CN114841157A - Online interaction method, system, equipment and storage medium based on data analysis - Google Patents
Online interaction method, system, equipment and storage medium based on data analysis Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
Abstract
The invention provides an online interaction method, a system, equipment and a storage medium based on data analysis, wherein the method comprises the following steps: receiving a question request and corresponding question data from a teacher terminal, and analyzing the question data to obtain a course ID; inquiring the ID of the current online student from the course management server according to the course ID; calculating the matching degree of the problem data and each current online student; and sequentially selecting students to answer according to the matching degree from high to low, and sending answer reminders to the corresponding student terminals. By adopting the scheme of the invention, students answering questions are automatically selected based on data analysis, and answering reminders are automatically sent to the student terminals without manual operation of teachers, so that the use experience of users is improved, and the pertinence and the effectiveness of an interaction process can be improved.
Description
Technical Field
The invention relates to the technical field of intelligent education, in particular to an online interaction method, system, equipment and storage medium based on data analysis.
Background
The online education means that teachers and students can enter a classroom on user terminals of the teachers and the students and are connected with a server through the internet to achieve online teaching. In the course of course, some questioning links are often added in order to strengthen the interactivity of teaching. In the teacher teaching process, in order to guarantee audio quality, can be to the silence of whole student, when needing to be answered the problem by a certain student, just open the microphone function that this student corresponds. Specifically, when a student is asked for a plurality of students, the teacher asks each student about a knowledge point, the teacher generally randomly selects a student to answer the student at present, or each student is sequentially arranged to answer the student in turn, and the method requires the teacher to manually select the student to answer the question, so that the operation is complex, the use experience of the teacher is poor, and the teacher asks the student to lack pertinence and randomness on the other hand, and the good interaction effect cannot be realized.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an online interaction method, system, equipment and storage medium based on data analysis, which automatically select students answering questions based on data analysis and automatically send answering reminders to student terminals without manual operation of teachers.
The embodiment of the invention provides an online interaction method based on data analysis, which comprises the following steps:
receiving a question request and corresponding question data from a teacher terminal, and analyzing the question data to obtain a course ID;
inquiring the ID of the current online student from the course management server according to the course ID;
calculating the matching degree of the problem data and each current online student;
and sequentially selecting students to answer according to the matching degree from high to low, and sending answer reminders to the corresponding student terminals.
Optionally, the calculating the matching degree of the question data and each current online student includes the following steps:
analyzing the problem data to obtain a courseware ID adopted currently;
inquiring a plurality of knowledge points corresponding to the courseware ID and keywords of each knowledge point from a courseware management server;
analyzing the problem data to obtain a problem keyword;
comparing the question keywords with keywords of each knowledge point corresponding to the courseware ID, and determining the knowledge point closest to the question keywords;
and calculating the matching degree of the closest knowledge point and each current online student as the matching degree of the problem data and each current online student.
Optionally, calculating the matching degree of the closest knowledge point to each current online student includes the following steps:
querying a category x1 to which the closest knowledge point belongs;
acquiring preset weak knowledge point category x2 and well-known knowledge point category x3 of each current online student from the user management server;
respectively acquiring feature vectors y1, y2 and y3 corresponding to the category x1, the category x2 and the category x3 according to a preset mapping relation between the category of the knowledge points and the feature vectors of the knowledge points;
and combining the obtained feature vectors y1, y2 and y3, and inputting the combined feature vectors into a trained matching degree calculation model to obtain a matching degree value output by the matching degree calculation model, wherein the matching degree calculation model is a model constructed and trained on the basis of deep learning.
Optionally, after the answer reminder is sent to the corresponding student terminal, the method further includes the following steps:
receiving student response confirmation data from the teacher terminal, and judging whether the student response is correct or not;
sending the student answering results and the corresponding knowledge point categories to the user management server;
the user management server is configured to count the knowledge point category with the largest number of wrong answers and the knowledge point category with the largest number of correct answers in the course answering data of each student at preset time intervals, and the knowledge point categories are respectively used as weak knowledge point category x2 and well-known knowledge point category x3 of the student.
Optionally, calculating the matching degree of the closest knowledge point to each current online student includes the following steps:
inquiring the category to which the closest knowledge point belongs, and acquiring the historical error rate of the current online student corresponding to the category from a user management server;
and determining the matching degree of the closest knowledge point and each current online student according to the historical error rate, wherein the historical error rate is positively correlated with the matching degree.
