CN116797052A - Resource recommendation method, device, system and storage medium based on programming learning - Google Patents
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Abstract
The application relates to a resource recommendation method, a device, a system and a storage medium based on programming learning, wherein the resource recommendation method based on programming learning comprises the following steps: obtaining a programming exercise result of a learner, and determining the mastering degree of each programming knowledge point by the learner according to the programming exercise result; determining evaluation weights of the programming knowledge points according to the contribution degree of the programming knowledge points to the programming capability of the learner; generating a programming capability portrait of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point; and recommending programming learning resources to the learner according to the programming capability portrait of the learner. The application provides a personalized resource recommendation method for programming learning, which solves the problem that an effective resource recommendation method for programming learning is lacked in the related technology.
Description
Technical Field
The present application relates to the field of resource recommendation, and in particular, to a method, apparatus, system, and storage medium for resource recommendation based on programming learning.
Background
With the rapid development of computer and artificial intelligence technologies, theoretical teaching courses are widely adopting a learning content recommendation method aiming at individual characteristics of learners. These techniques can generate a learning portraits by analyzing learning process data of a learner, and recommend learning resources, learning routes, teaching activities, etc. closer to the learner according to the learning portraits. In contrast, personalized recommendations for online programming learning are relatively late. Extraction and analysis of learning process data and intelligent recommendation of learning resources are always facing great challenges, and systemization and automation of personalized recommendation are difficult to achieve. Therefore, there is currently a lack of an effective resource recommendation method for programming learning.
Aiming at the problem of lack of an effective resource recommendation method for programming learning in the related art, no effective solution is proposed at present.
Disclosure of Invention
The invention provides a resource recommendation method, device, system and storage medium based on programming learning, which are used for solving the problem that an effective resource recommendation method aiming at programming learning is lacked in the related technology.
In a first aspect, the present invention provides a resource recommendation method based on programming learning, the method comprising:
Obtaining a programming exercise result of a learner, and determining the mastering degree of each programming knowledge point by the learner according to the programming exercise result;
determining evaluation weights of the programming knowledge points according to the contribution degree of the programming knowledge points to the programming capability of the learner;
generating a programming capability portrait of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point;
and recommending programming learning resources to the learner according to the programming capability portrait of the learner.
In some of these embodiments, the learner's programming practice results include actual assessment scores of the learner at each of the programming knowledge points;
the determining the mastery degree of the learner on each programming knowledge point according to the programming exercise result comprises the following steps:
and determining the mastery degree of the learner on each programming knowledge point according to the actual evaluation score of the learner on each programming knowledge point and the full score of each programming knowledge point.
In some of these embodiments, the method further comprises:
determining effective evaluation scores of the learner at all the programming knowledge points according to the actual evaluation scores of the learner at all the programming knowledge points and the evaluation weights of all the programming knowledge points;
And determining the comprehensive programming capability of the learner according to the effective evaluation scores of the learner at each programming knowledge point.
In some of these embodiments, after generating the learner's programming ability representation, the method further comprises:
displaying the programming capability portrait of the learner through a visual graph;
wherein the color brightness of each graphic area in the visual graphic represents the mastery degree of the learner on each programming knowledge point; the area ratio of each graphic area in the visual graphic represents the evaluation weight of the programming knowledge point.
In some embodiments, each programming knowledge point is associated with a different programming learning resource;
the recommending programming learning resources to the learner according to the programming capability representation of the learner comprises:
and determining the recommendation priority of programming learning resources associated with each programming knowledge point by taking the mastery degree of the learner on each programming knowledge point as a primary consideration index and taking the evaluation weight of each programming knowledge point as a secondary consideration index.
In some of these embodiments, the method further comprises:
Acquiring initial programming log data generated when the learner performs programming exercise, and analyzing the initial programming log data to obtain target programming log data;
extracting data indexes capable of representing programming ability from the target programming log data according to the preset evaluation modes of the programming knowledge points;
and determining the programming exercise result of the learner according to the data index of the expressive programming ability.
In some embodiments thereof, the programming learning resources include programming teaching resources and/or programming training resources;
the method further comprises the steps of:
constructing a knowledge body system in the programming learning field, and determining each programming knowledge point based on the knowledge body system;
and establishing association relations between each programming knowledge point and different programming teaching resources and/or programming exercise resources.
