Task data decomposition implementation management platform for action-oriented courses

Abstract

This invention discloses a task data decomposition and implementation management platform for action-oriented courses, relating to the fields of educational informatization and intelligent education technology. The platform includes the following steps: a behavior data collection module for collecting basic behavior data generated during task execution; a task structure configuration module for assessing operational deviations; a process feature analysis module for extracting path fit features; a behavior pattern comparison module for comparing and classifying student behaviors; an intelligent feedback adjustment module for pushing recommended paths, guidance prompts, task replacement, and correction information; and a multi-dimensional result output module for comprehensively evaluating standardization, matching, and efficiency performance. This solves the problem that existing teaching platforms can only evaluate task results but cannot determine whether students' operational processes are standardized or proficient.

Description

Technical Field

[0001] This invention relates to the field of educational informatization and intelligent education technology, specifically to a task data decomposition and implementation management platform for action-oriented courses. Background Technology

[0002] With the increasing adoption of action-oriented courses in vocational education, teaching activities are increasingly emphasizing real-world task-driven learning and hands-on experience. These courses typically organize their content around project tasks and typical work processes, placing higher demands on process management and task execution. However, most current teaching platforms still focus primarily on resource presentation and result submission, making it difficult to support dynamic management during task progress.

[0003] For example, the invention patent with announcement number CN119577465B discloses an information-based sports education platform management system, specifically involving the field of sports training data management. This system addresses the problem of accurate classification and feedback of multi-equipment training data. First, based on the fluctuation characteristics of physiological data, it captures data during the transition period to ensure the continuity and timeliness of training data. Then, using the transition response consistency index and the target equipment feature matching degree, it accurately defines effective transition data, clearly distinguishes the physiological characteristic data of each equipment, and improves classification accuracy. The defined effective transition data undergoes fine classification and label generation through the target equipment dynamic feature model, ensuring data independence and consistency. Finally, an adaptive elimination mechanism is used to filter out abnormal data, retaining high-quality data to generate personalized feedback, enhancing the relevance of the feedback. This constructs a closed-loop system from data collection to feedback generation, providing scientific support for sports training data management.

[0004] For example, invention patent CN116611022B discloses a method and platform for big data fusion in smart campus education. This method includes the following steps: acquiring student educational activity data; standardizing the student educational activity data to obtain standard real-time data; fusing and constructing student behavior models based on the standard real-time data; predicting behavioral anomalies based on the student behavior models to obtain abnormal behavior data; and conducting intelligent event tracing and evaluation based on the abnormal behavior data to obtain detailed information on abnormal behaviors for tracking abnormal events in smart campus education. By acquiring, standardizing, and fusing student educational activity data, student behavior models can be quickly built, and multi-source data can be integrated for comprehensive analysis, improving the efficiency of student education management.

[0005] Most existing platforms rely on static task assignment and periodic result assessment, lacking the ability to continuously record and identify the learning process. In multi-step tasks, students are prone to problems such as disordered execution order, mid-process pauses, and skipping key steps, which are difficult to detect and address effectively. Judgment, intervention, and adjustment during the teaching process often depend on manual processing by teachers, resulting in common problems such as low efficiency, incomplete information, and limited control methods, making it difficult to meet the needs of full-process supervision in complex course scenarios.

[0006] To address the above issues, there is an urgent need for a task data decomposition and implementation management platform for action-oriented courses. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a task data decomposition and implementation management platform for action-oriented courses, which solves the problem that existing teaching platforms can only evaluate task results but cannot determine whether students' operation processes are standardized and proficient.

[0009] Technical solution

[0010] To achieve the above objectives, this invention provides the following technical solution: a task data decomposition and implementation management platform for action-oriented courses, comprising the following steps: a behavior data collection module, used to collect basic behavior data generated during task execution, and construct a traceable behavior sequence through time-series processing; a task structure configuration module, used to map a standard path map based on the behavior sequence, compare the student's path order and evaluate the degree of operational deviation, synchronously mark the progress status, and trigger path control and guidance rules; a process feature analysis module, used to perform matching analysis on the student's path in terms of structure and rhythm, extract path fit features, and push rhythm prompts, jump interventions, and path recommendations; a behavior pattern comparison module, used to compare and classify student behavior patterns based on the standard path map and group benchmarks, identify structural and rhythm differences, extract key segments, and label behavior; an intelligent feedback adjustment module, used to match intervention strategies based on behavior labels, and push recommended paths, guidance prompts, task replacement, and correction information; and a multi-dimensional result output module, used to integrate the degree of operational deviation and path fit features, comprehensively evaluate standardization, matching, and efficiency performance, and drive differentiated responses in task structure, resource push, and feedback mechanisms.

[0011] Furthermore, the specific steps for constructing a traceable behavior sequence from the basic behavioral data generated during the task execution are as follows: By collecting the interface operation sequence, jump relationships, and interaction time information formed during task execution in real time, basic behavioral data that can be used for time-series analysis and behavior evaluation is obtained. This basic behavioral data includes: the total number of task steps, the student's actual operation sequence, the standard operation sequence, the number of path matching edges, the number of student path edges, the number of standard path edges, the student click timestamp sequence, the standard time interval sequence, the task loading timestamp, the task submission timestamp, and the historical baseline duration. During the preprocessing of the collected basic behavioral data, data cleaning and format unification are completed. Standardization processing is performed on the structured data in the basic behavioral data to unify field and path formats. Normalization processing is performed on the numerical data in the basic behavioral data to eliminate dimensional differences. Task node alignment and path mapping are completed.

