaching effectiveness and resource utilization efficiency, achieved precise matching of teaching resources and dynamic optimization of teaching strategies, and enhanced the intelligence and personalization of teaching.
Smart Images
Figure CN121031983A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of online teaching, and particularly relates to a method, system, storage medium and processor for online teaching. BACKGROUND
[0002] With the development of information technology and the popularity of the Internet, online education as a new teaching mode has rapidly emerged, and its characteristics of not being restricted by region and time greatly enrich the distribution mode of educational resources and improve the flexibility and efficiency of educational activities. However, the existing online teaching scheme still has some deficiencies in practical application, and it is difficult to meet the personalized teaching needs.
[0003] However, in actual situations, the learning understanding abilities of a plurality of students are not the same, and the existing online teaching method is difficult to accurately evaluate the learning situation of each student, so that the teaching teacher is difficult to adjust the teaching mode according to the learning situation of the students, thereby resulting in poor learning effect of online teaching. SUMMARY
[0004] The present application aims to at least solve one of the technical problems existing in the prior art; for this purpose, the present application provides a method, system, storage medium and processor for online teaching, which is used to solve the technical problem that the existing online teaching method is difficult to accurately evaluate the learning situation of each student, so that the teaching teacher is difficult to adjust the teaching mode according to the learning situation of the students, thereby resulting in poor learning effect of online teaching.
[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a method for online teaching, comprising: obtaining learning behavior characteristics of each student in the process of learning online teaching video; determining a difficult point set of each student based on the learning behavior characteristics of each student; inductively analyzing the difficult point set of each student to obtain a common difficult point set and an individual difficult point set of each student; improving the online teaching video based on the common difficult point set; generating a special exercise set of each student based on the individual difficult point set; conducting exercise test on the corresponding student based on the special exercise set of each student to obtain the course mastery degree of each student; adjusting the teaching mode of the teacher based on the course mastery degree of each student.
[0006] Preferably, the determination of the difficult point set of each student based on the learning behavior characteristics of each student comprises: Extract the learning behavior characteristics of each student; among which, the learning behavior characteristics include the student's correct answer rate for each course, video pause duration, number of video replays, video pause position, and video rewind position; Each student's learning efficiency score is calculated based on the accuracy of their answers, the duration of video pauses, and the number of times the video is replayed. If the learning efficiency score is less than the preset threshold, the knowledge points corresponding to the pause and rewind positions of the corresponding student's video will be marked as key and difficult knowledge points. If the learning efficiency score is greater than the preset threshold, the knowledge points corresponding to the pause and rewind positions of the video for the corresponding student will be marked as mastered knowledge points. Multiple key and difficult knowledge points are integrated into a set of difficult points; among them, key and difficult knowledge points refer to knowledge points that are not fully understood or are easily confused.
[0007] Preferably, the calculation of each student's learning efficiency score based on the correct answer rate, video pause duration, and number of video replays includes: Extract each student's answer accuracy rate, video pause duration, and video replay count; then use the formula... Calculate student i's learning efficiency score ;in, For student i, the correct answer rate The duration of the video pause for student i. Let a1, a2, and a3 be the number of times the video is played back for student i; a1, a2, and a3 are all proportionality coefficients greater than 0; i = 1, 2, ..., n, where n is the total number of students.
[0008] Preferably, the step of summarizing and analyzing the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student includes: Extract the set of difficulties for each student and count the number of key and difficult knowledge points; calculate the ratio of the number of key and difficult knowledge points to the total number of students, and mark the corresponding ratio as the feedback ratio; if the feedback ratio is greater than the preset ratio threshold, mark the corresponding key and difficult knowledge points as common difficulties; if the feedback ratio is less than or equal to the preset ratio threshold, mark the corresponding key and difficult knowledge points as individual difficulties; integrate several common difficulties into a common difficulty set, and integrate several individual difficulties of each student into the individual difficulty set of the corresponding student.
[0009] Preferably, the improvement of online teaching videos based on a set of common difficulties includes: Extract a set of common difficulties; send several common difficulties from the set to the corresponding teachers, who will then re-record course improvement videos for these common difficulties.
[0010] Preferably, the step of generating a specialized set of exercises for each student based on their individual difficulty set includes: Extract individual student difficulty sets; input key and difficult knowledge points from these individual difficulty sets into a problem database for matching to obtain corresponding problems; integrate these problems into a specialized problem set.
[0011] Preferably, the step of conducting practice tests on students based on their respective specialized practice sets includes: Extract each student's specific practice set; administer practice tests to each student and tally their scores. If the answer score is greater than the preset score threshold, the corresponding student's course mastery level will be marked as mastered; If the answer score is less than or equal to the preset score threshold, the corresponding student's course mastery level will be marked as not mastered; The level of course mastery includes both "mastered" and "not mastered".
