Visual analysis method, device and electronic equipment for learning design, and storage medium

Abstract

The application discloses a kind of visual analysis methods, devices, electronic equipment and storage medium of learning design, wherein the visual analysis method of learning design includes: obtaining original learning design data;Obtain the preset learning design metadata standard, according to the learning design metadata standard, the original learning design data is parsed, and standardized learning design data is obtained;The standardized learning design data is analyzed, and the analysis result of the learning design data is obtained;The analysis result of the learning design data is displayed.By introducing learning design metadata standard, the problem that the depth of learning design visualization analysis result is insufficient, the breadth is deficient and the like is solved.

Description

Technical Field

[0001] This invention relates to the field of educational technology, specifically to a visualization analysis method, device, electronic device, and storage medium for learning design. Background Technology

[0002] The continuous accumulation of educational big data and the gradual maturation of big data mining technology are driving a shift in educational decision-making from relying on teachers' individual intuition and experience to being supported by real data evidence. Data visualization analysis methods and tools have a significant advantage in extracting clear and intuitive information from complex data, thereby enhancing individual cognitive abilities. They offer direct data support for learning design decisions, learning process improvement, and learning outcome enhancement, gaining widespread recognition from researchers and practitioners in the field of education, and related research and practices are receiving extensive attention and advancement.

[0003] Learning design is the result of designing a learning experience, recorded in a symbolic form. It includes the learning experiences and support conditions designed by teachers to help students achieve specific learning objectives. It typically includes elements such as learning goals and learning activities, and serves as the starting point of the entire teaching and learning process, significantly determining teaching efficiency and quality. How to quantitatively analyze and visually present teachers' learning design decisions to promote their comprehensive understanding and in-depth comprehension, thereby enabling reflection and optimization to improve teaching effectiveness, has become a crucial breakthrough for researchers and practitioners in improving teaching quality. However, due to the rich content and complex data contained in learning designs, frontline teachers face the challenge of easily accessing intuitive information. Systematically mining and analyzing learning designs and visually presenting design decisions has become an urgent challenge for researchers and practitioners.

[0004] While data visualization and analysis methods and tools are widely used in business, engineering, and other fields, their application in education is limited to learning analytics due to factors such as poor data standardization, high data contextualization, and low data information density. Their exploration and application in learning design are still in their early stages. A comprehensive survey of the current state of research and practice in learning design and related fields reveals that most existing learning design tools primarily explore aspects such as standardizing learning design frameworks and elements, providing scaffolding to guide the learning design process, and sharing and implementing learning design results. Some tools explore descriptive statistical analysis and simple visualization methods and functions for certain elements of learning design. Few tools can provide comprehensive and in-depth visualization analysis and presentation of learning designs, and thus cannot effectively support teachers in gaining a comprehensive understanding, reflecting deeply, and making evidence-based improvements to their learning designs. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus, electronic device, and storage medium for visual analysis of learning designs, so as to effectively support teachers in gaining a comprehensive understanding, deep reflection, and evidence-based optimization and improvement of their learning designs.

[0006] According to a first aspect, embodiments of the present invention provide a visualization analysis method for learning design, comprising the following steps: acquiring original learning design data; acquiring a preset learning design metadata standard, parsing the original learning design data according to the learning design metadata standard to obtain standardized learning design data; analyzing the standardized learning design data to obtain analysis results of the learning design data; and displaying the analysis results of the learning design data.

[0007] Specifically, obtaining the original learning design data includes: obtaining a learning design created according to preset requirements, and extracting the original learning design data from the created learning design; or, obtaining a learning design document input according to preset template requirements, and extracting the original learning design data from the learning design document.

[0008] Specifically, the learning design metadata standard defines learning design elements, element attributes of the learning design elements, and threshold spaces for the element attributes.

[0009] Specifically, the step of analyzing the standardized learning design data to obtain the analysis results includes: acquiring a preset analysis indicator system and preset analysis indicator calculation rules; determining the analysis indicators to be analyzed based on the analysis indicator system; for any analysis indicator, finding the corresponding calculation rule in the analysis indicator calculation rules, analyzing the standardized learning design data based on the calculation rule of the analysis indicator, and obtaining the analysis result of the analysis indicator; and traversing all analysis indicators in the analysis indicator system to obtain the analysis results of the learning design data.

[0010] Specifically, the analytical indicator system includes a micro-element layer, a meso-element relationship layer, and a macro-overall layer; wherein the micro-element layer uses the element attribute characteristics of each learning design element as the analytical indicator, the meso-element relationship layer uses the relationship characteristics between two learning design elements as the analytical indicator, and the macro-overall layer uses the composite relationship characteristics between multiple learning design elements as the analytical indicator.

[0011] Specifically, displaying the analysis results of the learning design data includes: obtaining preset visualization rules, wherein the visualization rules include the presentation format of the analysis results of each analysis indicator in the analysis indicator system; processing the analysis results of the learning design data using the visualization rules to obtain the visualization results of the learning design data, and displaying the visualization results.

[0012] Specifically, before displaying the visualization results, the process includes: obtaining preset visualization integration rules; and integrating the visualization results based on the visualization integration rules.

[0013] According to a second aspect, embodiments of the present invention also provide a learning design analysis device, including a first acquisition module, a second acquisition module, a preprocessing module, an analysis module, and a display module. The first acquisition module is used to acquire raw learning design data; the second acquisition module is used to acquire a preset learning design metadata standard; the preprocessing module is used to parse the raw learning design data according to the learning design metadata standard to obtain standardized learning design data; the analysis module is used to analyze the standardized learning design data to obtain the analysis result of the learning design data; and the display module is used to display the analysis result of the learning design data.

