Digitization-based teaching resource sharing system and method

Abstract

The invention discloses a teaching resource sharing system and method based on digitization, and relates to the technical field of education resource sharing. According to the method, the prepared resources are subjected to multi-dimensional evaluation before recommendation, the user evaluation data are extracted to determine the user feedback index, and the feedback of the user to the teaching resources is considered; uploader information is obtained to obtain a user reliability index, and the reliability of the resource creator is evaluated; the resource timeliness index is calculated, the timeliness of recommended resources is guaranteed, evaluation is more comprehensive, a basis is provided for personalized recommendation, feedback acceptance preference, uploader acceptance preference and content timeliness preference of the user are considered, and the sharing evaluation index of teaching resources is calculated according to different preferences. And the teaching resources are pushed to the user according to the sharing evaluation index, so that the problem that in the prior art, user feedback data, uploader data and the like of the teaching resources are not fully considered, and personalized requirements of the user cannot be met is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of educational resource sharing, and specifically provides a digital-based teaching resource sharing system and method. Background Art

[0002] With the rapid development of information technology, the education field is also constantly undergoing digital transformation. The traditional teaching mode relies on limited teaching materials and the personal knowledge reserves of teachers, resulting in relatively scarce teaching resources and difficulty in achieving extensive sharing.

[0003] However, the existing teaching resource sharing systems in the prior art still have the following deficiencies in the actual application process:

[0004] The recommendation function of the teaching resource sharing systems in the prior art is relatively single, mainly based on the user's search history or popular resources for recommendation, without fully considering user feedback data and uploader data of teaching resources, etc., and cannot meet the personalized needs of users;

[0005] In addition, the existing sharing platforms have insufficient incentive measures for users to participate in resource sharing, unable to fully mobilize the enthusiasm of users. Users who upload high-quality resources do not receive sufficient recognition and rewards, resulting in many users lacking the motivation to share.

[0006] Therefore, a digital-based teaching resource sharing system and method are proposed. Summary of the Invention

[0007] The purpose of the present invention is to solve the problems pointed out in the background art, and provide a digital-based teaching resource sharing system and method.

[0008] The purpose of the present invention can be achieved through the following technical solutions: A digital-based teaching resource sharing system, including:

[0009] Resource management module: Identify the teaching resources uploaded by users, identify the corresponding subject, grade, and teaching type of the teaching resources and classify them into the corresponding storage areas; the teaching types include courseware, videos, and documents;

[0010] Resource recommendation module: After the user inputs the subject, grade, and teaching type of the required teaching resources, trigger a retrieval signal, extract each group of teaching resources that meet the user's input requirements as preparatory resources, and perform resource evaluation processing on each group of preparatory resources and then push them to the user. After the push is completed, the user selects the required teaching resources from each group of preparatory resources, and the download count of the corresponding teaching resources increases by one;

[0011] Shared incentive module: Pre-build a user incentive mechanism, and update the user's sharing activity value in real time according to the user incentive mechanism. When the user's sharing activity value reaches the corresponding set reference value, trigger an exchange signal and push it to the user.

[0012] As a preferred embodiment of the present invention, the specific steps of resource evaluation and processing are as follows:

[0013] Extract the user evaluation data of each group of preparatory resources, and after performing keyword matching processing, determine the user feedback index Uy of each group of preparatory resources;

[0014] Obtain the user type, the number of uploaded resources, and the number of downloads of the uploaders corresponding to each group of preparatory resources, and after processing, determine the user reliability index Ut of each group of preparatory resources;

[0015] Extract the time of the most recent update of each group of preparatory resources from the current time point, and calculate the time difference between the time of the most recent update and the current time point to obtain the resource timeliness index Ur of each group of preparatory resources;

[0016] Obtain the user feedback index Uy, the user reliability index Ut, and the resource timeliness index Ur of each group of preparatory resources, and push them to the user. After the user determines the preference and further evaluates, push them to the user; the preferences include feedback approval preference, uploader approval preference, and content timeliness preference, and are represented by the numbers a, b, and c;

[0017] Based on the preferences determined by the user, substitute the user feedback index Uy, the user reliability index Ut, and the resource timeliness index Ur of each group of preparatory resources into the corresponding formula for weighted calculation to obtain the sharing evaluation index Ue of each group of preparatory resources;

[0018] Extract the sharing evaluation index Ue of each group of preparatory resources, and sort each group of preparatory resources from large to small according to the size of the sharing evaluation index Ue. After sorting, push it to the user.

