CN114267214A - Cloud education resource custom approval distribution method and system - Google Patents
Cloud education resource custom approval distribution method and system Download PDFInfo
- Publication number
- CN114267214A CN114267214A CN202111656602.9A CN202111656602A CN114267214A CN 114267214 A CN114267214 A CN 114267214A CN 202111656602 A CN202111656602 A CN 202111656602A CN 114267214 A CN114267214 A CN 114267214A
- Authority
- CN
- China
- Prior art keywords
- user
- client
- class
- data
- excitation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention relates to a cloud education resource custom approval distribution method and system. At present, the cloud education resource course setting is not matched with the actual learning basis and environment of a user, so that the utilization rate of education resources is reduced, and the energy and time of learners are wasted, and the cloud education resource custom approval distribution method and the cloud education resource custom approval distribution system comprise the following steps: the analysis server receives a user-defined form which is submitted by the client and contains the personal condition and environmental condition information of a user, judges the learning level of the user and whether the learning level of the user needs to be tested in real time or not by detecting a first user-defined field and other user-defined fields in the user-defined form, corrects the real-time test result according to the information of other user-defined fields, comprehensively judges the learning level of the user according to the information of the user-defined form and the corrected test result, and sends a corresponding course. Through user segmentation, the learning interest of the user is stimulated, the confidence of the user is enhanced, and the learning efficiency of the user is improved.
Description
Technical Field
The invention belongs to the field of internet education, and particularly relates to a cloud education resource custom approval distribution method and system.
Background
With the development of information and network technology and the development of various electronic devices with network functions, the convenience of learning, working and living of people is greatly improved. The internet education enables learners to develop learning activities at any time and place, and greatly meets the individual learning requirements.
Taking typing learning as an example, due to the requirements of writing documents, handling business, daily chatting and other items on the typing speed, a large number of learners who need to practice typing to improve the typing efficiency appear. There are also many types of typing practice software in the market, such as jinshan typing, pinyin typing, and rainy and sunny typing. The existing typing software on the market enters from a new hand, and proceeds from one stage to the next stage, different courses are not set for different crowds, the time of certain typing basic crowds is wasted, and the rapid improvement of the typing speed is difficult to achieve; moreover, the typing training is a very monotonous and boring process, and the interest of a practicer is difficult to arouse, so that people are difficult to insist. Obviously, the existing typing software cannot be well matched with the actual typing basic condition and typing environment of a user, so that the typing training speed is quickly increased, and the purposes of saving the typing training time and improving the typing training efficiency are achieved.
Similar to the above-mentioned problems of mismatching course settings with the actual learning basis and environment of the user existing in the typing software, the problems are commonly existed in various education training software and systems, thus greatly reducing the utilization rate of education resources, causing serious waste of energy and time of learners, and being difficult to achieve the purpose of rapidly improving the learner level.
Disclosure of Invention
The application aims to provide a cloud education resource custom approval distribution method and system to overcome the defect that the existing education software or system cannot well match a learning course with the actual learning basic condition and the learning environment of a user, so that the learning efficiency of the user is improved.
In order to achieve the purpose, the technical scheme of the application is as follows:
the invention provides a cloud education resource custom approval distribution method, which comprises the following steps:
the analysis server receives a user-defined form which is submitted by a client and contains user personal condition and environmental condition information, and judges whether the first user-defined field contains data or not by detecting the first user-defined field;
if the first custom field has no data, the resource server sends first class learning course resources to the client; if the first custom field contains data, the resource server sends a learning level test program to the client;
receiving test data returned by the client;
for users with test data not less than the first defined value, the resource server sends second class learning course resources to the client; for the user with the test data smaller than the first defined value, the analysis server detects whether the user-defined form contains other user-defined fields except the first user-defined field again;
if no other custom fields exist, the resource server sends first class learning course resources to the client;
if other custom fields exist, the analysis server corrects the test data according to the data of the other custom fields to obtain a test data correction value;
if the correction value is not less than the first limit value, the resource server sends the second class of learning course resources to the client; and if the correction value is smaller than the first defined value, the resource server sends the first class of learning course resources to the client.