[0014] According to a third aspect, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the visualization analysis method of learning design as described in the first aspect or any one of the first aspects.

[0015] According to a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing the computer to perform the visualization analysis method of the learning design described in the first aspect or any one of the first aspects.

[0016] The visualization analysis method, apparatus, electronic device, and storage medium for learning design according to embodiments of the present invention have the following beneficial effects:

[0017] (1) By introducing the learning design metadata standard, the obstacles that affect the depth and breadth of the visualization analysis results of learning design are eliminated from the perspective of data source standardization. Furthermore, the embodiments of the present invention comprehensively survey and sort out the learning design elements and their attributes involved in various learning design theories and existing learning design tools, and extensively survey the learning design elements and attributes identified by front-line teachers as key. Then, the identified learning design elements and their attributes are systematically sorted out and integrated to generate a more extensive and richer set of learning design elements and attribute sets, thereby generating a learning design metadata standard that can cover and meet the metadata required by teachers for the visualization analysis of learning design.

[0018] (2) Currently, existing data visualization analysis dimensions and indicators for learning design are mainly determined by the recorded data; that is, what data defines what indicators, rather than following teaching theories or addressing teachers' actual needs. Furthermore, the analysis indicators are scattered and incomplete. This data-driven visualization analysis logic leads to analysis results that are detached from the educational context and fail to provide guidance and inspiration. At the same time, the visualization analysis dimensions and indicators are relatively simple and scattered. Although they focus on learning activities, a core element in learning design, they neglect to analyze and mine the relationships between elements at the meso-level and the overall learning design at the macro-level, failing to fully reflect the valuable information hidden in the learning design data. In contrast, the embodiments of this invention, through surveying teachers' actual needs and expectations, construct a hierarchical analysis indicator system and analysis indicator calculation rules, enabling the analysis indicators to take into account all analytical granularities from the local to the overall.

[0019] (3) Currently, the visualization analysis of existing learning design data mainly adopts simple descriptive statistical analysis methods, focusing on specific attributes of a single element in the learning design. Cross-correlation analysis is rarely used to conduct in-depth analysis of multiple attributes of a single element, and intelligent mining technology is even less frequently applied to explore the relationships between multiple types of elements. This is not conducive to the comprehensive revelation and profound insight into the implicit information and patterns in the learning design data. In contrast, the embodiments of this invention not only clarify the calculation rules and methods for each analytical indicator, but also construct a hierarchical indicator calculation rule system based on the level to which the analytical indicators belong. This improves the standardization and efficiency of the analytical indicator calculation, and promotes efficient and high-quality visualization of the indicator analysis results.

[0020] (4) Current technical solutions' visualization analysis results for learning designs only remain at a superficial level of describing the current situation. They not only fail to comprehensively, systematically, clearly, and accurately express the information implicit in the learning design data, but also struggle to play an intervention role in evaluating the quality of learning designs and optimizing learning design decisions. In contrast, the visualization presentation rules of this invention define the types of analysis indicators and the visualization methods for different types of analysis indicators. At the same time, it defines the rules for integrating and outputting visualization analysis results, and clarifies the method for integrating multiple visualization results, thereby comprehensively and systematically expressing and describing the information implicit in the learning design. Attached Figure Description

[0021] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0022] Figure 1 This is a flowchart illustrating the learning design visualization analysis method in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram illustrating an example of visual analysis of learning objectives at the level of displaying micro-elements of a region for visualization analysis;

[0024] Figure 3 A schematic diagram illustrating an example of visual analysis of learning strategies at the level of micro-elements in a region.

[0025] Figure 4 A schematic diagram illustrating the first example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis.

[0026] Figure 5 A schematic diagram illustrating the second example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis.

[0027] Figure 6 A schematic diagram of the third example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis;

[0028] Figure 7 A schematic diagram of the fourth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis;

[0029] Figure 8 A schematic diagram of the fifth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis;

[0030] Figure 9 A schematic diagram of the sixth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis;

[0031] Figure 10A schematic diagram illustrating an example of visual analysis tools and resources for displaying micro-level elements in a region;

[0032] Figure 11 A schematic diagram illustrating an example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning activities;

[0033] Figure 12 This is a schematic diagram illustrating another example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning activities.

[0034] Figure 13 This is a schematic diagram illustrating another example of visual analysis of the relationship between learning objectives and learning activities at the mid-level of regional elements.

[0035] Figure 14 A schematic diagram illustrating an example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning evaluation.

[0036] Figure 15 This is a schematic diagram of the structure of the learning design visualization analysis device in an embodiment of the present invention;

[0037] Figure 16 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0039] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0040] Research has found that the reasons why comprehensive and in-depth visualization analysis of learning design cannot be carried out at present are: (1) Insufficient data breadth, which cannot fully represent and describe all elements and attributes of a learning design, and there is also the problem of coarse data granularity, which leads to insufficient and incomplete visualization analysis of learning design from the root; (2) Lack of data analysis depth, which is specifically reflected in the incomplete data analysis indicator system, which does not meet the practical needs of teachers, and ignores the analysis and mining at different levels such as between different elements and the entire learning design; (3) The data analysis method is superficial, mainly using simple descriptive statistical analysis methods, which is not conducive to the comprehensive revelation and profound insight of the implicit information and patterns in the learning design data.