[0019] As a preferred embodiment of the present invention, to determine the user feedback index Uy of each group of preparatory resources, specifically:

[0020] Preset each group of keywords corresponding to user comments, input the preset groups of keywords into the user comment data in each group of preparatory resources for matching. If a certain group of user comment data of the corresponding preparatory resources matches the preset keywords, the experience score of the corresponding preparatory resources is increased by one. After the matching is completed, obtain the final experience score of each group of preparatory resources, denoted as the number of satisfied times;

[0021] Count the number of sharing times and the number of commenting users of each group of preparatory resources; calculate the proportion of the number of satisfied times and the number of sharing times of each group of preparatory resources in the number of commenting users respectively, so as to obtain the satisfaction ratio and sharing ratio of each group of preparatory resources;

[0022] Multiply the satisfaction ratio and sharing ratio of each group of preliminary resources by the corresponding set weight coefficients respectively, and then sum to obtain the user feedback index Uy of each group of preliminary resources.

[0023] As a preferred embodiment of the present invention, determine the user reliability index Ut of each group of preliminary resources, specifically:

[0024] Obtain the user types of the uploaders corresponding to each group of preliminary resources, where the user types include education experts, senior teachers, and in-service teachers; set a basic gold content score corresponding to each different user type, so as to determine the basic gold content score of each group of preliminary resources;

[0025] Count the number of uploaded resources and the number of downloads of the uploaders of each group of preliminary resources, and form data pairs; preset the intervals where each group of data pairs corresponding to the data pairs are located, and each interval where a group of data pairs is located corresponds to a gold content additional coefficient, and each interval where a group of data pairs is located includes a range of the number of uploaded resources and a range of the number of downloads; match the data pairs of each group of preliminary resources with the intervals where each group of data pairs is located to determine the gold content additional coefficient of each group of preliminary resources;

[0026] Multiply the determined basic gold content score of each group of preliminary resources by the corresponding gold content additional coefficient to obtain the user reliability index Ut of each group of preliminary resources.

[0027] As a preferred embodiment of the present invention, obtain the sharing evaluation index Ue of each group of preliminary resources, specifically:

[0028] The formula is expressed as Uy 及格 、Ut 及格 、Ur 参考 are respectively the preset passing feedback index, passing reliability index, and reference timeliness index; α1, α2, α3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user-determined feedback recognition preference; β1, β2, β3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user-determined uploader recognition preference; η1, η2, η3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user-determined content timeliness preference.

[0029] As a preferred embodiment of the present invention, the construction process of the user incentive mechanism is:

[0030] Pre-set various incentive behaviors, and various incentive behaviors include resource upload, resource download, user evaluation, and resource sharing;

[0031] Count the number of various incentive behaviors of users within a set time window, set corresponding weight coefficients, multiply the number of various incentive behaviors of users within the set time window by the corresponding weight coefficients respectively, and then sum to obtain the active evaluation index of users within the set time window;

[0032] Preset four groups of index ranges corresponding to the active evaluation index, and set that each group of index ranges corresponds to an active level respectively; among them, the active levels include bronze, silver, gold, and diamond, and different active levels correspond to a group of active reward points respectively; after determining the active level of the user, add the corresponding active reward points to the user's current shared active value and update it.

[0033] As a preferred embodiment of the present invention, the construction process of the user incentive mechanism further includes:

[0034] Obtain the user feedback index Uy of the teaching resources uploaded by the user within the set time window, set the value range of each group of indexes corresponding to the user feedback index Uy, and set that each group of index value ranges corresponds to a resource contribution score respectively; determine the resource contribution score of the teaching resources uploaded by the user within the set time window, and add the corresponding resource contribution score to the user's current shared active value and update it.