[0012] Furthermore, the specific steps for comparing student path order and evaluating operation deviation based on the behavior sequence mapping standard path graph are as follows: Teachers add operation step nodes sequentially in the task modeling interface and manually connect their execution logic paths. After constructing a directed graph structure, a standard path graph is formed. The operation records in the behavior sequence are established in correspondence with the task nodes in the standard path graph, constructing a structural mapping foundation that can be used for sequence comparison. The total number of task steps, the student's actual operation order at step i, and the standard operation order at step i are obtained from the basic behavior data. The order deviation tolerance is calculated by comparing the number of differences in node positions between the student's actual operation order and the standard operation order, obtaining the order deviation tolerance at step i. Using the total number of task steps as the total number of iterations, the difference between the student's actual operation order at step i and the standard operation order at step i is calculated sequentially for each step. The absolute value is used as the numerator of the deviation degree, and the order deviation tolerance at step i plus 1 is used as the denominator to form the standardized deviation value for each step. The deviation values ​​of all steps are then summed, and the sum is divided by the total number of task steps to obtain the average standardized order deviation degree after averaging all steps, which is the task execution deviation value.

[0013] Furthermore, the specific steps for synchronously marking the progress status and triggering path control and guidance rules are as follows: Real-time comparison of task execution offset values ​​with offset thresholds, including primary and secondary offset thresholds; when the task execution offset value is greater than or equal to the primary offset threshold, a forced switch to sequential constraint mode is implemented, allowing students to proceed only along the standard path, restricting skipping and backtracking behaviors; clicking on an incorrect step will trigger a structural warning and record an anomaly marker; when the task execution offset value is greater than the secondary offset threshold but less than the primary offset threshold, a standard path comparison diagram is inserted to highlight misaligned steps, a critical step locking mechanism is activated, and sequential clicking is forced, while additional guidance prompts provide operation instructions; when the task execution offset value is less than or equal to the secondary offset threshold, entry into subsequent tasks is allowed, marked as a standard behavior sample, and the behavior trajectory is archived.

[0014] Furthermore, the specific steps for performing matching analysis on student paths in the structural and rhythmic dimensions and extracting path fitting features are as follows: Extract the number of matching edges, student click timestamp sequence, standard time interval sequence, number of student path edges, and number of standard path edges from the basic behavioral data; by comparing the student click timestamp sequence and the standard time interval sequence, take the absolute value of the time difference for each pair of matching edges and calculate the arithmetic mean to obtain the average rhythm difference; use the number of matching edges as the numerator and the smaller value between the number of student path edges and the number of standard path edges as the denominator, and divide the numerator by the denominator to obtain the structural matching rate; multiply the structural matching rate by a negative exponential function with the average rhythm difference as the power variable and the rhythm penalty coefficient as the control parameter to obtain the behavioral path matching value.

[0015] Furthermore, the specific steps for push rhythm prompts, jump interventions, and path recommendations are as follows: Real-time comparison of behavioral path matching values ​​with matching thresholds, including a primary matching threshold and a secondary matching threshold; when the behavioral path matching value is less than the primary matching threshold, restricting some free jump operations, forcing the use of the task navigator, pushing the process simulation task requiring students to repeatedly practice under the structure template, and marking the current operation as a weak graph sample; when the behavioral path matching value is greater than or equal to the primary matching threshold and less than the secondary matching threshold, generating a structure echo graph to compare the student with the standard path, guiding repeated execution of missing nodes, unlocking the remaining modules after completion, and simultaneously initiating rhythm optimization suggestions to prompt abnormal operation rhythm sections; when the behavioral path matching value is greater than or equal to the secondary matching threshold, enabling fast path permissions, allowing free exploration and skipping of demonstration steps.

[0016] Furthermore, the specific steps for comparing and classifying student behavior patterns based on standard path maps and group benchmarks, identifying structural and rhythmic differences, extracting key segments, and labeling behavior are as follows: The path structure and operational rhythm features in the student behavior sequence are compared and analyzed with the standard path map, high-performing samples, and group behavior reference system stored in the platform. Based on the node order, jump method, and operational rhythm, the skill template is matched to determine the degree of structural fit. Behavioral features are clustered and mapped to determine their relative position in the group distribution reference system, and are classified into subgroups of experienced, novice, and abnormal individuals. The behavior sequence is then used for reverse analysis to trace and locate out-of-order segments, abnormal jumps, and stagnant areas in the path, identifying key nodes that lead to decreased efficiency and result deviations. Dense jump segments and rhythmic abrupt change points in the path execution process are extracted, marked as key behavioral segments, and behavior labels are generated.

[0017] Furthermore, the specific steps of the behavior tag matching intervention strategy, which pushes recommended paths, guidance prompts, task replacement and correction information, are as follows: The behavior tags generated in the behavior pattern comparison module are invoked to determine the student's current behavior path deviation type, rhythm state, and group position; when the tag indicates structural deviation and path instability, the optimal path recommendation, tool combination paradigm, and visual navigation scheme are pushed to guide the student to complete the task according to the recommended structure; when dynamic feature tags indicating behavioral mutation, lengthy path, and rhythm stagnation are detected, phased operation guidance and voice prompts are triggered to help the student understand and correct the current steps; when the behavior bottleneck tag indicates repeated errors and abnormal dwell time in a certain task segment, the task structure is replaced with a simplified version; and prompt information associated with the current deviation behavior is generated simultaneously.