[0041] Based on this, embodiments of the present invention provide a visualization analysis method for learning design, which is applied to a learning design visualization analysis tool and is applicable to technical fields such as teacher lesson preparation, teaching research, big data analysis and application. Figure 1 This is a flowchart illustrating the learning design visualization analysis method in an embodiment of the present invention, such as... Figure 1 As shown, the learning design visualization analysis method of this invention includes the following steps:

[0042] S101: Obtain the raw learning design data.

[0043] Specifically, the following two methods can be used to obtain the original learning design data.

[0044] The first method is to obtain the learning design created according to preset requirements, and then extract the original learning design data from the created learning design.

[0045] The second method is to obtain a learning design document that is input according to a preset template requirement, and then extract the original learning design data from the learning design document.

[0046] In other words, the learning design visualization and analysis tool acquires learning design data in two ways: first, users can import their learning designs into the tool by filling out a Word document according to the template requirements; second, users can create learning designs online within the tool, which automatically records and stores these designs. The learning design data generated through both methods is presented on the page as a list of learning designs, including the design name, and is stored in the backend learning design storage module for visualization analysis. Users can select any learning design from the list for visualization analysis.

[0047] S102: Obtain the preset learning design metadata standard, and parse the original learning design data according to the learning design metadata standard to obtain standardized learning design data.

[0048] In this embodiment of the invention, the learning design metadata standard defines learning design elements, element attributes of the learning design elements, and threshold spaces for the element attributes. The learning design elements include: learning objectives, learning strategies, learning steps, learning activities, learning tools and resources, and learning evaluation.

[0049] Learning design metadata includes basic learning design information and learning design content information. Basic learning design information identifies and displays the fundamental characteristics of the learning design, while content information characterizes the specific content of the learning design, reflecting its essence and features, and is used for analysis and visualization.

[0050] The learning design metadata standard consists of multiple related data tables with the learning design ID as the primary key. Specifically, it includes a learning design basic information table and a learning design content information table. The latter can be further divided into six data tables: learning objectives table, learning strategy table, learning process table, learning activity table, learning tools and resources table, and learning evaluation table. Each data table defines relevant data fields, field thresholds, field values, and other content.

[0051] 1) Learn to design basic information tables

[0052] This data table defines fields such as learning design ID, learning design name, subject, grade level, and teacher. The threshold range for subject includes common subject types such as Chinese, mathematics, and English; the threshold range for grade level includes kindergarten, primary school, junior high school, and senior high school.

[0053] 2) Learning Design Content Information Table

[0054] A. Learning Objectives Table

[0055] This table defines the fields related to the learning objectives in the learning design, specifically including five attribute fields: objective number, objective type, objective content, objective difficulty coefficient, and objective importance coefficient. The objective type attribute involves four thresholds: cognitive, ability, metacognitive, and meaning / value. The threshold ranges for the objective difficulty coefficient and objective importance coefficient are both [1, 10].

[0056] B. Learning Strategy Table

[0057] This table defines the fields related to learning strategy attributes in learning design, specifically including three attribute fields: strategy type, preset learning step sequence, and alignment index with target type. The learning strategy attribute includes strategy type, preset learning step sequence, and alignment index with target type. The strategy type attribute involves seven thresholds: lecture-based teaching, project-based learning, collaborative learning, case-based learning, deep learning, inquiry-based learning, and contextualized teaching. The threshold space for the strategy type attribute is lecture-based teaching, project-based learning, collaborative learning, case-based learning, deep learning, inquiry-based learning, and contextualized teaching. The preset learning step sequence attribute includes three sub-attributes: preset step name, preset step order, and associated strategy. The name sub-attribute involves multiple thresholds such as context introduction, problem identification, and outcome presentation. The associated strategy sub-attribute involves seven thresholds for teaching strategy types to represent the correspondence between steps and strategies. The alignment index with target type has a threshold range of [0%, 100%].

[0058] C. Learning Module Table

[0059] This table defines fields related to learning stage attributes in the learning design, specifically including three attribute fields: stage order, stage name, and stage execution time. The execution time involves three thresholds: before class, during class, and after class.

[0060] D. Learning Activity Schedule

[0061] This table defines fields related to learning activity attributes in the learning design, specifically including 10 attribute fields: activity sequence, activity name, activity type, activity implementation method, activity duration, activity importance coefficient, activity difficulty coefficient, activity content, associated learning links, and corresponding learning objectives. Among them, the activity type involves 13 thresholds, including icebreaker, reading, video viewing, teacher explanation, case study, discussion, collaboration, practice, presentation, voting, test, exercise, and summary. The activity implementation method involves two thresholds, online and offline. The activity duration threshold ranges from [1, ∞]. The threshold ranges for both the activity difficulty coefficient and the activity importance coefficient are [1, 10]. The threshold for associated learning links is the name of the learning link, and the threshold for corresponding learning objectives is the content of the learning objective.

[0062] E. Learning Tools and Resources List

[0063] This table defines fields related to learning tools and resources in learning design, specifically including four attribute fields: tool / resource order, tool / resource type, tool / resource name, and corresponding learning activity. The tool / resource type involves 16 thresholds, including learning design tools, video playback tools, text reading tools, smart attendance tools, classroom instant feedback tools, communication and collaboration tools, resource publishing and sharing tools, graphical thinking tools, collaborative writing tools, practical operation tools, peer assessment tools, teacher assessment tools, smart reminder tools, evaluation visualization tools, opinion collection tools, and traditional teaching aids. The corresponding learning activity threshold is the name of the learning activity.

