CN115129971A - Course recommendation method and device based on capability evaluation data and readable storage medium - Google Patents
Course recommendation method and device based on capability evaluation data and readable storage medium Download PDFInfo
- Publication number
- CN115129971A CN115129971A CN202110361082.2A CN202110361082A CN115129971A CN 115129971 A CN115129971 A CN 115129971A CN 202110361082 A CN202110361082 A CN 202110361082A CN 115129971 A CN115129971 A CN 115129971A
- Authority
- CN
- China
- Prior art keywords
- information
- capability
- recommended
- acquiring
- ability
- 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
- 238000011156 evaluation Methods 0.000 title claims abstract description 149
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012545 processing Methods 0.000 claims description 20
- 238000011161 development Methods 0.000 claims description 11
- 230000003993 interaction Effects 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 4
- 238000012549 training Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 11
- 230000000694 effects Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 4
- 238000009223 counseling Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Educational Technology (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present disclosure provides a course recommendation method, device and readable storage medium based on capability evaluation data, the method comprising: obtaining a course recommendation request, wherein the course recommendation request comprises: the method comprises the following steps of identifying an object to be recommended and the personal basic information of the object to be recommended; acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request; and analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended. Therefore, the personalized initial teaching scheme can be matched according to the characteristics of the object to be recommended, so that the initial teaching scheme is more in accordance with the actual situation of the object to be recommended, and the teaching quality is improved.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a course recommendation method and apparatus based on capability evaluation data, and a readable storage medium.
Background
Preschool education is an integral part of basic education and is the initial stage of school education and lifelong education. It has been increasingly emphasized because it can affect many aspects of the development of children, such as cognition, emotion, and personality.
In the existing education system, generally, the unified learning content and teaching scheme are provided. The teacher can teach all students according to the unified learning content and the learning scheme.
However, in the teaching process by using the above method, since different students have different characteristics and capabilities, teaching is performed by using uniform learning content and learning scheme, the characteristics of each student cannot be maximally excited, and the quality of teaching courses cannot be guaranteed.
Disclosure of Invention
The invention provides a course recommending method and device based on capability evaluation data and a readable storage medium, which are used for solving the technical problems that an existing teaching method cannot accurately match a proper teaching course for each student and teaching quality is not high.
A first aspect of the present disclosure is to provide a course recommendation method based on capability evaluation data, including:
obtaining a course recommendation request, wherein the course recommendation request comprises: the method comprises the following steps of identifying an object to be recommended and the personal basic information of the object to be recommended;
acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request;
and analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
A second aspect of the present disclosure is to provide a course recommending apparatus based on ability evaluation data, including:
an interaction module, configured to obtain a course recommendation request, where the course recommendation request includes: the method comprises the following steps of identifying an object to be recommended and the personal basic information of the object to be recommended;
the processing module is used for acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request;
and analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
A third aspect of the present disclosure is to provide a course recommending apparatus based on capability evaluation data, including: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to invoke program instructions in the memory to perform the course recommendation method based on the capability assessment data according to the first aspect.
A fourth aspect of the present disclosure is to provide a computer-readable storage medium having stored therein computer-executable instructions for implementing the course recommending method based on capability evaluation data according to the first aspect when the computer-executable instructions are executed by a processor.
The invention provides a course recommending method and device based on capability evaluation data and a readable storage medium. And determining an initial teaching scheme corresponding to the object to be recommended by adopting a preset ability course matching model according to the ability evaluation data information and the personal basic information of the object to be recommended by acquiring the ability evaluation data information corresponding to the object to be recommended. Therefore, the personalized initial teaching scheme can be matched according to the characteristics of the object to be recommended, so that the initial teaching scheme is more in accordance with the actual situation of the object to be recommended, and the teaching quality is improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present disclosure, the drawings used in the embodiments or technical solutions of the present disclosure will be briefly described below, it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art according to these drawings.