[0018] Furthermore, the specific steps for comprehensively evaluating standardization, matching, and efficiency performance based on the degree of deviation and path fit characteristics of the fusion operation are as follows: Obtain the task loading timestamp, task submission timestamp, and historical baseline duration from the basic behavioral data; extract the task execution offset value and behavioral path matching value calculated by the extraction module; obtain the student completion time by calculating the difference between the task loading timestamp and the task submission timestamp; subtract the task execution offset value from one and add it to the behavioral path matching value to obtain the joint score; divide the student completion time by the historical baseline duration, add it to the joint score, and subtract the scoring correction constant to obtain the complete comprehensive input item; multiply this comprehensive input item by the scoring steepness parameter and add a negative sign before the product to obtain the power of the exponential function; calculate the exponential function value using the base of the natural logarithm as the base; add one to the exponential function value as the denominator, and finally divide the denominator by the constant one to obtain the comprehensive skill performance value.

[0019] Furthermore, the specific steps of the differentiated response of the driving task structure, resource push, and feedback mechanism are as follows: Real-time comparison of the comprehensive skill performance value with the comprehensive performance response threshold group, which includes a first-level performance threshold and a second-level performance threshold; when the comprehensive skill performance value is greater than or equal to the second-level performance threshold, open challenging tasks and multi-path task structures, reduce demonstration dependence, allow skipping low-level training stages, and generate high-performance behavior summaries; when the comprehensive skill performance value is greater than or equal to the first-level performance threshold but less than the second-level performance threshold, push optional path optimization suggestion cards, activate adjustable task paths, allow some steps to be set in their own execution order, and mark the current student as a growth sample; when the comprehensive skill performance value is less than the first-level performance threshold, forcibly switch to path standardization mode, restrict non-standard operation paths, push behavior feedback summaries and standard operation video demonstrations, prevent access to advanced tasks, and suggest completing basic tasks before applying for the advancement process.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This invention collects the operation sequence, jump relationship and interaction time during the task execution process, and completes field standardization and path mapping, thereby realizing the structural organization and temporal restoration of student behavior data. It solves the problem that the existing teaching system cannot effectively construct continuous behavior sequences and is difficult to support process data analysis, which is conducive to forming a calculable and comparable basic data structure.

[0023] (2) By constructing a standard path diagram, the present invention maps the student behavior sequence to the structural nodes and calculates the task execution offset value based on the sequence difference and tolerance coefficient. It can accurately identify non-standard behaviors such as disordered operation sequence and process jump, effectively overcome the problem that traditional systems lack a judgment mechanism for behavior offset, and enable the task advancement process to have dynamic recognition and hierarchical control capabilities.

[0024] (3) This invention introduces a structure matching rate and a rhythm penalty function to generate a comprehensive path matching value, and sets a threshold based on this value to trigger rhythm prompts, jump restrictions and path recommendations. This enables the identification of behavioral deviations from two dimensions: path structure and operation rhythm, and solves the problem that existing teaching systems cannot accurately intervene in execution rhythm and operation habits.

[0025] (4) Based on the comprehensive skill performance value, the present invention divides the performance level and dynamically adjusts the task structure and resource push strategy. It can realize the opening of challenge tasks, the strengthening of process restrictions and the push of path optimization suggestions, making up for the shortcomings of the single feedback mechanism and the fixed task advancement method in the existing software, so that students can obtain task configuration and guidance resources that match their performance at different levels.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a structural diagram of the task data decomposition and implementation management platform for action-oriented courses according to the present invention;

[0028] Figure 2 This is a comparison diagram of the student behavior path and the standard path of the present invention;

[0029] Figure 3 This is a comparison chart of student and standard rhythm trends in this invention;

[0030] Figure 4 This is a trend chart of the overall skill performance value of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figures 1-4 This invention provides a technical solution: a task data decomposition and implementation management platform for action-oriented courses, comprising the following steps: a behavior data collection module, used to collect basic behavior data generated during task execution, and construct a traceable behavior sequence through time-series processing; a task structure configuration module, used to map a standard path map based on the behavior sequence, compare the student path order and evaluate the degree of operation deviation, synchronously mark the progress status, and trigger path control and guidance rules; a process feature analysis module, used to perform matching analysis on the student path in terms of structure and rhythm, extract path fit features, and push rhythm prompts, jump interventions, and path recommendations; a behavior pattern comparison module, used to compare and classify student behavior patterns based on the standard path map and group benchmarks, identify structural and rhythm differences, extract key segments, and label behavior; an intelligent feedback adjustment module, used to match intervention strategies based on behavior labels, and push recommended paths, guidance prompts, task replacement, and correction information; and a multi-dimensional result output module, used to integrate the degree of operation deviation and path fit features, comprehensively evaluate standardization, matching, and efficiency performance, and drive differentiated responses in task structure, resource push, and feedback mechanisms.

