Mobile micro course auditing method and system supporting breakpoint continuation and template guidance

Abstract

This invention discloses a mobile micro-lesson review method and system supporting breakpoint continuation and template guidance, relating to the field of information processing technology. The method includes receiving a micro-lesson creation request from a mobile device, loading template enhancement rules into a target template node, reconstructing field constraints, inserting enhanced fields, and injecting guidance prompts into the target template node to generate a templated editing interface, while simultaneously establishing a node-level editing state object. During mobile editing, when a node content change editing event is detected, incremental state recording is performed on the node-level editing state object. When the course is re-entered on the mobile device, the most recently saved node-level editing state object is restored to retrieve the current editing state of the target template node. This invention achieves targeted characterization of the relationship between learning effectiveness and template nodes based on big data processing, improving the rule adaptability and consistency of review criteria during the micro-lesson creation process.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a mobile micro-lesson review method and system that supports breakpoint continuation and template guidance. Background Technology

[0002] With the development of mobile learning, job training, and online review services, mobile micro-courses have been widely used in knowledge transfer, skills training, and process assessment scenarios. In existing technologies, micro-course templates are typically used to organize course titles, content fields, prompts, and interactive elements. These are combined with mobile editing, breakpoint saving, submission for review, and learning feedback collection to complete the production and publication of micro-courses. At the same time, relying on big data processing technology to summarize and analyze micro-course editing behavior, learning outcomes, and review records is gradually becoming an important technical path to improve the efficiency of training resource allocation and the level of refinement in review management.

[0003] However, conventional methods often rely on static rules and personal experience for audits, and templates fail to deeply integrate historical training effectiveness big data, making it difficult to achieve evidence-based quantitative assessment and continuous iteration. Templates focus on providing formal structures and fail to dynamically embed validated and effective design elements and prompts based on historical audit conclusions and learning effectiveness data, resulting in insufficient pre-emptive quality control and a lack of precise guidance based on data evidence during the development process. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a mobile micro-course review method that supports breakpoint continuation and template guidance, solving the problems of disconnect between template guidance and review decision-making, as well as the lack of quantitative basis in the development process.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, this invention provides a mobile micro-course review method that supports breakpoint continuation and template guidance, comprising: collecting template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set; performing micro-course training effect attribution analysis on the template utility analysis sample set, identifying template node feature combinations, and classifying and grading nodes based on template node position, feature function type, and utility enhancement direction to obtain template enhancement rules; receiving a micro-course creation request sent by the mobile terminal, loading the template enhancement rules into the target template node, and performing field constraint reconstruction, enhanced field insertion, and guidance prompt injection on the target template node to generate a templated editing interface, while establishing a node-level editing state object; during the editing process on the mobile terminal, when a node content change editing event is detected, performing incremental state recording on the node-level editing state object, and performing continuation and restoration processing on the most recently saved node-level editing state object when the mobile terminal re-enters the course to obtain the current editing state of the target template node; and after receiving a review instruction, performing compliance review on the current micro-course based on the template enhancement rules and the current editing state of the target template node to generate a review report.

[0008] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for forming the template utility analysis sample set are as follows:

[0009] The template node editing feature data includes the template node identifier, node type, and node fill content;

[0010] The learning outcome feedback data includes course completion rate, interactive test scores, and on-the-job skills assessment data.

[0011] Using the micro-lesson identifier as the association key, the template node editing feature data and learning effectiveness feedback data are associated and matched to form a template utility analysis sample set.

[0012] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for identifying template node feature combinations are as follows:

[0013] The inter-group difference was calculated for the course completion rate, interactive test score and on-the-job skills assessment data corresponding to the content filled at different nodes in the template utility analysis sample set to obtain the node utility difference.

[0014] Perform difference validity tests and increase calculations on the node utility difference data, and select the node filling content that meets the difference conditions and the increase is greater than the increase threshold, and use it as a valid feature;

[0015] The effective features are merged according to the template node position, and the feature combinations that work together to improve the learning effect at the same template node position are identified to obtain the template node feature combination.

[0016] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for obtaining template enhancement rules are as follows:

[0017] Based on the combination of features of each template node, the improvement value of course completion rate, the improvement value of interactive test score and the improvement value of on-the-job skills assessment data are calculated, and the feature effect type is determined according to the improvement item with the highest proportion.

[0018] The template node feature combinations are categorized according to their feature function type, and the utility improvement direction and magnitude of each template node feature combination are calculated to generate utility classification basic data.

[0019] Based on the utility classification data, the utility level is divided for each template node feature combination and then transcribed into template enhancement rules.

[0020] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for generating the templated editing interface are as follows:

[0021] Receive micro-course creation requests sent from mobile devices, extract course topics, course categories, and training task identifiers, and match them with target micro-course templates;

[0022] Based on the template node identifier in the target micro-lesson template, the target template node for the template enhancement rules is determined, and the enhancement field type, field triggering condition and prompt content are extracted to form a node enhancement configuration table;

[0023] Based on the node enhancement configuration table, identify missing fields, replacement fields, and priority fields in the original field configuration and rule requirements of the target template node to form a list of node field adjustments.

[0024] Based on the node field adjustment list, missing fields are added to the target template node, replacement fields are updated to enhanced fields, and prompt content is loaded into the target template node to generate a templated editing interface.