The method comprises the steps of receiving a first class of learning course resource by a client, wherein the first class of learning course resource comprises a first class of excitation program, if the real-time practice data of the client is smaller than a first defined value, the excitation frequency of the first class of excitation program is in direct proportion to the difference value of the real-time practice data and the first defined value, and if the real-time practice data of the client is not smaller than the first defined value, the first class of excitation program adopts the excitation frequency of 1 time/detection period.
Further, the second class of learning course resources received by the client comprise a second class of incentive program, the second class of incentive program sequences the real-time exercise data and all recorded other client exercise data of the cloud server according to the client real-time exercise data, and sends a sequencing result back to the client.
Furthermore, the first custom field is a typing base, and the other custom fields at least comprise a keyboard type field, a typing environment temperature field and a daily exercise time field
Further, the first defined value is 150 letters/minute.
Further, the first-type excitation program detects the real-time exercise data of the client once per detection period, and sets the excitation frequency of the next detection period according to the difference between the real-time exercise data and 150 letters/minute: if the difference is 1-50 letters/minute, the excitation frequency is 1 time/detection period, if the difference is 50-100 letters/minute, the excitation frequency is 2 times/detection period, if the difference is more than 100 letters/minute, the excitation frequency is 3 times/detection period, and if the real-time exercise data is not less than 150 letters/minute, the excitation frequency is 1 time/detection period.
Further, the first class of learning course resources received by the client comprise a first class of excitation program, if the real-time practice data of the client is smaller than a first defined value, the excitation utterance intensity of the first class of excitation program is inversely proportional to the difference value between the real-time practice data and the first defined value, and if the real-time practice data of the client is not smaller than the first defined value, the first class of excitation program adopts high-intensity excitation utterances.
Further, the first-type excitation program detects the real-time exercise data of the client once per detection period, and sets the excitation speaking intensity of the next detection period according to the difference value between the real-time exercise data and 150 letters/minute: if the difference is 1-50 letters/minute, the high-intensity excitation words are played, if the difference is 50-100 letters/minute, the medium-intensity excitation words are played, and if the difference is more than 100 letters/minute, the low-intensity excitation words are played.
The invention also provides a cloud education resource custom approval distribution system, which comprises the following steps:
the first analysis unit is used for receiving a user-defined form which is submitted by a client and contains user personal conditions and environmental condition information, and judging whether the first user-defined field contains data or not by detecting the first user-defined field;
the first resource distribution unit is used for sending first class learning course resources and/or learning level test programs to the client; if the first custom field has no data, the first resource distribution unit sends first class of learning course resources to the client; if the first custom field contains data, the first resource distribution unit sends a learning level test program to the client;
the first analysis unit receives test data returned by the client;
the second resource distribution unit is used for sending the second class of learning course resources to the client; for the user with the test data not less than the first defined value, the second resource distribution unit sends the second class of learning course resources to the client; for a user with test data smaller than a first defined value, the first analysis unit detects whether the user-defined form contains other user-defined fields except the first user-defined field again;
if no other user-defined fields exist, the first resource distribution unit sends first class of learning course resources to the client;
if other custom fields exist, the first analysis unit corrects the test data according to the data of the other custom fields to obtain a test data correction value;
if the correction value is not less than the first defined value, the second resource distribution unit sends the second class of learning course resources to the client; and if the correction value is smaller than a first defined value, the first resource distribution unit sends the first class of learning course resources to the client.
According to the cloud education resource custom approval distribution method and system, the analysis server comprehensively judges the actual learning level of the user by analyzing the user personal information and the custom form information submitted by the client and the actual test data of the user, and the resource server distributes an exercise course and an excitation program which are in accordance with the actual situation of the user to the client according to the judgment result provided by the analysis server, so that the difficulty and the content setting of the course are in accordance with the actual situation of the user, and the purpose of improving the exercise efficiency is achieved; in the excitation mode, the excitation frequency and the excitation speech intensity are controlled to correspond to the user exercise condition, so that the confidence and interest of the user are increased, the user is aroused, and finally the purpose of quickly improving the user exercise level is achieved.