In a second aspect, the present invention provides a resource recommendation device based on programming learning, the device comprising:
the result acquisition module is used for acquiring programming exercise results of the learner and determining the mastering degree of each programming knowledge point of the learner according to the programming exercise results;
The weight determining module is used for determining the evaluation weight of each programming knowledge point according to the contribution degree of each programming knowledge point to the programming capability of the learner;
the image generation module is used for generating a programming capability image of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point;
and the resource recommending module is used for recommending programming learning resources to the learner according to the programming capability portrait of the learner.
In a third aspect, the present invention provides a resource recommendation system based on program learning, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the resource recommendation method based on program learning according to the first aspect when executing the computer program.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the resource recommendation method based on programming learning described in the first aspect.
Compared with the related art, the resource recommendation method, device, system and storage medium based on programming learning provided by the invention are mainly used for determining the programming capability representation of the learner based on the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point of the learner. The programming capability image can embody the mastering condition of a learner on each programming knowledge point, and based on the programming capability image of the learner, programming learning resources can be recommended to weak links of the learner, so that personalized recommendation of learning resources of different learners is realized. Therefore, the invention provides a personalized resource recommendation method aiming at programming learning, which solves the problem that the effective resource recommendation method aiming at programming learning is lacking in the related technology.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a hardware block diagram of a terminal for executing a resource recommendation method based on programming learning provided by the application;
FIG. 2 is a flow chart of a resource recommendation method based on programming learning provided by the application;
FIG. 3 is a flow chart of a resource recommendation method based on programming learning in some embodiments of the application;
FIG. 4 is a flow chart of a resource recommendation method based on programming learning in some embodiments of the application;
FIG. 5 is a flow chart of a resource recommendation method based on programming learning in one embodiment of the application;
fig. 6 is a block diagram of a resource recommendation device based on programming learning.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples for a clearer understanding of the objects, technical solutions and advantages of the present application.
Unless defined otherwise, technical or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these" and similar terms in this application are not intended to be limiting in number, but may be singular or plural. The terms "comprising," "including," "having," and any variations thereof, as used herein, are intended to encompass non-exclusive inclusion; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (units) is not limited to the list of steps or modules (units), but may include other steps or modules (units) not listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this disclosure are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. Typically, the character "/" indicates that the associated object is an "or" relationship. The terms "first," "second," "third," and the like, as referred to in this disclosure, merely distinguish similar objects and do not represent a particular ordering for objects.
The method embodiments provided in the present embodiment may be executed in a terminal, a computer, or similar computing device. For example, the method runs on a terminal, and fig. 1 is a hardware structure block diagram of the terminal for executing the resource recommendation method based on programming learning provided by the invention. As shown in fig. 1, the terminal may include one or more (only one is shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like. The terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the terminal. For example, the terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store computer programs, such as software programs of application software and modules, such as those corresponding to the resource recommendation method based on program learning provided in the present invention, and the processor 102 executes the computer programs stored in the memory 104 to perform various functional applications and data processing, i.e., implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The invention provides a resource recommendation method based on programming learning. Fig. 2 is a flowchart of a resource recommendation method based on programming learning, as shown in fig. 2, where the flowchart includes the following steps:
step S210, obtaining a programming exercise result of the learner, and determining the mastering degree of each programming knowledge point by the learner according to the programming exercise result.
Specifically, prior to this step, programming exercises may be provided to the learner, after which the learner may determine the level of mastery of the individual programming knowledge points. Therefore, in this step, the result of the programming exercise by the learner is obtained after the learner performs the programming exercise, and the degree of mastery of each programming knowledge point by the learner can be determined according to the result. For example, a learner may be provided with programming questions or project works, the learner may program based on the programming questions or project works, and program codes written by the learner may be evaluated by a preset evaluation rule or teacher, and the evaluation result is a programming exercise result of the learner.
Step S220, determining the evaluation weight of each programming knowledge point according to the contribution degree of each programming knowledge point to the programming ability of the learner.
In particular, several programming knowledge points are involved in the programming knowledge hierarchy, but the contribution of different programming knowledge points to the learner's programming ability is different. For example, if a learner wants to have a class a programming capability, a programming knowledge point with a strong correlation with the class a programming capability has a large contribution to the learner's programming capability. Therefore, in this step, for the learner desiring the class A programming capability, the programming knowledge point with the stronger correlation with the class A programming capability will get the higher evaluation weight. Similarly, for a learner desiring class B programming capability, a higher evaluation weight will be obtained for a programming knowledge point with a higher degree of relevance to class B programming capability.
Step S230, according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point, generating a programming ability image of the learner.