[0025] As a preferred embodiment of the mobile micro-course review method supporting breakpoint continuation and template guidance described in this invention, the node-level editing state object is generated by extracting the initial editing state information of the target template node from the templated editing interface, forming a node state index, and performing state mapping processing based on the node state index.

[0026] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the step of performing incremental state recording on node-level editing state objects includes the following steps:

[0027] During mobile editing, when a node content change editing event is detected, the target template node identifier, field identifier, changed field content, and current node filling status are extracted to form a node change record;

[0028] Write the changed field content in the node change record into the target template node in the node-level edit status object, and synchronously update the field fill status, enhancement field trigger status, and prompt hit status to form a status update object;

[0029] Extract the incremental state of the state update object and write it to the most recently saved version to form the latest node state version.

[0030] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for obtaining the current editing state of the target template node are as follows:

[0031] When re-entering the course on a mobile device, the target template node and incremental status of the most recent node content change are determined based on the latest node status version, forming a continuation and recovery index;

[0032] Based on the continuation and recovery index, the target template node is restored to the editing position and node state after the most recent node content change, and the current editing state of the target template node is obtained.

[0033] As a preferred embodiment of the mobile micro-lesson review method supporting breakpoint continuation and template guidance described in this invention, the steps for generating the review report are as follows:

[0034] Compare the current editing state of the target template node with the enhancement field requirements and prompt requirements in the template enhancement rules to determine whether the target template node meets the rule requirements and calculate the degree of rule compliance;

[0035] Based on the rule compliance level of the target template node and the target template node information corresponding to the rule-unmet items, problematic nodes are marked, and an audit report is generated.

[0036] Secondly, the present invention provides a mobile micro-lesson review system that supports breakpoint continuation and template guidance, including: a data acquisition module, used to collect template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set;

[0037] The enhanced rule transcription module is used to perform micro-course training effect attribution analysis on the template utility analysis sample set, identify template node feature combinations, and perform node classification and utility grading based on template node position, feature function type and utility enhancement direction to obtain template enhancement rules;

[0038] The guided editing module is used to receive micro-lesson creation requests sent by mobile devices, load template enhancement rules into the target template node, and reconstruct field constraints, insert enhanced fields, and inject guided prompts into the target template node to generate a templated editing interface, while establishing a node-level editing state object.

[0039] The continuation state recovery module is used to perform incremental state recording on the node-level editing state object when a node content change editing event is detected during the mobile editing process, and to perform continuation recovery processing on the most recently saved node-level editing state object when the course is re-entered on the mobile device, so as to obtain the current editing state of the target template node.

[0040] The compliance review module is used to perform compliance review on the current micro-lesson after receiving a review instruction, based on the template enhancement rules and the current editing status of the target template node, and generate a review report.

[0041] The beneficial effects of this invention are as follows: By performing attribution analysis on the template utility analysis sample set for micro-course training effects, identifying template node feature combinations and obtaining template enhancement rules, a targeted characterization of the relationship between learning outcomes and template nodes based on big data processing is achieved, improving the rule adaptability and consistency of review criteria in the micro-course creation process; by reconstructing field constraints, inserting enhanced fields, and injecting guiding prompts into the target template nodes, dynamic enhancement of the template editing interface is achieved, improving the continuity of breakpoint continuation editing and the accuracy of review reports. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0043] Figure 1 A flowchart illustrating the mobile micro-lesson review method that supports breakpoint continuation and template-guided editing.

[0044] Figure 2 A schematic diagram of a mobile micro-lesson review system that supports breakpoint continuation and template guidance.

[0045] Figure 3 A chart comparing the time taken to resume editing from a breakpoint under different editing methods.

[0046] Figure 4 This is a chart comparing the degree of rule compliance under different editing methods. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 This is one embodiment of the present invention, which provides a mobile micro-lesson review method that supports breakpoint continuation and template guidance, including the following steps:

[0051] S1. Collect template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set.

[0052] S1.1: Template node editing feature data includes template node identifier, node type, and node fill content.

[0053] It should be noted that the template node identifier is the identification information used to uniquely mark each node in the micro-lesson template, and is used to distinguish different target template nodes; the node type is the type information used to characterize the functional attributes of the template node, and is used to distinguish the role category of different nodes in the micro-lesson template; the node fill content refers to the content information that the developer actually fills in, inserts or configures in the corresponding template node, and is used to characterize the specific implementation of the template node in the micro-lesson creation process, including text content, enhanced field content and prompt response content.

[0054] S1.2: Learning outcome feedback data includes course completion rate, interactive test scores, and on-the-job skills assessment data.

[0055] It should be noted that the course completion rate refers to the proportion of learners who complete the entire learning process of the target micro-lesson, reflecting the learners' continued participation in the micro-lesson content and their overall learning completion level; the interactive test score refers to the performance information obtained by learners through interactive tests during or after completing the target micro-lesson, used to characterize the learners' mastery of the micro-lesson content; and the on-the-job skills assessment data refers to the evaluation information formed after learners are assessed on skills related to the target micro-lesson in actual work positions or simulated job tasks, used to characterize the effect of the target micro-lesson on improving learners' job skills and practical application abilities.

[0056] S1.3: Using the micro-lesson identifier as the association key, the template node editing feature data and learning effectiveness feedback data are associated and matched to form a template utility analysis sample set.

