Intelligent education and teaching management optimization system

Abstract

The invention relates to the technical field of intelligent education and management, and discloses an intelligent education teaching management optimization system, which comprises a teaching effect evaluation module, a teaching video evaluation module and a social interaction learning module. And the teaching effect evaluation module is used for evaluating the mastering degree of the user on the teaching video. The teaching video evaluation module is used for evaluating the content quality of the teaching video. And the social interaction learning module is used for the user to select to join the learning group with high activeness. The teaching effect evaluation module can plan the most suitable learning path for the students according to the learning requirements and learning interests of the students. Through cooperative work of the display touch screen, the learning video database and the learning planning processor, the system can track the learning progress and mastering degree of students in real time, dynamically adjust learning paths according to the actual conditions and learning targets of the students, and ensure that the students can efficiently and orderly complete learning tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent education and management, and specifically to an intelligent education and teaching management optimization system. Background Art

[0002] When the current learning platform is in use, users can conduct corresponding special assessments after watching teaching videos, and users can also like, comment, give negative comments, and forward the teaching videos. The planning of the learning path often lacks personalization and cannot meet the learning needs and learning rhythms of different students. The evaluation of teaching videos usually relies on manual review or simple user feedback, and it is difficult to accurately reflect the true quality and teaching effect of the videos. In addition, the function of the social interaction learning module is relatively single, lacking an effective evaluation and recommendation mechanism for the activity of students' study groups. In the aspect of learning path planning in traditional systems, a one-size-fits-all method is usually adopted, providing the same learning path for all students, ignoring the individual differences and different learning interests among students. This planning method not only fails to stimulate students' learning interests, but may also lead to low learning efficiency and poor learning effects. Traditional systems often rely on simple feedback such as users' likes and comments, which are often subjective and one-sided and cannot comprehensively and objectively reflect the quality of teaching videos. At the same time, the lack of scientific evaluation indicators and evaluation methods makes it difficult to screen and optimize teaching videos. Although traditional systems provide the function of study groups, they lack an effective evaluation and recommendation mechanism for group activity. Students often have difficulty finding a study group suitable for themselves and cannot understand the learning atmosphere and communication quality within the group, thus affecting the learning effect and learning experience.

[0003] To solve the above problems, an intelligent education and teaching management optimization system is proposed in the present invention. Summary of the Invention

[0004] The present invention proposes an intelligent education and teaching management optimization system, which solves the problems of lack of personalization in learning path planning, inaccurate evaluation of teaching videos, and single function of the social interaction learning module in traditional teaching management systems.

[0005] The technical solution of the present invention is as follows:

[0006] The intelligent education and teaching management optimization system includes: a teaching effect evaluation module, a teaching video evaluation module, and a social interaction learning module.

[0007] The teaching effect evaluation module is used to evaluate the degree of mastery of teaching videos by users.

[0008] The teaching video evaluation module is used to evaluate the content quality of teaching videos.

[0009] The social interaction learning module is used for users to choose to join study groups with high activity.

[0010] Preferably, the teaching effect evaluation module includes a touch display screen and a learning planning processor.

[0011] The touch display screen is used for the user to input the expected assessment score and name, and then send the expected assessment score to the learning planning processor for display after receiving the sorted teaching videos.

[0012] The learning planning processor is communicatively connected to the database of the learning platform and the touch display screen. After receiving the expected assessment score and name, it is used to call all the teaching videos that the user has taken an assessment on, the total assessment score of the special assessment corresponding to the teaching video, the assessment time used by the user in the special assessment corresponding to the teaching video, the assessment score, and the total number of assessments. Then, subtract 1 from the data obtained by dividing the assessment score in the special assessment by the total score of the special assessment, and then take the absolute value to obtain the assessment error rate in the special assessment of the teaching video. Then, calculate the mastery degree value of the user for the teaching video based on the total number of special assessments corresponding to the teaching video, the assessment score in the special assessment corresponding to the teaching video, the assessment error rate in the special assessment of the teaching video, and the assessment time used by the user in the special assessment of the teaching video. Then, send the mastery degree value of the user for the teaching video to the teaching video evaluation module.