Optionally, after the answer reminder is sent to the corresponding student terminal, the method further includes the following steps:
receiving student response confirmation data from the teacher terminal, and judging whether the student response is correct or not;
sending the student answering results and the corresponding knowledge point categories to the user management server;
and the user management server is configured to judge whether each student has a new student answering result for each knowledge point category at preset intervals, and if so, the historical error rate of the student for the knowledge point category is updated according to the new student answering result.
Optionally, the student who answers according to the selection from high to low of the matching degree comprises the following steps:
inquiring to obtain the importance value of the closest knowledge point in the courseware from a courseware management server;
determining the number m of questioners according to the mapping relation between the preset importance value and the number of questioners;
and selecting m students from high to low according to the matching degree.
Optionally, the determining the number m of questioning persons and selecting m students from high to low according to the matching degree further includes the following steps:
counting the number a of knowledge points which are asked for and finished at present and the number b of knowledge points which are not asked for and finished in a courseware which is adopted at present;
the planned remaining time period t2 is calculated using the following formula:
t2＝t1*b/(a+b)
wherein t1 is the preset total curriculum duration;
obtaining the residual time t3 of the current course from the course management server;
if the remaining time length t2 is greater than the remaining time length t3 and t2-t3 is greater than a preset time threshold value, the number m of questioners is reduced;
and if the remaining time length t2 is less than the remaining time length t3 and t3-t2 is greater than a preset time threshold value, the number m of questioners is increased.
Optionally, analyzing the problem data to obtain a problem keyword, including performing word segmentation on the problem data, removing words meeting preset deleting conditions in the word segmentation result, and taking the remaining words as the problem keyword;
comparing the question keywords with the keywords of each knowledge point corresponding to the courseware ID, comprising the following steps:
counting the number n of the keywords which are coincident with the question keywords for each knowledge point corresponding to the courseware ID;
and selecting the knowledge point with the highest number n of overlapped keywords as the closest knowledge point.
The embodiment of the invention also provides an online interaction system based on data analysis, which is applied to the online interaction method based on data analysis, and the system comprises:
the first communication module is used for interacting with the teacher terminal, receiving a question request and corresponding question data from the teacher terminal, and analyzing the question data to obtain a course ID;
the second communication module is used for interacting with the course management server and inquiring the ID of the current online student from the course management server according to the course ID when the first communication module receives a question request;
the answer selection module is used for calculating the matching degree of the question data and each current online student and selecting the students to answer according to the matching degree from high to low;
and the third communication module is used for interacting with the student terminals and sending the answering reminding to the student terminals selected by the answering selection module.
The embodiment of the invention also provides an online interaction device based on data analysis, which comprises:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the data analysis-based online interaction method via execution of the executable instructions.
The embodiment of the invention also provides a computer-readable storage medium for storing a program, and the program realizes the steps of the online interaction method based on data analysis when being executed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
The online interaction method, the online interaction system, the online interaction equipment and the online interaction storage medium based on data analysis have the following advantages:
the invention solves the problems in the prior art, calculates the matching degree between the problems and students based on data analysis, automatically selects the students answering the problems according to the matching degree, automatically sends answering prompts to the student terminals after the students select the students, does not need manual operation of teachers, reduces the steps of manual operation of teachers in the course of teaching, improves the automation of online courses, improves the use experience of users, and can improve the pertinence and the effectiveness of the interaction process.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of an online interaction method based on data analysis according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an online interactive system based on data analysis according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of data interaction between an online interactive system based on data analysis and each server according to an embodiment of the present invention;
FIG. 4 is a flow chart of calculating a degree of matching of a problem with a student according to an embodiment of the present invention;
FIG. 5 is a flow chart of calculating a closest knowledge point to student match according to one embodiment of the invention;
FIG. 6 is another flow chart of calculating a closest knowledge point to student match according to an embodiment of the invention;
FIG. 7 is a flow chart of a student selecting answers in accordance with an embodiment of the present invention;
FIG. 8 is a flowchart of adjusting the number of students according to the progress of the lesson in accordance with one embodiment of the invention;
FIG. 9 is a schematic diagram of an online interaction device based on data analysis according to an embodiment of the present invention;
fig. 10 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
As shown in fig. 1, to solve the above technical problem, an embodiment of the present invention provides an online interaction method based on data analysis, where the method includes the following steps:
s100: receiving a question request and corresponding question data from a teacher terminal, and analyzing the question data to obtain a course ID; the teacher terminal is a terminal device used by the teacher in class, and includes but is not limited to a mobile phone, a tablet computer, a notebook computer and the like used by the teacher;
the question data can comprise a course ID and question contents, wherein the question contents can be audio collected by a microphone of the teacher terminal or character input contents collected by a keyboard of the teacher terminal;
s200: inquiring ID of a current online student from a course management server according to the course ID, wherein the course management server is configured to manage the current ongoing course and realize interaction between a teacher terminal and a student terminal through the course management server, and the current online student is the student who is currently participating in the course;
s300: calculating the matching degree of the problem data and each current online student;
s400: and sequentially selecting students to answer according to the matching degree from high to low, and sending answer reminders to the corresponding student terminals. The student terminal is a terminal device used by the student in class, and includes but is not limited to a mobile phone, a tablet computer, a notebook computer and the like used by the student.
Further, in step S400, after the answer alert is sent to the corresponding student terminal, the microphone function corresponding to the student terminal of the answer student may be automatically turned on. Here, the automatic turning on of the microphone function of the student terminal may be sending a notification of the turning on of the microphone function of the student terminal to a course management server, and the course management server is configured to receive, from the student terminal, student voice data collected by the microphone of the student terminal, and push the data to the teacher terminal, or push the data to the teacher terminal and all the student terminals currently in class.
Therefore, the on-line interaction method of the invention firstly obtains the question request and question data sent by the teacher terminal through the step S100, then inquires the ID of the current on-line student through the step S200, namely determines the ID of the current student which can be selected to answer, then calculates the matching degree of the question and the student through the step S300, automatically selects the student who answers according to the matching degree through the step S400, preferably selects the student with higher matching degree, and automatically sends the answer reminder to the corresponding student terminal after selecting the student who answers, thereby realizing the automation of the question process without manual operation of the teacher. For the teacher, the questioning process can be started only by sending a questioning request through a button or a specific operation on the teacher terminal and recording or inputting the question content of the questioning, and the subsequent operations are all automatically executed. And the students with high matching degree are preferably selected according to the matching degree when the students with answers are selected, and the pertinence of questioning and answering is improved, so that the interaction effect in an online classroom can be further improved.
As shown in fig. 2, an embodiment of the present invention further provides an online interaction system M100 based on data analysis, which is applied to the online interaction method based on data analysis. Fig. 3 is a schematic diagram of data interaction between the online interactive system M100 based on data analysis and each server according to the present invention. The system comprises:
the first communication module M110 is configured to interact with the teacher terminal M200, receive a question request and corresponding question data from the teacher terminal, and analyze the question data to obtain a course ID;
a second communication module M120, configured to interact with the course management server M400, and query, when the first communication module receives a question request, an ID of a current online student from the course management server M400 according to the course ID;
the answer selection module M130 is used for calculating the matching degree of the question data and each current online student and selecting an answer student from high to low according to the matching degree;
and the third communication module M140 is configured to interact with the student terminal M300, and send the response reminder to the student terminal selected by the response selection module.
Therefore, the online interaction system of the invention firstly obtains the question request and question data sent by the teacher terminal through the first communication module M100, then queries the ID of the current online student through the second communication module M200, i.e. determines the ID of the current student who can choose to answer, then calculates the matching degree of the question and the student through the answer selection module M300, and automatically selects the student who answers according to the matching degree, preferably selects the student with higher matching degree, and after selecting the student who answers, automatically sends the answer reminder to the corresponding student terminal through the third communication module M300, thereby realizing the automation of the question process without manual operation of the teacher.
As shown in fig. 3, the online interactive system also performs communication interaction with the courseware management server M500. The courseware management server M500 is configured to manage data of all courseware, where the data of courseware specifically includes a plurality of preset knowledge points in the courseware and keywords of each knowledge point. Specifically, the online interaction system may further include a fourth communication module, configured to interact with the courseware management server M500.
As shown in fig. 4, in this embodiment, the step S300: calculating the matching degree of the problem data and each current online student, comprising the following steps:
s310: analyzing the problem data to obtain a courseware ID adopted currently;
s320: inquiring a plurality of knowledge points corresponding to the courseware ID and keywords of each knowledge point from a courseware management server; the courseware management server is configured to store a plurality of knowledge points and keywords of each knowledge point in each courseware in advance;
s330: analyzing the problem data to obtain a problem keyword;
s340: comparing the question keywords with keywords of each knowledge point corresponding to the courseware ID, and determining the knowledge point closest to the question keywords;
s350: and calculating the matching degree of the closest knowledge point and each current online student as the matching degree of the problem data and each current online student, so that the matching degree of the problem and the students is converted into the matching degree of the knowledge point and the students for calculation.
Therefore, the invention further converts the matching degree of the question and the student into the matching degree of the knowledge point and the student through the step S310-S350, and preferentially selects the student with the high matching degree of the knowledge point for questioning, thereby improving the pertinence of questioning in the interactive classroom and enhancing the questioning consolidation effect.
As shown in fig. 3, the online interactive system also performs communication interaction with the user management server M600. The user management server M600 is configured to manage personal data of each student user, for example, personal information (including name, gender, hobbies, and the like) of each student can be managed, and historical lesson data of each student can be further managed, including performance of each class, completion of each post-lesson assignment, and the like. Specifically, the online interactive system may further include a fifth communication module, configured to interact with the user management server M600.
In one embodiment, a machine learning model may be used to calculate the matching degree between the knowledge point and the student, specifically, as shown in fig. 5, the step S350: calculating the matching degree of the closest knowledge point and each current online student can comprise the following steps:
S351-A: inquiring the category x1 to which the closest knowledge point belongs, for example, presetting a plurality of categories: the current grammar category, the general past grammar category, the interview english category, the teleconference language category, and the like, the category of each knowledge point may be stored in advance in the courseware management server M500, and the category x1 to which the closest knowledge point belongs may be queried in the courseware management server M500 through the fourth communication module;
S352-A: acquiring preset weak knowledge point category x2 and well-known knowledge point category x3 of each current online student from the user management server;
the weak knowledge point category x2 and the familiar knowledge point category x3 may be preset by the user, or may be obtained by analyzing the historical course data of the user;
S353-A: respectively acquiring feature vectors y1, y2 and y3 corresponding to the category x1, the category x2 and the category x3 according to a preset mapping relation between the category of the knowledge points and the feature vectors of the knowledge points;
the mapping relation between the knowledge point categories and the knowledge point feature vectors is preset, namely, the corresponding feature vectors are preset for each knowledge point category;
S354-A: and combining the obtained feature vectors y1, y2 and y3, and inputting the combined feature vectors into a trained matching degree calculation model to obtain a matching degree value output by the matching degree calculation model, wherein the matching degree calculation model is a model constructed and trained on the basis of deep learning.
The matching degree calculation model may be, for example, a convolutional neural network model. After a model is built based on a convolutional neural network, a plurality of combined characteristic vectors are collected in advance, matching degree values are marked, the combined characteristic vectors are added into a training set, the convolutional neural network model is trained by the aid of the training set in a gradient descent mode until loss function values are smaller than a preset loss threshold value, and a trained matching degree calculation model is obtained.
Therefore, the matching degree of the knowledge points and the students can be calculated more accurately and rapidly by adopting the matching degree calculation model based on machine learning, so that the matching degree of the problems and the students is obtained, and the most matched students can be selected to push the answering reminding information more objectively and pertinently.
When training the matching degree calculation model, the training set can be partially derived from the historical course data of real students, and the artificial marking of the matching degree is carried out, so that a more objective and accurate matching degree calculation model can be obtained, a new combined feature vector can be obtained after the change according to the real historical course data of the students, and the artificial marking of the matching degree is carried out, so that the training set of the matching degree calculation model is expanded, and the model can be trained to be convergent quickly.
In this embodiment, the step S400: after the answering reminding is sent to the corresponding student terminal, the method further comprises the following steps:
s510: receiving student answering confirmation data from the teacher terminal, and judging whether the student answers correctly; for the teacher, after receiving student response data (audio, video or characters), the teacher terminal can click on an answer confirmation button and select whether the response is correct or not;
s520: and sending the student answering results and the corresponding knowledge point categories to the user management server. And the user management server stores the student answering results and the corresponding knowledge point categories as historical course data of the students.
The user management server is configured to count the knowledge point category with the largest number of wrong answers and the knowledge point category with the largest number of correct answers in the course answering data of each student at preset time intervals, and the knowledge point categories are respectively used as weak knowledge point category x2 and well-known knowledge point category x3 of the student.
In another implementation of this embodiment, the degree of matching may also be calculated based on the historical error rate of the class that the student associated with the closest knowledge point. Specifically, as shown in fig. 6, the step S350: calculating the matching degree of the closest knowledge point and each current online student, and further comprising the following steps:
S351-B: the class to which the closest knowledge point belongs is queried, and similarly, the class of each knowledge point can be stored in the courseware management server M500 in advance, and the class to which the closest knowledge point belongs can be queried in the courseware management server M500 through the fourth communication module;
S352-B: acquiring historical error rates of the current online students corresponding to the category from a user management server; the historical error rate is equal to the ratio of the number of times that the online student has answered the category of questions before to the total number of times that the category of questions has been answered;
S353-B: and determining the matching degree of the closest knowledge point and each current online student according to the historical error rate, wherein the historical error rate is positively correlated with the matching degree. That is, the higher the historical error rate, the higher the degree of matching. Specifically, for a student, the historical error rate is proportional to the degree of matching. Specifically, the inverse of the historical error rate may be used as the matching degree between the closest knowledge point and the student, or the inverse of the historical error rate may be further multiplied by a preset coefficient to be used as the matching degree between the closest knowledge point and the student.
Corresponding to the implementation mode of calculating the matching degree of the knowledge points and the students, the user management server can be further configured to judge whether each student answers each knowledge point category with a new student answer result at preset intervals, and if so, the historical error rate of each student for the knowledge point category is updated according to the new student answer result, so that the real-time updating of the historical error rate of each student stored in the user management server for each knowledge point category is ensured. If the historical error rate of a student corresponding to a category is higher before and the historical error rate corresponding to the category is lower after updating, the knowledge point is better mastered, and the matching degree of the student and the problem of the category is correspondingly reduced.
As shown in fig. 7, in this embodiment, the step S400: the students answering according to the selection from high to low of the matching degree comprise the following steps:
s410: inquiring to obtain the importance value of the closest knowledge point in the courseware from a courseware management server;
s420: determining the number m of questioners according to the mapping relation between the preset importance value and the number of questioners;
s430: and selecting m students from high to low according to the matching degree.
As shown in fig. 8, in this embodiment, the step S420: determining the number m of questioners and step S430: selecting m students from high to low according to the matching degree, and further comprising the following steps of:
s421: counting the number a of knowledge points which are asked for and finished at present and the number b of knowledge points which are not asked for and finished in a courseware which is adopted at present;
s422: the plan remaining time t2 is calculated using the following formula:
t2＝t1*b/(a+b)
wherein t1 is the preset total curriculum duration;
s423: obtaining the residual time t3 of the current course from the course management server;
s424: judging whether the remaining duration t2 is greater than the remaining duration t 3;
s425: if the remaining time t2 is greater than the remaining time t3, and t2-t3 is greater than a preset time threshold, it is described that the current course progress is slow, the course progress needs to be accelerated, and the course task is prevented from being incomplete, the number m of questioning people can be reduced, for example, 1 can be reduced on the basis of the number m of original people, or the reduction amplitude is selected according to the size of t2-t3, when the time of t2-t3 is very long, a plurality of questioned objects can be reduced, the course can be ensured to be completed on time by shortening the time of the interaction link, the influence on the normal operation of subsequent knowledge points is avoided, and when the number m of original people is 1, the reduction is not needed;
s426: if the remaining time length t2 is less than the remaining time length t3, and t3-t2 is greater than a preset time threshold, which indicates that the current course progress is faster, and the course progress can be slowed appropriately, the number m of questioning people can be increased, for example, 1 can be increased on the basis of the number m of original people, or the increasing range is selected according to the size of t3-t2, when the time length of t3-t2 is very long, a plurality of questioned objects can be increased, the time length of an interactive link is further increased, and when the number m of people reaches the number of current online students, the number m of people does not need to be increased continuously;
and if the remaining time length t2 is equal to the remaining time length t3 or the absolute value of the time difference between the remaining time length t2 and the remaining time length t3 is less than or equal to a preset time threshold, keeping the questioning people number m unchanged.
Therefore, the invention can calculate the plan residual time length according to the comparison between the current completed knowledge point quantity a and the total quantity a + b of all knowledge points in the courseware, and then can judge whether the current course progress meets the requirements of the plan or not by comparing the plan residual time length with the actual residual time length. And when the course progress deviates from the preset requirement, the course progress can be effectively adjusted by adjusting the number of questioners, and a teacher is assisted to finish a teaching task.
In this embodiment, analyzing the question data to obtain a question keyword includes performing word segmentation on the question data, removing words meeting a preset deletion condition from the result of the word segmentation, and using the remaining words as the question keyword. The problem data is segmented, if the problem data is English, segmentation can be performed according to spaces, punctuation marks and the like, and if the problem data is Chinese, segmentation can be achieved by means of j ieba segmentation and the like. The words meeting the preset deletion condition generally refer to preset linguistic words, conjunctions and the like.
The step S340: comparing the question keywords with the keywords of each knowledge point corresponding to the courseware ID, comprising the following steps:
counting the number n of the keywords which are coincident with the question keywords for each knowledge point corresponding to the courseware ID; the keyword coincidence not only can include the condition that a question keyword is completely consistent with a keyword of a knowledge point, but also can include the condition that the question keyword and the keyword of the knowledge point belong to a predefined synonym or a near synonym;
the knowledge point with the highest number n of overlapped keywords is selected as the closest knowledge point, so that the matching degree of the question and the student is converted into the matching degree of the knowledge point and the student in step S350 to calculate.
The embodiment of the invention also provides online interaction equipment based on data analysis, which comprises a processor; a memory having stored therein executable instructions of the processor; wherein the processor is configured to perform the steps of the data analysis-based online interaction method via execution of the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 9. The electronic device 600 shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 9, the electronic device 600 is embodied in the form of a general purpose computing device. The combination of the electronic device 600 may include, but is not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting different platform combinations (including memory unit 620 and processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned online course resource processing method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 1. Specifically, when the processing unit 610 executes each step in fig. 1, a specific step execution manner may adopt a specific implementation manner of each step of the online interaction method based on data analysis, which is not described again.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (1/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The embodiment of the invention also provides a computer-readable storage medium for storing a program, and the program realizes the steps of the online interaction method based on data analysis when being executed. In some possible embodiments, the various aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned online course resource processing method section of this specification, when said program product is run on the terminal device.
Referring to fig. 10, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, compared with the prior art, the online interaction method, system, device and storage medium based on data analysis provided by the present invention have the following advantages:
the invention solves the problems in the prior art, calculates the matching degree between the problems and students based on data analysis, automatically selects the students answering the problems according to the matching degree, automatically sends answering reminders to the student terminals after the students select the students, does not need manual operation of teachers, reduces the steps of manual operation of the teachers in the teaching process, improves the automation of online courses, improves the use experience of users, and can improve the pertinence and effectiveness of the interaction process.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (12)
1. An online interaction method based on data analysis is characterized by comprising the following steps:
receiving a question request and corresponding question data from a teacher terminal, and analyzing the question data to obtain a course ID;
inquiring the ID of the current online student from the course management server according to the course ID;
calculating the matching degree of the problem data and each current online student;
and sequentially selecting students to answer according to the matching degree from high to low, and sending answer reminders to the corresponding student terminals.
2. The data analysis-based online interaction method according to claim 1, wherein the calculating of the matching degree of the problem data with each current online student comprises the following steps:
analyzing the problem data to obtain a courseware ID adopted currently;
inquiring a plurality of knowledge points corresponding to the courseware ID and keywords of each knowledge point from a courseware management server;
analyzing the problem data to obtain a problem keyword;
comparing the question keywords with keywords of each knowledge point corresponding to the courseware ID, and determining the knowledge point closest to the question keywords;
and calculating the matching degree of the closest knowledge point and each current online student as the matching degree of the problem data and each current online student.
3. The data analysis-based online interaction method of claim 2, wherein calculating the matching degree of the closest knowledge point with each current online student comprises the following steps:
querying a category x1 to which the closest knowledge point belongs;
acquiring preset weak knowledge point category x2 and familiar knowledge point category x3 of each current online student from a user management server;
respectively acquiring feature vectors y1, y2 and y3 corresponding to the category x1, the category x2 and the category x3 according to a preset mapping relation between the category of the knowledge points and the feature vectors of the knowledge points;
and combining the obtained feature vectors y1, y2 and y3, and inputting the combined feature vectors into a trained matching degree calculation model to obtain a matching degree value output by the matching degree calculation model, wherein the matching degree calculation model is a model constructed and trained on the basis of deep learning.
4. The data analysis-based online interaction method according to claim 3, wherein after sending the response reminder to the corresponding student terminal, the method further comprises the following steps:
receiving student answering confirmation data from the teacher terminal, and judging whether the student answers correctly;
sending the student answering results and the corresponding knowledge point categories to the user management server;
the user management server is configured to count the knowledge point category with the largest number of wrong answers and the knowledge point category with the largest number of correct answers in the course answering data of each student at preset time intervals, and the knowledge point categories are respectively used as weak knowledge point category x2 and well-known knowledge point category x3 of the student.
5. The data analysis-based online interaction method of claim 2, wherein calculating the matching degree of the closest knowledge point with each current online student comprises the following steps:
inquiring the category to which the closest knowledge point belongs, and acquiring the historical error rate of the current online student corresponding to the category from a user management server;
and determining the matching degree of the closest knowledge point and each current online student according to the historical error rate, wherein the historical error rate is positively correlated with the matching degree.
6. The data analysis-based online interaction method according to claim 5, wherein after sending the response reminder to the corresponding student terminal, the method further comprises the following steps:
receiving student answering confirmation data from the teacher terminal, and judging whether the student answers correctly;
sending the student answering results and the corresponding knowledge point categories to the user management server;
and the user management server is configured to judge whether each student has a new student answering result for each knowledge point category at preset intervals, and if so, the historical error rate of the student for the knowledge point category is updated according to the new student answering result.
7. The data analysis-based online interaction method according to claim 2, wherein the students who answer the questions are selected from high to low according to the matching degree, and the method comprises the following steps:
inquiring to obtain the importance value of the closest knowledge point in the courseware from a courseware management server;
determining the number m of questioners according to the mapping relation between the preset importance value and the number of questioners;
and selecting m students from high to low according to the matching degree.
8. The data analysis-based online interaction method according to claim 7, wherein the step of determining the number m of questioning persons and selecting m students from high to low according to the matching degree further comprises the steps of:
counting the number a of knowledge points which are asked for and finished at present and the number b of knowledge points which are not asked for and finished in a courseware which is adopted at present;
the planned remaining time period t2 is calculated using the following formula:
t2＝t1*b/(a+b)
wherein t1 is the preset total curriculum duration;
obtaining the residual time t3 of the current course from the course management server;
if the remaining time length t2 is greater than the remaining time length t3 and t2-t3 is greater than a preset time threshold value, the number m of questioners is reduced;
and if the remaining time length t2 is less than the remaining time length t3 and t3-t2 is greater than a preset time threshold value, the number m of questioners is increased.
9. The data analysis-based online interaction method according to claim 2, wherein the problem data is analyzed to obtain a problem keyword, the problem keyword is segmented, words meeting preset deletion conditions in segmented results are removed, and the remaining words are used as the problem keyword;
comparing the question keywords with the keywords of each knowledge point corresponding to the courseware ID, comprising the following steps:
counting the number n of the keywords which are coincident with the question keywords for each knowledge point corresponding to the courseware ID;
and selecting the knowledge point with the highest number n of overlapped keywords as the closest knowledge point.
10. An online interaction system based on data analysis, which is applied to the online interaction method based on data analysis of any one of claims 1 to 9, and comprises:
the first communication module is used for interacting with the teacher terminal, receiving a question request and corresponding question data from the teacher terminal, and analyzing the question data to obtain a course ID;
the second communication module is used for interacting with the course management server and inquiring the ID of the current online student from the course management server according to the course ID when the first communication module receives a question request;
the answer selection module is used for calculating the matching degree of the question data and each current online student and selecting the students to answer according to the matching degree from high to low;
and the third communication module is used for interacting with the student terminals and sending the answering reminding to the student terminals selected by the answering selection module.
11. An online interaction device based on data analysis, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the data analysis based online interaction method of any of claims 1 to 9 via execution of the executable instructions.
12. A computer-readable storage medium storing a program which, when executed, performs the steps of the data analysis-based online interaction method of any one of claims 1 to 9.
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