[0035] As a preferred embodiment of the present invention, if the number of teaching resources uploaded by the user is greater than one, determine the resource contribution scores of each group of teaching resources uploaded within the set time window and then accumulate them, and add the accumulated value to the user's current shared active value and update it.

[0036] A digital-based teaching resource sharing method includes:

[0037] Resource management: After the user completes registration and uploads teaching resources, identify the teaching resources uploaded by the user, identify the subject, grade, and teaching type corresponding to the teaching resources and classify them into the corresponding storage areas;

[0038] Resource recommendation: After the user inputs the subject, grade, and teaching type of the required teaching resources, trigger a retrieval signaling, extract each group of teaching resources that meet the user's input requirements and record them as preparatory resources, and perform resource evaluation processing on each group of preparatory resources and then push them to the user. After the push is completed, the user selects the required teaching resources from each group of preparatory resources;

[0039] User incentive: Pre-construct a user incentive mechanism, and update the user's shared active value in real time according to the user incentive mechanism. When the user's shared active value reaches the corresponding set reference value, trigger a redemption signaling and push it to the user.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] Through multi-dimensional evaluation of preparatory resources before recommendation, the present invention extracts user evaluation data to determine the user feedback index, taking into account the user's feedback on teaching resources; obtains uploader information to obtain the user reliability index, evaluating the reliability of resource creators; calculates the resource timeliness index to ensure the timeliness of recommended resources, making the evaluation more comprehensive, providing a basis for personalized recommendation, and considering the user's feedback recognition preference, uploader recognition preference, and content timeliness preference. The sharing evaluation index of teaching resources is calculated according to different preferences, and teaching resources are pushed to users according to the sharing evaluation index, solving the problem in the prior art that user feedback data and uploader data of teaching resources are not fully considered and cannot meet the personalized needs of users;

[0042] After the user receives the recommendation result, the present invention can change the preference and push again. If the user is not satisfied with the current recommendation, the preference can be adjusted to recalculate the sharing evaluation index and sort and push, ensuring that the recommendation result is more in line with the user's needs;

[0043] When the user sharing active value reaches the set reference value, the present invention triggers an exchange signaling to remind the user to exchange goods, enabling the user to truly feel the value of participating in resource sharing and continuously maintaining enthusiasm. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0045] Figure 1 is the principle block diagram of the present invention;

[0046] Figure 2 is the flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. 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 work belong to the scope of protection of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 shown, a digital-based teaching resource sharing system includes a resource management module, a resource recommendation module, and a sharing incentive module;

[0050] The resource management module is used to identify the teaching resources uploaded by users, identify the disciplines, grades, and teaching types corresponding to the teaching resources, and classify them into corresponding storage areas; the teaching types include courseware, videos, and documents;

[0051] It should be noted that after classification, natural language processing technology can be used to automatically annotate teaching resources, extract keywords, facilitate users' quick retrieval, and determine the corresponding disciplines (such as Chinese, mathematics, physics, etc.), grades (lower grades of primary school, higher grades of primary school, junior high school, high school, etc.) and teaching types (courseware, videos, documents) of the resources, and store them in corresponding areas in a classified manner. This classified storage method is similar to the partition storage of different types of books in a library, greatly improving the orderliness of resource management and facilitating subsequent retrieval and invocation;

[0052] For the teaching resources uploaded by users, they are first evaluated by reviewers, and after passing the evaluation, they are identified and classified; set the resource update time interval, and delete expired or invalid resources after reaching the set update time interval; for example, some courseware supporting old versions of textbooks will be deleted when the update time interval is reached as their content is no longer applicable with the update of textbooks. At the same time, users are encouraged to upload the latest teaching resources to maintain the timeliness and practicality of the resource library and ensure that users can always obtain the latest and most useful teaching materials;

[0053] The resource recommendation module is used to trigger a retrieval signal after the user inputs the discipline, grade, and teaching type of the required teaching resources, extract each group of teaching resources that meet the user's input requirements as preliminary resources, and perform resource evaluation processing on each group of preliminary resources and then push them to the user. After the push is completed, the user selects the required teaching resources from each group of preliminary resources, and the download count of the corresponding teaching resources is incremented by one;

[0054] The specific steps of the resource evaluation processing are as follows:

