Learning System with Intelligent Teaching Scenario Switching

Abstract

The present invention discloses a learning system capable of intelligently switching teaching scenarios, which includes a test module, a learning module, a re-study module, and a review module; the test module is used to test the user's mastery of knowledge points; the learning module is used for the user to learn knowledge points; the re-study module is used for the user to re-study knowledge points; the review module is used for the user to review knowledge points. The present invention divides the modules corresponding to each learning scenario and separately equips a pool for storing knowledge points. On the one hand, for each operation of the system, only one module and a smaller pool are required, which occupies less computer resources and runs more smoothly. On the other hand, it is more flexible for users to learn. Each learning scenario is independently separated, and only the knowledge points in the current scenario need to be focused on.

Description

Technical Field

[0001] The present invention belongs to the technical field of online education, and particularly relates to a learning system capable of intelligently switching teaching scenarios. Background Art

[0002] The sequence of teaching scenarios of the first-generation intelligent adaptation recommendation algorithm engine is fixed. When a student needs to learn a knowledge point, the knowledge point will directly flow through each teaching scenario, that is, a knowledge point will directly undergo testing - learning - review. This will cause a linear programming for the student's learning, which is not only not conducive to improving learning efficiency, but also makes the entire learning system very rigid. If a problem occurs in one scenario or the entire knowledge point storage pool, it will cause the entire system to crash. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a learning system capable of intelligently switching teaching scenarios in view of the above-mentioned deficiencies in the prior art. The modules corresponding to each learning scenario are separated, and a pool for storing knowledge points is separately equipped. On the one hand, for each operation of the system, only one module and a smaller pool are required, which occupies less computer resources and runs more smoothly. On the other hand, for user learning, it is more flexible. Each learning scenario is independently separated, and only the knowledge points in the current scenario need to be focused on.

[0004] To solve the above technical problem, the technical solution adopted by the present invention is: a learning system capable of intelligently switching teaching scenarios, including a testing module, a learning module, a re-learning module, and a review module;

[0005] The testing module is used for testing the user's mastery of knowledge points;

[0006] The learning module is used for the user to learn knowledge points;

[0007] The re-learning module is used for the user to re-learn knowledge points;

[0008] The review module is used for the user to review knowledge points;

[0009] The testing module is configured with a testing pool, and the testing pool is used for storing the knowledge points in the course that have not been tested;

[0010] The learning module is configured with a learning pool, and the learning pool is used for storing the knowledge points in the course that have not been learned;

[0011] The re-learning module is configured with a re-learning pool, and the re-learning pool is used for storing the knowledge points in the course that have been learned but not mastered by the user;

[0012] The review module is configured with a review pool, and the review pool is used for storing the knowledge points in the course that have been mastered by the user;

[0013] The knowledge points in the test pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for testing. If the test result is mastery, the non-isolated knowledge points are transferred from the test pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are transferred from the test pool to the learning pool.

[0014] The knowledge points in the learning pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for learning. If the learning result is mastery, the non-isolated knowledge points are transferred from the learning pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are transferred from the learning pool to the re-learning pool.

[0015] The knowledge points in the re-learning pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for re-learning. If the learning result is mastery, the non-isolated knowledge points are transferred from the re-learning pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are converted into isolated knowledge points.

[0016] In the above learning system capable of intelligently switching teaching scenarios, the knowledge points in the review pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for review. After the isolated knowledge points are stored in the review pool for Th, the isolated knowledge points are converted into non-isolated knowledge points.

[0017] In the above learning system capable of intelligently switching teaching scenarios, when the test module, learning module, and / or re-learning module operate, they include the following steps:

[0018] Obtain the user's current ability level P2;

[0019] Compare the difficulty level P1 of each isolated knowledge point in the test pool with the user's current ability level P2; if P1 ≤ P2, convert the isolated knowledge point into a non-isolated knowledge point;

[0020] Compare the difficulty level P1 of each non-isolated knowledge point in the test pool with the user's current ability level P2; if P1 > P2, convert the non-isolated knowledge point into an isolated knowledge point.