The technical scheme is applied to typing training, and the actual typing level of the user can be comprehensively judged according to the typing test data of the user and the test environment condition; considering that environmental conditions can affect the typing speed of the user, in the judging process, the system corrects the typing test data of the user according to the influence conditions such as the keyboard type, the environmental temperature, the daily average exercise time and the like to obtain the accurate typing level of the user, so that the typing test data is sent to the user with a pointed typing course, the time and the energy of the user are saved, and the exercise efficiency of the user is improved. Meanwhile, the excitation program sent according to the actual level of the user meets the psychological needs of users at different levels in the aspects of excitation frequency and excitation speech intensity, the confidence of the users at low level can be better improved, the struggle of the users at high level is excited, different crowds can generate the interest in typing practice, and finally, the typing level is continuously and quickly improved.
Drawings
FIG. 1 is a flow chart of a custom approval distribution method for cloud education resources.
Fig. 2 is a flow chart of a cloud education resource custom approval distribution method taking typing training as an example.
Fig. 3 is a flow chart of a cloud education incentive.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Example 1
In this embodiment, as shown in fig. 1, a cloud education resource custom approval distribution method is provided, including:
step S1, the analysis server receives the user-defined form submitted by the client, the form at least includes one user-defined field, and judges whether the first user-defined field contains data or not by detecting the first user-defined field. For example: the first custom field is "learning basis".
Step S2, if there is no data in the first self-defined field, or the filled value is ' none ' (no filling ' is regarded as no data), the system judges that the user has no learning basis, the resource server sends the first class of learning course resource suitable for zero basis or beginner to the client; and if the first custom field contains data, the analysis server sends a learning level test program to the client to detect the actual learning level of the user.
Step S3, receiving the test data returned from the client, for the user whose test data is not less than the first defined value, the system judges that the user has better learning basis, the resource server sends the second class of learning course resource which is suitable for the user with basis and hopes to reach higher level to the client; and for the user with the test data smaller than the first defined value, the analysis server detects whether the user-defined form contains other user-defined fields except the first user-defined field again. For example: other custom fields may be "keyboard type", "ambient temperature", "average training duration" etc.
Step S4, if there is no other self-defined field, the resource server sends the first class of learning course resource to the client; and if other custom fields exist, the analysis server corrects the test data according to the data of the other custom fields to obtain the corrected value of the test data. For example: the test data can be corrected by giving corresponding correction coefficients according to different value ranges in the user-defined field, and the product of the test data and the correction coefficients is the test data correction value.
Step S5, if the correction value is not less than the first limit value, the resource server sends the second class of learning course resource to the client; and if the correction value is smaller than the first defined value, the resource server sends the first class of learning course resources to the client. The first class of learning course resources and the second class of learning course resources respectively comprise first class of incentive programs and second class of incentive programs.
The first class of excitation programs is implemented in such a way that the excitation frequency is proportional to the magnitude of the difference between the real-time exercise data and the defining value, while the intensity of the excited speech is inversely proportional to the difference between the real-time exercise data and the defining value; the second type of incentive program is implemented by ranking the real-time exercise data and other user data in the cloud.
Example 2
As shown in fig. 2, taking typing training as an example, a user submits a custom form through a client that fills in information including gender, age, school calendar, typing base, keyboard type, and ambient temperature.
The analysis server is used for analyzing a user-defined form submitted by a user through the client, the first user-defined field is a typing base, the analysis server checks whether the field of the typing base of the user has data, if the field has no data or the filled numerical value is 'none' (the filled 'none' is regarded as no data), the analysis server sends a key position exercise program to the client, and if the field has the numerical value, the analysis server sends a horizontal test program to the client to detect the real typing level of the user.
Receiving test data returned by the client, comparing the numerical value of the returned test data with a first defined value by the analysis server, setting the first defined value as 150 letters/minute, and if the test result is not less than 150 letters/minute, sending a case and case integrated typing practice program page to the client; if the test result is less than 150 letters/minute, the analysis server again checks whether there are any other custom fields outside the typing base.