[0055] Preset each group of keywords corresponding to user comments; keywords such as strong practicality, rich cases, and clear content, etc.; input the preset groups of keywords into the user comment data in each group of preliminary resources for matching. If a certain group of user comment data in the corresponding preliminary resource matches the preset keywords successfully, the experience score of the corresponding preliminary resource is incremented by one. After the matching is completed, obtain the final experience scores of each group of preliminary resources, which is recorded as the number of satisfied times;

[0056] It should be noted that if the number of groups of keywords that a certain group of user comment data content matches exceeds one group, the experience score is still determined to be incremented by one to avoid data falsification caused by a certain group of users' brushing of comments;

[0057] Count the sharing times and the number of commenting users of each group of preliminary resources; calculate the proportions of the number of satisfied times and sharing times of each group of preliminary resources in the number of commenting users respectively, so as to obtain the satisfaction ratio and sharing ratio of each group of preliminary resources;

[0058] Multiply the satisfaction ratio and sharing ratio of each group of preliminary resources by the corresponding set weight coefficients respectively, and then sum them up to obtain the user feedback index Uy of each group of preliminary resources;

[0059] It should be noted that by presetting keywords related to user experience and matching them with user comment data, valuable information can be extracted from the actual feedback of users, objectively evaluating the user experience of teaching resources. The setting of the experience score enables the performance of resources in terms of user experience to be quantified, avoiding the limitations of subjective judgment and allowing users to more accurately understand the satisfaction of users with resources when selecting teaching resources;

[0060] Obtain the user types of the uploaders corresponding to each group of preliminary resources, where the user types include educational experts, senior teachers, and in-service teachers; set a basic gold content score corresponding to different user types respectively; the basic gold content score of educational experts > the basic gold content score of senior teachers > the basic gold content score of in-service teachers; thus, determine the basic gold content score of each group of preliminary resources;

[0061] Count the number of uploaded resources and the number of downloads of the uploaders of each group of preliminary resources, and form data pairs; preset the intervals where each group of data pairs corresponding to the data pairs are located, and each interval where a group of data pairs is located corresponds to a gold content additional coefficient, and each interval where a group of data pairs is located includes a range of the number of uploaded resources and a range of the number of downloads; the value range of the gold content additional coefficient is set at 0.975 - 1.118; match the data pairs of each group of preliminary resources with the intervals where each group of data pairs is located to determine the gold content additional coefficient of each group of preliminary resources;

[0062] Multiply the determined basic gold content score of each group of preliminary resources by the corresponding gold content additional coefficient to obtain the user reliability index Ut of each group of preliminary resources;

[0063] It should be noted that through the calculation and application of the user reliability index, teaching resources can be better managed and organized, and high-quality resources can be more accurately pushed to users. For users, it is easier to find resources from reliable creators and popular resources, improving learning and teaching efficiency;

[0064] Extract the time of the most recent update of each group of preliminary resources from the current time point, and calculate the time difference between the time of the most recent update and the current time point to obtain the resource timeliness index Ur of each group of preliminary resources;

[0065] It should be noted that newer teaching resources are closer to the actual educational situation;

[0066] Obtain the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur for each group of preliminary resources, and push them to the user. After the user determines their preferences and further evaluates, push them to the user. Preferences include feedback approval preference, uploader approval preference, and content timeliness preference, and are represented by the numbers a, b, and c.

[0067] Based on the preferences determined by the user, substitute the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur of each group of preliminary resources into the corresponding formulas for weighted calculation to obtain the sharing evaluation index Ue of each group of preliminary resources.

[0068] The formula is expressed as Uy 及格 、Ut 及格 、Ur 参考 are respectively the preset passing feedback index, passing reliability index, and reference timeliness index; α1, α2, α3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user's determined feedback approval preference; β1, β2, β3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user's determined uploader approval preference; η1, η2, η3 are the influence weight factors of the user feedback index Uy, user reliability index Ut, and resource timeliness index Ur under the user's determined content timeliness preference.

[0069] It should be noted that considering the different preferences of users and using different weight factors to perform weighted calculations on the corresponding indexes enables the subsequent push results of teaching resources to be generated according to the personalized preferences of users.