[0013] Preferably, the calculation formula for the mastery degree value of the user for the teaching video is:

[0014]

[0015] Where: G i is the mastery degree value of the user for the teaching video, V is the total number of special assessments corresponding to the teaching video, unit: times, e i is the assessment score in the special assessment corresponding to the teaching video, unit: points, s i is the expected assessment score, unit: points, r i is the assessment error rate in the special assessment of the teaching video, t i is the assessment time used by the user in the special assessment of the teaching video, unit: minutes.

[0016] Preferably, the teaching video evaluation module is communicatively connected to the learning platform. On the first day of each month, it is used to call the like, dislike, view, and share information of all the teaching videos that the user has taken an assessment on in the database of the learning platform, and calculate the quality index of the teaching video according to the ratio of the number of likes to the number of dislikes and the ratio of the number of views to the number of shares.

[0017] Preferably, the calculation formula for the quality index of the teaching video is:

[0018] Y i = α × H + β × Z.

[0019] Wherein: Y i is the quality index of the teaching video, H is the ratio of the number of likes to the number of dislikes, Z is the ratio of the number of views to the number of forwards, and α and β are weight coefficients and α + β = 1.

[0020] Preferably, the teaching video evaluation module is communicatively connected to the learning planning processor, and is used for sending the quality index information of the teaching video to the learning planning processor after calculating the quality index of the teaching video.

[0021] The learning planning processor is used for calculating the comprehensive score of the teaching video according to the user's mastery degree value of the teaching video, the quality index of the teaching video, and the preset weight coefficients after receiving the quality index information of the teaching video, then sorting the teaching videos from small to large according to the comprehensive score of the teaching video, and then sending the sorted teaching videos to the touch display screen.

[0022] Preferably, the calculation formula for the comprehensive score of the teaching video is:

[0023] C i = k×G i +(1 - k)×Y i .

[0024] Wherein, C i is the comprehensive score of the teaching video, and k is the preset weight coefficient.

[0025] Preferably, the social interaction learning module includes a group database, an interaction processor, and a second display screen.

[0026] The group database is used for the user to store the preset learning groups, the total number and names of the group members in the learning group, the preset standard times, and the preset member participation weight coefficients.

[0027] The interaction processor is communicatively connected to the group database, the touch display screen, and the learning platform, and is used for calling the preset learning groups, the total number and names of the group members in the learning group, the preset standard times, and the preset member participation weight coefficients, and then calling the number of group members who participated in the comments within a week in the database of the learning platform, calculating the activity of the learning group according to the preset learning groups, the total number of group members in the learning group, the number of group members who participated in the comments within a week, the total number of comments of the group members within the group within a week, the preset standard times, and the preset member participation weight coefficients, then sorting the activity of the learning group from large to small, and then sending the sorting information of the activity of the learning group from large to small to the second display screen.

[0028] The second display screen is used for the user to view after receiving the sorting information of the activity of the learning group from large to small.

[0029] Preferably, the activity calculation formula of the learning group is as follows:

[0030]

[0031] Where: I is the activity of the learning group, P is the number of group members participating in comments within a week, unit: person, M is the total number of members in the learning group, unit: person, C is the total number of comments among group members within a week, unit: times, T is the preset standard number of times, unit: times, and γ is the preset member participation weight coefficient.

[0032] Preferably, the group database is a cloud server.

[0033] The beneficial effects of the present invention are as follows:

[0034] 1. The teaching effect evaluation module can plan the most suitable learning path for students according to their learning needs and interests. Through the coordinated work of the display touch screen, learning video database, and learning planning processor, the system can real-time track the learning progress and mastery level of students, and dynamically adjust the learning path according to the actual situation and learning goals of students to ensure that students can complete learning tasks efficiently and orderly.

[0035] 2. The teaching video evaluation module can comprehensively and objectively evaluate the quality of teaching videos by extracting key indicators such as the number of likes, dislikes, views, and forwards of the videos, combined with scientific evaluation algorithms and weight coefficients.