[0012] A second aspect of the present invention provides a system for online teaching, comprising: a data processing module, and a data acquisition module and a teaching adjustment module connected thereto; The data acquisition module is used to acquire the learning behavior characteristics of each student during the online teaching video learning process; The data processing module is used to: determine the set of difficulties for each student based on their learning behavior characteristics; summarize and analyze the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student; send the set of common difficulties to the instructors of the corresponding courses and generate improved courses for the corresponding courses; and generate a set of specialized exercises for each student based on the set of individual difficulties. The teaching adjustment module is used to conduct exercises and tests on students based on their respective problem sets to determine their level of mastery of the course; and to adjust the teaching methods of teachers based on the students' level of mastery of the course.
[0013] A third aspect of the present invention provides a storage medium comprising a stored executable program, wherein, when the executable program is executed, it controls the device where the storage medium is located to perform the aforementioned method for online teaching.
[0014] A fourth aspect of the present invention provides a processor for running an executable program, wherein the executable program, when running, performs the above-described method for online teaching.
[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention systematically collects students' behavioral characteristics data during the learning process and combines this with difficulty identification and classification analysis techniques to achieve dynamic optimization of teaching content and construction of personalized learning paths, thereby significantly improving teaching effectiveness and resource utilization efficiency. The method first accurately identifies common and individual difficulties based on students' behavioral characteristics, enhances the universality and relevance of resources by improving teaching video content, and generates specialized exercise sets to strengthen individual weaknesses, effectively improving learning efficiency and knowledge mastery. Furthermore, through quantitative analysis of exercise test results and dynamic adjustment of teaching methods, a closed-loop feedback mechanism of "data collection - difficulty identification - resource optimization - teaching improvement" is formed. This not only solves the limitations of the "one-size-fits-all" model in traditional online teaching but also enhances the intelligence and personalization of teaching. This method uses technological means to achieve precise adaptation of teaching resources and dynamic optimization of teaching strategies, taking into account both the common needs of the group and individual differences, thus contributing to improving the teaching quality and learning outcomes of online teaching. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Fig. 1 This is an overall flowchart of the method for online teaching according to the present invention; Fig. 2 This is a schematic diagram of the principle of the system for online teaching according to the present invention. Detailed Implementation
[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Please see Figs. 1-2 The first aspect of this invention provides a method for online teaching, comprising: S1: Obtain the learning behavior characteristics of each student during the online teaching video learning process; S2: Determine the set of difficulties for each student based on their learning behavior characteristics; S3: Summarize and analyze the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student; S4: Improve online teaching videos based on a set of common difficulties; S5: Generate a set of specialized practice problems for each student based on their individual difficulty set; S6: Based on each student's specific exercise set, conduct exercise tests on the corresponding students to obtain each student's course mastery level; S7: Adjust the teacher's teaching methods based on each student's level of mastery of the course.
[0020] In this embodiment, the set of difficulties for each student is determined based on their learning behavior characteristics, including: A1: Extract the learning behavior characteristics of each student; among which, the learning behavior characteristics include the student's correct answer rate for each course, video pause duration, number of video replays, video pause position, and video rewind position; A2: Calculate each student's learning efficiency score based on the correctness of answers, video pause duration, and number of video replays; A3: If the learning efficiency score is less than the preset threshold, the knowledge points corresponding to the pause and rewind positions of the corresponding student's video will be marked as key and difficult knowledge points. A4: If the learning efficiency score is greater than the preset threshold, then the knowledge points corresponding to the pause and rewind positions of the video for the corresponding student will be marked as mastered knowledge points; A5: Integrate multiple key and difficult knowledge points into a set of difficult points; among them, key and difficult knowledge points refer to knowledge points that are not fully understood or are easily confused.
[0021] In this embodiment, the learning efficiency score for each student is calculated based on the correct answer rate, video pause duration, and number of video replays, including: Extract each student's answer accuracy rate, video pause duration, and video replay count; then use the formula... Calculate student i's learning efficiency score ;in, For student i, the correct answer rate The duration of the video pause for student i. The number of times video is played back for student i; a1, a2, and a3 are all proportional coefficients greater than 0, and the specific values of a1, a2, and a3 are set by relevant experts based on experience; i = 1, 2, ..., n, where n is the total number of students.
[0022] For example, set the proportionality coefficients a1=100, a2=1.2, and a3=1.5; the student 1's answer accuracy rate =80%, Student 1's video pause duration =5 minutes, Number of video playback times for Student 1 =4; Student 1's learning efficiency score was calculated using the formula. ≈22.44.