[0033] Specifically, basic behavioral data generated during task execution is collected and processed temporally to construct a traceable behavioral sequence. The specific steps are as follows: By collecting the interface operation sequence, jump relationships, and interaction time information generated during task execution in real time, the order of operations, dwell time, and jump paths between task segments of key nodes are extracted to obtain basic behavioral data that can be used for temporal analysis and process evaluation. The basic behavioral data includes: total number of task steps, actual student operation sequence, standard operation sequence, number of path matching edges, number of student path edges, number of standard path edges, student click timestamp sequence, standard time interval sequence, task loading timestamp, task submission timestamp, and historical baseline duration, which are used to support the quantitative characterization of the completeness, standardization, and efficiency of the execution trajectory. After data acquisition, preprocessing operations are performed sequentially: First, data cleaning is carried out to remove invalid events and duplicate records, and to standardize the format and encoding rules; then, structural data is standardized to regulate node numbering, path format, and connection relationships; numerical data is normalized to unify the scope of units and improve the effectiveness of comparisons between multiple variables; finally, task nodes are aligned in conjunction with the task structure diagram to anchor each behavior record to the corresponding task segment, completing path mapping and structure organization, forming a temporal behavior sequence that can be used for subsequent path comparison and pattern recognition.

[0034] In this implementation plan, by collecting the operation sequence, jump relationships, and time information during task execution, and completing preprocessing and path mapping, a structurally sound and temporally complete behavioral data sequence is constructed. After cleaning, standardization, and normalization, the data possesses good comparability and traceability, providing a foundation for subsequent path comparison, rhythm analysis, and behavior recognition.

[0035] Specifically, based on the behavior sequence mapping standard path graph, the student path order is compared and the degree of operation deviation is evaluated. The specific steps are as follows: Teachers add operation step nodes sequentially in the task modeling interface and manually connect their execution logic paths to generate a directed graph structure, forming a standard path graph for comparison. A one-to-one correspondence is established between the operation records in the student behavior sequence and the task nodes in the standard path graph, forming the basis of structural mapping. Combining the basic behavior data, the total number of task steps, the actual operation order of the student in step i, and the standard operation order in step i are extracted. Based on the difference between the two in the node positions in the path graph, the number of skips, backscrambles, and out-of-order phenomena are counted, the order deviation tolerance is calculated, and the tolerance value of step i for each step is obtained. Using the total number of task steps as the number of iterations, the standard deviation value of each step is calculated sequentially: the absolute value of the difference between the actual order and the standard order is taken as the numerator, and the corresponding step tolerance plus 1 is taken as the denominator to obtain the standardized deviation value. The deviation values ​​of all steps are summed and divided by the total number of steps to obtain the average standardized order deviation degree, which is used as the task execution deviation value to measure the standardization of the operation path and the stability of the order.

[0036] The specific calculation method for the task execution offset is as follows:

[0037]

[0038] In the formula, This represents the task execution offset value. This indicates the total number of steps in the task. This indicates the order in which students actually perform operations in step i. This indicates the standard operation sequence for step i. This represents the sequential offset tolerance at step i.

[0039] like Figure 2 The diagram shows a comparison between the student behavior path and the standard path provided in this embodiment. The student behavior path and the standard path differ significantly in execution order. The standard path is: "Start Task → Read Data → Prepare Tools → Perform Operation A → Perform Operation B → Check Results → Submit Task". However, in actual operation, students execute "Prepare Tools" before "Read Data", resulting in skipping steps and causing two sequential deviations. The diagram uses solid green lines to indicate the standard path set by the teacher and red dashed lines to indicate the student path. The offset nodes "Read Data" and "Prepare Tools" are highlighted with red circles to indicate structural inconsistencies between the student's behavior and the standard process, facilitating subsequent identification of the source of behavioral deviation and triggering path control strategies.

[0040] In this implementation plan, by constructing a standard path diagram and mapping it to student behavior sequences, it is possible to progressively compare the operation sequence and calculate the offset. The standardized offset values ​​reflect the overall difference between each step of the student's task execution and the standard operation sequence, thereby quantifying the degree of path deviation. This process provides a crucial basis for identifying abnormal execution sequences and controlling task progress, and is a core step in judging behavioral norms and process consistency.

[0041] Specifically, the progress status is simultaneously marked, triggering path control and guidance rules. The specific steps are as follows: The current task execution offset value is compared in real time with a set offset threshold. The threshold is divided into two levels: a primary offset threshold and a secondary offset threshold, used to distinguish different degrees of path deviation. When the task execution offset value is greater than or equal to the primary threshold, the system switches to sequential constraint mode. Students must strictly follow the standard path to complete the task step by step, prohibiting skipping steps and backtracking. Clicking on a step other than the current one will immediately trigger a structural warning and generate an exception record for process marking and subsequent tracking. When the task execution offset value is between the primary and secondary thresholds, a standard path comparison diagram is inserted, misaligned nodes are highlighted, and a critical step locking mechanism is activated to restrict skipping important nodes. Simultaneously, accompanying guidance prompts assist students in correcting the process. If the task execution offset value is less than or equal to the secondary threshold, the behavior is deemed compliant, and subsequent tasks can continue. The current behavior is marked as a standard sample, and its operation trajectory is archived as the data basis for subsequent path optimization.

[0042] In this implementation plan, the task progress status is dynamically marked according to different ranges of task execution offset values, and corresponding path control and guidance measures are taken. By setting graded thresholds, the degree of behavioral compliance can be effectively distinguished, and skipping operations, locking key nodes, and pushing correction prompts can be restricted accordingly, achieving fine-grained management of the task progress rhythm and operation sequence. For cases with small offsets, the degree of freedom of progress is preserved and the behavioral trajectory is archived, providing a foundation for subsequent structure optimization and sample accumulation.

[0043] Specifically, a matching analysis of student paths in terms of structure and rhythm was conducted to extract path fit features. The specific steps are as follows: Extract the number of matching edges, student click timestamp sequence, standard time interval sequence, and the number of edges in the student path and the standard path from the basic behavioral data. Based on the operation time record, the click interval of each pair of matching edges was compared with the standard rhythm, the absolute value of the time difference was calculated, and their arithmetic mean was taken to obtain the average rhythm difference, which reflects the degree of rhythm deviation of students during operation. Using the number of matching edges as the numerator and the smaller of the number of student path edges and the number of standard path edges as the denominator, the structure matching rate was calculated to characterize the consistency of the path jump structure. A rhythm penalty coefficient was obtained by fitting the difference between operation rhythm and score between high and low level samples, with a value between 0.1 and 0.5. The structure matching rate and the average rhythm difference were combined and weighted by a negative exponential function with the rhythm difference as the power variable and the rhythm penalty coefficient as the control factor to obtain a behavioral path matching value that comprehensively considers the structure and rhythm dimensions, used to characterize the overall fit between the student's operation path and the reference path.

[0044] The specific calculation method for behavior path matching value is as follows:

[0045]

[0046] In the formula, Indicates the behavior path matching value. Indicates the number of edges in the path matching. This represents the number of edges on the student's path. Indicates the number of edges in a standard path. t represents the average rhythm difference. This represents the rhythm penalty coefficient.

[0047] like Figure 3 The figure shows a comparison of student and standard rhythm trends provided in this application embodiment. The figure illustrates the time intervals between student and standard operating rhythms for each task step. The horizontal axis represents the task operation steps, and the vertical axis represents the operation interval time. The blue broken line represents the actual operation interval between adjacent steps, and the orange dashed line represents the standard rhythm set by the teacher. At the "Read Data" node, the student's operation interval is 3 seconds, 2 seconds earlier than the standard rhythm; "Prepare Tools" and "Execute Operation A" are both 2 seconds slower than the standard; "Execute Operation B" is delayed by 3 seconds; the operation interval for the "Check Results" step is significantly shortened, 4 seconds earlier than the standard rhythm; and the "Submit Task" node is completed 2 seconds earlier. This figure reflects that the student's rhythm is unstable during execution, especially in the "Check Results" step where there is an excessively rapid jump, indicating an imbalance in the operation rhythm. This provides a quantitative basis for judging rhythm intervention and guidance prompts.

[0048] In this implementation scheme, a matching value between the fusion path structure and the operational rhythm is generated by calculating the structure matching rate and the rhythm penalty function. This value is used to measure the degree of conformity between the student's execution path and the reference standard. This matching value can reflect the consistency of the behavioral trajectory in two dimensions: logical jumps and execution rhythm, providing quantitative support for judging the degree of path deviation and identifying rhythm anomalies.

[0049] Specifically, the system pushes rhythm prompts, provides jump interventions, and recommends paths, with the following steps: Based on the matching value of the behavioral path, it compares it in real time with the set matching threshold range. The matching threshold includes a primary matching threshold and a secondary matching threshold, used to grade the degree of path fit. When the matching value is below the primary threshold, it is determined that the path structure and rhythm deviate significantly. Some free jump operations are immediately restricted, the task navigator is activated for forced guidance, and a process simulation task under the structure template is pushed, requiring students to repeat the operation within the standardized path. Simultaneously, the current execution process is marked as a weak graph sample to identify unstable operational behavior. When the matching value is between the two thresholds, a structure echo graph is generated, showing the difference between the student and the standard path. Students are guided to complete operations on missing nodes to correct the path structure. Upon completion, the remaining task modules are unlocked. At the same time, fluctuation areas in the operation rhythm are identified, and rhythm optimization suggestions are pushed, highlighting rhythm anomalies in key sections. When the matching value is above the secondary threshold, it is determined that the path fit is high. Fast path permissions are granted, allowing students to skip demonstration steps and independently advance the task process.

[0050] This implementation plan employs a tiered response strategy based on the range differences in behavioral path matching values. It intervenes in the task execution process through rhythmic prompts, jump restrictions, and path guidance. This effectively identifies structural deviations and rhythmic anomalies, dynamically adjusts operational freedom, guides students to correct critical paths and optimize execution rhythm, and improves the standardization and stability of task progress.

[0051] Specifically, student behavior patterns are compared and categorized based on standard path maps and group benchmarks to identify structural and rhythmic differences, extract key segments, and label them with behavioral tags. The specific steps are as follows: The path structure and operational rhythm features in the student behavior sequence are compared and analyzed with stored standard path maps, high-performing samples, and group reference behaviors. Skill matching is performed based on node order, jump method, and rhythmic pattern to determine the degree of structural fit. The extracted behavioral features are clustered and mapped onto a group behavior distribution map to determine their relative position in the overall behavioral space, and categorized into experienced, novice, and abnormal individuals based on their performance status. Reverse tracing is performed based on the complete behavioral sequence to locate disordered segments, abrupt jumps, and high-frequency stagnation areas in the path, identifying key links causing rhythm interruptions and efficiency declines. Segments with dense jumps and prominent rhythmic changes are further extracted as high-attention operational areas, and structured behavioral tags are generated accordingly to support subsequent guidance and intervention.

[0052] This implementation plan identifies and categorizes student behaviors by comparing path structure and rhythm characteristics, clarifying their position within the group. Combining key segment extraction and behavior tag generation effectively marks abnormal features and stage-specific bottlenecks in the process, providing a basis for subsequent path correction and strategy intervention.

[0053] Specifically, based on the behavioral label matching intervention strategy, recommended paths, guidance prompts, task replacements, and correction information are pushed out. The specific steps are as follows: The behavioral labels generated in the behavioral pattern comparison module are invoked to determine the student's current behavioral path deviation type, rhythm state, and relative position within the group reference. If the label represents a structural deviation or unstable path execution, the corresponding optimal path recommendation, tool combination paradigm, and visual navigation scheme are pushed out to guide the student to complete the task according to the suggested structure. For dynamic features marked as behavioral mutations, path extensions, and rhythm stagnation, phased guidance operations are triggered, using interface prompts and voice explanations to help students understand the current task requirements and correct abnormal operations. If the behavioral bottleneck label shows that the student has made multiple mistakes and abnormal stops in a specific task segment, the task structure of that segment is replaced with a simplified version to reduce the execution difficulty and ensure continuity of progress. Simultaneously, prompts related to the deviation behavior are generated, including text descriptions, task demonstration videos, and error replay materials, to reinforce cognitive correction and task understanding.

[0054] This implementation scheme identifies the offset type and path characteristics represented by behavioral tags, matches corresponding intervention strategies, and achieves precise guidance and personalized correction of the task execution process. It can dynamically push recommended paths, auxiliary prompts, and task alternatives, effectively alleviating execution obstacles and improving the continuity and stability of task progress.

[0055] Specifically, by integrating operational offset and path fit characteristics, a comprehensive evaluation of standardization, matching, and efficiency is conducted. The specific steps are as follows: Obtain the task loading timestamp, task submission timestamp, and historical baseline duration from the basic behavioral data. Extract the task execution offset value and behavioral path matching value calculated by the extraction module. Calculate the total time taken by the student to complete the task based on the timestamp difference between the task loading time and submission time, reflecting execution efficiency. Subtract the task execution offset value from this and add it to the path matching value to obtain a joint score measuring operational standardization and structural fit. By analyzing the location of the main sample concentration interval in the comprehensive scoring function and considering the function's symmetric structure, determine the offset parameter that makes the score fall within the middle interval to obtain a scoring correction constant, with a value between 1.5 and 2.5. Divide the student's completion time by the historical baseline duration to obtain the relative time, add it to the joint score, and subtract the scoring correction constant to form the comprehensive input item. By analyzing the range of input differences in the comprehensive score items between high-level and mid-level samples, and considering the shape requirements of the scoring function, a scoring steepness parameter is calculated where the slope of the function's derivative reaches its maximum rate of change, with a value between 5 and 15. The comprehensive input item is multiplied by the scoring steepness parameter and the result is negative, which is used as the power of the exponential function. This exponential value is calculated using the base of the natural logarithm, and the result is incremented by one to form the denominator. Finally, this value is divided by a constant to obtain the comprehensive skill performance value, which is used to quantify the student's overall performance level during task completion.

[0056] The specific calculation method for the comprehensive skill performance value is as follows:

[0057]

[0058] In the formula, This indicates the overall skill performance value. This represents the task execution offset value. Indicates the behavior path matching value. Indicates the time taken for students to complete the task. Indicates the historical baseline duration. This represents the scoring correction constant. This represents the scoring steepness parameter.

[0059] Table 1 shows the comprehensive skill performance value data table provided in this application embodiment. In this embodiment, the task execution offset value of Task 1 is set to 0.35, the behavior path matching value is set to 0.55, the student completion time is set to 200, and the historical baseline time is set to 120; the task execution offset value of Task 2 is set to 0.22, the behavior path matching value is set to 0.72, the student completion time is set to 160, and the historical baseline time is set to 120; the task execution offset value of Task 3 is set to 0.10, the behavior path matching value is set to 0.86, the student completion time is set to 130, and the historical baseline time is set to 120; the task execution offset value of Task 4 is set to 0.05, the behavior path matching value is set to 0.90, the student completion time is set to 120, and the historical baseline time is set to 120; and the task execution offset value of Task 5 is set to 0.01, the behavior path matching value is set to 0.96, the student completion time is set to 100, and the historical baseline time is set to 120.

[0060] Table 1. Comprehensive Skills Performance Data Table

[0061]

[0062] like Figure 4The figure shows a trend chart of the comprehensive skill performance value provided in the embodiments of this application. According to the data in the image and table, the set first-level performance threshold is 0.85, the second-level performance threshold is 0.70, and the comprehensive skill performance values ​​of the five tasks are distributed between 0.60 and 1.02, showing an overall step-like upward trend. Task 1 has a performance value of 0.60, lower than the second-level threshold, reflecting significant deviations in structural standardization, path matching, and time efficiency. Task 2 has a performance value of 0.75, between the second-level and first-level thresholds, which is considered acceptable. Tasks 3 and 4 have performance values ​​of 0.89 and 0.94 respectively, both exceeding the first-level threshold, demonstrating a high level of structural consistency and operational efficiency. Task 5 has a performance value of 1.02, significantly higher than the threshold, indicating that this task achieves excellent standards in terms of behavioral rhythm, path standardization, and time consumption. This figure can be used to intuitively judge the execution quality of different tasks, serving as a basis for subsequent task structure optimization, feedback strategy adjustment, and differentiated instructional guidance.

