Teaching optimization method and device based on teaching courseware and cognitive data

Abstract

The invention relates to the technical field of intelligent education, in particular to a teaching optimization method and device based on teaching courseware and cognitive data, and the method comprises the steps: compiling the teaching courseware including various forms of contents based on a teaching target and the demands of a learner; carrying out modularization processing on the written teaching courseware to obtain a plurality of teaching modules, and deploying a monitoring point in each teaching module; acquiring cognitive data of the learner at each monitoring point in the running process of the teaching courseware in real time, and comparing the acquired cognitive data with preset cognitive data to determine the cognitive state of the learner; and adjusting the form content of the teaching courseware according to the cognitive state, and re-determining the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware is matched with the cognitive state of the learner. Therefore, the cognitive data of the learner is collected and analyzed in real time, the teaching courseware is dynamically adjusted according to the analysis result to adapt to the cognitive state of the learner, and the learning effect is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent education technology, and in particular, to a teaching optimization method and device based on teaching courseware and cognitive data. Background Art

[0002] As the core carrier of education and teaching, the quality of the design of teaching courseware directly affects the teaching effect and the learning experience of students. Moreover, the design of teaching courseware is not only the transmission of knowledge, but more importantly, the guidance and promotion of the cognitive process of students. As the main body of learning, the cognitive process of students has individual differences and dynamic changes, and it is necessary to meet the learning needs of different students through in-depth understanding and data support. However, the existing teaching courseware can only rely on the subjective experience of teachers to update and optimize, lacking in-depth understanding of the cognitive process of students and data support, and cannot fit the cognitive levels and learning needs of different learners. Summary of the Invention

[0003] To overcome the deficiencies in the prior art, this application provides a teaching optimization method and device based on teaching courseware and cognitive data, which can monitor and analyze the cognitive process of students in real time, provide personalized guidance and feedback for teaching, and improve the teaching effect.

[0004] In a first aspect, this application provides a teaching optimization method based on teaching courseware and cognitive data, and the method includes the following steps:

[0005] Compile a teaching courseware including various forms of content based on teaching objectives and learner needs;

[0006] Perform modular processing on the compiled teaching courseware to obtain multiple teaching modules, and deploy monitoring points in each of the teaching modules;

[0007] Collect in real time the cognitive data of learners at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with the preset cognitive data to determine the cognitive state of the learners;

[0008] Adjust the form content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learners based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learners.

[0009] In a possible implementation manner, the performing modular processing on the compiled teaching courseware to obtain multiple teaching modules, and deploying monitoring points in each of the teaching modules includes the following steps:

[0010] Perform modular processing on the compiled teaching courseware based on the cognitive structure to obtain multiple teaching modules;

[0011] Divide each of the teaching modules into multiple steps based on the teaching process;

[0012] Divide each of the steps into multiple teaching scaffolds based on the teaching content, and divide each of the steps into multiple cognitive micro-steps including evolutionary level meta-scenarios based on the cognitive law;

[0013] Deploy monitoring points at the positions of each of the cognitive micro-steps and the teaching scaffolds.

[0014] In a possible implementation manner, the real-time acquisition of the cognitive data of the learner at each of the monitoring points during the running of the teaching courseware includes the following steps:

[0015] During the running of the teaching courseware, the cognitive data of the learner is real-time acquired at the positions of each of the pre-deployed monitoring points; the cognitive data includes at least one of reaction time, correct rate, and page viewing duration;

[0016] Standardize the cognitive data in a unified format to obtain a learner cognitive data set.

[0017] In a possible implementation manner, the comparison of the acquired cognitive data with the preset cognitive data to determine the cognitive state of the learner includes the following steps:

[0018] Preprocess the learner cognitive data set; the preprocessing includes at least one of missing value processing, outlier processing, and duplicate value processing;

[0019] Based on the trained cognitive evaluation model, perform feature extraction on each of the preset cognitive data and each of the acquired cognitive data in the learner cognitive data set, and compare the extracted features to obtain the cognitive state of the learner.