[0023] It should be noted that the values of the proportionality coefficients a1, a2, and a3 are related to...; the larger the value of..., the larger the actual values of the proportionality coefficients a1, a2, and a3.
[0024] In this embodiment, the set of difficulties for each student is summarized and analyzed to obtain a set of common difficulties and a set of individual difficulties for each student, including: Extract the set of difficulties for each student and count the number of key and difficult knowledge points; calculate the ratio of the number of key and difficult knowledge points to the total number of students, and mark the corresponding ratio as the feedback ratio; if the feedback ratio is greater than the preset ratio threshold, mark the corresponding key and difficult knowledge points as common difficulties; if the feedback ratio is less than or equal to the preset ratio threshold, mark the corresponding key and difficult knowledge points as individual difficulties; integrate several common difficulties into a common difficulty set, and integrate several individual difficulties of each student into the individual difficulty set of the corresponding student.
[0025] For example, the total number of students is set to 83, the number of key and difficult knowledge points 1 is 5, the number of key and difficult knowledge points 2 is 12, the number of key and difficult knowledge points 3 is 18, and the number of key and difficult knowledge points 4 is 24. The ratio of the number of responses to each key and difficult knowledge point to the total number of students was calculated. The results showed that the feedback rate for key and difficult knowledge point 1 was 6.02%, the feedback rate for key and difficult knowledge point 2 was 14.46%, the feedback rate for key and difficult knowledge point 3 was 21.69%, and the feedback rate for key and difficult knowledge point 4 was 28.92%. The percentage threshold is set to 20%. Since the feedback percentages of key and difficult knowledge points 3 and 4 are greater than the preset percentage threshold, key and difficult knowledge points 3 and 4 are marked as common difficulties. Since the feedback percentages of key and difficult knowledge points 1 and 2 are less than the preset percentage threshold, key and difficult knowledge points 1 and 2 are marked as individual difficulties.
[0026] In this embodiment, online teaching videos are improved based on a set of common difficulties, including: Extract a set of common difficulties; send several common difficulties from the set to the corresponding teachers, who will then re-record course improvement videos for these common difficulties.
[0027] In this embodiment, a specialized set of practice questions is generated for each student based on a set of individual difficulties, including: Extract individual student difficulty sets; input key and difficult knowledge points from these individual difficulty sets into a problem database for matching to obtain corresponding problems; integrate these problems into a specialized problem set.
[0028] In this embodiment, based on each student's specific problem set, a problem test is conducted on the corresponding students, including: Extract each student's specific practice set; administer practice tests to each student and tally their scores. If the answer score is greater than the preset score threshold, the corresponding student's course mastery level will be marked as mastered; If the answer score is less than or equal to the preset score threshold, the corresponding student's course mastery level will be marked as not mastered; The level of course mastery includes both "mastered" and "not mastered".
[0029] For example, a test is given to Student 1 based on the specific exercise set. Student 1's answer score is set to 94, and the score threshold is set to 90. Since Student 1's answer score is greater than the preset score threshold, Student 1's course mastery level is marked as mastered.
[0030] A second aspect of the present invention provides a system for online teaching, comprising: a data processing module, and a data acquisition module and a teaching adjustment module connected thereto; Data acquisition module: used to acquire the learning behavior characteristics of each student during the online teaching video learning process; Data processing module: used to determine the set of difficulties for each student based on their learning behavior characteristics; to summarize and analyze the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student; to send the set of common difficulties to the instructors of the corresponding courses and generate improved courses for the corresponding courses; and to generate a set of specialized exercises for each student based on the set of individual difficulties. The teaching adjustment module is used to conduct exercises and tests on students based on their specific exercise sets to determine their level of mastery of the course; and to adjust the teaching methods of teachers based on the students' level of mastery of the course.
[0031] A third aspect of the present invention provides a storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the storage medium is located to execute the above-described method for online teaching.
[0032] A fourth aspect of the present invention provides a processor for running an executable program, wherein the executable program executes the above-described method for online teaching.
[0033] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.
[0034] Working principle of the invention: This invention obtains the learning behavior characteristics of each student during the online teaching video learning process; determines the set of difficulties for each student based on the learning behavior characteristics; summarizes and analyzes the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student; improves the online teaching videos based on the set of common difficulties; generates a set of specialized exercises for each student based on the set of individual difficulties; conducts exercises tests on the corresponding students based on the set of specialized exercises to obtain the students' course mastery level; and adjusts the teaching methods of teachers based on the students' course mastery level.