[0021] In the above learning system capable of intelligently switching teaching scenarios, when the test module, learning module, and / or re-learning module operate, they include the following steps:

[0022] Judge whether the user has completed the corresponding operations for n non-isolated knowledge points;

[0023] Or, judge whether the user has operated for a full M minutes in the current module;

[0024] Or, determine whether the user has completed the corresponding operations for n non-isolated knowledge points and has operated for M minutes in the current module;

[0025] Or, determine whether there are non-isolated knowledge points currently;

[0026] If so, exit the current module, where the current module is a test module, a learning module, or a re-study module.

[0027] The above learning system capable of intelligently switching teaching scenarios, when the learning system is running, includes the following steps:

[0028] Call the test module to run after initialization;

[0029] When exiting the test module, learning module, re-module, or review module, perform the following steps:

[0030] Step a: Determine whether there are non-isolated knowledge points in the re-study pool. If so, enter the re-study module; if not, proceed to the next step;

[0031] Step b: Determine whether there are non-isolated knowledge points in the learning pool. If so, enter the learning module; if not, proceed to the next step;

[0032] Step c: Determine whether there are non-isolated knowledge points in the test pool. If so, enter the test module; if not, proceed to the next step;

[0033] Step d: Determine whether there are non-isolated knowledge points in the review pool. If so, enter the review module.

[0034] The above learning system capable of intelligently switching teaching scenarios, the learning system is configured with an original pool for a single course, and the original pool is used to store the knowledge points of a single course; after the learning system is initialized, the knowledge points in the learning pool are transferred to the test pool.

[0035] Compared with the prior art, the present invention has the following advantages: The present invention divides the modules corresponding to each learning scenario and separately equips a pool for storing knowledge points. On the one hand, for each operation of the system, only one module and a smaller pool are required, occupying less computer resources and running more smoothly. On the other hand, for user learning, it is more flexible. Each learning scenario is independently separated, and only the knowledge points in the current scenario need to be focused on.

[0036] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Brief Description of the Drawings

[0037] Figure 1 For the states of each pool of the present invention Figure 1 .

[0038] Figure 2 For the status of each pool of the present invention Figure 2 。

[0039] Figure 3 For the status of each pool of the present invention Figure 3 。

[0040] Figure 4 For the status of each pool of the present invention Figure 4 。

[0041] Figure 5 For the status of each pool of the present invention Figure 5 。

[0042] Figure 6 For the status of each pool of the present invention Figure 6 。 Detailed implementation manners I、

[0044] A learning system capable of intelligently switching teaching scenarios, comprising a test module, a learning module, a re-study module and a review module;

[0045] The test module is used for a user to test the mastery degree of knowledge points;

[0046] The learning module is used for a user to learn knowledge points;

[0047] The re-study module is used for a user to re-study knowledge points;

[0048] The review module is used for a user to review knowledge points;

[0049] The test module is configured with a test pool, and the test pool is used for storing knowledge points in the course that have not been tested;

[0050] The learning module is configured with a learning pool, and the learning pool is used for storing knowledge points in the course that have not been learned;

[0051] The re-study module is configured with a re-study pool, and the re-study pool is used for storing knowledge points that have been learned in the course but not mastered by the user;

[0052] The review module is configured with a review pool, and the review pool is used for storing knowledge points that have been mastered by the user in the course;

[0053] The knowledge points in the test pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for testing. If the test result is mastery, the non-isolated knowledge points are transferred from the test pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are transferred from the test pool to the learning pool;

[0054] The knowledge points in the learning pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for learning. If the learning result is mastery, the non-isolated knowledge points are transferred from the learning pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are transferred from the learning pool to the re-study pool.

[0055] The knowledge points in the re-study pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for re-study. If the learning result is mastery, the non-isolated knowledge points are transferred from the re-study pool to the review pool; if the test result is non-mastery, the non-isolated knowledge points are converted into isolated knowledge points.