[0020] In a possible implementation manner, the cognitive state includes at least one of the degree of attention concentration, the degree of knowledge understanding, and the learning progress.

[0021] In a possible implementation manner, the adjustment of the formal content of the teaching courseware according to the cognitive state includes the following steps:

[0022] Determine the formal content of the current teaching courseware;

[0023] Adjust the formal content of the current teaching courseware according to the pre-developed adjustment plan and the cognitive state of the learner.

[0024] In a possible implementation, the adjustment scheme includes at least one of content difficulty adjustment, presentation mode change, and content order adjustment for different cognitive states.

[0025] In a second aspect, the present application provides a teaching optimization device based on teaching courseware and cognitive data, and the device includes:

[0026] A compilation module, configured to compile a teaching courseware including various forms of content based on teaching objectives and learner needs;

[0027] A division module, configured to perform modular processing on the compiled teaching courseware to obtain a plurality of teaching modules, and deploy monitoring points in each of the teaching modules;

[0028] A determination module, configured to collect in real time the cognitive data of the learner at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with preset cognitive data to determine the cognitive state of the learner;

[0029] An adjustment module, configured to adjust the formal content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner.

[0030] In a third aspect, the present application provides an electronic device, including: a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the steps of the teaching optimization method based on teaching courseware and cognitive data according to any one of the first aspects are executed.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the teaching optimization method based on teaching courseware and cognitive data according to any one of the first aspects are executed.

[0032] A teaching optimization method and device based on teaching courseware and cognitive data provided by this embodiment. The teaching courseware including various forms of content is compiled based on teaching objectives and learners' needs; the compiled teaching courseware is modularized to obtain multiple teaching modules, and monitoring points are deployed in each of the teaching modules; cognitive data of learners at each of the monitoring points during the operation of the teaching courseware is collected in real time, and the collected cognitive data is compared with preset cognitive data to determine the cognitive state of the learners; the form content of the teaching courseware is adjusted according to the cognitive state, and the cognitive state of the learners is re-determined based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learners. Thus, cognitive data of learners is collected and analyzed in real time, and the teaching courseware is dynamically adjusted according to the analysis results to adapt to the cognitive state of the learners, improving the learning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 Shows a flowchart of the teaching optimization method based on teaching courseware and cognitive data according to an embodiment of the present application;

[0035] Figure 2 Shows a flowchart of dividing the compiled teaching courseware into multiple teaching modules and setting monitoring points in each of the teaching modules;

[0036] Figure 3 Shows a flowchart of comparing the collected cognitive data with preset cognitive data to determine the cognitive state of the learners;

[0037] Figure 4 Shows a structural schematic diagram of the teaching optimization device based on teaching courseware and cognitive data according to an embodiment of the present application;

[0038] Figure 5 Shows a structural block diagram of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. It should be understood that the accompanying drawings in this application only serve the purposes of illustration and description, and are not used to limit the protection scope of this application. Additionally, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of this application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without a logical context relationship may be reversed or implemented simultaneously. Furthermore, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of this application.

[0040] In addition, the described embodiments are only some embodiments of this application, rather than all embodiments. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application claimed, but merely represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0041] It should be noted that the term "including" will be used in the embodiments of this application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0042] In view of the technical problems proposed in the background art, this application provides a teaching optimization method and device based on teaching courseware and cognitive data, which can monitor and analyze the cognitive process of students in real time, provide personalized guidance and feedback for teaching, and improve the teaching effect.

[0043] In one embodiment, referring to the appended Figure 1 description, a teaching optimization method based on teaching courseware and cognitive data provided by this application includes the following steps:

[0044] S1. Compile a teaching courseware including various forms of content based on teaching objectives and learner needs;

[0045] S2. Modularize the compiled teaching courseware to obtain multiple teaching modules, and deploy monitoring points in each of the teaching modules;

[0046] S3. Real-time collect the cognitive data of learners at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with the preset cognitive data to determine the cognitive state of the learners;

[0047] S4. Adjust the formal content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner.