[0064] F. Learning Evaluation Form

[0065] This table defines the fields related to learning evaluation attributes in the learning design, specifically including four attribute fields: evaluation type, evaluation name, evaluation content, and corresponding learning objective. The evaluation type involves three thresholds: student self-evaluation, teacher evaluation, and student peer evaluation. The corresponding learning objective threshold is the learning objective content.

[0066] Therefore, step S102 enables the identification and transformation of user-imported Word documents or learning design data created within the tool according to the learning design metadata standard, thereby forming standardized and normalized metadata for further analysis, calculation, and visualization. In other words, this embodiment of the invention, by introducing the learning design metadata standard, eliminates the obstacle of insufficient depth and breadth in the visualization analysis results of learning designs caused by data source issues, in terms of the standardization and comprehensiveness of the data source.

[0067] Furthermore, this invention comprehensively surveys and organizes the learning design elements and their attributes involved in multiple learning design theories and existing learning design tools, and extensively surveys front-line teachers on the key learning design elements and attributes. Then, the collected learning design elements and their attributes are systematically organized and integrated to generate a more extensive and richer set of learning design elements and attribute sets, thereby generating a learning design metadata standard that can cover and meet the metadata required for teachers' learning design visualization and analysis needs.

[0068] S103: Analyze the standardized learning design data to obtain the analysis results of the learning design data.

[0069] In this embodiment of the invention, the analysis of the standardized learning design data to obtain the analysis results can be achieved using the following method: First, a preset analysis indicator system and preset analysis indicator calculation rules are obtained. Second, the analysis indicators to be analyzed are determined based on the analysis indicator system. Third, for any analysis indicator, a calculation rule corresponding to that analysis indicator is found in the analysis indicator calculation rules. The standardized learning design data is then analyzed based on the calculation rule of that analysis indicator to obtain the analysis result of that analysis indicator. Finally, all analysis indicators in the analysis indicator system are traversed to obtain the analysis results of the learning design data.

[0070] Specifically, the analysis indicator system includes a micro-element layer, a meso-element relationship layer, and a macro-overall layer. The micro-element layer uses the element attribute characteristics of each learning design element as the analysis indicator, the meso-element relationship layer uses the relationship characteristics between two learning design elements as the analysis indicator, and the macro-overall layer uses the composite relationship characteristics between multiple learning design elements as the analysis indicator.

[0071] For example, the micro-level features layer includes the following analytical indicators:

[0072] (1) Learning objectives related indicators: number of objectives, types of objectives, average difficulty coefficient of objectives, average importance coefficient of objectives, proportion of each type of objective, change curve of objective importance coefficient, and importance curve of objective difficulty coefficient.

[0073] (2) Learning strategy related indicators: strategy type.

[0074] (3) Learning process related indicators: number of processes; proportion of processes in each period.

[0075] (4) Learning activity related indicators: number of activities, types of activities, average duration of activities, average difficulty coefficient of activities, average importance coefficient of activities, proportion of various activities, proportion of activities in each type of activity, change curve of activity importance coefficient, and change curve of activity difficulty coefficient.

[0076] (5) Learning tools and resources related indicators: number of tools and resources, number of types of tools and resources.

[0077] (6) Learning evaluation related indicators: number of evaluations and types of evaluations.

[0078] The meso-level element relationship layer includes the following analytical indicators:

[0079] (1) Relationship index between learning objectives and learning strategies: matching index.

[0080] (2) Relationship indicators between learning strategies and learning stages: similarity index and completeness index.

[0081] (3) Relationship indicators between learning segments and learning activities: segment duration, number of activities included in the segment, types of activities included in the segment, segment difficulty coefficient, segment importance coefficient, proportion of activity duration in segments for each time period, number of activities included in segments for each time period, types of activities included in segments for each time period, difficulty coefficient of segments for each time period, and importance coefficient of segments for each time period.

[0082] (4) Relationship indicators between learning objectives and learning activities: number of activities corresponding to objectives, average duration of activities corresponding to objectives, average importance coefficient of activities corresponding to objectives, average difficulty coefficient of activities corresponding to objectives, number of activities corresponding to each type of objective, average duration of activities corresponding to each type of objective, average importance coefficient of activities corresponding to each type of objective, average difficulty coefficient of activities corresponding to each type of objective, and correlation coefficient between objective difficulty coefficient and activity duration.

[0083] (5) Relationship indicators between learning objectives and learning evaluation: the number of evaluations for each objective, the types of evaluations for each objective, the types of evaluations for each type of objective, and the number of evaluations for each type of objective.

[0084] The macro-level focuses on the complex relationships between multiple learning design elements, aiming to provide a comprehensive and visual analysis of learning design from a holistic perspective. Specifically, it includes the cascading relationships between learning objectives, learning strategies, learning processes, learning activities, learning tools and resources, and learning assessment.

[0085] Specifically, the rules for calculating analytical indicators define the indicator name, the method of indicator calculation, and the metadata required for indicator calculation, clarifying the basis and method for designing visual analytical indicators for computational learning.

[0086] For example, corresponding to the analysis indicator system above, the calculation rules of each analysis indicator are presented according to the analysis indicator hierarchy.

[0087] The calculation rules for each analytical indicator in the micro-element layer are as follows:

[0088] (1) Learning Objective Related Indicators: Following the order of the analytical indicators in the above analytical indicator system, the calculation rules for each indicator are as follows: non-repeatingly count the learning objective sequence number; non-repeatingly count the learning objective type; calculate the average difficulty coefficient of all learning objectives; calculate the average importance coefficient of all learning objectives; calculate the ratio of the number of each type of learning objective to the total number of objectives; read the importance coefficient values ​​sequentially according to the learning objective order; and read the difficulty coefficient values ​​sequentially according to the learning objective order. (Example) Figure 2 This is a schematic diagram illustrating an example of visual analysis of micro-level elements in a learning objective.

[0089] (2) Learning Strategy Related Indicators. Following the order of the analytical indicators in the above analytical indicator system, the calculation rule for each indicator is as follows: Read the learning strategy name. For example, Figure 3 This is a schematic diagram illustrating an example of visual analysis of learning strategies at the level of micro-elements in a region.

[0090] (3) Learning process related indicators. According to the order of the analytical indicators in the above analytical indicator system, the calculation rules for each analytical indicator are as follows: count the learning process serial number without repetition, summarize the number of learning processes in each period and calculate the ratio of the number of learning processes in each period to the total number of all processes.

[0091] (4) Learning Activity Related Indicators. Following the order of the analytical indicators in the above analytical indicator system, the calculation rules for each indicator are as follows: non-repeating count of learning activity sequence number, non-repeating count of activity type, calculation of the average duration of all learning activities, calculation of the average difficulty coefficient of all learning activities, calculation of the average importance coefficient of all learning activities, calculation of the ratio of the number of each type of learning activity to the total number of activities, calculation of the ratio of the number of learning activities of each implementation method to the total number of activities, reading the importance coefficient values ​​sequentially according to the learning activity order, and reading the difficulty coefficient values ​​sequentially according to the learning activity order. For example, Figure 4 A schematic diagram illustrating the first example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis. Figure 5 A schematic diagram illustrating the second example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis. Figure 6 A schematic diagram of the third example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis; Figure 7 A schematic diagram of the fourth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis; Figure 8 A schematic diagram of the fifth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis; Figure 9 This is a schematic diagram of the sixth example of visual analysis of learning activities, showing the micro-level elements of a region for visualization analysis.

[0092] (5) Indicators related to learning tools and resources. Following the order of the analytical indicators in the above analytical indicator system, the calculation rules for each indicator are as follows: unique learning tool and resource serial number, unique learning tool and resource type. For example, Figure 10 A schematic diagram illustrating an example of visual analysis tools and resources for displaying micro-level elements in a region.

[0093] (6) Calculation of relevant indicators for learning evaluation. According to the order of the analytical indicators in the above analytical indicator system, the calculation rules for each analytical indicator are as follows: non-repeating counted learning evaluation sequence number, non-repeating counted learning evaluation type.

[0094] The calculation rules for each analytical indicator in the meso-level element relationship layer are as follows:

[0095] (1) Indicators related to the relationship between learning objectives and learning strategies. According to the order of the analytical indicators in the above analytical indicator system, the calculation rule for each analytical indicator is: directly read the matching index value.

[0096] (2) Relationship indicators between learning strategies and learning stages. According to the order of the analytical indicators in the above analytical indicator system, the calculation rules for each analytical indicator are as follows: summarize the number of stages with the same order and name as the actual stages and calculate the ratio of the actual stage to the total number of the preset stages; summarize the number of stages with the same order as the preset stages and calculate the ratio of the actual stage to the total number of the preset stages.

[0097] (3) Indicators related to the relationship between learning stages and learning activities. Following the order of the analytical indicators in the above analytical indicator system, the calculation rules for each indicator are as follows: Summarize the duration of learning activities included in each stage; summarize the number of learning activities included in each stage; summarize the types of learning activities included in each stage; calculate the average difficulty coefficient of learning activities included in each stage; calculate the average importance coefficient of learning activities included in each stage; calculate the ratio of the duration of learning stages in each type of implementation period to the total duration; summarize the number of learning activities included in learning stages in each type of implementation period; summarize the types of learning activities included in learning stages in each type of implementation period; calculate the average difficulty coefficient of learning activities included in learning stages in each type of implementation period; calculate the average importance coefficient of learning activities included in stages in each type of implementation period.

[0098] (4) Indicators relating learning objectives to learning activities. Following the order of the analytical indicators in the above analysis indicator system, the calculation rules for each indicator are as follows: calculate the number of activities associated with each objective; calculate the average duration of activities associated with each objective; calculate the average importance coefficient of activities associated with each objective; calculate the average difficulty coefficient of activities associated with each objective; calculate the types of activities associated with each type of objective; calculate the average duration of activities associated with each type of objective; calculate the average importance coefficient of activities associated with each type of objective; calculate the average difficulty coefficient of activities associated with each objective; and calculate the correlation between the objective difficulty coefficient and the activity duration. For example, Figure 11 A schematic diagram illustrating an example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning activities; Figure 12This is a schematic diagram illustrating another example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning activities. Figure 13 This is a schematic diagram illustrating another example of visual analysis of the relationship between learning objectives and learning activities at the mid-level of regional elements.

[0099] (5) Indicators related to the relationship between learning objectives and learning evaluation. Following the order of the analytical indicators in the above analytical indicator system, the analytical indicators for the relationship between learning objectives and learning evaluation include: summarizing the number of evaluations associated with each objective, summarizing the types of evaluations associated with each objective, summarizing the number of evaluations associated with each type of objective, and summarizing the types of evaluations associated with each type of objective. For example, Figure 14 This is a schematic diagram illustrating an example of visual analysis of the relationship between mid-level elements in a region—the relationship between learning objectives and learning evaluation.

[0100] It should be noted that current data visualization analysis dimensions and indicators in learning design are primarily determined by the recorded data—that is, indicators are defined based on available data—rather than following teaching theories or addressing teachers' actual needs. Furthermore, the analysis indicators are scattered and incomplete. This data-driven visualization analysis logic leads to results detached from the educational context and fails to provide guidance or inspiration. Simultaneously, the visualization analysis dimensions and indicators are relatively simple and scattered. While they focus on learning activities—a core element of learning design—they neglect analyzing and mining the relationships between elements at the meso-level and the overall macro-level of the learning design, failing to fully reveal the valuable information hidden within the learning design data. In contrast, this invention, through surveying teachers' actual needs and expectations, constructs a hierarchical analysis indicator system and calculation rules, enabling the analysis indicators to encompass all analytical granularities from the local to the overall.

[0101] Meanwhile, current visualization analysis of learning design data mainly employs simple descriptive statistical analysis methods, focusing on specific attributes of single elements in the learning design. Cross-correlation analysis is rarely used to conduct in-depth analysis of multiple attributes of a single element, and intelligent data mining techniques are even less frequently applied to explore the relationships between multiple types of elements. This hinders the comprehensive revelation and profound insight into the implicit information and patterns within the learning design data. In contrast, this invention not only clarifies the calculation rules and methods for each analytical indicator but also constructs a hierarchical indicator calculation rule system based on the level to which the analytical indicators belong. This improves the standardization and efficiency of analytical indicator calculations, promoting efficient and high-quality visualization of the indicator analysis results.

[0102] S104: Display the analysis results of the learning design data.

[0103] In this embodiment of the invention, the method for displaying the analysis results of the learning design data can be as follows: obtaining a preset visualization rule, wherein the visualization rule includes the presentation format of the analysis results of each type of analysis indicator in the analysis indicator system; processing the analysis results of the learning design data using the visualization rule to obtain the visualization results of the learning design data, and displaying the visualization results.

[0104] Specifically, the visualization rules define the types of analytical indicators and the visualization methods for different types of analytical indicators. For example, analytical indicators can be divided into several types, and the visualization rules for each type are as follows:

[0105] (1) Visualization rules for directly reading content-based indicators / indicators with simple calculations and single results

[0106] These metrics include the number of learning objectives, the type of learning strategies, the number of learning activities, and the type of learning evaluation. Presenting the original content or calculation results in a visually intuitive way is the most concise and clear approach. Therefore, text and numerical values ​​are used to directly present the content or results.

[0107] (2) Visualization rules for classification and comparison indicators

[0108] These indicators aim to compare and describe the relative relationships between different categories. Using bar charts or column charts is relatively simple and clear. Therefore, this method is used to present the calculation results of indicators such as the number of activities corresponding to each type of learning objective and the number of activity types corresponding to each learning stage.

[0109] (3) Visualization rules for proportional structural indicators

[0110] These indicators aim to express the proportional relationship between multiple categories within a whole. The indicators include the proportional structure of the number of various learning objectives and the proportional structure of the duration of various learning activities. Presenting the proportional structure in the form of a pie chart is the most ideal approach, so pie charts are used to visualize these indicators.

[0111] (4) Visualization rules for trend indicators

[0112] These types of indicators aim to describe and present how a certain element changes over time / sequence. Line charts have the ability and advantage to express this information. Therefore, line charts are used to visualize and present trend-type indicators such as the changing trend of the importance coefficient of learning objectives and the changing trend of the difficulty coefficient of learning activities.

[0113] (5) Visualization rules for exponential indicators

[0114] These indicators aim to characterize the relative relationship between two values, and their value range is generally [0%, 100%]. Since the pie chart can clearly express the relative relationship between the current state and the ideal state, the pie chart is chosen to visualize the three indicators: matching index, similarity index, and completeness index.

[0115] (6) Visualization rules for correlation indexes

[0116] This type of indicator aims to describe the correlation between two variables. Scatter plots have the ability to express this type of relationship. Therefore, scatter plots are used to visualize the correlation between the difficulty coefficient of learning objectives and the duration of learning activities.

[0117] (7) Visualization rules for corresponding relationship indicators

[0118] These types of indicators are often used to express the correspondence between two variables, both of which can contain multiple types. Because Sankey diagrams can accurately represent many-to-many relationships between multiple types of values ​​of two variables, they are used to visualize type-correspondence indicators, such as the correspondence between learning objective types and learning evaluation types.

[0119] (8) Visualization rules for composite indicators

[0120] These types of indicators typically integrate and present a combination of various data and their analytical results. Learning design requires the presentation of the overall learning design, including hierarchical indicators representing the relationships between learning objectives, learning strategies, learning stages, and learning activities, as well as time-series indicators representing the relationships between multiple learning stages / activities. Tree diagrams effectively present the hierarchical relationships between multiple levels of indicators, while timelines use time as a key element to reflect the evolution or state of variables. Therefore, integrating tree diagrams and timelines to visualize composite indicators of learning design is a good approach.

[0121] Furthermore, before displaying the visualization results, the process includes: obtaining preset visualization integration rules; and integrating the visualization results based on the visualization integration rules.

[0122] For example, the integration rules for the visualization analysis results of the analysis indicators in the micro-element layer are as follows: Read the visualization sub-result table of micro-element and present the visualization analysis results of each type of element in the order of learning objectives, learning strategies, learning links, learning activities, learning tools and resources, and learning evaluation; in the visualization results of the same element, they are presented in the order of visualization from simple to complex, that is, text first and then charts.

[0123] The integration rules for the visualization analysis results of the analysis indicators in the meso-level element relationship layer are as follows: Read the visualization sub-result table of meso-level element relationships, and present the visualization analysis results of each type of element relationship in the order of element relationship between learning objectives and learning strategies, learning strategies and learning links, learning links and learning activities, learning objectives and learning activities, and learning objectives and learning evaluation; in the same group of element relationship visualization results, present them in the order of visualization from simple to complex.

[0124] The integration rules for the visualization analysis results of the macro-level overall analysis indicators are as follows: Read the visualization analysis result table of the macro-level overall design, and present the multiple relationships between the learning design elements in a vertical hierarchical division and a horizontal time coherence manner, according to the element order from learning objectives to learning evaluation, so as to present the overall analysis results of the learning design.

[0125] It should be noted that the visualization analysis results of current technical solutions for learning designs only remain at a superficial level of describing the current situation. They not only fail to comprehensively, systematically, clearly, and accurately express the information implicit in the learning design data, but also struggle to play a significant role in evaluating and optimizing learning design decisions. In contrast, the embodiments of this invention define learning design metadata standards, clarify learning design data analysis methods, and design rules for visualizing learning design data. They cover the complete process of visualization analysis, from source data collection and analysis indicator calculation to the visualization output of calculation results, enabling a comprehensive and systematic expression and description of the information implicit in the learning design.

[0126] Furthermore, after processing the analysis results of the learning design data using the visualization rules to obtain the visualization results of the learning design data, the method further includes: saving the visualization results.

[0127] Specifically, a data structure system can be constructed with the learning design ID as the primary key, linking a micro-level element visualization result table, a meso-level element relationship visualization analysis result table, and a macro-level overall visualization analysis result table. The micro-level element visualization analysis result table stores fields such as the learning design ID, learning design element analysis indicators, and the calculation results of these indicators. The meso-level element relationship visualization analysis result table stores fields such as the learning design ID, learning design element relationship analysis indicators, and the calculation results of these indicators. The macro-level overall design visualization analysis result table stores fields such as the learning design ID, overall learning design analysis indicators, and the overall learning design element analysis indicator results.

[0128] This allows visualization results to be stored instantly for easy retrieval.

[0129] To implement the aforementioned learning design visualization analysis method, the learning design visualization analysis tool includes front-end and back-end functional modules.

[0130] The front-end functional module is designed to support teachers in selecting any learning design or creating a new learning design to present the results of the visualization analysis.

[0131] The backend functional modules include a data reading and parsing module, a data storage module, a data analysis and visualization module, and a visualization analysis result storage and output module. These four modules have functions such as learning and designing data reading and parsing, data storage, data calculation and analysis, analysis result visualization generation, visualization result storage and output, which can support the implementation of the frontend functional modules.

[0132] Specifically, the data reading and parsing module has a built-in learning design metadata standard, which is used to parse the original learning design data according to the learning design metadata standard to obtain standardized learning design data.

[0133] The data storage module is used to store standardized learning design data.

[0134] The data analysis and visualization modules perform real-time calculations, dynamic visualizations, and output visualization results through the data analysis engine and data visualization engine. The data analysis engine includes an analysis indicator system and calculation rules for these indicators, enabling it to calculate the learning design data and output the results. The data visualization engine includes data visualization rules, allowing it to visualize the calculation results of the learning design data analysis indicators.

[0135] In the visualization analysis results storage and output module, the visualization results will be stored according to the indicator visualization results storage rules, and integrated and output according to the indicator visualization integration rules.

[0136] Corresponding to the above-described visualization analysis method for learning design, this embodiment of the invention also provides a visualization analysis device for learning design. Figure 15 This is a schematic diagram of the structure of the learning design visualization analysis device in an embodiment of the present invention, such as... Figure 15 As shown, the learning design visualization analysis device of this embodiment includes a first acquisition module 20, a second acquisition module 21, a preprocessing module 22, an analysis module 23, and a display module 24.

[0137] Specifically, the first acquisition module 20 is used to acquire the original learning design data;

[0138] The second acquisition module 21 is used to acquire preset learning design metadata standards;

[0139] The preprocessing module 22 is used to parse the original learning design data according to the learning design metadata standard to obtain standardized learning design data;

[0140] Analysis module 23 is used to analyze the standardized learning design data and obtain the analysis results of the learning design data;

[0141] Display module 24 is used to display the analysis results of the learning design data.

[0142] The first acquisition module 20 is specifically used to: acquire a learning design created according to preset requirements, and extract the original learning design data from the created learning design; or, acquire a learning design document input according to preset template requirements, and extract the original learning design data from the learning design document.

[0143] The analysis module 23 is specifically used for: acquiring a preset analysis indicator system and preset analysis indicator calculation rules respectively; determining the analysis indicators to be analyzed according to the analysis indicator system; for any analysis indicator, finding the calculation rule corresponding to the analysis indicator in the analysis indicator calculation rules, analyzing the standardized learning design data based on the calculation rule of the analysis indicator, and obtaining the analysis result of the analysis indicator; and traversing all analysis indicators in the analysis indicator system to obtain the analysis result of the learning design data.