If there are no other custom fields, the pure lower case typing exercise program page is sent to the client. If other self-defined fields exist, such as the keyboard type, the environmental temperature and other influencing factors, the server corrects the test result according to the numerical values of the influencing factors, and the corrected typing speed is obtained. Taking the keyboard type as an example, three input keyboards, i.e., a notebook computer self-contained keyboard, a common external keyboard and an external mechanical keyboard, have different typing speeds when a same typewriter uses different keyboards due to different keyboard layouts, such as the keyboard structure, the key position spacing and the like, generally, the typing speed when the external mechanical keyboard is used is the highest, and the typing speed when the notebook computer self-contained keyboard is used is the lowest. Therefore, when the user test speed is corrected, the set correction coefficient for filling the keyboard type of the external mechanical keyboard is 1, the set correction coefficient for filling the common external keyboard is 1.1, the set correction coefficient for filling the notebook computer self-contained keyboard is 1.2, and the typing speed correction value is the product of the actual test speed and the correction coefficient. Similarly, the temperature of the typing environment affects the typing speed of the typewriter, and people feel uncomfortable in the environment with low temperature (such as below 10 ℃) and high temperature (such as above 30 ℃) so as to slow down the typing speed, and people can often play the best typing speed in the proper temperature range (10 ℃ to 30 ℃). Therefore, when the user test speed is corrected, the set correction coefficient of "10 degrees celsius to 30 degrees celsius" for the filling environment temperature, the set correction coefficient of "1.1" for the filling environment temperature, "30 degrees celsius or higher", and the set correction coefficient of "1.2" for the filling environment temperature, "10 degrees celsius or lower", are the product of the actual test speed and the correction coefficient. For example, if a user actually tests at 120 words/minute, the keyboard type field of the user is filled with 'notebook computer own keyboard', and the ambient temperature field of the user is filled with '30 degrees centigrade later', the corrected typing speed is 120 × 1.2 × 1.1=158.4 letters/minute.
The analysis server compares the corrected typing speed with 150 letters/minute again, and sends the upper and lower case integrated typing practice program pages to the client if the corrected value is not less than 150 letters/minute. If the correction value is still less than 150 letters/minute, the pure lower case typing exercise program page is sent to the client.
In this embodiment, the typing speed detection period of the excitation program is 15 seconds, and the excitation degree (including the excitation frequency and the excitation speech intensity) of the next period is automatically set according to the average typing speed of the previous period; the high-intensity excitation words are "you are really too excellent" or "soon successful", the medium-intensity excitation words are "you are not wrong" or "go step with last, and the low-intensity excitation words are" oil-filled "or" you have a high potential ".
As shown in fig. 3, the key position exercise program and the pure lower case typing exercise program include a first type of excitation program, and the excitation mode is as follows: if the user typing speed in the last period is less than 150 letters/minute and the difference value between the user typing speed and the first defined value is in the range of 1-50 letters/minute, the next period is excited once, and a high-intensity excitation speech of 'you are really too big' is used; if the user's typing speed differs from the first defined value by a value in the range of 50-100 letters/minute, the next cycle is stimulated twice, and a medium intensity stimulation utterance such as "you are not wrong" is used; if the difference value between the user typing speed and a first defined value is more than 100 letters/minute, exciting the next period for three times, and using low-intensity excitation words such as 'oil-above'; if the user's typing speed in the previous cycle is not less than 150 letters/minute, the next cycle uses a 1/test cycle excitation frequency and uses a high intensity excitation word such as "you are really tai chi".
The case comprehensive typing practice program comprises a second type of excitation program, and the excitation mode is as follows: the real-time typing speed of the user can sequence the uploaded cloud together with the typing speeds of other users, sequencing results are sent back to the user client, sequencing is divided into forms of friend ranking, area ranking and the like, and the struggle of a practicer can be effectively stimulated through a sequencing stimulation mode.
The above examples are merely representative of preferred embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, various changes, modifications and substitutions can be made without departing from the spirit of the present invention, and these are all within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A cloud education resource custom approval distribution method is characterized by comprising the following steps:
the analysis server receives a user-defined form which is submitted by a client and contains user personal condition and environmental condition information, and judges whether the first user-defined field contains data or not by detecting the first user-defined field;
if the first custom field has no data, the resource server sends first class learning course resources to the client; if the first custom field contains data, the resource server sends a learning level test program to the client;
receiving test data returned by the client, and sending second class of learning course resources to the client by the resource server for users of which the test data is not less than the first defined value; for the user with the test data smaller than the first defined value, the analysis server detects whether the user-defined form contains other user-defined fields except the first user-defined field again;
if no other custom fields exist, the resource server sends first class learning course resources to the client;
if other custom fields exist, the analysis server corrects the test data according to the data of the other custom fields to obtain a test data correction value;
if the correction value is not less than the first limit value, the resource server sends the second class of learning course resources to the client; and if the correction value is smaller than the first defined value, the resource server sends the first class of learning course resources to the client.