[0057] Furthermore, the micro-lesson identifier corresponding to each record is read from the template node editing feature data, and the course completion rate, interactive test score, and on-the-job skills assessment data corresponding to the same micro-lesson identifier are retrieved from the learning effectiveness feedback data. The template node identifier, node type, node filling content under the same micro-lesson are associated and marked with the corresponding effectiveness feedback data to form a template utility analysis sample set.

[0058] S2. Perform attribution analysis on the template utility analysis sample set for micro-course training effect, identify template node feature combinations, and classify and grade nodes based on template node position, feature function type and utility enhancement direction to obtain template enhancement rules.

[0059] S2.1: Calculate the inter-group differences in course completion rate, interactive test score, and on-the-job skills assessment data corresponding to different node content in the template utility analysis sample set to obtain the node utility difference.

[0060] Furthermore, the template utility analysis sample set is grouped according to the content filled in the nodes. The average course completion rate, average interactive test score, and average on-the-job skills assessment data of all records in each node content group are calculated. The difference between the average values ​​of each indicator (course completion rate, interactive test score, and on-the-job skills assessment data) between each node content group is calculated, and the node utility difference is obtained by summing them up.

[0061] S2.2: Perform difference validity test and increase calculation on the node utility difference data, and select the node filling content that meets the difference condition and the increase is greater than the increase threshold, and use it as a valid feature.

[0062] Furthermore, for each node in the node utility difference data, the number of occurrences of positive differences, negative differences, and zero differences are counted for the differences in course completion rate, interaction test scores, and on-the-job skills assessment data corresponding to the content filled in each node. The average difference and fluctuation range of the differences in course completion rate, interaction test scores, and on-the-job skills assessment data are calculated. The differences in course completion rate, interaction test scores, and on-the-job skills assessment data that meet the proportion condition of positive differences, the minimum difference condition of average differences, and the lower limit of the fluctuation range are determined to meet the difference condition. Based on the differences in course completion rate, interaction test scores, and on-the-job skills assessment data that meet the difference condition, the improvement rate relative to the average value of the corresponding control group is calculated and compared with the improvement threshold. The node content filled in that simultaneously meets the difference condition and the improvement rate is greater than the improvement threshold is retained as a valid feature.

[0063] It should be noted that the difference conditions refer to the criteria used to determine whether the differences in course completion rates, interactive test scores, and on-the-job skills assessment data have effective distinguishing significance. These include the proportion condition, the minimum difference condition, and the lower limit condition of the fluctuation range (lower limit of the fluctuation range > 0). The proportion condition is set by statistically analyzing the number of times the differences in course completion rates, interactive test scores, and on-the-job skills assessment data are positive in all group comparisons of the same node's content, and calculating the proportion of positive values ​​to the total number of comparisons. An example value is 80%. The normal fluctuation range is defined as the 25th percentile to the 75th percentile of the absolute value of the differences between similar sample control groups in the historical template utility analysis sample set. For percentile intervals, the minimum effective increment above the upper limit of the normal fluctuation range is used as the minimum difference condition. For example, the minimum difference condition for the course completion rate difference is ≥0.03, the minimum difference condition for the interactive test score difference is ≥3 points, and the minimum difference condition for the on-the-job skills assessment data difference is ≥0.05. The improvement threshold is set by statistically analyzing the overall distribution level of the improvement magnitude corresponding to the content filled in each node, and selecting the improvement magnitude corresponding to the 75th percentile. For example, the improvement threshold for the course completion rate is set to 0.05~0.15, the improvement threshold for the interactive test score is set to 5~12 points, and the improvement threshold for the on-the-job skills assessment data is set to 0.08~0.20.

[0064] S2.3: Merge effective features according to template node position, identify feature combinations that jointly contribute to the learning effect at the same template node position, and obtain template node feature combinations.

[0065] Furthermore, effective features are grouped according to template node positions to obtain a set of effective features corresponding to each template node position. In the set of effective features corresponding to each template node position, the effectiveness of individual effective features is pre-screened first. Then, based on the co-occurrence relationship of node filling content under the same micro-course identifier, pairwise and multi-group enumerations are performed to obtain candidate node filling content combinations. The improvement rate of course completion rate, the improvement rate of interactive test score, and the improvement rate of on-the-job skills assessment data corresponding to each candidate node filling content combination are statistically analyzed. The improvement direction of each node filling content in each candidate node filling content combination is compared to see if they are consistent (if the improvement values ​​are all positive, they are considered to be consistent in improvement direction; if there are negative improvement values ​​or different main improvement directions, they are considered to be inconsistent in improvement direction), whether the improvement after combination is greater than the improvement threshold, whether the positive improvement ratio meets the ratio condition, whether the minimum difference meets the minimum difference condition, and whether the lower limit of the fluctuation range is greater than zero. Candidate node filling content combinations that simultaneously meet the above conditions are retained as template node feature combinations.

[0066] S2.4: Based on the feature combination of each template node, calculate the improvement value of course completion rate, the improvement value of interactive test score and the improvement value of on-the-job skills assessment data, and determine the feature effect type according to the improvement item with the highest proportion.