[0056] In this embodiment, the knowledge points in the review pool include isolated knowledge points and non-isolated knowledge points; the non-isolated knowledge points are used for review. After the isolated knowledge points are stored in the review pool for Th, the isolated knowledge points are converted into non-isolated knowledge points.

[0057] In this embodiment, when the test module, learning module, and / or re-study module runs, it includes the following steps:

[0058] Obtain the user's current ability level P2;

[0059] Compare the difficulty level P1 of each isolated knowledge point in the test pool with the user's current ability level P2; if P1 ≤ P2, convert the isolated knowledge point into a non-isolated knowledge point.

[0060] Compare the difficulty level P1 of each non-isolated knowledge point in the test pool with the user's current ability level P2; if P1 > P2, convert the non-isolated knowledge point into an isolated knowledge point.

[0061] In this embodiment, when the test module, learning module, and / or re-study module runs, it includes the following steps:

[0062] Judge whether the user has completed the corresponding operations for n non-isolated knowledge points;

[0063] Or, judge whether the user has operated for M minutes in the current module;

[0064] Or, judge whether the user simultaneously satisfies the conditions of having completed the corresponding operations for n non-isolated knowledge points and having operated for M minutes in the current module;

[0065] Or, judge whether there are non-isolated knowledge points currently;

[0066] If so, exit the current module, and the current module is the test module, learning module, or re-study module.

[0067] In this embodiment, when the learning system runs, it includes the following steps:

[0068] Call the test module to run after initialization;

[0069] When the test module, learning module, re - learning module, or review module exits, perform the following steps:

[0070] Step a: Determine whether there are non - isolated knowledge points in the re - learning pool. If so, enter the re - learning module; if not, proceed to the next step;

[0071] Step b: Determine whether there are non - isolated knowledge points in the learning pool. If so, enter the learning module; if not, proceed to the next step;

[0072] Step c: Determine whether there are non - isolated knowledge points in the test pool. If so, enter the test module; if not, proceed to the next step;

[0073] Step d: Determine whether there are non - isolated knowledge points in the review pool. If so, enter the review module.

[0074] In this embodiment, the learning system is configured with an original pool for a single course, and the original pool is used to store the knowledge points of a single course; after the learning system is initialized, the knowledge points in the learning pool are transferred to the test pool. Two,

[0076] The following is an example to illustrate the operation process of the above learning system that can intelligently switch teaching scenarios:

[0077] As Figure 1 shown, assume that the current lesson is to learn 3 knowledge points, and the 3 knowledge points are respectively {① Linear equation with one unknown [Difficulty 1, Frequency of examination 3]}, {② Quadratic equation with one unknown [Difficulty 2, Frequency of examination 2]}, and {③ Quadratic equation with one unknown [Difficulty 3, Frequency of examination 1]}.

[0078] For this lesson, initially, the 3 knowledge points are placed in the original pool.

[0079] The first step: Move the knowledge points in the lesson to the initial pool according to the initial teaching scenario preset by the teacher (refer to Figure 1 )

[0080] By default, all knowledge points in the lesson will be stored in the original pool. When the student starts learning, unless the teacher makes special settings, the default initial teaching scenario is testing.

[0081] For example, assume a lesson has 3 knowledge points, namely linear equation with one unknown, quadratic equation with one unknown, and binary linear equation. Then these three knowledge points will be moved to the test pool by the intelligent adaptive recommendation algorithm engine.

[0082] The second step: The test teaching scenario triggers the strategic selection logic (refer toFigure 2 )

[0083] Traverse all knowledge points in the test pool, and match the difficulty and frequency attributes of the knowledge points with the difficulty and frequency suitable for the student level given by the teacher. The knowledge points that do not match are strategically abandoned and moved to the test isolation pool [When entering the test next time, the test strategy selection will be triggered again. After the student level changes, some of the knowledge points that were originally strategically abandoned will be released back to the test pool].