FIG. 1 is a schematic diagram of a network architecture upon which the present disclosure is based;
FIG. 2 is a flowchart illustrating a course recommending method based on capability evaluation data according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of a capability course matching model training scenario provided by an embodiment of the present disclosure;
FIG. 4 is a flowchart illustrating a course recommending method based on capability evaluation data according to a second embodiment of the disclosure;
FIG. 5 is a schematic flow chart illustrating the generation of a teaching plan according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of underlying data based on which a course recommendation method based on capability assessment data according to an embodiment of the present disclosure is provided;
FIG. 7 is a schematic diagram of a display interface of a teacher's terminal according to an embodiment of the disclosure;
FIG. 8 is a flowchart illustrating a course recommending method based on capability evaluation data according to a third embodiment of the disclosure;
FIG. 9 is a flowchart illustrating a course recommendation method based on capability assessment data according to a fourth embodiment of the disclosure;
fig. 10 is a schematic structural diagram of a course recommending apparatus based on capability evaluation data according to a fifth embodiment of the disclosure;
fig. 11 is a schematic structural diagram of a course recommending apparatus based on capability evaluation data according to a sixth embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments obtained based on the embodiments in the present disclosure belong to the protection scope of the present disclosure.
In order to solve the technical problems that the existing teaching method cannot accurately match a proper teaching course for each student and the teaching quality is not high, the disclosure provides a course recommending method based on capability evaluation data, a device and a readable storage medium.
It should be noted that the course recommendation method, device and readable storage medium provided by the present disclosure based on capability evaluation data can be applied to various education scenarios.
The preschool education is planned education on the infants before entering primary schools according to a certain cultivation target and physical and psychological characteristics of the infants. In the prior art, students in the same age group generally adopt the same teaching materials for teaching. However, the characteristics and abilities of different students are different, so that the teaching with the same teaching material cannot obtain a good effect.
In the process of solving the problems, the inventor finds out through research that in order to improve the teaching effect and stimulate the specialties of students more greatly, the teaching scheme can be matched according to the capability information of the students, and a personalized teaching scheme is provided for each student. When the teaching scheme is matched according to the ability information of students, the inventor finds that the scheme of artificial intelligence has outstanding performance through further research in order to match the teaching scheme more accurately. Therefore, the most representative ability information and the matching model are required to be obtained to complete the personalized teaching scheme provided for each student. Specifically, since the most representative of the student capability information is the student's personal basic information and capability evaluation data information, the student's personal basic information and capability evaluation data information are used as the student's capability information. And then analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
Fig. 1 is a schematic diagram of a network architecture based on the present disclosure, and as shown in fig. 1, the network architecture based on the present disclosure at least includes: the system comprises terminal equipment 1 and a server 2, wherein the server 2 is provided with a course recommending device based on capability evaluation data, and the course recommending device based on the capability evaluation data can be written by adopting languages such as C/C + +, Java, Shell or Python; the terminal device 1 may be a desktop computer, a tablet computer, or the like. Wherein, the terminal device 1 is connected with the server 2 in a communication way, so that the two can carry out information interaction.
Fig. 2 is a schematic flowchart of a course recommendation method based on capability evaluation data according to an embodiment of the present disclosure, and as shown in fig. 2, the method includes:
The execution topic of the embodiment is a course recommending device based on capability evaluation data, and the course recommending device based on capability evaluation data can be coupled to the server.
In this embodiment, in order to make the teaching plan more suitable for the actual situation of the object to be recommended, the course recommending apparatus based on the capability evaluation data may first obtain a course recommending request, where the course recommending request includes: the method comprises the steps of identifying an object to be recommended and the personal basic information of the object to be recommended, wherein the personal basic information comprises but is not limited to basic information such as age and gender. Therefore, initial course recommendation can be subsequently carried out on each object to be recommended according to the course recommendation request.
Specifically, the server may be in communication connection with a terminal device of the object to be recommended, and the course recommendation request may be sent by the terminal device for the object to be recommended.