[0048] Specifically, in step S1, the teaching objectives can be obtained by studying the curriculum syllabus, and the learner needs can be obtained through questionnaire surveys. Then, based on the teaching objectives and learner needs, screen and organize the teaching content, and select diverse teaching methods according to the teaching content, and select appropriate teaching elements from the rich teaching resource library, including text, pictures, videos, etc. For example, the text is highlighted to explain concepts, principles, and experimental procedures; the pictures are intuitive, such as the object movement trajectory diagram, to help students better understand abstract knowledge; the videos are vivid and interesting, such as experimental demonstration videos, to enhance the fun and attraction of teaching.

[0049] See the attached instructions Figure 2 In step S2, the steps of dividing the compiled teaching courseware into multiple teaching modules and setting monitoring points in each teaching module are as follows:

[0050] S201. Process the compiled teaching courseware modularly based on the cognitive structure to obtain multiple teaching modules;

[0051] S202. Divide each teaching module into multiple steps based on the teaching process;

[0052] S203. Divide each step into multiple teaching scaffolds based on the teaching content, and divide each step into multiple cognitive micro-steps containing evolutionary level meta-scenes based on the cognitive law;

[0053] S204. Deploy monitoring points at the positions of each cognitive micro-step and the teaching scaffold.

[0054] In this application, modularizing, step-by-step processing, and formulating cognitive micro-steps containing evolutionary level meta-scenes for the compiled teaching courseware. This structured design can, on the one hand, make the teaching process more standardized, and on the other hand, can organize the teaching content more quickly, meet the learning needs of different learners, and lay a foundation for quickly adjusting the teaching courseware later.

[0055] In step S201, in order to comprehensively cover various abilities and knowledge categories that learners need to cultivate during the learning process, the prepared teaching courseware is divided into thirteen teaching modules according to the cognitive structure, namely motivation, activity, concept, technical thinking, scientific thinking, cultural thinking, philosophical thinking, application, PBL (Project-Based Learning), STEAM (Science, Technology, Engineering, Arts, Mathematics Interdisciplinary Education), innovation, reflection, and academic summary.

[0056] In step S202, in order to guide learners to study gradually in depth, each of the teaching modules is divided into multiple steps based on the teaching process; for example, according to the sequence and internal logic of teaching activities, the scientific thinking teaching module is divided into seven steps, namely: Step 1, observe scientific facts and phenomena, sort out and establish a conceptual relationship diagram from basic concepts to big concepts; Step 2, clarify the research topic, formulate hypotheses based on real situations and phenomena, implement and adjust strategies, and use critical thinking methods to boldly put forward hypotheses or conjectures about a certain relevance; Step 3, based on the research purpose and research conditions, clarify the research content and framework, determine the research steps, select the correct research ideas, research directions and methods, and obtain knowledge evidence; Step 4, retrieve literature, consult materials, classify and summarize, conduct analysis and judgment, and information screening; conduct analogy and comparison, analyze data, identify authenticity, and obtain knowledge evidence; Step 5, evaluate the rationality of the research process and research results, and based on new evidence, phenomena and principles, revise the original hypothesis and optimize the research plan; Step 6, according to scientific theories, reconstruct the knowledge concept system, establish a principle model, follow industry rules, and transform it into a mathematical model and formula; Step 7, use the principle to solve practical problems, try cross-field applications, and try to put forward innovative hypotheses.

[0057] In step S203, in order to deepen students' understanding, ten teaching scaffolds are constructed for each step around the key parts of the teaching content, namely case library, famous quotes, resource library, video library, VR library, question bank, exercise bank, link, club, and AI assistant; in order to reflect students' mastery of knowledge and skills, seven micro-steps are constructed for each step, namely scenario, task, question, path, method, assessment, and expression, and in order to represent the characteristics of different learning stages, seven evolutionary level meta-scenarios are defined for each micro-step, namely generativity, rooting, growth, derivation, ecology, creation, and vitality.