Through the above two steps, the learning degree of each programming knowledge point by the learner and the evaluation weight of each programming knowledge point of the learner are obtained, and the programming ability image of the learner can be generated based on the two indexes. Specifically, the programmability image may represent the mastering situation of the learner on each programming knowledge point, and may be roughly divided into four situations: firstly, the learner has high mastery degree of programming knowledge points with high evaluation weight, secondly, the learner has high mastery degree of programming knowledge points with low evaluation weight, thirdly, the learner has low mastery degree of programming knowledge points with high evaluation weight, and thirdly, the learner has low mastery degree of programming knowledge points with low evaluation weight.
Step S240, recommending programming learning resources to the learner according to the programming capability representation of the learner.
In this step, the programmability image may represent the learner's knowledge of each programming knowledge point. Thus, a learner's programming weaknesses may be determined based on the learner's representation of the programming capabilities. Therefore, programming learning resources can be recommended to the learner mainly aiming at weak links of the learner, and personalized recommendation of the learning resources aiming at different learners is realized. For example, a programming knowledge point with low mastery degree and high evaluation weight of a learner is determined first, and then learning resources for the programming knowledge point are recommended to the learner.
Through the steps, the programming capability representation of the learner is determined mainly based on the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point of the learner. The programming capability image can embody the mastering condition of a learner on each programming knowledge point, and based on the programming capability image of the learner, programming learning resources can be recommended to weak links of the learner, so that personalized recommendation of learning resources of different learners is realized. Therefore, the invention provides a personalized resource recommendation method aiming at programming learning, which solves the problem that the effective resource recommendation method aiming at programming learning is lacking in the related technology.
In some of these embodiments, the learner's programming exercise results include actual assessment scores of the learner at various programming knowledge points; and step S210, determining the mastering degree of each programming knowledge point by the learner according to the programming exercise result, wherein the method specifically comprises the following steps:
and determining the mastery degree of the learner on each programming knowledge point according to the actual evaluation score of the learner on each programming knowledge point and the full score of each programming knowledge point.
In this embodiment, a specific technical means for determining the degree of mastery of each programming knowledge point by the learner is provided. As described above, before step S210, the programming exercise may be provided to the learner, and after the learner performs the programming exercise, the program code written by the learner may be evaluated by a preset evaluation mode or by a teacher. In the present embodiment, the evaluation process is a process of analyzing program codes written by learners and scoring grasping situations of individual programmed knowledge points. I.e. the learner's programming training results include the actual assessment scores of the learner at each programming knowledge point. And before the evaluation, standard scores, i.e., full scores, for each programmed knowledge point may be set. Further, for any programming knowledge point, the ratio of the actual evaluation score to the full score of the learner reflects the mastery degree of the learner on the programming knowledge point. For example, when the actual evaluation score of a learner at a certain programming knowledge point is a full score, it is indicated that the learner has completely mastered the programming knowledge point. Through the technical means provided by the embodiment, the mastering degree of each programming knowledge point by the learner can be determined.
Further, in some embodiments, the resource recommendation method based on programming learning in the present invention further includes:
step S231, determining effective evaluation scores of the learner at all programming knowledge points according to actual evaluation scores of the learner at all programming knowledge points and evaluation weights of all programming knowledge points; step S232, determining the comprehensive programming capability of the learner according to the effective evaluation scores of the learner at each programming knowledge point.
In this embodiment, a technical means for determining the comprehensive programming ability of the learner is also provided. Specifically, the comprehensive programming ability of the learner should be considered in combination with the level of knowledge of the learner in each programming knowledge point. However, the contribution degree of different programming knowledge points to the programming capability is different, so that the evaluation weight determination of each programming knowledge point is also needed. Specifically, the actual evaluation score of the learner at each programmed knowledge point may be multiplied by the respective evaluation weight, thereby obtaining an effective evaluation score of the learner at each programmed knowledge point. The effective evaluation score of each programming knowledge point combines the mastery degree of the learner on the corresponding programming knowledge point and the evaluation weight of the corresponding programming knowledge point, thereby being capable of more accurately reflecting the programming capability of the learner. At this time, the effective evaluation scores of the programming knowledge points can be added, and the total score of the effective evaluation scores represents the comprehensive programming capability of the learner.
Preferably, the total score of the effective assessment score may also be converted into a percent representation. The following formula can be specifically adopted:
wherein, the liquid crystal display device comprises a liquid crystal display device,Arepresenting the comprehensive programming ability of the learner, namely, the percent conversion result of the total score of the effective evaluation score,n i is the first of learnersiThe actual evaluation score of each knowledge point,n j is the firstjThe full score of the individual knowledge points,W i andW j respectively the firstiAnd (b)jThe evaluation weights of the individual programmed knowledge points,mto program the number of knowledge points.