And 102, acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request.
In this embodiment, in order to accurately recommend a teaching plan for an object to be recommended, first, a capability evaluation needs to be performed on the object to be recommended. Specifically, according to the course recommendation request, the capability evaluation questionnaire corresponding to the object to be recommended may be acquired, and the capability evaluation is performed according to the response of the object to be recommended, so as to obtain the capability evaluation data information corresponding to the identifier of the object to be recommended.
By pre-evaluating the capability of the object to be recommended, the subsequently matched initial teaching scheme can be more fit with the capability of the object to be recommended, and a better teaching effect is achieved.
In this embodiment, after the capability evaluation data information corresponding to the identifier of the object to be recommended is acquired, the matching operation of the initial teaching scheme may be performed according to the capability evaluation data information corresponding to the identifier of the object to be recommended.
Specifically, a preset ability course matching model may be adopted to analyze the personal basic information and the ability evaluation data information of the object to be recommended, so as to generate an initial teaching scheme matched with the object to be recommended.
It should be noted that the ability course matching model is trained on a large amount of personal basic information, ability evaluation data and initial teaching schemes of the users, which are adopted in advance, so that the corresponding initial teaching schemes can be accurately matched according to the personal basic information and the ability evaluation data information of the object to be recommended.
Fig. 3 is a schematic diagram of a training scenario of a capability course matching model provided in the embodiment of the present disclosure, and as shown in fig. 3, a training set may be generated according to a large amount of personal basic information of users, capability evaluation data, and an initial teaching scheme. Respectively randomly training data of the training sets to a first training set 31, a second training set 32 and a third training set 33, training a preset basic model 34 by adopting the three training sets to obtain a first result 35, a second result 36 and a third result 37 output by the basic model 34 based on each training set, and carrying out comprehensive operation on the first result 35, the second result 36 and the third result 37 to obtain a prediction result 38. So that parameters of the model can be adjusted subsequently according to the prediction result 38 and the initial teaching scheme in the training set until the model converges, and the capability course matching model is obtained.
According to the course recommending method based on the ability evaluation data, the ability evaluation data information corresponding to the object to be recommended is obtained, and the initial teaching scheme corresponding to the object to be recommended is determined by adopting the preset ability course matching model according to the ability evaluation data information and the personal basic information of the object to be recommended. Therefore, the personalized initial teaching scheme can be matched according to the characteristics of the object to be recommended, so that the initial teaching scheme is more in accordance with the actual situation of the object to be recommended, and the teaching quality is improved.
Fig. 4 is a schematic flowchart of a course recommendation method based on capability evaluation data according to a second embodiment of the present disclosure, where on the basis of the first embodiment, as shown in fig. 4, step 102 specifically includes:
In this embodiment, in order to evaluate the ability of the object to be recommended and obtain the ability evaluation data information, a long ability axis may be established in advance. The growth ability axis comprises a plurality of ability assessment questionnaires corresponding to different ability points. The capability points include, but are not limited to, language, art, creativity, life, mathematics, scientific culture, social emotional feelings, sense, sports, english, basic health, and the like.
Specifically, the ability point corresponding to the personal basic information may be acquired on the growth ability axis according to the personal basic information of the object to be recommended. In practical application, the object to be recommended may have different ability points for different ages, so that accurate acquisition of the ability points can be performed according to personal basic information.
Further, the ability evaluation questionnaire corresponding to each ability point can be obtained, and the object to be recommended replies to the ability evaluation questionnaire to obtain a reply result. And further calculating the capability evaluation data information corresponding to the object to be recommended according to the reply result.
Further, on the basis of the first embodiment, the step 103 specifically includes:
and acquiring a capability point and a capability evaluation value section corresponding to the personal basic information, and a difficulty level at each capability point.
And analyzing the capability evaluation data information according to the capability points and the capability evaluation value intervals corresponding to the personal basic information and the difficulty level of each capability point to obtain a capability evaluation value of the object to be recommended.
And processing the capability evaluation value by adopting the pre-configured capability course matching model to generate the initial teaching scheme.
In this embodiment, the ability evaluation values corresponding to the ability points of the objects to be recommended of different age groups are different, and therefore the ability point and the ability evaluation value section corresponding to the personal basic information, and the difficulty level at each ability point can be acquired. And further, the ability evaluation data information can be analyzed according to the ability points and the ability evaluation value intervals corresponding to the personal basic information and the difficulty level of each ability point, and the ability evaluation numerical value of the object to be recommended is obtained. The ability evaluation data information includes but is not limited to behavior evaluation data, language evaluation data, expression evaluation data and the like.
For example, in practical applications, when the age of the object to be recommended is one year, two capability points are corresponding to the object to be recommended, and the capability evaluation value interval corresponding to each capability point may be 0 to 3. The two capacity points respectively correspond to the following difficulties: the difficulty 1 is the minimum value of the interval +10, and the difficulty 2 is the maximum value of the interval-10. If the age of the object to be recommended is two years old, three capability points are corresponding to the object to be recommended, and the capability evaluation value interval corresponding to each capability point can be 0-4. The 3 corresponding difficulty grades are also changed, and the difficulty grades are respectively 1-interval minimum value + 10; difficulty t ═ difficulty (t-1) + (difficulty n-difficulty 1)/(n-1); the difficulty n is the maximum value of the interval-10; wherein t is a positive integer less than n.
After obtaining the ability evaluation value of the object to be recommended, the ability evaluation value may be input to the ability course matching model, and the ability evaluation value may be processed to generate the initial teaching plan.
Fig. 5 is a schematic flow chart of teaching scheme generation provided by the embodiment of the present disclosure, and as shown in fig. 5, based on information such as a course axis 51, a capability axis 52, and a bottom-layer data core algorithm 53, capability evaluation data 54 of an object to be recommended may be determined, an initial teaching scheme 55 may be determined according to the capability evaluation data 54, and an intelligent course arrangement may be performed according to the initial teaching scheme 55.
Fig. 6 is a schematic diagram of the underlying data based on the course recommendation method based on capability evaluation data according to the embodiment of the present disclosure, as shown in fig. 6, the underlying data based on the course recommendation method based on capability evaluation data specifically includes an algorithm library 61 and an underlying database 62, where the algorithm library 61 includes, but is not limited to, a knowledge graph algorithm 63, a collaborative recommendation algorithm 64, a matching algorithm 65, a Neuro-Linguistic Programming (NLP for short) 66, and other algorithms. Included in the underlying database 62 are, but are not limited to, a competency library 67, a course library 68, a knowledge library 69, an assessment library 610, and the like. The generation of the initial teaching plan can be accurately realized by the algorithms and data in the algorithm library 61 and the underlying database 62.
Further, on the basis of the first embodiment, the method further includes:
and acquiring comment information of the teacher under the initial teaching scheme.
And processing the capability evaluation numerical value and the teacher evaluation information by adopting the pre-configured capability course matching model to generate an updated teaching scheme.
In this embodiment, in order to enable the matched teaching scheme to better fit the actual situation of the object to be recommended, comment information of daily activities of the object to be recommended by the teacher may also be acquired, and a final teaching scheme is acquired together with the teacher comment information according to the capability evaluation value.
Specifically, comment information of the teacher on the object to be recommended during the implementation of the initial teaching plan may be acquired. And inputting the comment information and the ability evaluation value into a pre-configured ability course matching model together to generate an updated teaching scheme.
The updated teaching scheme is generated by adopting the commenting information and the capability evaluation value together, so that the teaching scheme can be matched with the object to be recommended, and the teaching effect is improved.
Fig. 7 is a schematic view of a display interface of a teacher terminal according to an embodiment of the present disclosure, and as shown in fig. 7, a teacher may interface with the display interface, and implement a comment operation on a learning status and a living status of a target student within a certain time period by clicking a preset comment icon.