[0144] The display module 24 is specifically used for: acquiring preset visualization rules, wherein the visualization rules include the presentation format of the analysis results of each type of analysis indicator in the analysis indicator system; processing the analysis results of the learning design data using the visualization rules to obtain the visualization results of the learning design data, and displaying the visualization results.

[0145] Before displaying the visualization results, the display module 24 is further configured to: obtain preset visualization integration rules; and integrate the visualization results based on the visualization integration rules.

[0146] For specific details regarding the aforementioned learning design visualization analysis device, please refer to the relevant documentation. Figures 1 to 14 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0147] This invention also provides an electronic device, such as... Figure 16 As shown, the electronic device may include a processor 31 and a memory 32, wherein the processor 31 and the memory 32 may be connected by a bus or other means.

[0148] Processor 31 can be a central processing unit (CPU). Processor 31 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0149] Memory 32, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the visual analysis method of learning design in this embodiment of the invention (e.g., Figure 2 The first acquisition module 20, the second acquisition module 21, the preprocessing module 22, the analysis module 23, and the display module 24 are shown. The processor 31 executes various functional applications and data processing by running non-transitory software programs, instructions, and modules stored in the memory 32, thereby realizing the visualization analysis method of the learning design in the above method embodiments.

[0150] The memory 32 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 31, etc. Furthermore, the memory 32 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 32 may optionally include memory remotely located relative to the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The one or more modules are stored in the memory 32, and when executed by the processor 31, they perform actions such as... Figures 1 to 14 The illustrated embodiment presents a visualization analysis method for learning design.

[0152] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figures 1 to 15 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0153] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0154] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A visualization analysis method for learning design, characterized in that, include: Obtain the original learning design data; Obtain the preset learning design metadata standard, and parse the original learning design data according to the learning design metadata standard to obtain standardized learning design data; The standardized learning design data is analyzed to obtain the analysis results of the learning design data. Display the analysis results of the learning design data; The learning design metadata standard defines learning design elements, element attributes of the learning design elements, and threshold space for the element attributes. The analysis of the standardized learning design data to obtain the analysis results of the learning design data includes: Obtain the preset analysis indicator system and preset analysis indicator calculation rules respectively; The analytical indicators that need to be analyzed are determined based on the aforementioned analytical indicator system; For any analytical indicator, the calculation rule corresponding to the analytical indicator is found in the calculation rules of the analytical indicator. The normalized learning design data is analyzed based on the calculation rule of the analytical indicator to obtain the analytical result of the analytical indicator. By traversing all the analytical indicators in the analytical indicator system, the analytical results of the learning design data are obtained; The analytical indicator system includes a micro-element layer, a meso-element relationship layer, and a macro-overall layer; wherein the micro-element layer uses the element attribute characteristics of each learning design element as the analytical indicator, the meso-element relationship layer uses the relationship characteristics between two learning design elements as the analytical indicator, and the macro-overall layer uses the composite relationship characteristics between multiple learning design elements as the analytical indicator.

2. The method according to claim 1, characterized in that, The acquisition of the original learning design data includes: Obtain the learning design created according to preset requirements, and extract the original learning design data from the created learning design; Alternatively, obtain a learning design document that is input according to a preset template requirement, and extract the original learning design data from the learning design document.

3. The method according to claim 1, characterized in that, The analysis results displayed for the learning design data include: Obtain preset visualization rules, wherein the visualization rules include the presentation format of the analysis results of each analysis indicator in the analysis indicator system; The analysis results of the learning design data are processed using the visualization rules to obtain the visualization results of the learning design data, and the visualization results are displayed.

4. The method according to claim 3, characterized in that, Before displaying the visualization results, the following are included: Obtain preset visualization integration rules; The visualization results are integrated based on the visualization integration rules.

5. A visualization analysis device for learning design, characterized in that, include: The first acquisition module is used to acquire the original learning design data; The second acquisition module is used to acquire the preset learning design metadata standard; The preprocessing module is used to parse the original learning design data according to the learning design metadata standard to obtain standardized learning design data. The analysis module is used to analyze the standardized learning design data and obtain the analysis results of the learning design data; The display module is used to display the analysis results of the learning design data; The learning design metadata standard defines learning design elements, element attributes of the learning design elements, and threshold space for the element attributes. The analysis module is specifically used for: Obtain the preset analysis indicator system and preset analysis indicator calculation rules respectively; The analytical indicators that need to be analyzed are determined based on the aforementioned analytical indicator system; For any analytical indicator, the calculation rule corresponding to the analytical indicator is found in the calculation rules of the analytical indicator. The normalized learning design data is analyzed based on the calculation rule of the analytical indicator to obtain the analytical result of the analytical indicator. By traversing all the analytical indicators in the analytical indicator system, the analytical results of the learning design data are obtained; The analytical indicator system includes a micro-element layer, a meso-element relationship layer, and a macro-overall layer; wherein the micro-element layer uses the element attribute characteristics of each learning design element as the analytical indicator, the meso-element relationship layer uses the relationship characteristics between two learning design elements as the analytical indicator, and the macro-overall layer uses the composite relationship characteristics between multiple learning design elements as the analytical indicator.

6. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the visualization analysis method of learning design according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the visualization analysis method of the learning design according to any one of claims 1 to 4.

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