[0070] For example, for users who pay more attention to the feedback of other users (feedback approval preference), a higher weight will be given to the user feedback index when calculating the sharing evaluation index, and teaching resources that perform well in this regard will be recommended first; while for users who value the qualifications of resource creators (uploader approval preference), the role of the user reliability index will be emphasized. This personalized evaluation method can better meet the diverse needs of users and improve user satisfaction with resources.

[0071] Extract the sharing evaluation index Ue of each group of preliminary resources, and sort each group of preliminary resources from largest to smallest according to the size of the sharing evaluation index Ue. After the sorting is completed, push it to the user.

[0072] It should be noted that after the user receives the push result, they can return to the previous step to change their preferences and then push a new result again.

[0073] The sharing incentive module is used to pre - construct a user incentive mechanism and update the user's sharing activity value in real - time according to the user incentive mechanism; the sharing activity value is initially 0 after registration; when the user's sharing activity value reaches the corresponding set reference value, a redemption signal is triggered and pushed to the user;

[0074] It should be noted that triggering the redemption signal means that the user's sharing activity value can be used to exchange for some goods, reminding the user to make a timely exchange.

[0075] The construction process of the user incentive mechanism is as follows:

[0076] Pre - set various incentive behaviors, including resource upload, resource download, user evaluation, and resource sharing;

[0077] Count the number of various incentive behaviors of the user within a set time window, set corresponding weight coefficients, multiply the number of various incentive behaviors of the user within the set time window by the corresponding weight coefficients respectively, and then sum to obtain the activity evaluation index of the user within the set time window;

[0078] Preset four groups of index intervals corresponding to the activity evaluation index, and set an activity level corresponding to each group of index intervals; among them, the activity levels include bronze, silver, gold, and diamond, and the higher the activity evaluation index, the higher the possibility of the corresponding activity level being diamond; different activity levels respectively correspond to a group of activity reward points; the activity reward points can be set to 2, 4, 6, 8; after determining the user's activity level, add the corresponding activity reward points to the user's current sharing activity value and update it;

[0079] It should be noted that multiple incentive behaviors such as resource upload, resource download, user evaluation, and resource sharing are clearly set, so that users clearly know through which ways they can obtain rewards, thus encouraging users to participate more in the activity of teaching resource sharing. In order to obtain higher activity reward points, users will be more active in uploading high - quality teaching resources, increasing the richness of resources; or seriously evaluate the resources they have used, providing references for other users and also improving the quality of platform resources;

[0080] Obtain the user feedback index Uy of the teaching resources uploaded by the user within the set time window, set the value range of each group of indexes corresponding to the user feedback index Uy, and set a resource contribution score corresponding to each group of index value ranges; the resource contribution score range is set from 1 to 10, and the higher the user feedback index Uy, the higher the corresponding resource contribution score;

[0081] Determine the resource contribution score of the teaching resources uploaded by the user within the set time window, add the corresponding resource contribution score to the user's current sharing activity value and update it;

[0082] If the number of teaching resources uploaded by a user is greater than one, after determining the resource contribution scores of each group of teaching resources uploaded within the set time window and accumulating them, the accumulated value is added to the user's current sharing activity value and updated;

[0083] It should be noted that when obtaining the user feedback index Uy of the teaching resources uploaded by the user, only the newly added user evaluation data and user analysis data within the set time window are considered during the calculation of the user feedback index Uy;

[0084] The quality of the teaching resources uploaded by the user is measured by the user feedback index, and corresponding resource contribution points are given according to this index. The higher the user feedback index, the more resource contribution points are obtained. This encourages users to strive to upload high-quality, practical teaching resources that can meet the needs of other users in order to obtain higher points, ensuring the quality of teaching resource sharing and enabling users to spend more time and effort to improve the content of the courseware and optimize the teaching method of the teaching video, thereby improving the overall quality of the teaching resources;

[0085] Embodiment 2

[0086] Please refer to Figure 2 As shown, based on a digital-based teaching resource sharing system provided in Embodiment 1 of the present application, Embodiment 2 of the present application proposes a digital-based teaching resource sharing method. Embodiment 2 is merely a preferred manner of Embodiment 1, and the implementation of Embodiment 2 will not affect the independent implementation of Embodiment 1.