In some embodiments, the resource recommendation method based on programming learning in the present invention further includes, after generating the programming capability representation of the learner:
step S233, displaying the programming ability portrait of the learner through a visual graph; wherein, the color brightness of each graphic area in the visual graphic represents the mastery degree of each programming knowledge point by a learner; the area ratio of each graphic region in the visualized graphic represents the evaluation weight of the programming knowledge point.
In the embodiment, the programming capability portrait of the learner is displayed through the visual graph, so that the learner can more intuitively know the self programming capability, and simultaneously, an intuitive reference basis is provided for learning diagnosis and guidance. Specifically, a plurality of graphic areas are included in the visual image, and each graphic area corresponds to each programming knowledge point one by one, i.e. one graphic area represents one programming knowledge point. The graphic area represents the mastering degree of the learner on the corresponding programming knowledge point through the color brightness, namely the color shade. Through the graph area passing area ratio, namely the proportion of the graph area to the total visualized graph area, the evaluation weight of the corresponding programming knowledge point is represented. For example, the darker the color of the graphic region, the higher the level of mastery of the corresponding programming knowledge point by the learner, the higher the area occupation ratio of the graphic region, and the larger the evaluation weight of the corresponding programming knowledge point. Illustratively, a pie thermodynamic diagram may be employed to demonstrate a learner's representation of programming capabilities.
In some of these embodiments, each programming knowledge point is associated with a different programming learning resource, respectively; step S240, recommending programming learning resources to the learner according to the programming capability portrait of the learner, specifically comprising:
in step S241, the learning level of each programming knowledge point is taken as a primary consideration index, and the evaluation weight of each programming knowledge point is taken as a secondary consideration index, so as to determine the recommendation priority of the programming learning resource associated with each programming knowledge point.
As described above, the programmability image may represent the mastering situation of the learner on each programming knowledge point, and may be roughly divided into four situations: firstly, the learner has high mastery degree of programming knowledge points with high evaluation weight, secondly, the learner has high mastery degree of programming knowledge points with low evaluation weight, thirdly, the learner has low mastery degree of programming knowledge points with high evaluation weight, and thirdly, the learner has low mastery degree of programming knowledge points with low evaluation weight. In this embodiment, when determining the recommendation priority of the programming learning resources associated with each programming knowledge point, the learning level of each programming knowledge point by the learner is considered first, that is, the programming learning resources associated with the programming knowledge points with low learning level by the learner are recommended preferentially. On the basis, the evaluation weights of all programming knowledge points are further considered, and the higher the evaluation weights, the higher the priority is. Therefore, the priority of each programming knowledge point in the programming learning resource recommendation process can be determined as follows: the method comprises the steps of programming knowledge points with low mastering degree and high evaluation weight of a learner, programming knowledge points with low mastering degree and low evaluation weight of the learner, programming knowledge points with high mastering degree and high evaluation weight of the learner, and programming knowledge points with high mastering degree and low evaluation weight of the learner.
Specifically, the number, type and learning difficulty of corresponding programming learning resources can be set according to the number and learning difficulty of weak programming knowledge points of learners. And generating a personalized learning plan of the learner according to the set learning sequence of the programmed learning resources. Recommending the generated personalized learning plan to a learner, guiding the learner to strengthen exercise according to the learning plan, and improving the mastery degree of the learner on weaker programming knowledge points.
Furthermore, a teacher feedback module can be constructed, the teacher can supplement and correct feedback contents (programming learning resources and learning plans) generated by the system according to own professional knowledge and experience, and the system pushes updated feedback contents to a learner for strengthening exercise. Meanwhile, an incremental learning mechanism can be established, and as new data of a learner are generated, the new data are analyzed through loop iteration, and feedback content and a learning guidance scheme are updated, so that the learning instruction is dynamically adapted to the latest learning current situation of the learner.
FIG. 3 is a flow chart of a resource recommendation method based on programming learning in some embodiments of the invention. Referring to FIG. 3, in some embodiments thereof, the programming learning resources include programming teaching resources and/or programming training resources; the resource recommendation method based on programming learning in the invention further comprises the following steps before the step S210:
Step S201, constructing a knowledge body system in the programming learning field, and determining each programming knowledge point based on the knowledge body system; step S202, establishing association relations between each programming knowledge point and different programming teaching resources and/or programming practice resources.
Because the learner's programming ability representation needs to represent the learner's mastery of each programming knowledge point, and personalized recommendation of programming learning resources is implemented based on each programming knowledge point. Therefore, in this embodiment, before the learner's programming capability image is created, the knowledge points of the programming knowledge system are classified and disassembled, so as to obtain each programming knowledge point possibly involved in the programming process, and corresponding programming learning resources are associated for each programming knowledge point.
For example, knowledge engineering methods can be adopted to build a knowledge ontology system in the programming learning field, and knowledge modeling, knowledge organization and association, interface query and other technologies are implemented. Specifically, in knowledge modeling, knowledge points in the programming field are formally described and abstracted by using a knowledge ontology system construction technology, and a programming knowledge base is constructed. In terms of the organization of the knowledge, And carrying out multi-level classification on the programming knowledge points by using a knowledge classification and coding technology, giving formal coding, and establishing a classification tree structure of the programming knowledge points. For example, the acquired knowledge points areK 1 、K 2 、K 3 、...、K n . In the aspect of knowledge association, an association relation mapping technology is utilized, on the basis of a constructed knowledge point classification tree, a structured many-to-many association relation is established between programming knowledge points and programming teaching resources (theoretical teaching materials, practical cases, teaching videos and the like) and programming exercise resources (exercises, project works and the like), and dynamic mapping of the programming knowledge points to programming learning resources is realized. And storing the knowledge graph by adopting a relational database technology, providing a corresponding query interface, and supporting the complex knowledge graph query. A query interface based on programming learning resources and programming knowledge points is provided, receiving a learner's learning requirement query and returning matched programming learning resources.
FIG. 4 is a flow chart of a resource recommendation method based on programming learning in some embodiments of the invention. Referring to fig. 4, in some embodiments, the resource recommendation method based on programming learning in the present invention further includes, before step S210:
step S203, initial programming log data generated when a learner performs programming exercise are obtained, and the initial programming log data are analyzed to obtain target programming log data; step S204, extracting data indexes capable of representing programming ability from target programming log data according to the preset evaluation modes of each programming knowledge point; step S205, determining the programming exercise result of the learner according to the data index capable of representing the programming ability.
In this embodiment, a specific technical means of determining the result of a learner's programming exercise is provided. Firstly, in the process of programming exercise of a learner, the programming data are collected, and initial programming log data are obtained. Then, the initial programming log data is parsed, and the parsing process is mainly divided into two parts. On the one hand, language analysis technologies such as lexical analysis, grammar analysis and semantic analysis are utilized to analyze codes written by learners, code semantic information, grammar information, operation information and the like are extracted, and operation traces of the learners are recorded; on the other hand, natural language information such as code notes written by learners, search query keywords and the like in the programming log data is analyzed by using a natural language processing technology and a rule reasoning technology. The analyzed programming log data is the target programming log data. Further, according to the preset evaluation mode of each programming knowledge point, data indexes which show the mastering degree of the learner on each programming knowledge point, namely data indexes which can show the programming ability, are extracted from the target programming log data. By way of example, the data indicators that may represent programming capabilities may be indicators of code normalization, debug efficiency, problem solving capabilities, and the like. Specifically, a machine learning technology can be used for establishing a programming log feature space, extracting data indexes capable of representing programming ability in log data, mapping the data indexes to the feature space for quantitative analysis, and obtaining evaluation scores of learners at all programming knowledge points, so that programming exercise results of the learners are determined.
The technical scheme of the invention is described below through a specific embodiment.
FIG. 5 is a flow chart of a resource recommendation method based on programming learning in one embodiment of the invention. Referring to fig. 5, the resource recommendation method based on programming learning in this embodiment includes the following steps:
step S310, knowledge graph disassembly and learning resource management.
Specifically, a knowledge engineering method is adopted to establish a knowledge ontology system in the programming learning field, and knowledge modeling, knowledge organization and association, interface query and other technologies are implemented. Specifically, in knowledge modeling, knowledge points in the programming field are formally described and abstracted by using a knowledge ontology construction technology, and a knowledge base is constructed. In knowledge organization, knowledge points are classified in multiple levels by using knowledge classification and coding technology and formally coded, and a classification tree structure of the knowledge points is built, for example, the obtained knowledge points areK 1 、K 2 、K 3 、...、K n . In knowledge association, the association relation mapping technology is utilized to carry out knowledge point and learning resources (theoretical teaching materials, practical cases, teaching videos and the like) on the basis of the constructed knowledge classification treeL 1 、L 2 、L 3 、...、L n Evaluation exercises (exercises, project works, etc.) P 1 、P 2 、P 3 、...、P n And a structured many-to-many association relation is established between the knowledge points, so that the knowledge points are dynamically mapped to related learning resources and evaluation exercises. And storing the knowledge graph by adopting a relational database technology, providing a corresponding query interface, and supporting the complex knowledge graph query. A query interface based on learning resources and knowledge points is provided, and a learner's learning requirement query is received and matched learning resources and assessment exercises are returned.