In the course recommendation method based on capability evaluation data provided in this embodiment, the capability points corresponding to the personal basic information are acquired on the growth capability axis according to the personal basic information of the object to be recommended. And acquiring the capability evaluation questionnaire corresponding to each capability point, and answering the capability evaluation questionnaire by the object to be recommended to obtain an answering result. Therefore, the capability evaluation data information of the object to be recommended can be accurately determined, and then the initial teaching scheme can be accurately recommended according to the capability evaluation data information, so that the initial teaching scheme is more suitable for the capability of the object to be recommended, and a better teaching effect is achieved.
Fig. 8 is a schematic flowchart of a course recommendation method based on capability evaluation data according to a third embodiment of the present disclosure, where on the basis of any of the foregoing embodiments, as shown in fig. 8, the method further includes:
And step 804, generating and recommending a matched teaching service mode according to the development data.
In this embodiment, the course recommendation method based on the ability evaluation data provided by the disclosure can recommend a teaching plan for a single object to be recommended, and can also match batched teaching service modes for schools and classes.
Specifically, the counseling information of the user may be collected, wherein the counseling information includes basic personal information of the object to be counseled and counseling content. And acquiring capability points corresponding to the basic personal information of the object to be consulted, analyzing the consultation content, and acquiring key information related to the capability points of the consulted object in the consultation content. Consultation term information corresponding to the key information is acquired to guide the user, and development data related to the subject to be consulted is acquired. And generating and recommending a matched teaching service mode according to the development data.
According to the course recommendation method based on the capability evaluation data, the matched teaching service mode is generated and recommended according to the consultation information of the user, so that the teaching scheme is recommended in various different scenes, and the applicability is higher.
Fig. 9 is a schematic flowchart of a course recommendation method based on capability evaluation data according to a fourth embodiment of the present disclosure, and based on any one of the foregoing embodiments, as shown in fig. 9, the method further includes:
and step 901, acquiring and obtaining tracking information related to the object to be recommended in a preset stage of the initial teaching scheme or the updated teaching scheme.
And 902, updating the pre-configured ability course matching model according to the tracking information in each preset stage and the expected ability evaluation value in each preset stage.
The tracking information related to the object to be recommended comprises one or more of the following items: new capability evaluation data information, basic personal information change data, evaluation information of teachers, and environmental information of objects to be recommended.
In this embodiment, in order to improve the matching accuracy of the ability course matching model, the ability course matching model needs to be updated.
Specifically, the tracking information related to the object to be recommended may be acquired and obtained at a preset stage of the initial teaching plan or the updated teaching plan. And updating the pre-configured ability course matching model according to the tracking information in each preset stage and the expected ability evaluation value in each preset stage.
It should be noted that the ability course matching model may be updated according to a preset time interval. The ability course matching model can also be updated when the data volume of the tracking information reaches a preset threshold value. The ability course matching model can also be updated according to the triggering operation of the user, which is not limited by the disclosure.
In the course recommendation method based on capability evaluation data provided by this embodiment, tracking information related to an object to be recommended, tracking information in each preset stage, and an expected capability evaluation value in each preset stage are acquired and obtained at preset stages of an initial teaching scheme or an updated teaching scheme, and a preconfigured capability course matching model is updated. Therefore, the matching accuracy of the ability course matching model can be improved, the teaching scheme obtained through matching can be more fit with the ability of the object to be recommended, and a better teaching effect can be achieved.
Fig. 10 is a schematic structural diagram of a course recommending apparatus based on capability evaluation data according to a fifth embodiment of the present disclosure, as shown in fig. 10, the apparatus includes an interaction module 1001 and a processing module 1002. The interaction module 1001 is configured to obtain a course recommendation request, where the course recommendation request includes: the identification of the object to be recommended and the personal basic information of the object to be recommended. The processing module 1002 is configured to obtain, according to the course recommendation request, capability evaluation data information corresponding to the identifier of the object to be recommended. And analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
Further, on the basis of the fifth embodiment, the processing module is configured to:
and acquiring an ability point corresponding to the personal basic information on a pre-configured growth ability axis. And respectively acquiring the capability evaluation data information corresponding to the object to be recommended under each capability point.
Further, on the basis of the fifth embodiment, the processing module is configured to:
and acquiring the capability points and capability evaluation value intervals corresponding to the personal basic information, and difficulty levels at each capability point. And analyzing the capability evaluation data information according to the capability points and the capability evaluation value intervals corresponding to the personal basic information and the difficulty level of each capability point to obtain a capability evaluation value of the object to be recommended. And processing the capability evaluation value by adopting the pre-configured capability course matching model to generate the initial teaching scheme.
Further, on the basis of the fifth embodiment, the interaction module is further configured to: and acquiring comment information of the teacher under the initial teaching scheme. The processing module is further used for processing the capability evaluation numerical value and the teacher assessment information by adopting the pre-configured capability course matching model so as to generate an updated teaching scheme.
Further, on the basis of any of the above embodiments, the method further includes: the device comprises a communication module and a generation module. The communication module is used for acquiring the consultation information of the user, wherein the consultation information comprises the basic personal information of the object to be consulted and the consultation content. Acquiring capability points corresponding to basic personal information of a subject to be consulted, analyzing the consultation content, and acquiring key information related to the capability points of the consulted subject in the consultation content. And acquiring consultation term information corresponding to the key information to guide the user, and acquiring development data related to the object to be consulted. And the generating module is used for generating and recommending the matched teaching service mode according to the development data.
Further, on the basis of any of the above embodiments, the method further includes: the device comprises a collection module and an updating module, wherein the collection module is used for collecting and acquiring tracking information related to the object to be recommended at a preset stage of the initial teaching scheme or the updated teaching scheme. And the updating module is used for updating the pre-configured ability course matching model according to the tracking information in each preset stage and the expected ability evaluation value in each preset stage. The tracking information related to the object to be recommended comprises one or more of the following items: new capability evaluation data information, basic personal information change data, evaluation information of teachers, and environmental information of objects to be recommended.
Fig. 11 is a schematic structural diagram of a course recommending apparatus based on capability evaluation data according to a sixth embodiment of the disclosure, and as shown in fig. 11, the course recommending apparatus based on capability evaluation data includes: memory 1101, processor 1102;
a memory 1101; a memory 1101 for storing instructions executable by the processor 1102;
wherein the processor 1102 is configured to call program instructions in the memory 1101 to execute a course recommendation method based on capability evaluation data according to any one of the above embodiments.
The memory 1101 stores programs. In particular, the program may include program code comprising computer operating instructions. The memory 1101 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 1102 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present disclosure.
Alternatively, in a specific implementation, if the memory 1101 and the processor 1102 are implemented independently, the memory 1101 and the processor 1102 may be connected to each other through a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
Still another embodiment of the present disclosure further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement a course recommendation method based on capability evaluation data as described in any one of the above embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (14)
1. A course recommendation method based on capability evaluation data is characterized by comprising the following steps:
obtaining a course recommendation request, wherein the course recommendation request comprises: the method comprises the following steps of identifying an object to be recommended and the personal basic information of the object to be recommended;
acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request;
and analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
2. The course recommending method based on ability evaluation data according to claim 1, wherein said obtaining ability evaluation data information corresponding to the identification of the object to be recommended comprises:
acquiring an ability point corresponding to the personal basic information on a pre-configured growth ability axis;
and respectively acquiring the capability evaluation data information corresponding to the object to be recommended under each capability point.
3. The ability evaluation data-based course recommendation method according to claim 2, wherein the analyzing the personal basic information of the object to be recommended and the ability evaluation data information by using a pre-configured ability course matching model to generate an initial teaching plan matching the object to be recommended comprises:
acquiring a capability point and a capability evaluation value interval corresponding to the personal basic information and a difficulty level of each capability point;
analyzing the capability evaluation data information according to the capability points and capability evaluation value intervals corresponding to the personal basic information and the difficulty level of each capability point to obtain a capability evaluation value of the object to be recommended;
and processing the capability evaluation value by adopting the pre-configured capability course matching model to generate the initial teaching scheme.
4. The method for recommending courses based on ability evaluation data according to claim 3, further comprising:
acquiring comment information of a teacher under the initial teaching scheme;
and processing the capability evaluation numerical value and the teacher evaluation information by adopting the pre-configured capability course matching model to generate an updated teaching scheme.
5. The method for recommending courses according to any of claims 1 to 4, further comprising:
acquiring consultation information of a user, wherein the consultation information comprises basic personal information of an object to be consulted and consultation content;
acquiring capability points corresponding to basic personal information of an object to be consulted, analyzing the consultation content, and acquiring key information related to the capability points of the consulted object in the consultation content;
acquiring consulting term information corresponding to the key information to guide the user and acquiring development data related to the object to be consulted;
and generating and recommending a matched teaching service mode according to the development data.
6. The method for recommending courses based on ability evaluation data according to any of claims 1 to 4, further comprising:
acquiring and acquiring tracking information related to the object to be recommended at a preset stage of the initial teaching scheme or the updated teaching scheme;
updating the pre-configured ability course matching model according to the tracking information in each preset stage and the expected ability evaluation value in each preset stage;
the tracking information related to the object to be recommended comprises one or more of the following items: new capability evaluation data information, basic personal information change data, evaluation information of teachers, and environmental information of objects to be recommended.
7. A course recommending apparatus based on ability evaluation data, comprising:
an interaction module, configured to obtain a course recommendation request, where the course recommendation request includes: the method comprises the following steps of identifying an object to be recommended and the personal basic information of the object to be recommended;
the processing module is used for acquiring capability evaluation data information corresponding to the identification of the object to be recommended according to the course recommendation request;
and analyzing the personal basic information of the object to be recommended and the capability evaluation data information by adopting a pre-configured capability course matching model to generate an initial teaching scheme matched with the object to be recommended.
8. The ability-assessment-data-based course recommendation device of claim 7, wherein said processing module is configured to:
acquiring an ability point corresponding to the personal basic information on a pre-configured growth ability axis;
and respectively acquiring the capability evaluation data information corresponding to the object to be recommended under each capability point.
9. The ability-assessment-data-based course recommendation device of claim 8, wherein said processing module is configured to:
acquiring an ability point and an ability evaluation value interval corresponding to the personal basic information, and a difficulty level of each ability point;
analyzing the capability evaluation data information according to the capability points and capability evaluation value intervals corresponding to the personal basic information and the difficulty level of each capability point to obtain a capability evaluation value of the object to be recommended;
and processing the capability evaluation value by adopting the pre-configured capability course matching model to generate the initial teaching scheme.
10. The ability-assessment-data-based course recommendation device of claim 9, wherein said interaction module is further configured to: acquiring comment information of a teacher under the initial teaching scheme;
the processing module is further used for processing the capability evaluation numerical value and the teacher assessment information by adopting the pre-configured capability course matching model so as to generate an updated teaching scheme.
11. The lesson recommending apparatus according to any one of claims 7 to 10, further comprising:
the communication module is used for acquiring the consultation information of the user, wherein the consultation information comprises basic personal information of an object to be consulted and consultation content;
acquiring capability points corresponding to basic personal information of an object to be consulted, analyzing the consultation content, and acquiring key information related to the capability points of the consulted object in the consultation content;
acquiring consultation term information corresponding to the key information to guide the user, and acquiring development data related to the object to be consulted;
and the generating module is used for generating and recommending the matched teaching service mode according to the development data.