[0063] This implementation plan integrates key indicators such as task execution deviation, path matching degree, and completion time to generate a comprehensive skill performance value that reflects standardization, matching, and efficiency. This value can quantify students' overall level in structured execution, pace control, and time utilization, providing a basis for subsequent classification assessment and differentiated advancement.

[0064] Specifically, the differentiated response driven by task structure, resource delivery, and feedback mechanisms follows these steps: Based on the comprehensive skill performance value, a set response threshold group is compared in real-time. This threshold group includes a primary performance threshold and a secondary performance threshold, used to categorize students' current performance status. When the value is greater than or equal to the secondary performance threshold, challenging tasks and multi-path structures are enabled, reducing reliance on demonstrations and allowing students to skip initial task processes. A high-performance behavior summary is generated as an individual performance record. When the value is between the primary and secondary performance thresholds, the student is considered to be in the growth stage. A path optimization suggestion card is pushed, an adjustable task path is activated, allowing students to independently set the order of some steps, and this is marked as a growth sample to track progress. When the value is below the primary threshold, the system switches to a path standardization mode, disables free operation permissions, standardizes process requirements, and pushes a behavior feedback summary and standard operation video. Entry into higher task stages is temporarily restricted, and it is recommended to complete basic tasks before applying for advancement.

[0065] This implementation plan employs differentiated task structure adjustments and resource allocation strategies based on the distribution of comprehensive skill performance scores. By setting tiered response thresholds, performance levels are dynamically categorized, corresponding to open challenge tasks, path optimization suggestions, and standardized guidance. This enables a gradual release of task difficulty and dynamic control of operational freedom, enhancing the adaptability of task progress and the targeted nature of the feedback mechanism.

[0066] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A task data decomposition and implementation management platform for action-oriented courses, characterized in that, include: The behavior data acquisition module is used to collect basic behavior data generated during task execution, and then organize it into a traceable behavior sequence. The task structure configuration module is used to map a standard path map based on behavior sequence, compare the student's path order and evaluate the degree of operation deviation, synchronously mark the progress status, and trigger path control and guidance rules. The specific steps for comparing student path order and evaluating operation offset based on the behavior sequence mapping standard path graph are as follows: Teachers add operation step nodes sequentially in the task modeling interface and manually connect their execution logic paths. After constructing a directed graph structure, a standard path graph is formed. The operation records in the behavior sequence are then matched with the task nodes in the standard path graph to build a structural mapping foundation that can be used for sequential comparison. Obtain the total number of task steps, the actual operation order of the student in step i, and the standard operation order in step i from the basic behavioral data; The order offset tolerance is calculated by comparing the number of differences in node positions between the student's actual operation sequence and the standard operation sequence, and the order offset tolerance for step i is obtained. Using the total number of task steps as the total number of iterations, for each step, calculate the difference between the student's actual operation order in step i and the standard operation order in step i. Take the absolute value as the numerator of the deviation degree, and use the value of the order deviation tolerance of step i plus 1 as the denominator to form the standardized deviation value of each step. Then, sum the deviation values ​​of all steps and divide the sum by the total number of task steps to obtain the standardized order deviation degree after averaging all steps, which is the task execution deviation value. The process feature analysis module is used to match and analyze student paths in terms of structure and rhythm, extract path fit features, and push rhythm prompts, jump interventions, and path recommendations. The behavior pattern comparison module is used to compare and classify student behavior patterns based on standard path maps and group benchmarks, identify structural and rhythmic differences, extract key segments, and label behavior. The intelligent feedback adjustment module is used to push recommended paths, guidance prompts, task replacement and correction information based on behavioral tag matching intervention strategies. The multi-dimensional results output module is used to integrate the degree of operation offset and path fitting characteristics to comprehensively evaluate the standardization, matching and efficiency performance, and drive the differentiated response of task structure, resource push and feedback mechanism.

2. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The basic behavioral data generated during the collection task is processed in a time series to construct a traceable behavioral sequence. The specific steps are as follows: By collecting the interface operation sequence, jump relationship and interaction time information formed during the task execution process in real time, basic behavioral data that can be used for time series analysis and behavior evaluation is obtained. The basic behavioral data includes: total number of task steps, actual operation sequence of students, standard operation sequence, number of path matching edges, number of student path edges, number of standard path edges, student click timestamp sequence, standard time interval sequence, task loading timestamp, task submission timestamp and historical baseline duration. When preprocessing the collected basic behavioral data, data cleaning and format unification are completed; structural data in the basic behavioral data are standardized to unify field and path formats; numerical data in the basic behavioral data are normalized to eliminate dimensional differences; and task node alignment and path mapping are completed.

3. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for triggering path control and guidance rules in the synchronous annotation and advancement status are as follows: The system compares the task execution offset value with the offset threshold in real time. The offset threshold includes a primary offset threshold and a secondary offset threshold. When the task execution offset value is greater than or equal to the primary offset threshold, the system forces a switch to sequential constraint mode, allowing students to proceed only step by step along the standard path. Skipping steps and backtracking are restricted. Clicking on an incorrect step will trigger a structural warning and record an anomaly. When the task execution offset value is greater than the secondary offset threshold but less than the primary offset threshold, a standard path comparison diagram is inserted to highlight misaligned steps. A critical step locking mechanism is activated, and sequential clicking is forced. A guiding prompt is also provided to explain the operation. When the task execution offset value is less than or equal to the secondary offset threshold, subsequent tasks are allowed, the task is marked as a standard behavior sample, and the behavior trajectory is archived.

4. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for matching and analyzing student paths in terms of structure and rhythm to extract path fit features are as follows: Extract the path matching edge count, student click timestamp sequence, standard time interval sequence, student path edge count, and standard path edge count from the basic behavioral data. By comparing the student click timestamp sequence and the standard time interval sequence, take the absolute value of the time difference for each pair of matching edges and calculate the arithmetic mean to obtain the average rhythm difference. Use the path matching edge count as the numerator and the smaller value between the student path edge count and the standard path edge count as the denominator. Divide the numerator by the denominator to obtain the structure matching rate. Multiply the structure matching rate by a negative exponential function with the average rhythm difference as the power variable and the rhythm penalty coefficient as the control parameter to obtain the behavioral path matching value.

5. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for push notification rhythm prompts, redirect intervention, and path recommendation are as follows: The system compares the behavior path matching value with the matching threshold in real time. The matching threshold includes a first-level matching threshold and a second-level matching threshold. When the behavior path matching value is less than the first-level matching threshold, some free jump operations are restricted, the task navigator is forced to be used, a process exercise task is pushed to require students to repeat the practice under the structure template, and the current operation is marked as a weak graph sample. When the behavior path matching value is greater than or equal to the first-level matching threshold and less than the second-level matching threshold, a structure echo graph is generated to compare the student with the standard path, guide the repeated execution of missing nodes, unlock the remaining modules after completion, and initiate rhythm optimization suggestions to prompt abnormal operation rhythm sections. When the behavior path matching value is greater than or equal to the second-level matching threshold, fast path permissions are enabled, allowing free exploration and skipping of demonstration steps.

6. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for comparing and classifying student behaviors based on standard path maps and group benchmarks, identifying structural and rhythmic differences, extracting key segments, and labeling behaviors are as follows: The path structure and operation rhythm features in the student behavior sequence are compared and analyzed with the standard path map, high-performance samples and group behavior reference system stored in the platform. Based on the node order, jump method and operation rhythm, the skill template is matched to determine the degree of structural fit. The behavior features are clustered and mapped to determine their relative position in the group distribution reference system, and are classified into subgroups of experienced, novice and abnormal individuals. By calling the behavior sequence for reverse analysis, the source of disordered segments, abnormal jumps and stalled areas in the path is traced and located, and key nodes that cause efficiency loss and result deviation are identified. Extract the jump-intensive segments and rhythmic abrupt change points in the path execution process, mark them as key behavior segments, and generate behavior labels.

7. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps of the behavior tag matching-based intervention strategy, which pushes recommended paths, guidance prompts, task replacement and correction information, are as follows: The system uses behavior tags generated in the behavior pattern comparison module to determine the type of deviation, rhythm state, and group position of the student's current behavior path. When the tag indicates structural deviation and path instability, it pushes the optimal path recommendation, tool combination paradigm, and visual navigation scheme to guide the student to complete the task according to the recommended structure. When dynamic feature tags such as behavioral abrupt changes, lengthy paths, and rhythm stagnation are detected, it triggers phased operation guidance and voice prompts to help the student understand and correct the current steps. When the behavior bottleneck tag indicates that the student has repeatedly made mistakes or had abnormal dwell time in a certain task segment, it replaces the task structure with a simplified version and simultaneously generates prompt information associated with the current deviation behavior.

8. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for comprehensively evaluating the standardization, matching, and efficiency performance of the fusion operation, based on the degree of offset and path fitting characteristics, are as follows: Obtain the task loading timestamp, task submission timestamp, and historical baseline duration from the basic behavioral data; extract the task execution offset value and behavioral path matching value calculated by the module; obtain the student's completion time by calculating the difference between the task loading timestamp and the task submission timestamp; Subtract the task execution offset value from 1, add it to the behavior path matching value to obtain the joint score; divide the student's completion time by the historical baseline time, add it to the joint score, and subtract the scoring correction constant to obtain the complete integrated input item; multiply this integrated input item by the scoring steepness parameter and add a negative sign before the product to obtain the power of the exponential function; calculate the exponential function value with the base of the natural logarithm as the base; add one to the exponential function value to obtain the whole denominator, and finally divide the constant 1 by the denominator to obtain the integrated skill performance value.

9. The task data decomposition and implementation management platform for action-oriented courses according to claim 1, characterized in that: The specific steps for the differentiated response of the driving task structure, resource push, and feedback mechanism are as follows: The system compares the overall skill performance value with a set of overall performance response thresholds in real time. These thresholds include a primary performance threshold and a secondary performance threshold. When the overall skill performance value is greater than or equal to the secondary performance threshold, challenging tasks and multi-path task structures are enabled, reducing reliance on demonstrations, allowing skipping of lower-level training stages, and generating high-performance behavior summaries. When the overall skill performance value is greater than or equal to the primary performance threshold but less than the secondary performance threshold, optional path optimization suggestion cards are pushed, an adjustable task path is activated, allowing for the autonomous setting of the execution order of some steps, and the current student is marked as a growth-oriented sample. When the overall skill performance value is less than the primary performance threshold, a forced switch to path standardization mode is implemented, restricting non-standard operation paths, pushing behavior feedback summaries and standard operation video demonstrations, preventing access to advanced tasks, and suggesting that basic tasks be completed before applying for the advancement process.

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