Step S320, the learner programs exercises online.
Specifically, the front end is embedded with execution engines of different languages, so that a plurality of mainstream programming languages are supported, and a learner can select the languages at will for development. The code editor has the functions of code reading, automatic complement, grammar highlighting and the like, and can conveniently write and modify codes. The back-end server with automatic compiling function can automatically compile codes submitted by learners and detect grammar errors and logic errors. The compiling result is directly displayed at the front end, and comprises success information and error prompts, and a learner can modify codes according to feedback information. The back end uses the version control system to track and manage the code modification history of the learner, and the learner can trace back to the history version at any time. Supporting learner to preview and debug running code at front end or to deploy running through interface at back end. The back end uses container technology to start independent compiling and running environment for each learner, and ensures the safety of code execution.
Step S330, data acquisition of online programming exercises.
Specifically, structured evaluation rules and evaluation rules are set for the knowledge points in step S310Score [ ]n 1 、n 2 、n 3 、...、n n ) Weight of evaluationW 1 、W 2 、W 3 、...、W n ) And establishing evaluation management of the knowledge points and storing the evaluation management in a knowledge evaluation library. The score value is the full score value of each knowledge point. The programming environment is embedded into a log acquisition module, a code written by a learner is analyzed by using language Analysis technologies such as Lexical Analysis (Lexical Analysis), grammar Analysis (Paring), semantic Analysis (semantic Analysis) and the like, code semantic information, grammar information, operation information and the like are extracted, and operation traces of the learner are recorded to form structured programming log data. And analyzing natural language information such as code annotation written by a learner, search query keywords and the like in the programming log data by using a natural language processing technology and a rule reasoning technology, and extracting and collecting according to the knowledge points and evaluation rules of the knowledge points. And establishing a programming log feature space by using a machine learning technology, extracting feature indexes in log data, such as indexes of code normalization, debugging efficiency, problem solving capability and the like, mapping the feature indexes into the feature space, and performing quantitative analysis to obtain actual evaluation scores of all knowledge points of programming.
Step S340, intelligent assessment of the learner' S online programming exercises.
Specifically, the intelligent evaluation mainly comprises the following sub-steps:
a) Acquiring actual evaluation scores of all knowledge points, wherein the actual evaluation scores are obtained through programming exercise of learners on all knowledge points;
b) Setting evaluation weights for all knowledge points, wherein the evaluation weights reflect the contribution degree of all knowledge points to the programming capability of learners;
c) Calculating effective evaluation scores of all knowledge points, namely the product of the actual evaluation scores of all knowledge points and the evaluation weights of all knowledge points;
d) Adding the effective evaluation scores of the knowledge points to obtain an original score of the programming ability of the learner;
e) And converting the original score of the programming ability of the learner into a percentile to obtain the comprehensive programming ability score of the learner.
According to the learner programming ability evaluation method, the programming performance of the learner on each knowledge point is quantified, the evaluation weight is set according to the contribution degree of each knowledge point to the learner programming ability, and finally the comprehensive programming ability score of the learner is obtained, so that the learner programming ability is evaluated.
Specifically, the comprehensive programmability score of the learner may be calculated using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device, ARepresenting the learner's comprehensive programmability score,n i is the first of learnersiThe actual evaluation score of each knowledge point,n j is the firstjThe full score of the individual knowledge points,W i andW j respectively the firstiSum of alljThe evaluation weights of the individual programmed knowledge points,mto program the number of knowledge points.
Furthermore, the comprehensive programming capability obtained by the learner, namely the actual evaluation score of each knowledge point, can be visualized on the front page, and the performance of the learner on the programming capability of each dimension is presented in the form of a thermodynamic diagram (pie chart), so that the programming capability image of the learner is formed. The programming capability portrait shows the capability of the learner at the corresponding knowledge point through the color depth, and the darker the color is, the stronger the capability of the knowledge point is. The shade value of the color is calculated from the actual evaluation score of the knowledge point. The programming capability image reflects the importance degree of the knowledge points in programming practice through the proportion of the knowledge points in the pie chart, the larger the proportion in the pie chart is, the more important the knowledge points are, and the proportion of the pie chart is calculated by the evaluation weight. Through the thermodynamic diagram, the programming ability of the learner can be clearly seen at which knowledge points are weak and which important knowledge points need to be strengthened, and an intuitive reference basis is provided for learning diagnosis and guidance.
In particular, it is assumed that in thermodynamic diagramsFirst, theiThe color shade value of each knowledge point isS i ThenS i The calculation formula of (2) is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,n i is the first of learnersiThe actual evaluation score of each knowledge point,N i is the firstiAnd the evaluation value of each knowledge point.
And, assuming that in the thermodynamic diagram,R i is the firstiPie chart occupancy of individual knowledge points, thenR i The calculation formula of (2) is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,W i andW j respectively the firstiAnd (b)jThe evaluation weight of each knowledge point,mis the number of knowledge points.
Step S350, intelligent recommendation is performed on programming exercises of the learner.
Specifically, according to the programming capability representation of the learner, the evaluation results of the learner on each knowledge point are analyzed, and weak knowledge points of the learner are identified. Combining knowledge points and learning resources (theoretical teaching materials, practical cases, teaching videos and the like)L 1 、L 2 、L 3 、...、L n Evaluation exercise (exercises, project work, etc.)P 1 、P 2 、P 3 、...、P n And searching learning resources and assessment exercises associated with weak knowledge points of learners according to the many-to-many association relation. According to the weak knowledge points of the learner and the learning difficulty, corresponding learning resources, the number, the type and the learning difficulty of the assessment exercises are set. Generating a study according to the set learning resources and the learning sequence of the assessment exercises Personalized learning plans for the learner. Recommending the generated personalized learning plan to a learner, guiding the learner to strengthen exercise according to the learning plan, and improving the mastering degree of the learner on weaker knowledge points. And constructing a teacher feedback module, wherein the teacher can supplement and correct diagnosis feedback and learning guidance generated by the system according to own professional knowledge and experience, and the system pushes the updated feedback to a learner for strengthening exercise. And (3) establishing an incremental learning mechanism, circularly calling the intelligent evaluation and intelligent recommendation module along with the generation of new data of the learner, and updating feedback content and a learning guidance scheme to dynamically adapt to the latest learning current situation of the learner, namely repeatedly circularly executing the steps S320 to S350.
According to the above embodiment, the resource recommendation method based on programming learning in the present invention has the following technical advantages.
1. Improving the learning efficiency of the programming learner. The invention can enable the learner to quickly and efficiently master programming skills by generating programming ability portraits and personalized learning recommendations of the learner.
2. Enhancing the learning interest and power of the learner. Through personalized learning resources and assessment exercises, a learner can focus on learning knowledge that is needed and of interest by himself, rather than learning all knowledge from scratch.
3. The teacher and the learning platform are instructed to formulate a more accurate learning scheme. The learner capability image generated by the invention can provide more comprehensive and accurate learning condition analysis for teachers and learning platforms, and helps the teachers and the learning platforms to make an optimal learning scheme and path for each learner to actually need.
4. Shortening the learning period of new skills. By personalizing the learning recommendation, the learner may skip knowledge points that have been mastered, only need to pay attention to knowledge and skills that are not yet skilled or mastered by himself. The repeated learning of the prior knowledge can be avoided to the greatest extent, and the speed of learning the new knowledge and skills is increased.
The invention also provides a resource recommendation device based on programming learning, which is used for realizing the above embodiment and the preferred implementation, and the description is omitted. The terms "module," "unit," "sub-unit," and the like as used below may refer to a combination of software and/or hardware that performs a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.
FIG. 6 is a block diagram of a resource recommendation device based on programming learning, provided by the invention, as shown in FIG. 6, the device comprises:
The result obtaining module 610 is configured to obtain a programming exercise result of the learner, and determine a mastering degree of each programming knowledge point by the learner according to the programming exercise result;
the weight determining module 620 is configured to determine an evaluation weight of each programming knowledge point according to a contribution degree of each programming knowledge point to the programming ability of the learner;
an image generation module 630, configured to generate a programming capability image of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point;
the resource recommendation module 640 is used for recommending programming learning resources to the learner according to the programming capability representation of the learner.
By the device, the programming capability image of the learner is determined based on the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point of the learner. The programming capability image can embody the mastering condition of a learner on each programming knowledge point, and based on the programming capability image of the learner, programming learning resources can be recommended to weak links of the learner, so that personalized recommendation of learning resources of different learners is realized. Therefore, the invention provides a personalized resource recommendation device aiming at programming learning, which solves the problem that an effective resource recommendation method aiming at programming learning is lacked in the related technology.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
There is also provided in the present invention a resource recommendation system based on program learning, comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and are not described in detail in this embodiment.
In addition, in combination with the resource recommendation method based on programming learning provided in the above embodiment, a storage medium may be provided for implementation in the present invention. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the program learning-based resource recommendation methods of the above embodiments.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to be limiting. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure in accordance with the embodiments provided herein.
It is to be understood that the drawings are merely illustrative of some embodiments of the present application and that it is possible for those skilled in the art to adapt the present application to other similar situations without the need for inventive work. In addition, it should be appreciated that while the development effort might be complex and lengthy, it will nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and further having the benefit of this disclosure.
The term "embodiment" in this disclosure means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive. It will be clear or implicitly understood by those of ordinary skill in the art that the embodiments described in the present application can be combined with other embodiments without conflict.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the patent claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A resource recommendation method based on programming learning, the method comprising:
obtaining a programming exercise result of a learner, and determining the mastering degree of each programming knowledge point by the learner according to the programming exercise result;
Determining evaluation weights of the programming knowledge points according to the contribution degree of the programming knowledge points to the programming capability of the learner;
generating a programming capability portrait of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point;
and recommending programming learning resources to the learner according to the programming capability portrait of the learner.
2. The program learning based resource recommendation method according to claim 1, wherein the learner's programming practice results include actual evaluation scores of the learner at each of the programming knowledge points;
the determining the mastery degree of the learner on each programming knowledge point according to the programming exercise result comprises the following steps:
and determining the mastery degree of the learner on each programming knowledge point according to the actual evaluation score of the learner on each programming knowledge point and the full score of each programming knowledge point.
3. The program learning based resource recommendation method of claim 2, further comprising:
determining effective evaluation scores of the learner at all the programming knowledge points according to the actual evaluation scores of the learner at all the programming knowledge points and the evaluation weights of all the programming knowledge points;
And determining the comprehensive programming capability of the learner according to the effective evaluation scores of the learner at each programming knowledge point.
4. The program learning based resource recommendation method of claim 1, wherein after generating the learner's programming capability representation, the method further comprises:
displaying the programming capability portrait of the learner through a visual graph;
wherein the color brightness of each graphic area in the visual graphic represents the mastery degree of the learner on each programming knowledge point; the area ratio of each graphic area in the visual graphic represents the evaluation weight of the programming knowledge point.
5. The resource recommendation method based on programming learning according to claim 1, wherein each programming knowledge point is respectively associated with a different programming learning resource;
the recommending programming learning resources to the learner according to the programming capability representation of the learner comprises:
and determining the recommendation priority of programming learning resources associated with each programming knowledge point by taking the mastery degree of the learner on each programming knowledge point as a primary consideration index and taking the evaluation weight of each programming knowledge point as a secondary consideration index.
6. The program learning based resource recommendation method of claim 1, further comprising:
acquiring initial programming log data generated when the learner performs programming exercise, and analyzing the initial programming log data to obtain target programming log data;
extracting data indexes capable of representing programming ability from the target programming log data according to the preset evaluation modes of the programming knowledge points;
and determining the programming exercise result of the learner according to the data index of the expressive programming ability.
7. The program learning based resource recommendation method according to any one of claims 1 to 6, wherein the program learning resources include program teaching resources and/or program training resources;
the method further comprises the steps of:
constructing a knowledge body system in the programming learning field, and determining each programming knowledge point based on the knowledge body system;
and establishing association relations between each programming knowledge point and different programming teaching resources and/or programming exercise resources.
8. A resource recommendation device based on programming learning, the device comprising:
The result acquisition module is used for acquiring programming exercise results of the learner and determining the mastering degree of each programming knowledge point of the learner according to the programming exercise results;
the weight determining module is used for determining the evaluation weight of each programming knowledge point according to the contribution degree of each programming knowledge point to the programming capability of the learner;
the image generation module is used for generating a programming capability image of the learner according to the mastery degree of the learner on each programming knowledge point and the evaluation weight of each programming knowledge point;
and the resource recommending module is used for recommending programming learning resources to the learner according to the programming capability portrait of the learner.
9. A resource recommendation system based on program learning, comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the resource recommendation method based on program learning as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the program learning-based resource recommendation method of any one of claims 1 to 7.
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