[0035] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A method for online teaching, characterized in that, include: To obtain the learning behavior characteristics of each student during the process of learning online teaching videos; Determine the set of difficulties for each student based on their learning behavior characteristics. By summarizing and analyzing the set of difficulties for each student, we can obtain a set of common difficulties and a set of individual difficulties for each student. Improve online teaching videos based on a set of common difficulties; Based on individual student difficulties, specialized practice sets are generated for each student. Based on each student's specific set of exercises, corresponding tests are conducted on the students to determine each student's level of course mastery. The teacher's teaching methods are adjusted based on each student's level of mastery of the course.
2. The method for online teaching according to claim 1, characterized in that, The process of determining the set of difficulties for each student based on their learning behavior characteristics includes: Extract the learning behavior characteristics of each student; among which, the learning behavior characteristics include the student's correct answer rate for each course, video pause duration, number of video replays, video pause position, and video rewind position; Each student's learning efficiency score is calculated based on the accuracy of their answers, the duration of video pauses, and the number of times the video is replayed. If the learning efficiency score is less than the preset threshold, the knowledge points corresponding to the pause and rewind positions of the corresponding student's video will be marked as key and difficult knowledge points. If the learning efficiency score is greater than the preset threshold, the knowledge points corresponding to the pause and rewind positions of the video for the corresponding student will be marked as mastered knowledge points. Multiple key and difficult knowledge points are integrated into a set of difficult points; among them, key and difficult knowledge points refer to knowledge points that are not fully understood or are easily confused.
3. The method for online teaching according to claim 2, characterized in that, The learning efficiency score for each student is calculated based on the accuracy of answers, video pause duration, and number of video replays, including: Extract each student's answer accuracy rate, video pause duration, and video replay count; then use the formula... Calculate student i's learning efficiency score ;in, For student i, the correct answer rate The duration of the video pause for student i. Let a1, a2, and a3 be the number of times the video is played back for student i; a1, a2, and a3 are all proportionality coefficients greater than 0; i = 1, 2, ..., n, where n is the total number of students.
4. The method for online teaching according to claim 1, characterized in that, The analysis of the difficulties faced by each student yields a set of common difficulties and a set of individual difficulties for each student, including: Extract the set of difficulties for each student and count the number of key and difficult knowledge points; calculate the ratio of the number of key and difficult knowledge points to the total number of students, and mark the corresponding ratio as the feedback ratio; if the feedback ratio is greater than the preset ratio threshold, mark the corresponding key and difficult knowledge points as common difficulties; if the feedback ratio is less than or equal to the preset ratio threshold, mark the corresponding key and difficult knowledge points as individual difficulties; integrate several common difficulties into a common difficulty set, and integrate several individual difficulties of each student into the individual difficulty set of the corresponding student.
5. The method for online teaching according to claim 1, characterized in that, The improvement of online teaching videos based on a set of common difficulties includes: Extract a set of common difficulties; send several common difficulties from the set to the corresponding teachers, who will then re-record course improvement videos for these common difficulties.
6. The method for online teaching according to claim 1, characterized in that, The method of generating specialized practice sets for each student based on individual difficulty sets includes: Extract individual student difficulty sets; input key and difficult knowledge points from these individual difficulty sets into a problem database for matching to obtain corresponding problems; integrate these problems into a specialized problem set.
7. The method for online teaching according to claim 1, characterized in that, The practice tests conducted on students based on their respective specialized practice sets include: Extract each student's specific practice set; administer practice tests to each student and tally their scores. If the answer score is greater than the preset score threshold, the corresponding student's course mastery level will be marked as mastered; If the answer score is less than or equal to the preset score threshold, the corresponding student's course mastery level will be marked as not mastered; The level of course mastery includes both "mastered" and "not mastered".
8. A method for online teaching, used to implement the method for online teaching as described in any one of claims 1-7, characterized in that, include: The data processing module, and the data acquisition module and teaching adjustment module connected to it; The data acquisition module is used to acquire the learning behavior characteristics of each student during the online teaching video learning process; The data processing module is used to: determine the set of difficulties for each student based on their learning behavior characteristics; summarize and analyze the set of difficulties for each student to obtain a set of common difficulties and a set of individual difficulties for each student; send the set of common difficulties to the instructors of the corresponding courses and generate improved courses for the corresponding courses; and generate a set of specialized exercises for each student based on the set of individual difficulties. The teaching adjustment module is used to conduct exercises and tests on students based on their respective problem sets to determine their level of mastery of the course; and to adjust the teaching methods of teachers based on the students' level of mastery of the course.
9. A storage medium, characterized in that, The storage medium stores an executable program, which, when executed by a processor, is used to implement the method for online teaching as described in any one of claims 1-7.
10. A processor, characterized in that, The processor is used to run an executable program, wherein the executable program executes the method for online teaching as described in any one of claims 1-7.