2. The method of claim 1, wherein: the first class of learning course resources received by the client comprise a first class of excitation programs, if the real-time practice data of the client are smaller than a first defined value, the excitation frequency of the first class of excitation programs is in direct proportion to the difference value between the real-time practice data and the first defined value, and if the real-time practice data of the client are not smaller than the first defined value, the first class of excitation programs adopt 1-time/detection cycle excitation frequency.
3. The method of claim 1, wherein: the second class of learning course resources received by the client comprise second class of incentive programs, the second class of incentive programs rank the real-time exercise data and all recorded other client exercise data of the cloud server according to the client real-time exercise data, and a ranking result is sent back to the client.
4. The method of claim 1, wherein: the first custom field is a typing basis, and the other custom fields at least comprise a keyboard type field, a typing environment temperature field and a daily average exercise time field.
5. The method of claim 1, wherein: the first defined value is 150 letters/minute.
6. The method of claim 1, wherein: the first-class excitation program detects real-time exercise data of the client once in each detection period, and sets excitation frequency of the next detection period according to the difference value between the real-time exercise data and 150 letters/minute: if the difference is 1-50 letters/minute, the excitation frequency is 1 time/detection period, if the difference is 50-100 letters/minute, the excitation frequency is 2 times/detection period, if the difference is more than 100 letters/minute, the excitation frequency is 3 times/detection period, and if the real-time exercise data is not less than 150 letters/minute, the excitation frequency is 1 time/detection period.
7. The method of claim 1, wherein: the first class of learning course resources received by the client comprise a first class of excitation programs, if the real-time practice data of the client are smaller than a first defined value, the excitation speaking intensity of the first class of excitation programs is inversely proportional to the difference value between the real-time practice data and the first defined value, and if the real-time practice data of the client are not smaller than the first defined value, the first class of excitation programs adopt high-intensity excitation utterances; the first-class excitation program detects real-time exercise data of the client once in each detection period, and the excitation speaking intensity of the next detection period is set according to the difference value between the real-time exercise data and 150 letters/minute: if the difference is 1-50 letters/minute, the high-intensity excitation words are played, if the difference is 50-100 letters/minute, the medium-intensity excitation words are played, and if the difference is more than 100 letters/minute, the low-intensity excitation words are played.
8. The cloud education resource custom approval distribution system is characterized by comprising the following components:
the first analysis unit is used for receiving a user-defined form which is submitted by a client and contains user personal conditions and environmental condition information, and judging whether the first user-defined field contains data or not by detecting the first user-defined field;
the first resource distribution unit is used for sending first class learning course resources and/or learning level test programs to the client; if the first custom field has no data, the first resource distribution unit sends first class of learning course resources to the client; if the first custom field contains data, the first resource distribution unit sends a learning level test program to the client;
the first analysis unit receives test data returned by the client;
the second resource distribution unit is used for sending the second class of learning course resources to the client; for the user with the test data not less than the first defined value, the second resource distribution unit sends the second class of learning course resources to the client; for a user with test data smaller than a first defined value, the first analysis unit detects whether the user-defined form contains other user-defined fields except the first user-defined field again;
if no other user-defined fields exist, the first resource distribution unit sends first class of learning course resources to the client;
if other custom fields exist, the first analysis unit corrects the test data according to the data of the other custom fields to obtain a test data correction value;
if the correction value is not less than the first defined value, the second resource distribution unit sends the second class of learning course resources to the client; and if the correction value is smaller than a first defined value, the first resource distribution unit sends the first class of learning course resources to the client.
9. A storage medium, characterized by: the storage medium stores a program that executes the detection method according to any one of claims 1 to 7.