[0058] In step S204, by executing steps S201 - S203, the teaching process is finely divided, and thus the positions of the monitoring points are clarified, that is, the ten teaching scaffolds under the seven steps in the thirteen teaching modules and the seven micro-steps containing seven evolutionary level meta-scenarios. In one embodiment, cognitive data monitoring units can be deployed at these positions respectively to accurately collect learners' cognitive data.

[0059] In step S3, during the operation of the teaching courseware, the cognitive data monitoring units deployed at each monitoring point are activated. For each learner, the cognitive data monitoring unit collects relevant cognitive data in real time when using the teaching courseware. The cognitive data includes at least one of reaction time, correct rate, page view duration, mouse click frequency, and number of speeches. For example, taking a learner doing math exercises as an example, starting from seeing the question, the cognitive data monitoring unit begins to record the time. When the answer is submitted, the time taken to answer the question, that is, the reaction time, is recorded. At the same time, according to the preset answers and scoring criteria, it is judged whether the answer is correct or not to obtain the correct rate. And the collected cognitive data is standardized in a unified format to obtain the learner cognitive data set, which is stored through software or hardware devices. For example, in the database, it is stored in the corresponding data table according to the learner's identity information (student number, name, etc.), learning time, teaching courseware name, and cognitive data type, which is convenient for subsequent query and in-depth analysis at any time.

[0060] Further, referring to the attached drawings of the specification Figure 3 The comparison of the collected cognitive data with the preset cognitive data to determine the cognitive state of the learner includes the following steps:

[0061] S301. Preprocess the learner cognitive data set; the preprocessing includes at least one of missing value processing, outlier processing, and duplicate value processing;

[0062] S302. Based on the trained cognitive assessment model, feature extraction is performed on each preset cognitive data and each collected cognitive data in the learner cognitive data set, and the extracted features are compared to obtain the cognitive state of the learner.

[0063] In step S301, mainly through preprocessing, it is checked whether there are data missing or abnormal situations in the learner cognitive data set and processed. Among them, for the missing data, such as the answering time, correct rate, etc., statistical quantities such as the mean and median of other non-missing data can be calculated for filling; for the abnormal data, for example, using statistical methods, the interquartile range IQR of the data is calculated, and the data outside the normal range is regarded as an outlier, and then these outliers are corrected, deleted or marked separately according to the actual situation.

[0064] In step S302, the preprocessed learner cognitive data set and the preset cognitive data are input into the trained cognitive assessment model. For each preset cognitive data, relevant features are extracted from the collected learner cognitive data. For example, if we want to analyze the degree of concentration, features such as page viewing duration, mouse click frequency, and speaking times can be extracted; if we want to analyze the degree of knowledge understanding, features such as reaction time and accuracy rate can be extracted. And the extracted features are normalized to make different features in the same scale range. In addition, according to the characteristics of the analysis target, a suitable machine learning algorithm is selected for the constructed cognitive assessment model. For example, if it is to judge the degree of concentration (a classification problem, divided into categories such as concentrated, relatively concentrated, and not concentrated), classification algorithms such as logistic regression, decision tree, and support vector machine can be selected; if it is to predict the degree of understanding (a regression problem, such as represented by a score from 0 to 100), regression algorithms such as linear regression, ridge regression, and neural network can be selected. Thus, the prediction result of the learner's cognitive state is obtained.

[0065] Among them, the construction and training of the cognitive assessment model should be technical means well-known to those skilled in the art and will not be elaborated here.

[0066] In step S4, it is mainly to adjust the form and content of the current teaching courseware according to the pre-developed adjustment plan and the learner's cognitive state. The cognitive state covers multiple dimensions such as the learner's degree of concentration, degree of knowledge understanding, and learning progress; in-depth interpretation is carried out for the results of each dimension to clarify the specific difficulties and advantages of the learner. For example, in terms of the degree of knowledge understanding, determine which knowledge points the learner has obstacles in understanding; for the degree of concentration, analyze in which teaching links or time periods there is a situation of distracted attention.

[0067] The pre-developed adjustment plan includes: for the situation of inattention, design various interactive elements, such as online questions, group discussions, quiz games, etc., to stimulate the learner's enthusiasm for participation; adjust the visual effect of the courseware, including changing to a more eye-catching font, adjusting the color combination to enhance visual attraction, and optimizing the page layout to make the content clearer and easier to read. For the learner's difficulties, evaluate the existing order of the teaching content. If it is found that the connection between some knowledge points is unreasonable, re-plan the teaching order to make it conform to the logical structure of knowledge and the cognitive law of the learner. According to the learner's learning progress, adjust the difficulty of the teaching content. For learners with weak foundations, appropriately reduce the difficulty and increase the explanation and practice of basic knowledge. For learners with stronger learning abilities, provide more challenging expansion content.

[0068] Furthermore, a courseware editing tool is used to modify the teaching courseware according to the formulated adjustment strategy. Further, a teaching optimization method based on teaching courseware and cognitive data provided in this application further includes the following steps: re-determine the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner. Specifically, repeat step S3 and step S4, let the learner study using the adjusted teaching courseware, collect the cognitive data of the learner and conduct a cognitive state assessment. By comparing the assessment results of the cognitive state before and after the adjustment of the teaching courseware, judge whether the adjustment of the teaching courseware is effective. If it is found that the adjustment effect is not ideal, re-examine the adjustment plan and conduct further optimization to achieve the dynamic matching of the teaching courseware and the learner's cognitive data and improve the learning effect.

[0069] It can be seen that a teaching optimization method based on teaching courseware and cognitive data provided in this application makes data processing and analysis more efficient by modularizing, step-by-step processing the teaching courseware, and formulating cognitive micro-steps including evolution-level meta-scenarios, which helps to quickly adjust teaching content and adapt to the learning needs of different learners; by collecting and analyzing the cognitive data of the learner in real time to obtain the cognitive state of the learner, and then dynamically adjusting the courseware content to achieve the dynamic matching of the teaching courseware and the learner's cognitive data and improve the learning effect.

[0070] Based on the same inventive concept, an embodiment of this application also provides a teaching optimization device based on teaching courseware and cognitive data. Since the principle of solving problems by the device in the embodiment of this application is similar to that of the above-mentioned teaching optimization method based on teaching courseware and cognitive data in the embodiment of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0071] As shown in the attached Figure 4 description, a teaching optimization device based on teaching courseware and cognitive data provided in an embodiment of this application, the device includes:

[0072] A compilation module 401, configured to compile a teaching courseware including various forms of content based on teaching objectives and learner needs;

[0073] A division module 402, configured to modularize the compiled teaching courseware to obtain a plurality of teaching modules, and deploy monitoring points in each of the teaching modules;

[0074] A determination module 403, configured to collect in real time the cognitive data of the learner at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with preset cognitive data to determine the cognitive state of the learner;

[0075] An adjustment module 404 is configured to adjust the formal content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner.

[0076] In one embodiment, the apparatus further includes:

[0077] An optimization module is configured to re-determine the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner.

[0078] In one embodiment, the partitioning module 402 partitions the prepared teaching courseware into multiple teaching modules, and deploys monitoring points in each of the teaching modules, including: partitioning the prepared teaching courseware into multiple teaching modules based on the cognitive structure; partitioning each of the teaching modules into multiple steps based on the teaching process; partitioning each of the steps into multiple teaching scaffolds based on the teaching content, and partitioning each of the steps into multiple cognitive micro-steps including evolutionary level meta-scenes based on the cognitive law; deploying monitoring points at the positions of each of the cognitive micro-steps and the teaching scaffolds.