[0087] Specifically, the difference of a digital-based teaching resource sharing method provided in Embodiment 2 of the present application includes:

[0088] Resource management: After the user completes registration and uploads teaching resources, the teaching resources uploaded by the user are identified, and the subject, grade, and teaching type corresponding to the teaching resources are identified and classified into the corresponding storage areas;

[0089] Resource recommendation: After the user inputs the subject, grade, and teaching type of the required teaching resources, a retrieval signal is triggered, and each group of teaching resources that meet the user's input requirements is extracted and recorded as preliminary resources. After resource evaluation processing of each group of preliminary resources, they are pushed to the user. After the push is completed, the user selects the required teaching resources from each group of preliminary resources;

[0090] User incentive: A user incentive mechanism is pre-constructed, and the user's sharing activity value is updated in real time according to the user incentive mechanism. When the user's sharing activity value reaches the corresponding set reference value, a redemption signal is triggered and pushed to the user.

[0091] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments only. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A digital teaching resource sharing system, characterized in that: include: Resource management module: Identify the teaching resources uploaded by users, identify the subject, grade and teaching type corresponding to the teaching resources and classify them into the corresponding storage area; teaching types include courseware, videos and documents; Resource recommendation module: After the user inputs the subject, grade and teaching type of the required teaching resources, the search signal is triggered, and each group of teaching resources that meet the user's input requirements are extracted and recorded as preparatory resources. After the resource evaluation process is performed on each group of preparatory resources, they are pushed to the user. After the push is completed, the user selects the teaching resources required from each group of preparatory resources. After the selection is completed, the download count of the corresponding teaching resource increases by one; Sharing incentive module: pre-build the user incentive mechanism, and update the user's shared activity value in real time according to the user incentive mechanism. When the user's shared activity value reaches the corresponding set reference value, the redemption signal is triggered and pushed to the user.

2. A digital teaching resource sharing system according to claim 1, characterized in that: The specific steps of resource assessment processing are: Extract user evaluation data of each group of prepared resources, and determine the user feedback index Uy of each group of prepared resources after keyword matching processing; Obtain the user type, the number of uploaded resources, and the number of downloads of the uploader corresponding to each group of prepared resources, and determine the user reliability index Ut of each group of prepared resources after processing; Extract the time of the most recent update of each group of prepared resources from the current time point, and calculate the time difference between the most recent update time and the current time point to obtain the resource timeliness index Ur of each group of prepared resources; Obtain the user feedback index Uy, user reliability index Ut and resource timeliness index Ur of each group of prepared resources, and push them to the user. After the user determines the preference, further evaluation is performed and the resource timeliness index Ur is pushed to the user. The preference includes feedback recognition preference, uploader recognition preference and content timeliness preference, and is represented by numbers a, b and c. Based on the preferences determined by the user, the user feedback index Uy, user reliability index Ut and resource timeliness index Ur of each group of prepared resources are substituted into the corresponding formula for weighted calculation to obtain the sharing evaluation index Ue of each group of prepared resources; The shared evaluation index Ue of each group of prepared resources is extracted, and the groups of prepared resources are sorted from large to small according to the size of the shared evaluation index Ue. After the sorting is completed, it is pushed to the user.

3. A digital teaching resource sharing system according to claim 2, characterized in that: Determine the user feedback index Uy of each group of prepared resources, specifically: Preset each group of keywords corresponding to the user comments, input each group of keywords into the user comment data in each group of prepared resources for matching, if a group of user comment data corresponding to the prepared resources successfully matches the preset keywords, the experience score of the corresponding prepared resources will be increased by one, and after the matching is completed, the final experience score of each group of prepared resources is obtained and recorded as the number of satisfaction; Count the number of times each group of prepared resources is shared and the number of users who comment on them; Calculate the proportion of satisfaction and sharing times of each group of prepared resources in the number of commenting users, so as to obtain the satisfaction ratio and sharing ratio of each group of prepared resources; The satisfaction ratio and sharing ratio of each group of prepared resources are multiplied by the corresponding set weight coefficients respectively, and then the sum is calculated to obtain the user feedback index Uy of each group of prepared resources.