10. A processor, characterized in that: the processor is configured to execute a program, wherein the program executes the detection method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111656602.9A CN114267214A (en) | 2021-12-31 | 2021-12-31 | Cloud education resource custom approval distribution method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111656602.9A CN114267214A (en) | 2021-12-31 | 2021-12-31 | Cloud education resource custom approval distribution method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114267214A true CN114267214A (en) | 2022-04-01 |
Family
ID=80831868
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111656602.9A Pending CN114267214A (en) | 2021-12-31 | 2021-12-31 | Cloud education resource custom approval distribution method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114267214A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102610129A (en) * | 2012-02-14 | 2012-07-25 | 江苏省现代企业信息化应用支撑软件工程技术研发中心 | Method for realizing individual collaborative learning system based on multiple agents |
| CN108376381A (en) * | 2018-02-22 | 2018-08-07 | 河海大学 | A kind of multifactor impact Students ' break, changes the time computational methods per capita of building room |
| CN109670110A (en) * | 2018-12-20 | 2019-04-23 | 蒋文军 | A kind of educational resource recommended method, device, equipment and storage medium |
| CN112102675A (en) * | 2020-10-13 | 2020-12-18 | 上海市静安区和田路小学 | Teaching progress based tutorial management system and working method thereof |
| CN112185191A (en) * | 2020-09-21 | 2021-01-05 | 信阳职业技术学院 | Intelligent digital teaching model |
| CN113326996A (en) * | 2020-12-10 | 2021-08-31 | 国网山东省电力公司德州供电公司 | Safety risk assessment method for power grid in high-proportion new energy access region |
-
2021
- 2021-12-31 CN CN202111656602.9A patent/CN114267214A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102610129A (en) * | 2012-02-14 | 2012-07-25 | 江苏省现代企业信息化应用支撑软件工程技术研发中心 | Method for realizing individual collaborative learning system based on multiple agents |
| CN108376381A (en) * | 2018-02-22 | 2018-08-07 | 河海大学 | A kind of multifactor impact Students ' break, changes the time computational methods per capita of building room |
| CN109670110A (en) * | 2018-12-20 | 2019-04-23 | 蒋文军 | A kind of educational resource recommended method, device, equipment and storage medium |
| CN112185191A (en) * | 2020-09-21 | 2021-01-05 | 信阳职业技术学院 | Intelligent digital teaching model |
| CN112102675A (en) * | 2020-10-13 | 2020-12-18 | 上海市静安区和田路小学 | Teaching progress based tutorial management system and working method thereof |
| CN113326996A (en) * | 2020-12-10 | 2021-08-31 | 国网山东省电力公司德州供电公司 | Safety risk assessment method for power grid in high-proportion new energy access region |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210166582A1 (en) | Language learning system adapted to personalize language learning to individual users | |
| US8386240B2 (en) | Domain dictionary creation by detection of new topic words using divergence value comparison | |
| US20100075289A1 (en) | Method and system for automated content customization and delivery | |
| CN107038480A (en) | A kind of text sentiment classification method based on convolutional neural networks | |
| EP2733582A1 (en) | Character input method, device and system | |
| CN103699521A (en) | Text analysis method and device | |
| Biemiller et al. | Models of vocabulary acquisition: Direct tests and text-derived simulations of vocabulary growth | |
| CN101488120A (en) | Learning evaluation apparatus and method | |
| US11586830B2 (en) | System and method for reinforcement learning based controlled natural language generation | |
| Olney et al. | Assessing Computer Literacy of Adults with Low Literacy Skills. | |
| CN113610680A (en) | AI-based interactive reading material personalized recommendation method and system | |
| CN109858009A (en) | Device, method and its computer storage medium of control instruction are generated according to text | |
| CN111552773A (en) | Method and system for searching key sentence of question or not in reading and understanding task | |
| He et al. | A personalized e-learning services recommendation algorithm based on user learning ability | |
| CN113434755A (en) | Page generation method and device, electronic equipment and storage medium | |
| Havelka et al. | Age of acquisition in naming Japanese words | |
| CN111611269A (en) | Artificial intelligence emotion accompanying and attending system in conversation and chat mode | |
| Vastrick et al. | Measuring the frequency occurrence of handwritten numeral characteristics | |
| CN117151794A (en) | Advertisement task recommendation method and related device | |
| CN114267214A (en) | Cloud education resource custom approval distribution method and system | |
| US20170193620A1 (en) | Associate a learner and learning content | |
| JP2011013811A (en) | Device, system and method for word level decision, control program, and recording medium | |
| CN114398896A (en) | Information input method and device, electronic equipment and computer readable storage medium | |
| WO2016063403A1 (en) | Data analysis system, data analysis method, and data analysis program | |
| US20120100519A1 (en) | Training system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |