CN111950852A - User behavior analysis and guidance method and device, electronic equipment and readable storage device - Google Patents
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Abstract
The invention relates to the technical field of data analysis, in particular to a user behavior analysis and guidance method, a user behavior analysis and guidance device, electronic equipment and a readable storage medium. The user behavior analysis and guidance method comprises the following steps: collecting user behavior data; wherein the user is a targeted educational application online learner; analyzing the learner behavior data according to a preset rule; and outputting guidance suggestions according to the analysis result. Based on the technical scheme provided by the embodiment of the application, the method and the device can realize support of personalized content push, support of real-time feedback of student homework and examination, support of student behavior specification guidance and support of personalized learning plan guidance so as to improve the learning efficiency of learners.
Description
Technical Field
The invention relates to the field of data analysis, in particular to an online learner-user behavior analysis and guidance method, an online learner-user behavior analysis and guidance device, electronic equipment and a readable storage device.
Background
With the wide use of Online courses and the popularization of Online learning, the proportion of Online learning in the learning life of modern learners is increasing, and Online learning systems such as MOOC (Massive open Online Course) and SPOC (Small-scale restricted Online Course) enable learners not to be limited by time and space, and Online learning, Online discussion, Online evaluation and the like can be performed anytime and anywhere through the internet. However, the current online education platform mainly provides learning resources for users, particularly a learning platform of middle and primary schools, a set of relatively perfect online learning analysis guidance rules does not exist, learners mainly learn by means of self initiative and parent supervision, the online education platform cannot push high-quality related knowledge points according to the current mastery degree of the learners on the knowledge points, cannot judge learning attitudes of the learners according to behavior habits of the learners, and cannot further give guidance opinions.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a user behavior analysis and guidance method, where the method includes:
collecting user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
analyzing the learner behavior data according to a preset rule; the preset rule can be customized by a user;
and outputting guidance suggestions according to the analysis result.
Optionally, the behavior data at least comprises training data, evaluation data and interaction data; wherein the content of the first and second substances,
the training data includes at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sorted;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
Optionally, the analyzing the learner behavior data according to a preset rule includes:
analyzing training data according to a preset rule, and outputting a training guidance suggestion; or the like, or, alternatively,
analyzing the evaluation data according to a preset rule, and outputting an evaluation guidance suggestion; or the like, or, alternatively,
and analyzing the interactive data according to a preset rule, and outputting an individual learning guidance suggestion.
Optionally, the analyzing the learner behavior data according to a preset rule includes:
comprehensively analyzing the training data, the evaluation data and the interaction data of the learner according to a preset rule;
and reminding and recommending learning contents according to the comprehensive analysis result.
Optionally, the outputting of the guidance advice according to the analysis result includes:
if the analysis result does not meet the preset requirement condition, automatically skipping to the learning content related to the training or evaluation;
and in a set period, if the analysis result does not reach the preset requirement and exceeds the set times, skipping of the learning content is not performed.
In a second aspect, an embodiment of the present invention provides a user behavior analysis and guidance apparatus, where the apparatus includes: an acquisition module, a storage module, an analysis module and an output module, wherein,
the acquisition module is used for acquiring user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
the storage module is used for storing preset rules and collected behavior data; the preset rule can be customized by a user;
the analysis module is used for analyzing the learner behavior data according to a preset rule;
and the output module is used for outputting guidance suggestions according to the analysis results.
Optionally, the acquisition module is configured to acquire user training data, evaluation data, and interaction data; wherein the content of the first and second substances,
the training data includes at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sorted;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
Optionally, the acquisition module is configured to comprehensively analyze training data, evaluation data, and interaction data of the learner according to a preset rule;
and the output module is used for outputting learning content reminding and recommendation to a user according to the comprehensive analysis result.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing operation instructions;
the processor is used for executing the user behavior analysis and guidance method by calling the operation instruction.
In a fourth aspect, a computer-readable storage medium is characterized in that the storage medium stores thereon a computer program, and the computer program, when executed by a processor, implements the method for analyzing and guiding user behavior.
The technical scheme disclosed by the embodiment of the application has the following beneficial effects: according to the user behavior analysis and suggestion method provided by the embodiment of the application, online learners, namely behavior data of users are collected; analyzing the learner behavior data according to a preset rule; the preset rule can be customized by a user; and outputting guidance suggestions according to the analysis result. Based on the technical scheme provided by the embodiment of the application, personalized content push is supported, real-time feedback of student homework and examination is supported, student behavior specification guidance is supported, student learning state evaluation is supported, personalized learning plan guidance is supported, learning behavior characteristics of learners can be designed on the basis of online learning, the significance of learning characteristics is more clear, the learning process of learners can be reflected, the time regularity of learner login learning can be comprehensively evaluated, the learning autonomy of learners is determined, each online learning of learners is finely added into an evaluation system, so that personalized education and online education are linked, online learners are scientifically, reasonably and accurately analyzed, and learning methods corresponding to the personality of learners are recommended, and the learning efficiency of learners is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a user behavior analysis and guidance method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a user behavior analysis and guidance device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 shows a schematic flow diagram of a user behavior analysis and guidance method provided in an embodiment of the present application, and as shown in fig. 1, the method mainly includes:
s101, collecting user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
s102, analyzing the learner behavior data according to a preset rule; the preset rule can be customized by a user;
and S103, outputting guidance suggestions according to the analysis results.
In an optional embodiment of the present application, the behavior data at least comprises training data, evaluation data, and interaction data; wherein the content of the first and second substances,
the training data includes at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sequenced;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
Further, in this embodiment of the present application, the analyzing the learner behavior data according to a preset rule includes:
analyzing training data according to a preset rule, and outputting a training guidance suggestion; or the like, or, alternatively,
analyzing the evaluation data according to a preset rule, and outputting an evaluation guidance suggestion; or the like, or, alternatively,
analyzing the interactive data according to a preset rule, and outputting an individual learning guidance suggestion
In a specific embodiment, the training may be performed by completing the job data online for the online learner, and accordingly, a duration of completing the job by the learner (preferably, a duration of completing each question by the learner to determine a degree of mastery of the task by the learner), a task accuracy rate, a task modification rate, and a task completion rate are collected, when the training data does not meet a preset forward requirement, it may be determined that the learner does not master the portion of the content, the learner may be prompted or automatically jump to a knowledge point related to the job for the learner to learn again, and within a certain time period, for example, within 24 hours, the automatic training data does not meet the requirement more than a certain number of times, for example, after three times, the learner does not repeatedly prompt and cyclically jump to the related knowledge point. Optionally, the failing to meet the requirement of the training data further comprises:
acquiring training data of four dimensions, namely acquiring training duration, training accuracy, training modification rate and training completion rate data;
analyzing the training data comprises respectively giving weights to the data of the four dimensions according to the importance degree; analyzing by utilizing the rule to obtain a training result;
comparing the training result with the preset requirement condition;
if the requirement condition is not met, outputting an evaluation guidance suggestion, for example, comprising the step of jumping to the related knowledge point.
Similarly, in an embodiment, the evaluation may be performed by completing examination question data online by an online learner, and accordingly, collecting a time length for completing the examination question by the learner (preferably, collecting a time length for completing each question by the learner to determine how well the learner mastery the examination question is), a test question accuracy rate, a test question modification rate, a test question completion rate, a test score and a test result sorting, when the evaluation data does not meet a preset forward requirement, it may be determined that the learner does not grasp the portion of the content, the learner may be prompted to or automatically jump to a knowledge point associated with the examination question for the learner to learn again, and after the automatic evaluation data does not meet the requirement condition for a certain number of times, for example three times, within a certain time period, for example 24 hours, repeatedly reminding and circularly jumping to the related knowledge point are not carried out. Optionally, the evaluating that the data fails to meet the requirement further comprises:
collecting evaluation data of the following dimensions, namely collecting evaluation duration, evaluation accuracy, evaluation modification rate and evaluation completion rate;
the analysis training data is respectively given to the weight of the data of the four dimensions according to the importance degree, the evaluation result is obtained by utilizing the rule analysis,
comparing the evaluation result with the preset requirement condition;
if the requirement condition is not met, outputting an evaluation guidance suggestion, for example, comprising the step of jumping to the related knowledge point.
In a specific embodiment, the interaction data includes: the time when the learner uses the objective education APP may preferably be collected the time and period of the orientation specific module (interaction time), the accessed business module (interaction object), the frequency of accessing the same business module (interaction frequency), the access log, and the personalized operation performed by accessing the business module, such as annotation, note, etc. (interactive behavior personalized features). Optionally, a personalized learning guidance suggestion for the learner is made by analyzing the personalized interaction data, for example, by analyzing the interaction data or login time of the learner over a period of time, judging the frequent learning period of the learner to make a learning reminding plan, etc.
In an optional embodiment of the present application, the analyzing the learner behavior data according to a preset rule includes: comprehensively analyzing the training data, the evaluation data and the interaction data of the learner according to a preset rule; and prompting and recommending learning content according to the comprehensive analysis result, wherein the comprehensive analysis method can be determined by adopting common algorithms in the prior art such as weight assignment and the like. Learning content recommendation and reminding are substantially the same as the training and assessment, and are not described herein again.
Optionally, the preset rule, the preset requirement condition, and the set period and number of times may all be customized by a user.
Based on the user behavior and guidance method provided by the embodiment of the application, personalized content pushing, real-time feedback of student homework and examination, student behavior specification guidance, student learning state evaluation and personalized learning plan guidance are supported.
Based on the user behavior analysis and guidance method shown in fig. 1, another aspect of the present application provides a user behavior analysis and guidance apparatus, as shown in fig. 2, the apparatus includes: 201 acquisition module, 202 storage module, 203 analysis module and 204 output module, wherein,
the 201 acquisition module is used for acquiring user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
the 202 storage module is used for storing preset rules and collected behavior data; the preset rule can be customized by a user;
the 203 analysis module is used for analyzing the learner behavior data according to a preset rule;
and the 204 output module is used for outputting guidance suggestions according to the analysis results.
In an alternative embodiment, the acquisition module acquires the training data including at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sorted;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
In an optional embodiment of the present application, the acquisition module is configured to comprehensively analyze training data, evaluation data, and interaction data of the learner according to a preset rule;
and the output module is used for outputting learning content reminding and recommendation to a user according to the comprehensive analysis result.
In an optional embodiment of the present application, the analysis module is further configured to analyze training data according to a preset rule, and the output module is specifically configured to output a training guidance suggestion; or the like, or, alternatively,
the analysis module is further used for analyzing the evaluation data according to a preset rule, and the output module is specifically used for outputting an evaluation guidance suggestion; or the like, or, alternatively,
the analysis module is specifically used for analyzing the interactive data according to a preset rule, and the output module is specifically used for outputting an individual learning guidance suggestion.
In an optional embodiment of the present application, the apparatus further includes a determining module, where the determining module is configured to determine whether the analysis result meets a preset requirement, and if the analysis result does not meet the preset requirement,
the output module is also used for controlling the interface to automatically jump to the learning content related to the training or evaluation;
and if the analysis result does not reach the preset requirement condition and exceeds the set times within the set period, the learning content is not skipped.
It is understood that the above modules of the user behavior analysis and guidance device in the present embodiment have functions of implementing the corresponding steps of the method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module, reference may be specifically made to the corresponding description of the method in the embodiment shown in fig. 1, and details are not repeated here.
The embodiment of the application provides an electronic device, which comprises a processor and a memory;
a memory for storing operating instructions;
and the processor is used for executing the user behavior analysis and guidance method provided by any embodiment of the application by calling the operation instruction.
As an example, fig. 3 shows a schematic structural diagram of an electronic device to which an embodiment of the present application is applicable, and as shown in fig. 3, the electronic device 2000 includes: a processor 2001 and a memory 2003. Wherein the processor 2001 is coupled to a memory 2003, such as via a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied to the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 2004 may include a receiver and a transmitter, and the transceiver 2004 is applied to the embodiments of the present application to implement the functions of the electronic device of the embodiments of the present application to communicate with other devices when executed.
The Processor 2001 may be a CPU (Central Processing Unit), general Processor, DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (field programmable Gate Array) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The Memory 2003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact disk Read Only Memory) or other optical disk storage, optical disk storage (including Compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
Optionally, the memory 2003 is used for storing application program code for performing the disclosed aspects, and is controlled in execution by the processor 2001. The processor 2001 is used to execute the application program code stored in the memory 2003 to implement the user behavior analysis and guidance method provided in any of the embodiments of the present application.
The electronic device provided by the embodiment of the application is applicable to any embodiment of the method, and is not described herein again.
The embodiment of the present application provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the user behavior analysis and guidance method shown in the foregoing method embodiment.
The computer-readable storage medium provided in the embodiments of the present application is applicable to any of the embodiments of the foregoing method, and is not described herein again.
The user behavior analysis and guidance scheme provided by the embodiment of the application comprises the steps of collecting behavior data of an online learner, namely a user; analyzing the learner behavior data according to a preset rule; the preset rule can be customized by a user; and outputting guidance suggestions according to the analysis result. Based on the technical scheme provided by the embodiment of the application, personalized content push is supported, real-time feedback of student homework and examination is supported, student behavior specification guidance is supported, student learning state evaluation is supported, personalized learning plan guidance is supported, learning behavior characteristics of learners can be designed on the basis of online learning, the significance of learning characteristics is more clear, the learning process of learners can be reflected, the time regularity of learner login learning can be comprehensively evaluated, the learning autonomy of learners is determined, each online learning of learners is finely added into an evaluation system, so that personalized education and online education are linked, online learners are scientifically, reasonably and accurately analyzed, and learning methods corresponding to the personality of learners are recommended, and the learning efficiency of learners is improved.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for analyzing and guiding user behavior, the method comprising:
collecting user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
analyzing the learner behavior data according to a preset rule; the preset rule can be customized by a user;
and outputting guidance suggestions according to the analysis result.
2. The user behavior analysis and guidance method according to claim 1,
the training data includes at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sorted;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
3. The user behavior analysis and guidance method of claim 2, wherein the analyzing the learner behavior data according to a preset rule comprises:
analyzing training data according to a preset rule, and outputting a training guidance suggestion; or the like, or, alternatively,
analyzing the evaluation data according to a preset rule, and outputting an evaluation guidance suggestion; or the like, or, alternatively,
and analyzing the interactive data according to a preset rule, and outputting an individual learning guidance suggestion.
4. The user behavior analysis and guidance method of claim 2, wherein the analyzing the learner behavior data according to a preset rule comprises:
comprehensively analyzing the training data, the evaluation data and the interaction data of the learner according to a preset rule;
and reminding and recommending learning contents according to the comprehensive analysis result.
5. The user behavior and guidance method according to any one of claims 1 to 4, wherein outputting guidance suggestions according to the analysis results comprises:
if the analysis result does not meet the preset requirement condition, automatically skipping to the learning content related to the training or evaluation;
and in a set period, if the analysis result does not reach the preset requirement and exceeds the set times, skipping of the learning content is not performed.
6. A user behavior analysis and guidance apparatus, the apparatus comprising: an acquisition module, a storage module, an analysis module and an output module, wherein,
the acquisition module is used for acquiring user behavior data; wherein the user is a targeted educational application online learner; wherein the behavioral data includes at least: training data, evaluation data and interaction data;
the storage module is used for storing preset rules and collected behavior data; the preset rule can be customized by a user;
the analysis module is used for analyzing the learner behavior data according to a preset rule;
and the output module is used for outputting guidance suggestions according to the analysis results.
7. The apparatus according to claim 6, wherein the collecting module is used for collecting user training data, evaluation data and interaction data; wherein the content of the first and second substances,
the training data includes at least one of: training duration, training accuracy, training modification rate and training completion rate;
the evaluation data comprises: the evaluation duration, the evaluation accuracy, the evaluation completion rate, the evaluation value and the evaluation result are sorted;
the interaction data includes: interaction time, interaction objects, interaction frequency, access logs and interaction behavior personalization characteristics.
8. The user behavior analysis and guidance device according to claim 7, wherein the collection module is configured to comprehensively analyze training data, evaluation data, and interaction data of the learner according to a preset rule;
and the output module is used for outputting learning content reminding and recommendation to a user according to the comprehensive analysis result.
9. An electronic device comprising a processor and a memory;
the memory is used for storing operation instructions;
the processor is used for executing the method of any one of claims 1-5 by calling the operation instruction.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-5.
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| CN113034319A (en) * | 2020-12-24 | 2021-06-25 | 广东国粒教育技术有限公司 | User behavior data processing method and device in teaching management, electronic equipment and storage medium |
| CN113160009A (en) * | 2021-03-31 | 2021-07-23 | 北京大米科技有限公司 | Information pushing method, related device and computer medium |
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