[0036] 3. The social interaction learning module can real-time evaluate the activity of the learning group through the coordinated work of the group database and the interaction processor, and recommend the most suitable learning group for students according to the activity ranking information. This not only helps students find a learning group suitable for themselves, improve learning effects and experiences, but also promotes communication and cooperation within the learning group, forming a good learning atmosphere. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0038] Figure 1 It is a schematic block diagram of the intelligent education teaching management optimization system of the present invention; SPECIFIC EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of the present invention.

[0040] Please refer to Figure 1 , the present invention provides an intelligent education and teaching management optimization system, including: a teaching effect evaluation module, a teaching video evaluation module, and a social interaction learning module.

[0041] The teaching effect evaluation module is used to evaluate the user's mastery of the teaching video.

[0042] The teaching video evaluation module is used to evaluate the content quality of the teaching video.

[0043] The social interaction learning module is used for users to choose to join learning groups with high activity.

[0044] In this embodiment, the teaching effect evaluation module includes a touch display screen and a learning planning processor.

[0045] The touch display screen is used for the user to input the expected assessment score and name, and then send the expected assessment score to the learning planning processor for display after receiving the sorted teaching videos.

[0046] The learning planning processor is communicatively connected to the database of the learning platform and the touch display screen. After receiving the expected assessment score and name, it is used to call all the teaching videos that the user has been assessed on, the total assessment score of the special assessment corresponding to the teaching video, the assessment time used by the user in the special assessment corresponding to the teaching video, the assessment score, and the total number of assessments. Then, subtract 1 from the data obtained by dividing the assessment score in the special assessment by the total score of the special assessment, and then take the absolute value to obtain the assessment error rate in the special assessment of the teaching video. Then, based on the total number of special assessments corresponding to the teaching video, the assessment score in the special assessment corresponding to the teaching video, the assessment error rate in the special assessment of the teaching video, and the assessment time used by the user in the special assessment corresponding to the teaching video, calculate the mastery degree value of the user for this teaching video, and then send the mastery degree value of the user for the teaching video to the teaching video evaluation module.

[0047] The expected assessment score is the score that the user himself wants to obtain in this teaching video.

[0048] The total number of special assessments corresponding to the teaching video is the total number of user assessments in the special assessment corresponding to each teaching video. The assessment time used by the user in the special assessment of the teaching video is the total time from the start to the end of the special assessment.

[0049] In this embodiment, the calculation formula for the mastery degree value of the user for the teaching video is:

[0050]

[0051] Where: G iLet \(M\) be the user's mastery level value for the teaching video, \(V\) be the total number of corresponding special assessments for the teaching video, unit: times, \(e\) i Let \(s\) be the assessment score in the corresponding special assessment of the teaching video, unit: points i Let \(r\) be the expected assessment score, unit: points i Let \(t\) be the assessment error rate in the special assessment of the teaching video i Let \(T\) be the assessment time used by the user in the special assessment of the teaching video, unit: minutes

[0052] For example: Student Xiaoming selected "Functions" in mathematics as the teaching video, and the expected assessment score was 100 points. The total score of the special assessment of "Functions" in mathematics was 100 points. He conducted 3 special assessments for the teaching video of "Functions".

[0053] The score of the first assessment was 70 points, the assessment time used was 30 minutes, and the assessment error rate was 0.3

[0054] The score of the second assessment was 75 points, the assessment time used was 25 minutes, and the assessment error rate was 0.25

[0055] The score of the third assessment was 80 points, the assessment time used was 20 minutes, and the assessment error rate was 0.2

[0056] According to the formula, the user's mastery level value \(M\) for the teaching video is calculated i is 0.72

[0057] In this embodiment, the teaching video evaluation module is communicatively connected to the learning platform, and is used to call the like, dislike, view, and share information of all teaching videos that the users have been assessed for in the database of the learning platform on the first day of each month, and calculate the quality index of the teaching video according to the ratio of the number of likes to the number of dislikes and the ratio of the number of views to the number of shares

[0058] In this embodiment, the quality index calculation formula of the teaching video is

[0059] Y i =\(\alpha\times H+\beta\times Z\).