12. The lesson recommending apparatus according to any one of claims 7 to 10, further comprising:
the acquisition module is used for acquiring and acquiring tracking information related to the object to be recommended at a preset stage of the initial teaching scheme or the updated teaching scheme;
the updating module is used for updating the pre-configured ability course matching model according to the tracking information in each preset stage and the expected ability evaluation value in each preset stage;
the tracking information related to the object to be recommended comprises one or more of the following items: new capability evaluation data information, basic personal information change data, evaluation information of teachers, and environmental information of objects to be recommended.
13. A course recommendation method and device based on capability evaluation data is characterized by comprising the following steps: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to invoke program instructions in the memory to perform a course recommendation method based on capability assessment data as claimed in any one of claims 1 to 6.
14. A computer-readable storage medium having stored thereon computer-executable instructions for implementing the method for course recommendation based on capability assessment data as claimed in any one of claims 1-6 when executed by a processor.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2021103195868 | 2021-03-25 | ||
| CN202110319586 | 2021-03-25 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115129971A true CN115129971A (en) | 2022-09-30 |
Family
ID=83375671
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110361082.2A Pending CN115129971A (en) | 2021-03-25 | 2021-04-02 | Course recommendation method and device based on capability evaluation data and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115129971A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575265A (en) * | 2023-11-29 | 2024-02-20 | 新励成教育科技股份有限公司 | Intelligent course arrangement system, method and product based on multiple condition demands |
-
2021
- 2021-04-02 CN CN202110361082.2A patent/CN115129971A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575265A (en) * | 2023-11-29 | 2024-02-20 | 新励成教育科技股份有限公司 | Intelligent course arrangement system, method and product based on multiple condition demands |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Truong | Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities | |
| Lao | Reorienting machine learning education towards tinkerers and ML-engaged citizens | |
| CN112184500A (en) | Extraclass learning tutoring system based on deep learning and knowledge graph and implementation method | |
| Greene et al. | A two-tiered approach to analyzing self-regulated learning data to inform the design of hypermedia learning environments | |
| Xia et al. | Robots for future classrooms: A cross-cultural validation study of “negative attitudes toward robots scale” in the US context | |
| Yuan et al. | Assessment of collaborative problem solving based on process stream data: A new paradigm for extracting indicators and modeling dyad data | |
| CN113851020A (en) | Self-adaptive learning platform based on knowledge graph | |
| Giabbanelli et al. | Overcoming the PBL assessment challenge: Design and development of the incremental thesaurus for assessing causal maps (ITACM) | |
| Supianto et al. | Model-based analysis of thinking in problem posing as sentence integration focused on violation of the constraints | |
| CN114254127A (en) | Student ability portrayal method and learning resource recommendation method and device | |
| CN114398556A (en) | Learning content recommendation method, device, equipment and storage medium | |
| Adewale et al. | Design of a personalised adaptive ubiquitous learning system | |
| CN113283488A (en) | Learning behavior-based cognitive diagnosis method and system | |
| Dai et al. | An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study | |
| Pupara et al. | An institution recommender system based on student context and educational institution in a mobile environment | |
| Chaudhary et al. | Student future prediction using machine learning | |
| Zhang et al. | Learning preference: development in smart learning environments | |
| Jyothy et al. | Exploring large language models as an integrated tool for learning, teaching, and research through the Fogg Behavior Model: a comprehensive mixed-methods analysis | |
| CN115129971A (en) | Course recommendation method and device based on capability evaluation data and readable storage medium | |
| Khan et al. | Utilizing machine learning models to predict student performance from LMS activity logs | |
| Gupta et al. | Affective teacher tools: affective class report card and dashboard | |
| Ding et al. | EduAware: using tablet-based navigation gestures to predict learning module performance | |
| Zhao | [Retracted] Construction of College Chinese Mobile Learning Environment Based on Intelligent Reinforcement Learning Technology in Wireless Network Environment | |
| Armağan et al. | The Antecedents of the Consumers' Mobile Learning Intention during the Covid-19 Pandemic | |
| CN112231373A (en) | Method, device and equipment for processing knowledge point data and computer readable medium |
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 |