[0084] For example, the difficulty range of knowledge points is 1-9, and the higher the difficulty value, the more difficult the knowledge point. The frequency range of knowledge points is 1-9, and the higher the frequency, the more frequently the knowledge point appears in the exam. Assume that the difficulty of the linear equation with one variable is 1 / frequency is 3, the difficulty of the quadratic equation with one variable is 2 / frequency is 2, and the difficulty of the linear equation with two variables is 3 / frequency is 1. In the student level configuration table given by the teacher, students at the beginner level learn knowledge points within the range of difficulty 1 and frequency 3. Students at the intermediate level learn knowledge points within the range of difficulty 1-2 and frequency 2-3. Students at the advanced level learn all knowledge points within the range of difficulty 1-3 and frequency 1-3. By default, all students are at the intermediate level before starting to learn. Then, the linear equation with one variable and the quadratic equation with one variable will be stored in the test pool, and the linear equation with two variables will be stored in the test isolation pool.

[0085] Step 3: Test whether the teaching scenario meets the exit conditions

[0086] Once there are no measurable knowledge points in the test pool, or the number of unmastered knowledge points diagnosed exceeds n. Then the exit conditions of the test teaching scenario are met, or other exit conditions are set.

[0087] Step 4: Trigger the knowledge point transfer logic (refer to Figure 3 )

[0088] The knowledge points mastered during the test are moved to the review isolation pool, and the unmastered knowledge points are moved to the learning pool

[0089] For example, all tests on the linear equation with one variable and the quadratic equation with one variable are completed by the student. The student has mastered the linear equation with one variable but not the quadratic equation with one variable. Then the linear equation with one variable flows into the review isolation pool, and the quadratic equation with one variable flows into the learning pool.

[0090] Step 5: Switch to the next teaching scenario

[0091] Filter out all available teaching scenarios (pools). At this time, only the learning pool and the review pool have knowledge points. According to the definition of the importance of teaching scenarios [relearning > learning > testing > review], the learning pool has a higher priority than the review pool. Therefore, the next teaching scenario is the learning pool.

[0092] Step 6: The learning teaching scenario triggers the strategic selection logic (refer to Figure 4 )

[0093] Traverse all knowledge points. If the prerequisite knowledge points of this knowledge point are not mastered or not tested, then this knowledge point is in a non-learnable state (strategic abandonment), that is, an isolated knowledge point.

[0094] For example: Suppose a linear equation with one variable is a prerequisite for the knowledge point of a quadratic equation with one variable in the logic map. If the linear equation with one variable is not mastered, then the quadratic equation with one variable will be strategically abandoned even if it enters the learning process.

[0095] Step 7: The learning teaching scenario meets the exit conditions

[0096] If there are no learnable knowledge points in the learning pool, then the condition for exiting the learning teaching scenario is met.

[0097] For example, assume that the quadratic equation with one variable just entered for learning is not mastered, then the quadratic equation with one variable flows into the re-learning isolation pool.

[0098] Step 8: Switch to the next teaching scenario

[0099] Screen out all available teaching scenarios (pools). At this time, only the re-learning pool and the review pool have knowledge points. According to the definition of the importance of teaching scenarios [re-learning > learning > testing > review], the re-learning pool has a higher priority than the review pool. Therefore, the next teaching scenario is the re-learning pool.

[0100] Step 9: The re-learning teaching scenario triggers the strategic selection logic (refer to Figure 5 )

[0101] Traverse all knowledge points. If this knowledge point comes from the previous learning teaching scenario, it will be strategically abandoned.

[0102] For example, the knowledge point of the quadratic equation with one variable that was not mastered in the previous round of learning will be strategically abandoned at this time.

[0103] Step 10: The re-learning teaching scenario meets the exit conditions

[0104] When there are no knowledge points to recommend for re-learning, then the re-learning will exit. The mastered knowledge points are moved to the review, and the unmastered knowledge points are moved to the re-learning isolation pool (repeated re-learning).

[0105] Re-learning and mastering will lead to an improvement in the student's level. At this time, the knowledge points in the test isolation pool will be released to the test pool because the student's level has risen from a beginner to an intermediate or a top student. Then the test pool will be determined to be available and participate in the recommendation of teaching scenarios.

[0106] Step 11: Switch to the next teaching scenario

[0107] When there are no knowledge points to recommend in the test pool, learning pool, and re-learning pool, and there are knowledge points to recommend only in the review pool, the fault tolerance logic is triggered. Move all the knowledge points in the re-learning isolation pool to the re-learning pool. At this time, both the re-learning pool and the review pool are available. According to the importance of the teaching scenarios, the next teaching scenario is still re-learning. The re-learning logic is still from the ninth step to the eleventh step, which will not be demonstrated here. Assume that there are no recommendable knowledge points for re-learning at this time. Then only the review pool is available at this time. Therefore, the next teaching scenario is review.

[0108] Step 12: The review teaching scenario triggers the strategic selection logic (refer to Figure 6 )

[0109] Traverse all the knowledge points. If this knowledge point was mastered today, it is strategically abandoned.

[0110] For example: The linear equation with one variable and the quadratic equation with one variable learned today will not be reviewed today. If a student comes in tomorrow, these two knowledge points will be reviewed.

[0111] Step 13: The review teaching scenario meets the exit condition

[0112] Review 3 knowledge points at a time. After completion, the exit condition for review will be met.

[0113] It should be noted that the purpose of the test teaching scenario is to help students efficiently diagnose weak knowledge points.

[0114] The purpose of the learning teaching scenario is to help students learn weak knowledge points.

[0115] The purpose of the re-learning teaching scenario is to help students learn weak knowledge points. The weak knowledge points for re-learning will be re-learned repeatedly.

[0116] The purpose of the review teaching scenario is to help students review forgotten knowledge points. III.

[0118] It should be noted that the order of the teaching scenarios of the first-generation intelligent adaptive recommendation algorithm engine is fixed. Students will first conduct a diagnostic test to diagnose weak knowledge points, followed by efficient learning to learn weak knowledge points, and then extended learning to expand and improve the abilities of students who have learned well.

[0119] The implementation principle of the technology is based on a tree structure. The lesson is the root node of the tree, and the pre-test, efficient learning, and extended learning are the leaf nodes of the lesson. Only when the leaf node is completed, the status of this node is marked as the completed state, and then it will return to the parent node. The lesson (parent node) stores the status set of the leaf nodes. After updating the completed state of the latest completed leaf node, the next teaching scenario will be recommended according to the set order. IV.

[0121] To more clearly describe the principle process of the present invention, the inventive concept of the present invention is hierarchically described below:

[0122] 4.1 Teaching scenario

[0123] 4.1.1 Definition of teaching scenario

[0124] A teaching scenario is an abstraction of the teaching idea when a teacher tutors a student in class.

[0125] 4.1.2 Feature introduction of teaching scenario

[0126] The features of a teaching scenario include the use of the teaching scenario, the importance level of the teaching scenario, and the exit condition of the teaching scenario.

[0127] 4.1.3 Definition of the use of teaching scenario

[0128] The categories of teaching scenarios can be divided into test, learning, re - learning, and review according to teaching steps.

[0129] The uses of different categories of teaching scenarios are introduced as follows:

[0130] Use of test teaching scenario: Help students efficiently diagnose weak knowledge points.

[0131] Use of learning teaching scenario: Help students master weak knowledge points.

[0132] Use of re - learning teaching scenario: Help students master weak knowledge points. Weak knowledge points will be re - learned repeatedly.

[0133] Use of review teaching scenario: Help students review forgotten knowledge points.

[0134] 4.1.4 Definition of the importance level of teaching scenario

[0135] The importance level of a teaching scenario is that when recommending the next teaching scenario, all available teaching scenarios (the pool) are sorted, and the teaching scenario with the highest importance level will be recommended.

[0136] Current priority of teaching scenarios: Re - learning > Learning > Test > Review

[0137] 4.1.5 Definition of the exit condition of teaching scenario

[0138] Once the teaching scenario meets the exit condition, the intelligent adaptive recommendation algorithm engine will recommend the next available teaching scenario according to the priority of the importance level of the teaching scenario.

[0139] For example: Exit the current teaching scenario after completing n knowledge points

[0140] Exit the current teaching scenario after exceeding m minutes

[0141] Complete n tasks and exit the current teaching scenario after more than m minutes

[0142] Exit the current teaching scenario when there are no recommendable knowledge points

[0143] 4.2 Pool

[0144] 4.2.1 Definition of Pool

[0145] A pool is a container for storing and managing knowledge points.

[0146] 4.2.2 Introduction to the Characteristics of the Pool

[0147] The characteristics of the pool are: algorithm isolation status, pool flow management, and pool switching conditions

[0148] 4.2.3 Definition of the Algorithm Isolation Status of the Pool

[0149] The pool status is a condition for determining whether a teaching scenario is recommendable, and its status is divided into recommendable and non-recommendable.

[0150] The knowledge points managed by the pool in the recommendable state will be recommended by the algorithm of the teaching scenario. The non-recommendable pool will not be recommended. This part of the non-recommendable knowledge points will flow from the non-recommendable pool to the recommendable pool according to certain conditions.

[0151] The algorithm isolation status is designed to recommend appropriate knowledge points for different student levels: for students with basic learning ability, easy knowledge points are recommendable, and difficult knowledge points are non-recommendable. When students with basic learning ability become intermediate or advanced students, the originally difficult knowledge points will also change from the non-recommendable state to the recommendable state and be used by the algorithm for recommendation.

[0152] Definition of the algorithm isolation status of the test teaching scenario: According to the knowledge point difficulty and frequency configuration table provided by the teacher and the student level dimension, the matching knowledge point range is measurable, otherwise it is unmeasurable.

[0153] Definition of the algorithm isolation status of the learning teaching scenario: According to the knowledge point logic learning map provided by the teacher, the knowledge points that are prerequisites for the current knowledge point are determined to be learnable, otherwise they are unlearnable.

[0154] Definition of the algorithm isolation status of the re-study teaching scenario: The knowledge points that have not been learned in the re-study of the current teaching scenario are first isolated, and then re-studied (isolated removed) after an interval of one teaching scenario (when isolated).

[0155] Definition of the algorithm isolation status of the review teaching scenario: The knowledge points mastered on the same day cannot be reviewed (isolated), otherwise they are reviewable.

[0156] 4.2.4 Definition of Pool Availability / Unavailability Judgment

[0157] If there are no knowledge points in the pool, or all knowledge points are in an algorithmically isolated state, then it is determined that this teaching scenario (pool) is unavailable.

[0158] 4.2.5 Definition of Pool Flow Management

[0159] Once a knowledge point in the pool is determined to meet the standard, the status of the teaching scenario of the current knowledge point will immediately change, and the next teaching scenario will be determined based on whether the knowledge point is mastered.

[0160] Test the teaching scenario transfer rule: The knowledge points determined to be mastered by the system are transferred to the review pool, otherwise they are transferred to the learning pool.

[0161] Learning teaching scenario transfer rule: The knowledge points determined to be mastered by the system are transferred to the review pool, otherwise they are transferred to the re-study pool.

[0162] Re-study teaching scenario transfer rule: The knowledge points determined to be mastered by the system are transferred to the review pool, otherwise they are isolated.

[0163] Review teaching scenario transfer rule: There is no condition for determining compliance, and no transfer occurs.

[0164] 4.2.6 Definition of Pool Switching Conditions

[0165] Same as the definition of the exit conditions of the teaching scenario in 4.1.5

[0166] The above are only the preferred embodiments of the present invention, and do not impose any limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A learning system capable of intelligently switching teaching scenarios, characterized in that, It includes a test module, a learning module, a re - learning module, and a review module; The test module is used for the user to test the mastery level of knowledge points; The learning module is used for the user to learn knowledge points; The re - learning module is used for the user to re - learn knowledge points; The review module is used for the user to review knowledge points; The test module is configured with a test pool, and the test pool is used to store the knowledge points in the course that have not been tested; The learning module is configured with a learning pool, and the learning pool is used to store the knowledge points in the course that have not been learned; The re - learning module is configured with a re - learning pool, and the re - learning pool is used to store the knowledge points in the course that have been learned but not mastered by the user; The review module is configured with a review pool, and the review pool is used to store the knowledge points in the course that have been mastered by the user; The knowledge points in the test pool include isolated knowledge points and non - isolated knowledge points; the non - isolated knowledge points are used for testing. If the test result is mastery, the non - isolated knowledge points are transferred from the test pool to the review pool; If the test result is non - mastery, the non - isolated knowledge points are transferred from the test pool to the learning pool; the isolated knowledge points in the test pool refer to the knowledge points that do not match the user's level; The knowledge points in the learning pool include isolated knowledge points and non - isolated knowledge points; the non - isolated knowledge points are used for learning. If the learning result is mastery, the non - isolated knowledge points are transferred from the learning pool to the review pool; If the test result is non - mastery, the non - isolated knowledge points are transferred from the learning pool to the re - learning pool; If the prerequisite knowledge point of the current knowledge point is non - mastered, or the current knowledge point is an untested knowledge point, the current knowledge point is used as the isolated knowledge point in the learning pool; The knowledge points in the re - learning pool include isolated knowledge points and non - isolated knowledge points; the non - isolated knowledge points are used for re - learning. If the learning result is mastery, the non - isolated knowledge points are transferred from the re - learning pool to the review pool; If the test result is non - mastery, the non - isolated knowledge points are converted into isolated knowledge points; the isolated knowledge points in the re - learning pool refer to the knowledge points that the user has not mastered; 2. The learning system capable of intelligently switching teaching scenarios according to claim 1, wherein The knowledge points in the review pool include isolated knowledge points and non - isolated knowledge points; the non - isolated knowledge points are used for review. After the isolated knowledge points are stored in the review pool for Th, the isolated knowledge points are converted into non - isolated knowledge points; 3. The learning system capable of intelligently switching teaching scenarios according to claim 1 or 2, characterized in that When the test module, the learning module, and / or the re - learning module runs, it includes the following steps: Obtain the user's current ability level P2; Compare the difficulty level P1 of each isolated knowledge point in the test pool with the user's current ability level P2; if P1≤P2, convert the isolated knowledge point into a non - isolated knowledge point; Compare the difficulty level P1 of each non - isolated knowledge point in the test pool with the user's current ability level P2; if P1>P2, convert the non - isolated knowledge point into an isolated knowledge point; 4. The learning system capable of intelligently switching teaching scenarios according to claim 1, wherein, When the test module, the learning module, and / or the re - learning module runs, it includes the following steps: Judge whether the user has completed the corresponding operations for n non - isolated knowledge points; Or, judge whether the user has operated for M minutes in the current module; Or, determine whether the user has completed the corresponding operations for n non-isolated knowledge points and has operated for M minutes in the current module; Or, determine whether there are no non-isolated knowledge points in the current module; If so, exit the current module, where the current module is a test module, a learning module, or a re-study module.

5. The learning system capable of intelligently switching teaching scenarios according to claim 1, characterized in that When the learning system runs, it includes the following steps: Call the test module to run after initialization; When exiting the test module, learning module, re-study module, or review module, perform the following steps: Step a: Determine whether there are non-isolated knowledge points in the re-study pool. If so, enter the re-study module; if not, proceed to the next step; Step b: Determine whether there are non-isolated knowledge points in the learning pool. If so, enter the learning module; if not, proceed to the next step; Step c: Determine whether there are non-isolated knowledge points in the test pool. If so, enter the test module; if not, proceed to the next step; Step d: Determine whether there are non-isolated knowledge points in the review pool. If so, enter the review module.

6. The learning system capable of intelligently switching teaching scenarios according to claim 5, characterized in that, The learning system is configured with an original pool for a single course, and the original pool is used to store the knowledge points of a single course; after the learning system is initialized, the knowledge points in the learning pool are transferred to the test pool.

Images