[0060] Where: \(Y\) i is the quality index of the teaching video, \(H\) is the ratio of the number of likes to the number of dislikes, \(Z\) is the ratio of the number of views to the number of shares, and \(\alpha\) and \(\beta\) are weight coefficients and \(\alpha+\beta = 1\).

[0061] For example: There is a mathematics teaching video with 1000 likes, 100 dislikes, 5000 views, and 500 shares. The ratio of the number of likes to the number of dislikes is 10, the ratio of the number of views to the number of shares is 10, and the weight coefficients \(\alpha\) and \(\beta\) are 0.6 and 0.4 respectively. It can be calculated that the quality index of the teaching video is 10

[0062] In this embodiment, the teaching video evaluation module is communicatively connected to the learning planning processor, and is configured to send the quality index information of the teaching video to the learning planning processor after calculating the quality index of the teaching video.

[0063] The learning planning processor is configured to calculate the comprehensive score of the teaching video according to the user's mastery degree value of the teaching video, the quality index of the teaching video, and the preset weight coefficient after receiving the quality index information of the teaching video. Then, the teaching videos are sorted from small to large according to the comprehensive score of the teaching video, and then the sorted teaching videos are sent to the touch display screen.

[0064] In this embodiment, the formula for calculating the comprehensive score of the teaching video is:

[0065] C i = k × G i + (1 - k) × Y i .

[0066] Where C i is the comprehensive score of the teaching video, and k is the preset weight coefficient.

[0067] For example: Given that the mastery degree value M i of the teaching video is 0.83, the quality index of the teaching video is 10, and the preset weight coefficient is 0.7, the calculated comprehensive score C i of the teaching video is 3.58.

[0068] In this embodiment, the social interaction learning module includes a group database, an interaction processor, and a second display screen.

[0069] The group database is used for the user to store the preset learning groups, the total number and names of the group members in the learning group, the preset standard times, and the preset member participation weight coefficient.

[0070] The interaction processor is communicatively connected to the group database, the touch display screen, and the learning platform, and is configured to call the preset learning groups, the total number and names of the group members in the learning group, the preset standard times, and the preset member participation weight coefficient. Then, it calls the number of group members who participated in the comments within a week in the database of the learning platform, and calculates the activity of the learning group according to the preset learning groups, the total number of group members in the learning group, the number of group members who participated in the comments within a week, the total number of comments of the group members within the week, the preset standard times, and the preset member participation weight coefficient. Then, the activities of the learning groups are sorted from large to small, and then the sorting information of the activities of the learning groups from large to small is sent to the second display screen.

[0071] The second display screen is used for the user to view after receiving the sorting information of the activity levels of the learning groups from high to low.

[0072] In this embodiment, the calculation formula for the activity level of a learning group is:

[0073]

[0074] Where: I is the activity level of the learning group, P is the number of group members who participated in the comments within a week, unit: person, M is the total number of members in the learning group, unit: person, C is the total number of comments made by the members within the group within a week, unit: times, T is the preset standard number of times, unit: times, and γ is the preset member participation weight coefficient.

[0075] For example: There is a learning group with a total of 20 members. Within a week, 15 people participated in the comments, and the total number of comments posted by the members was 180 times. The preset standard number of times was 500 times. Assuming that the preset member participation influence degree coefficient γ = 0.4, the calculated activity level of this learning group is 0.51.

[0076] In this embodiment, the group database is a cloud server.

[0077] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Intelligent education and teaching management optimization system, characterized by: include: Teaching effectiveness evaluation module, teaching video evaluation module, social interactive learning module; The teaching effect evaluation module is used to evaluate the user's mastery of the teaching video; The teaching video evaluation module is used to evaluate the content quality of teaching videos; The social interactive learning module is used for users to choose to join highly active learning groups.

2. The intelligent education and teaching management optimization system according to claim 1 is characterized by: The teaching effect evaluation module includes a touch display screen and a learning planning processor; The touch screen display is used for the user to input the expected assessment score and name, and then the expected assessment score is sent to the learning planning processor for display after receiving the sorted teaching video; The learning planning processor is communicatively connected with the database and touch display screen of the learning platform, and is used to call all the teaching videos that the user has assessed, the total assessment score of the special assessment corresponding to the teaching video, the assessment time, assessment score, and total number of assessments of the user in the special assessment corresponding to the teaching video after receiving the expected assessment score and name, and then divide the assessment score in the special assessment by the total score of the special assessment to obtain the data minus 1, and then take the absolute value to obtain the assessment error rate in the special assessment of the teaching video, and then calculate the user's mastery of the teaching video based on the total number of special assessments corresponding to the teaching video, the assessment score in the special assessment corresponding to the teaching video, the assessment error rate in the special assessment of the teaching video, and the assessment time of the user in the special assessment of the teaching video to obtain the user's mastery of the teaching video, and then send the user's mastery of the teaching video to the teaching video evaluation module.

3. The intelligent education and teaching management optimization system according to claim 2 is characterized by: The calculation formula for the user's mastery of the teaching video is: Where: G i is the user's mastery of the teaching video, V is the total number of special assessments corresponding to the teaching video, unit: times, e i It is the assessment score of the corresponding special assessment of the teaching video, unit: points, s i is the expected assessment score, unit: points, r i is the error rate of teaching videos in special assessments, t i The time taken by the user in the special assessment of the teaching video, unit: minutes.

4. The intelligent education and teaching management optimization system according to claim 3 is characterized by: The teaching video evaluation module is connected to the learning platform for communication. It is used to call the likes, negative comments, views and forwarding information of all teaching videos that have been evaluated by users in the learning platform database on the first day of each month, and calculate the quality index of the teaching video based on the ratio of likes to negative comments and the ratio of views to forwarding.

5. The intelligent education and teaching management optimization system according to claim 4 is characterized by: The quality index calculation formula of the teaching video is: AND i (α×H+β×Z) Where: Y i is the quality index of the teaching video, H is the ratio of the number of likes to the number of negative comments, Z is the ratio of the number of views to the number of reposts, α and β are weight coefficients and α+β=1.

6. The intelligent education and teaching management optimization system according to claim 5 is characterized by: The teaching video evaluation module is in communication connection with the learning planning processor, and is used to send the quality index information of the teaching video to the learning planning processor after calculating the quality index of the teaching video; The learning planning processor is used to calculate the comprehensive score of the teaching video according to the user's mastery of the teaching video, the quality index of the teaching video and the preset weight coefficient after receiving the quality index information of the teaching video, and then sort the teaching videos from small to large according to the comprehensive scores of the teaching videos, and then send the sorted teaching videos to the touch display screen.

7. The intelligent education and teaching management optimization system according to claim 6 is characterized by: The comprehensive score calculation formula for the teaching video is: C i =k×G i +(1-k)×Y i ; Among them, C i is the comprehensive score of the teaching video, and k is the preset weight coefficient.

8. The intelligent education and teaching management optimization system according to claim 7 is characterized by: The social interaction learning module includes a group database, an interaction processor, and a second display screen; The group database is used for users to store preset study groups, the total number and names of group members in the study group, the preset standard times, and the preset member participation weight coefficients; The interactive processor is in communication connection with the group database, the touch display screen, and the learning platform, and is used to call the preset learning group, the total number and names of group members in the learning group, the preset standard number of times, and the preset member participation weight coefficient, and then call the number of group members who participated in the comment within a week in the database of the learning platform, calculate the activity of the learning group according to the preset learning group, the total number of group members in the learning group, the number of group members who participated in the comment within a week, the total number of group member comments within a week, the preset standard number of times, and the preset member participation weight coefficient, and then sort the activity of the learning group from large to small, and then send the sorting information of the activity of the learning group from large to small to the second display screen; The second display screen is used for users to view after receiving the information of sorting the activity levels of the study groups from large to small.

9. The intelligent education and teaching management optimization system according to claim 6 is characterized by: The activity calculation formula of the learning group is: Where: I is the activity of the learning group, P is the number of group members who participated in the comments within a week, unit: person, M is the total number of members in the learning group, unit: person, C is the total number of comments made by group members within a week, unit: times, T is the preset standard number of times, unit: times, γ is the preset member participation weight coefficient.

10. The intelligent education and teaching management optimization system according to claim 8, characterized in that: The group database is a cloud server.

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