[0067] Furthermore, for each template node feature combination, all micro-course samples containing this template node feature combination are selected from the template utility analysis sample set. The average course completion rate, average interactive test score, and average on-the-job skills assessment data of the micro-course samples are calculated. Other comparable micro-course samples that do not contain this template node feature combination are selected, and the corresponding average course completion rate, average interactive test score, and average on-the-job skills assessment data are calculated. The difference between the average of the samples containing the template node feature combination and the average of the samples not containing the template node feature combination is used to obtain the improvement value of course completion rate, improvement value of interactive test score, and improvement value of on-the-job skills assessment data for that template node feature combination. The improvement percentages of the three are calculated and compared. The performance indicator with the largest improvement percentage is determined as the highest percentage improvement item. The feature function type of this template node feature combination is uniquely determined according to the name of this performance indicator (course completion rate, interactive test score, or on-the-job skills assessment data), expressed as:

[0068] ;

[0069] in, Represents the combination of template node features In performance indicators The percentage of increase; This represents a combination of features for a template node; Indicates the name of the performance indicator to be compared; This indicates the rate of increase in course completion rate; This indicates the magnitude of the improvement in the interaction test score; This indicates the rate of improvement in on-the-job skills assessment data; Represents the combination of template node features In performance indicators The increase in the above; Represents the combination of template node features The extent of improvement in course completion rate; Represents the combination of template node features The improvement in interaction test scores; Represents the combination of template node features The extent of improvement in on-the-job skills assessment data.

[0070] S2.5: Classify the feature combinations of each template node according to the feature function type, calculate the utility improvement direction and utility improvement magnitude of each template node feature combination, and generate utility classification basic data.

[0071] Furthermore, template node feature combinations with the same type of function are grouped into the same group to form a set of template node feature combinations organized by function type. For each template node feature combination in the set, the direction of utility improvement is determined based on the positive or negative sign of the improvement value of course completion rate, interactive test score, and on-the-job skills assessment data (for example, if all three types of improvement values ​​are positive, it is a positive effect improvement; if all three types of improvement values ​​are negative, it is a negative effect improvement; if some of the three types of improvement values ​​are positive and some are negative, it is a mixed improvement). The absolute value of the improvement value corresponding to the highest proportion of improvement item in the template node feature combination is recorded as the utility improvement magnitude. The node classification information, utility improvement direction, and utility improvement magnitude of all template node feature combinations are collected to generate basic data for utility classification.

[0072] S2.6: Based on the utility level base data, perform utility level classification on the feature combination of each template node and transcribe it into template enhancement rules.

[0073] Furthermore, the utility improvement magnitude in the utility grading base data is compared with the utility level intervals one by one, and the utility level of each template node feature combination is determined by combining the utility improvement direction. Where the utility improvement direction is positive, if the utility improvement magnitude falls into the high level interval, it is classified as high efficiency level. If the utility improvement magnitude falls into the medium or low level interval, it is classified as medium efficiency level or low efficiency level, respectively. If the utility improvement direction is negative or mixed improvement, it is classified as restricted level. The template node position is used as the action node. According to the action mode generation rules, the course completion rate improvement type is converted into the content supplement type action mode, the interactive test score improvement type is converted into the test guidance type action mode, and the on-the-job skills assessment data improvement type is converted into the case or practical action mode. The utility level is converted into the execution intensity, and the utility improvement direction is used as the constraint direction to form the template enhancement rules.

[0074] It should be noted that the utility improvement in the utility classification basic data is divided into utility level intervals according to the distribution threshold. For example, the 75th percentile and above is the high-level interval, the 50th percentile to the 75th percentile is the medium-level interval, and the 25th percentile to the 50th percentile is the low-level interval.

[0075] It should also be noted that the template enhancement rules include the action node, action method, execution intensity, constraint direction, and corresponding utility enhancement magnitude. These rules are used to characterize which node content or feature combinations at a specific template node position have high relevance utility to course completion rate, interactive test score, or on-the-job skills assessment data. The relevance utility is organized into rule descriptions that can be used for editing guidance and review judgment.

[0076] S3. Receive the micro-course creation request sent by the mobile terminal, load the template enhancement rules into the target template node, and reconstruct the field constraints of the target template node, insert enhanced fields and inject guidance prompts to generate a templated editing interface, and at the same time establish a node-level editing state object.

[0077] S3.1: Receive the micro-course creation request sent by the mobile terminal, extract the course topic, course category and training task identifier, and match the target micro-course template.

[0078] Furthermore, the system receives micro-course creation requests sent from mobile devices, extracts course theme text, course category code, and training task identifier from these requests, and compares them with the applicable theme keywords, applicable category range, and bound task identifier of each template record in the micro-course template library. When the course theme text is completely consistent with the applicable theme keywords of the template, or when the course theme text contains any of the applicable theme keywords, the template record is determined to be the target micro-course template.

[0079] It should be noted that the micro-course template library is a collection of template resources used to store and manage different micro-course template records. It is constructed by pre-organizing the micro-course structure requirements corresponding to different course themes, course categories and training tasks, including template identifiers, applicable theme keywords, applicable category range, bound task identifiers, template node structure, field configurations corresponding to each template node and prompts corresponding to each template node.

[0080] S3.2: Based on the template node identifier in the target micro-lesson template, determine the target template node for the template enhancement rules, and extract the enhancement field type, field triggering conditions, and prompt content to form a node enhancement configuration table.

[0081] Furthermore, the system reads the action node, action method, execution intensity, and constraint direction corresponding to each template enhancement rule, matches the action node with the template node identifier in the target micro-course template one by one, and takes the matching template node identifier as the target template node; the action method description in the template enhancement rule is directly mapped to the specific enhancement field type (e.g., the action method "add case analysis" corresponds to the enhancement field type "case text field"); when the constraint direction in the template enhancement rule is positive improvement and the execution intensity is high efficiency level, the field trigger condition is defined as mandatory inclusion; if the execution intensity is medium efficiency level or low efficiency level, the field trigger condition is defined as recommended inclusion; by combining the feature action type, utility level, and utility improvement magnitude recorded in the template enhancement rule, prompt content is generated, such as generating the statement "It is recommended to add case analysis here. Historical data shows that this operation can improve the course completion rate by an average of about 10%"; the enhancement field type, field trigger condition, and prompt content are associated with the corresponding target template node identifier to form a node enhancement configuration table.

[0082] S3.3: Based on the node enhancement configuration table, identify missing fields, replacement fields, and priority fields in the original field configuration and rule requirements of the target template node, and form a list of node field adjustments.

[0083] Furthermore, each record in the node enhancement configuration table is traversed, and the enhanced field type in the record is compared with the original field configuration corresponding to the target template node. If there is no field with the same name as the enhanced field type in the original field configuration, the enhanced field type is marked as a missing field. If there is a field with the same name as the enhanced field type in the original field configuration but with a different specific type or attribute definition (e.g., the "Case" field in the original field configuration is a plain text input box, while the "Case" in the enhanced field type is defined as a composite component that supports video upload and text description, the two have the same name but different specific types, so the "Case" composite component will be marked as a replacement field), the original field type and the enhanced field type are mapped, and the enhanced field type is marked as a replacement field. The field triggering conditions in the node enhancement configuration table are checked, and enhanced field types whose triggering conditions are mandatory are marked as high-priority fields, while enhanced field types whose triggering conditions are recommended are marked as ordinary-priority fields. The missing fields, replacement fields, and priority information are summarized to generate a node field adjustment list.

[0084] S3.4: Based on the node field adjustment list, add missing fields to the target template node, update the replacement fields to the enhanced fields, load the prompt content to the target template node, and generate a templated editing interface.

[0085] Furthermore, for records marked as missing fields in the node field adjustment list, a new corresponding field component is created and inserted into the corresponding insertion position in the field sequence of the target template node according to the enhanced field type. For records marked as replacement fields, the original field corresponding to the enhanced field type is found in the field sequence of the target template node, and its field type and attribute definition are updated to the specification defined by the enhanced field type. For fields marked as high priority or normal priority, the display order is adjusted in the layout of the target template node, and the high priority field is placed at the top. The prompt content corresponding to the currently processed field in the node enhancement configuration table is associated and loaded into the prompt display area of ​​the corresponding field in the target template node. After completing the field sequence update, layout order adjustment, and prompt content binding of the target template node, the reconstructed target template node is integrated with the overall framework, style definition, and other unmodified template nodes of its micro-lesson template, and assembled according to the display rules of the mobile editing interface to generate a templated editing interface that users can directly fill in and interact with.

[0086] S3.5: Extract the initial editing state information of the target template node from the templated editing interface, form a node state index, and perform state mapping processing based on the node state index to generate a node-level editing state object.

[0087] Furthermore, the field identifier, field type, field layout position, associated prompt content, and initial fill value of the target template node are read from the template editing interface and organized into a record with the template node identifier as the key, forming a node status index. Based on the field structure in the node status index, a node-level editing status object is created, mapping each field in the node status index to a status attribute in the node-level editing status object, including field identifier, field type, current field value, field fill status, enhanced field trigger status, prompt hit status, and last modified timestamp (initial value is empty). An initial value is assigned to each status attribute, completing the construction of the node-level editing status object.

[0088] It should be noted that the initial value assignment rules are as follows: the initial values ​​of field identifiers and field types are directly taken from the corresponding information recorded in the node status index. The initial value of the current field is assigned to the preset content (such as default text or example content) if the node status index indicates that the field has preset content in the templated editing interface; otherwise, it is assigned an empty value. The initial value of the field filling state is uniformly assigned to "not edited". The initial value of the enhanced field trigger state is uniformly assigned to "not triggered". The initial value of the prompt hit state is assigned to "not triggered" if the field is associated with prompt content in the node status index; otherwise, it is assigned to "none".

[0089] S4. During the editing process on the mobile device, when a node content change editing event is detected, incremental state recording is performed on the node-level editing state object. When the course is re-entered on the mobile device, the most recently saved node-level editing state object is restored to obtain the current editing state of the target template node.

[0090] S4.1: During the editing process on the mobile device, when a node content change editing event is detected, the target template node identifier, field identifier, changed field content, and current node filling status of the changed node are extracted to form a node change record.

[0091] Furthermore, during the mobile editing process, the system listens for user input in the templated editing interface. When a node content change editing event is captured, the system parses the target template node identifier bound to the event, the field identifier where the content has changed, and the field content that the user has latest input or modified in this operation from the node content change editing event object. The system reads the node-level editing state object corresponding to the target template node identifier, obtains the current values ​​of all fields in the record, and uses them as the current node filling state. The system combines the target template node identifier, field identifier, changed field content, and current node filling state into a record containing a timestamp, i.e., the node change record.

[0092] It should be noted that a node content change editing event refers to a status change signal triggered when a user modifies the field content of a specific template node in the templated editing interface (such as entering text, uploading a file, or selecting an option) during micro-lesson editing on a mobile device. The core information includes the identifier of the target template node being operated on, the identifier of the specific field whose content has been changed, the field content that the user has latest entered or modified in this operation, and the specific interactive action that triggered the change (such as focus leaving or manual saving).

[0093] S4.2: Write the changed field content in the node change record into the target template node in the node-level edit status object, and synchronously update the field fill status, enhancement field trigger status, and prompt hit status to form a status update object.

[0094] Furthermore, based on the target template node identifier in the node change record, the corresponding node-level editing state object is located. Within the node-level editing state object, the field identifier corresponding to the changed field content is found, and the changed field content is written to the current value attribute of that field. The current value of the field after writing is checked. If it is not empty, the field fill status under the same field is updated to "filled"; if it is empty, it is updated to "not filled". Based on the node enhancement configuration table, it is determined whether the field identifier corresponds to an enhancement field type. If so, the enhancement field trigger status of this field is updated according to the field trigger condition (when the field trigger condition is "must include" or "recommended to include", the field being filled indicates it has been triggered; not being filled indicates it has not been triggered). The prompt validation rules associated with this field are matched to determine whether the changed field content meets the prompt validation rules. If it does, the prompt hit status of this field is updated to "hit". After completing the update of all attributes in the node-level editing state object, the event time corresponding to the current node content change editing event is written to the last modified timestamp, generating a status update object.

[0095] It should be noted that the prompt content includes the displayed prompt text and prompt verification rules. The prompt hit status is determined based on the prompt verification rules. The prompt verification rules are used to determine whether the content filled in the corresponding field in the target template node meets the guidance requirements. By extracting the verification elements directly related to the filling result based on the function, field triggering conditions and prompt content in the template enhancement rules, the verification elements are converted into executable judgment conditions for setting. The judgment conditions include at least one of the following: content length requirements, keyword inclusion requirements, format requirements, field integrity requirements or attachment submission requirements. Thus, the prompt content is not only used for interface display, but also for subsequent prompt hit status determination.

[0096] S4.3: Extract the incremental state of the state update object and write it to the most recently saved version to form the latest node state version.

[0097] Furthermore, the status update object is compared with the node-level edit status object with the same name recorded in the most recent saved version. All attributes in the status update object that have undergone value changes (current field value, field fill status, enhanced field trigger status, hint hit status, and last modified timestamp) are identified. These attributes and their new values ​​are recorded as incremental states. The data of the most recent saved version is read, and the new attribute values ​​recorded in the incremental states are written to the corresponding attributes of the corresponding node-level edit status object in the most recent saved version, overwriting the old values, and generating the latest node status version.

[0098] S4.4: When re-entering the course on a mobile device, the target template node and incremental status of the most recent node content change are determined based on the latest node status version, forming a continuation and recovery index.

[0099] Furthermore, when re-entering the course on the mobile device, the latest node status version is accessed, and all node-level edit status objects contained therein are identified. By comparing the last modification timestamps recorded in each node-level edit status object, the node-level edit status object with the latest timestamp is determined, and the corresponding template node identifier is the target template node identifier for the most recent node content change. All field identifiers with the field fill status "filled" and their corresponding current values ​​are extracted from the node-level edit status objects and organized together with the target template node identifier to form a continuation and recovery index.

[0100] S4.5 restores the target template node to the editing position and node state after the most recent change in node content based on the continuation and recovery index, and obtains the current editing state of the target template node.

[0101] Furthermore, the target template node identifier, field identifier, and current field value are read from the continuation and recovery index. The target template node corresponding to the target template node identifier is located in the templated editing interface, and the current field value is filled back into the editing position of the corresponding field in the target template node one by one. Based on the filled templated editing interface content, the node-level editing state object corresponding to the target template node is updated synchronously, and the field filling state, enhanced field trigger state, and prompt hit state of each field are updated to be consistent with the interface content, thereby obtaining the current editing state of the target template node.

[0102] Figure 3This paper demonstrates the overall trend of breakpoint resume recovery time under different editing methods as the cumulative number of node content change editing events changes. The upper part is an overview curve covering the entire range of node content change editing events, showing the characteristics and differences in recovery time under two conditions: the conventional template editing process and the templated editing interface proposed in this invention, as the cumulative number of node content change editing events gradually increases. The conventional template editing process is a conventional editing method that only fills in content according to the basic template structure and resumes editing after interruption. The templated editing interface of this invention is an editing method formed after loading template enhancement rules and recording incremental status during the editing process. When re-entering the course, it performs resume recovery processing based on the latest node status version. The lower part provides a magnified display of the stage with a low cumulative number of node content change editing events and marks the location with the greatest local difference, reflecting that at the stage of a specific editing event, the templated editing interface proposed in this invention has a more obvious advantage over the conventional template editing process in shortening the resume recovery positioning and status restoration time.

[0103] S5. Upon receiving the review instruction, based on the template enhancement rules and the current editing status of the target template node, perform a compliance review on the current micro-lesson and generate a review report.

[0104] S5.1: Compare the current editing state of the target template node with the enhancement field requirements and prompt requirements in the template enhancement rules to determine whether the target template node meets the rule requirements and calculate the degree of rule compliance.

[0105] Furthermore, based on the template node identifier of the target template node, a matching record is searched in the node enhancement configuration table to obtain the corresponding enhancement field type, field trigger condition, and prompt content, which constitute the rule item requirements. The current editing status of the target template node is compared one by one: for each enhancement field requirement, it is checked whether there is a field with the same name as the enhancement field type in the current editing status, and it is determined whether its field fill status is filled and whether the enhancement field trigger status meets the field trigger condition (such as the "must contain" requirement status being "triggered"). For each prompt requirement, it is checked whether the prompt hit status of the corresponding field is hit. The number of fully satisfied rule items is recorded, and the quotient of the number of satisfied rule items and the total number of rule items related to the target template node in the node enhancement configuration table is used as the rule compliance degree.

[0106] It should be noted that if there are no rule items related to the target template node in the node enhancement configuration table, the rule compliance degree of the target template node is recorded as 100%, and the field triggering conditions are that the rule items that must be included and those that are recommended to be included are calculated with the same weight.

[0107] S5.2: Based on the rule compliance level of the target template node and the target template node information corresponding to the unmet rule items, mark the problematic nodes and generate an audit report.

[0108] Furthermore, target template nodes with unmet mandatory inclusion rule items are marked as mandatory non-compliance problem nodes. Target template nodes without unmet mandatory inclusion rule items but with unmet suggested inclusion rule items or missed prompt verification rule items are marked as insufficient suggestion nodes. From the target template node information corresponding to the unmet rule items, the unmet enhanced field type, the unmet field trigger condition, and the missed prompt content are extracted and associated with the corresponding target template node identifier and feature function type to form problem description entries. All problem nodes and their corresponding problem description entries are summarized and combined with the rule compliance degree of the micro-lesson to generate an audit report containing a list of problem nodes, details of specific rule violations, and an overall compliance rating.

[0109] It should be noted that the overall compliance rating is based on the average rule compliance of all target template nodes in the current micro-lesson. Specifically, an average rule compliance of 90% or higher is rated as high, an average rule compliance of 75% or higher but less than 90% is rated as medium, and an average rule compliance of less than 75% is rated as low.

[0110] Figure 4 This paper compares the degree of rule compliance under different editing methods. The horizontal axis represents the sample number of different micro-lessons, and the vertical axis represents the degree of rule compliance. Grouped bar charts show the changes in rule compliance under different micro-lesson sample conditions between the conventional template editing process and the template-based editing interface proposed in this invention. Samples 1 to 8 are eight micro-lesson samples selected from the template utility analysis sample set based on the principles of consistent course category, consistent training task, and similar number of template nodes. Each micro-lesson sample includes the editing content and review result of the corresponding target template node. The conventional template editing process is the basic editing method without loading template enhancement rules, while the template-based editing interface of this invention is the editing method after loading template enhancement rules, making the editing process revolve around the template enhancement rules, and calculating the degree of rule compliance based on the current editing state during review. The overall distribution of the bars in each group clearly shows that under different micro-lesson sample conditions, the rule compliance degree corresponding to the template-based editing interface proposed in this invention is generally higher than that of the conventional template editing process, and a relatively stable upward trend is maintained among the samples. This indicates that this invention can guide the target template node to meet subsequent review requirements in advance during the editing stage. Therefore… Figure 4The study depicted the sample distribution characteristics of rule compliance under different editing methods, demonstrating that by embedding enhanced rules into the template-based editing interface in advance, the rule adaptability and consistency of review criteria in the micro-lesson creation process can be effectively improved, and the accuracy of review results can be further enhanced.

[0111] This embodiment also includes a mobile micro-course review system that supports breakpoint continuation and template guidance, comprising: a data acquisition module for collecting template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set; an enhancement rule transcription module for performing micro-course training effect attribution analysis on the template utility analysis sample set, identifying template node feature combinations, and classifying and grading nodes based on template node position, feature function type, and utility enhancement direction to obtain template enhancement rules; and a guided editing module for receiving micro-course creation requests sent by the mobile terminal, loading template enhancement rules into the target template node, and performing... The system includes: field constraint reconstruction, enhanced field insertion, and guided prompt injection; generation of a templated editing interface; and establishment of node-level editing state objects. The continuation state recovery module is used to perform incremental state recording on the node-level editing state object when a node content change editing event is detected during mobile editing. Upon re-entering the course on the mobile device, it performs continuation recovery processing on the most recently saved node-level editing state object to obtain the current editing state of the target template node. The compliance review module is used to perform compliance review on the current micro-lesson based on template enhancement rules and the current editing state of the target template node after receiving a review instruction, and generates a review report.

[0112] In summary, this invention achieves targeted characterization of the relationship between learning outcomes and template nodes based on big data processing by performing attribution analysis on the template utility analysis sample set, identifying template node feature combinations and obtaining template enhancement rules, thereby improving the rule adaptability and consistency of review criteria in the micro-course creation process; and by reconstructing field constraints, inserting enhanced fields, and injecting guiding prompts into the target template nodes, it achieves dynamic enhancement of the template editing interface, improving the continuity of breakpoint continuation editing and the accuracy of review reports.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A mobile micro-lesson review method that supports breakpoint continuation and template-guided editing, characterized by: include: Collect template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set; We performed attribution analysis on the template utility analysis sample set to identify template node feature combinations, and combined the template node position, feature function type and utility enhancement direction to classify nodes and classify utility to obtain template enhancement rules. Receive the micro-lesson creation request sent by the mobile terminal, load the template enhancement rules into the target template node, and reconstruct the field constraints of the target template node, insert enhanced fields and inject guidance prompts to generate a templated editing interface, while creating a node-level editing state object; During mobile editing, when a node content change editing event is detected, incremental state recording is performed on the node-level editing state object. When the course is re-entered on the mobile device, the most recently saved node-level editing state object is restored to obtain the current editing state of the target template node. Upon receiving the review instruction, the current micro-lesson undergoes a compliance review based on the template enhancement rules and the current editing status of the target template node, and a review report is generated.

2. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 1, characterized in that, The steps for forming the template utility analysis sample set are as follows: The template node editing feature data includes the template node identifier, node type, and node fill content; The learning outcome feedback data includes course completion rate, interactive test scores, and on-the-job skills assessment data. Using the micro-lesson identifier as the association key, the template node editing feature data and learning effectiveness feedback data are associated and matched to form a template utility analysis sample set.

3. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 1, characterized in that, The steps for identifying the combination of template node features are as follows: The inter-group difference was calculated for the course completion rate, interactive test score and on-the-job skills assessment data corresponding to the content filled at different nodes in the template utility analysis sample set to obtain the node utility difference. Perform difference validity tests and increase calculations on the node utility difference data, and select the node filling content that meets the difference conditions and the increase is greater than the increase threshold, and use it as a valid feature; The effective features are merged according to the template node position, and the feature combinations that work together to improve the learning effect at the same template node position are identified to obtain the template node feature combination.

4. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 3, characterized in that, The steps for obtaining template enhancement rules are as follows: Based on the combination of features of each template node, the improvement value of course completion rate, the improvement value of interactive test score and the improvement value of on-the-job skills assessment data are calculated, and the feature effect type is determined according to the improvement item with the highest proportion. The template node feature combinations are categorized according to their feature function type, and the utility improvement direction and magnitude of each template node feature combination are calculated to generate utility classification basic data. Based on the utility classification data, the utility level is divided for each template node feature combination and then transcribed into template enhancement rules.

5. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 1, characterized in that, The steps for generating the templated editing interface are as follows: Receive micro-course creation requests sent from mobile devices, extract course topics, course categories, and training task identifiers, and match them with target micro-course templates; Based on the template node identifier in the target micro-lesson template, the target template node for the template enhancement rules is determined, and the enhancement field type, field triggering condition and prompt content are extracted to form a node enhancement configuration table; Based on the node enhancement configuration table, identify missing fields, replacement fields, and priority fields in the original field configuration and rule requirements of the target template node to form a list of node field adjustments. Based on the node field adjustment list, missing fields are added to the target template node, replacement fields are updated to enhanced fields, and prompt content is loaded into the target template node to generate a templated editing interface.

6. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 5, characterized in that, The node-level editing state object is generated by extracting the initial editing state information of the target template node from the templated editing interface, forming a node state index, and performing state mapping processing based on the node state index.

7. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 1, characterized in that, The steps for performing incremental state recording on node-level edit state objects are as follows: During mobile editing, when a node content change editing event is detected, the target template node identifier, field identifier, changed field content, and current node filling status are extracted to form a node change record; Write the changed field content in the node change record into the target template node in the node-level edit status object, and synchronously update the field fill status, enhancement field trigger status, and prompt hit status to form a status update object; Extract the incremental state of the state update object and write it to the most recently saved version to form the latest node state version.

8. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 7, characterized in that, The steps to obtain the current editing state of the target template node are as follows: When re-entering the course on a mobile device, the target template node and incremental status of the most recent node content change are determined based on the latest node status version, forming a continuation and recovery index; Based on the continuation and recovery index, the target template node is restored to the editing position and node state after the most recent node content change, and the current editing state of the target template node is obtained.

9. The mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in claim 5 or 8, characterized in that, The steps for generating the audit report are as follows: Compare the current editing state of the target template node with the enhancement field requirements and prompt requirements in the template enhancement rules to determine whether the target template node meets the rule requirements and calculate the degree of rule compliance; Based on the rule compliance level of the target template node and the target template node information corresponding to the rule-unmet items, problematic nodes are marked, and an audit report is generated.

10. A mobile micro-lesson review system supporting breakpoint continuation and template guidance, based on the mobile micro-lesson review method supporting breakpoint continuation and template guidance as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect template node editing feature data and learning effectiveness feedback data to form a template utility analysis sample set; The enhanced rule transcription module is used to perform micro-course training effect attribution analysis on the template utility analysis sample set, identify template node feature combinations, and perform node classification and utility grading based on template node position, feature function type and utility enhancement direction to obtain template enhancement rules; The guided editing module is used to receive micro-lesson creation requests sent by mobile devices, load template enhancement rules into the target template node, and reconstruct field constraints, insert enhanced fields, and inject guided prompts into the target template node to generate a templated editing interface, while establishing a node-level editing state object. The continuation state recovery module is used to perform incremental state recording on the node-level editing state object when a node content change editing event is detected during the mobile editing process, and to perform continuation recovery processing on the most recently saved node-level editing state object when the course is re-entered on the mobile device, so as to obtain the current editing state of the target template node. The compliance review module is used to perform compliance review on the current micro-lesson after receiving a review instruction, based on the template enhancement rules and the current editing status of the target template node, and generate a review report.

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