4. A digital teaching resource sharing system according to claim 3, characterized in that: Determine the user reliability index Ut of each group of prepared resources, specifically: Obtain the user type of the uploader corresponding to each group of preparatory resources, where the user types include education experts, senior teachers, and in-service teachers; set a basic gold content score corresponding to each user type, thereby determining the basic gold content score of each group of preparatory resources; The number of uploaded resources and the number of downloads of each group of preparatory resource uploaders are counted and formed into data pairs; the intervals of each group of data pairs corresponding to the preset data pairs are respectively corresponding to a gold content additional coefficient, and the interval of each group of data pairs includes a range of the number of uploaded resources and a range of the number of downloads; the data pairs of each group of preparatory resources are matched with the intervals of each group of data pairs to determine the gold content additional coefficient of each group of preparatory resources; Multiply the basic gold content score determined for each group of reserve resources by the corresponding gold content additional coefficient to obtain the user reliability index Ut of each group of reserve resources.

5. A digital teaching resource sharing system according to claim 4, characterized in that: The shared evaluation index Ue of each group of prepared resources is obtained, specifically: The formula is expressed as Uy 及格 , Ut 及格 、Ur 参考 They are the preset passing feedback index, passing reliability index and reference timeliness index respectively; α1, α2, α3 are the influence weight factors of user feedback index Uy, user reliability index Ut and resource timeliness index Ur under the feedback recognition preference determined by the user; β1, β2, β3 are the influence weight factors of user feedback index Uy, user reliability index Ut and resource timeliness index Ur under the uploader recognition preference determined by the user; η1, η2, η3 are the influence weight factors of user feedback index Uy, user reliability index Ut and resource timeliness index Ur under the content timeliness preference determined by the user.

6. A digital teaching resource sharing system according to claim 5, characterized in that: The process of building the user incentive mechanism is as follows: Pre-set various incentive behaviors, including resource uploading, resource downloading, user evaluation and resource sharing; Count the number of various incentive behaviors of the user within the set time window, set corresponding weight coefficients, multiply the number of various incentive behaviors of the user within the set time window by the corresponding weight coefficients, and then sum them up to obtain the user's active evaluation index within the set time window; The four groups of indexes corresponding to the preset activity evaluation index are in intervals, and each group of indexes in the interval is set to correspond to an activity level; The activity levels include bronze, silver, gold and diamond, and different activity levels correspond to a set of activity reward points. After determining the user's activity level, the corresponding activity reward points are added to the user's current shared activity value and updated.

7. A digital teaching resource sharing system according to claim 6, characterized in that: The process of building a user incentive mechanism also includes: Obtain the user feedback index Uy of the teaching resources uploaded by the user within the set time window, set the index value ranges corresponding to the user feedback index Uy, and set each index value range to correspond to a resource contribution point; determine the resource contribution points of the teaching resources uploaded by the user within the set time window, add the corresponding resource contribution points to the user's current shared activity value and update it.

8. A digital teaching resource sharing system according to claim 7, characterized in that: If the number of teaching resources uploaded by the user is greater than one, the resource contribution points of each group of teaching resources uploaded within the set time window are determined and accumulated, and the accumulated value is added to the user's current shared activity value and updated.

9. A digital teaching resource sharing method, applied to a digital teaching resource sharing system as claimed in any one of claims 1 to 8, characterized in that: include: Resource management: After the user completes registration and uploads teaching resources, the teaching resources uploaded by the user are identified, and the corresponding subjects, grades and teaching types of the teaching resources are identified and classified into the corresponding storage areas; Resource recommendation: After the user enters the subject, grade, and teaching type of the required teaching resources, the search signaling is triggered, and each group of teaching resources that meets the user's input requirements is extracted and recorded as prepared resources. After the resource evaluation process is performed on each group of prepared resources, they are pushed to the user. After the push is completed, the user selects the required teaching resources from each group of prepared resources; User incentives: Build a user incentive mechanism in advance, and update the user's shared activity value in real time based on the user incentive mechanism. When the user's shared activity value reaches the corresponding set reference value, the redemption signal is triggered and pushed to the user.

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