[0079] In one embodiment, the determination module 403 collects the cognitive data of the learner at each of the monitoring points in real time during the operation of the teaching courseware, including: collecting the cognitive data of the learner in real time at the positions of each of the pre-set monitoring points during the operation of the teaching courseware; the cognitive data includes at least one of the reaction time, the correct rate, and the page browsing duration; standardizing the cognitive data in a unified format to obtain a learner cognitive data set.

[0080] In one embodiment, the determination module 403 compares the collected cognitive data with the pre-set cognitive data to determine the cognitive state of the learner, including: preprocessing the learner cognitive data set; the preprocessing includes at least one of missing value processing, outlier processing, and duplicate value processing; extracting features from each of the pre-set cognitive data and each of the collected cognitive data in the learner cognitive data set based on the trained cognitive evaluation model, and comparing the extracted features to obtain the cognitive state of the learner. The cognitive state includes at least one of the attention concentration degree, the knowledge understanding degree, and the learning progress.

[0081] In one embodiment, the determining module 403 adjusts the formal content of the teaching courseware according to the cognitive state, including: determining the formal content of the current teaching courseware; adjusting the formal content of the current teaching courseware according to a pre-developed adjustment plan and the cognitive state of the learner; the adjustment plan includes at least one of content difficulty adjustment, presentation mode change, and content order adjustment for different cognitive states.

[0082] A teaching optimization device based on teaching courseware and cognitive data provided by the present application, in which a writing module writes a teaching courseware including various forms of content based on teaching objectives and learner needs; a partitioning module modularizes the written teaching courseware to obtain a plurality of teaching modules, and deploys monitoring points in each of the teaching modules; a determining module collects in real time the cognitive data of the learner at each of the monitoring points during the operation of the teaching courseware, and compares the collected cognitive data with preset cognitive data to determine the cognitive state of the learner; an adjustment module adjusts the formal content of the teaching courseware according to the cognitive state, and re-determines the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learner. Thereby, the cognitive data of the learner is collected and analyzed in real time, and the teaching courseware is dynamically adjusted according to the analysis result to adapt to the cognitive state of the learner, improving the learning effect.

[0083] Based on the same concept of the present invention, the specification appendix Figure 5 As shown, a structure of an electronic device 500 provided by an embodiment of the present application, the electronic device 500 includes: at least one processor 501, at least one network interface 504 or other user interfaces 503, a memory 505, and at least one communication bus 502. The communication bus 502 is used to realize the connection communication between these components. The electronic device 500 optionally includes a user interface 503, including a display (for example, a touch screen, LCD, CRT, holographic imaging, or projector, etc.), a keyboard, or a pointing device (for example, a mouse, trackball, touchpad, or touch screen, etc.).

[0084] The memory 505 may include a read-only memory and a random access memory, and provide instructions and data to the processor 501. A part of the memory 505 may also include a non-volatile random access memory (NVRAM).

[0085] In some embodiments, the memory 505 stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof:

[0086] The operating system 5051 includes various system programs for implementing various basic services and handling hardware-based tasks;

[0087] The application program module 5052 includes various application programs, such as a launcher, a MediaPlayer, a Browser, etc., for implementing various application services.

[0088] In the embodiment of the present application, by invoking the programs or instructions stored in the memory 505, the processor 501 is used to execute the steps in a teaching optimization method based on teaching courseware and cognitive data, and can perform real-time monitoring and analysis according to the cognitive process of students, provide personalized guidance and feedback for teaching, and improve the teaching effect.

[0089] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps in a teaching optimization method based on teaching courseware and cognitive data.

[0090] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, it can execute the above-mentioned teaching optimization method based on teaching courseware and cognitive data.

[0091] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of devices or units can be in an electrical, mechanical or other form.

[0092] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, the functional units in the embodiments provided by the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0094] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0095] Finally, it should be noted that the above embodiments are only specific implementation manners of this application, used to illustrate the technical solutions of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A teaching optimization method based on teaching courseware and cognitive data, characterized in that, The method includes the following steps: Compile a teaching courseware including various forms of content based on teaching objectives and learners' needs; Perform modular processing on the compiled teaching courseware to obtain multiple teaching modules, and deploy monitoring points in each of the teaching modules; Collect in real time the cognitive data of learners at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with the preset cognitive data to determine the cognitive state of the learners; Adjust the form content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learners based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learners.

2. The teaching optimization method based on teaching courseware and cognitive data according to claim 1, wherein The performing modular processing on the compiled teaching courseware to obtain multiple teaching modules, and deploying monitoring points in each of the teaching modules includes the following steps: Perform modular processing on the compiled teaching courseware based on the cognitive structure to obtain multiple teaching modules; Divide each of the teaching modules into multiple steps based on the teaching process; Divide each of the steps into multiple teaching scaffolds based on the teaching content, and divide each of the steps into multiple cognitive micro-steps including evolutionary level meta-scenes based on the cognitive law; Deploy monitoring points at the positions of each of the cognitive micro-steps and the teaching scaffolds.

3. The teaching optimization method based on teaching courseware and cognitive data according to claim 2, wherein The collecting in real time the cognitive data of learners at each of the monitoring points during the operation of the teaching courseware includes the following steps: During the operation of the teaching courseware, collect in real time the cognitive data of learners at the positions of each of the pre-deployed monitoring points; the cognitive data includes at least one of reaction time, correct rate, and page viewing duration; Standardize the cognitive data in a unified format to obtain a set of learners' cognitive data.

4. The teaching optimization method based on teaching courseware and cognitive data according to claim 1, characterized in that, The comparing the collected cognitive data with the preset cognitive data to determine the cognitive state of the learners includes the following steps: Perform preprocessing on the set of learners' cognitive data; the preprocessing includes at least one of missing value processing, outlier processing, and duplicate value processing; Extract features from each of the preset cognitive data and each of the collected cognitive data in the set of learners' cognitive data based on a trained cognitive evaluation model, and compare the extracted features to obtain the cognitive state of the learners.

5. The teaching optimization method based on teaching courseware and cognitive data according to claim 4, characterized in that, The cognitive state includes at least one of the degree of concentration, the degree of knowledge understanding, and the learning progress.

6. The teaching optimization method based on teaching courseware and cognitive data according to claim 1, characterized in that, The adjusting the form content of the teaching courseware according to the cognitive state includes the following steps: Determine the form content of the current teaching courseware; Adjust the form content of the current teaching courseware according to the pre-developed adjustment plan and the cognitive state of the learners.

7. The teaching optimization method based on teaching courseware and cognitive data according to claim 6, characterized in that, The adjustment plan includes at least one of content difficulty adjustment, presentation method change, and content order adjustment under different cognitive states.

8. A teaching optimization device based on teaching courseware and cognitive data, characterized in that, The device includes: A compilation module for compiling a teaching courseware including various forms of content based on teaching objectives and learners' needs; A partitioning module, configured to perform modular processing on the prepared teaching courseware to obtain multiple teaching modules, and deploy monitoring points in each of the teaching modules; A determining module, configured to collect in real time the cognitive data of learners at each of the monitoring points during the operation of the teaching courseware, and compare the collected cognitive data with preset cognitive data to determine the cognitive state of the learners; An adjustment module, configured to adjust the formal content of the teaching courseware according to the cognitive state, and re-determine the cognitive state of the learners based on the adjusted teaching courseware until the adjusted teaching courseware matches the cognitive state of the learners.

9. An electronic device, characterized in that, Comprising: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the teaching optimization method based on teaching courseware and cognitive data according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the teaching optimization method based on teaching courseware and cognitive data according to any one of claims 1 to 7 are executed.

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