CN111708968B - Autonomous message reminding sending method and system in teaching management platform - Google Patents
Autonomous message reminding sending method and system in teaching management platform Download PDFInfo
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Abstract
The invention provides a method and a system for automatically sending message reminding in a teaching management platform, wherein the method comprises the following steps: acquiring a task scene type, task creation time and corresponding task parameters created by a teacher; under the examination scene, determining examination notification time according to the task creation time, determining a message reminding object and time according to the task submission state and the corresponding task parameters, and sending examination notification, examination advance notice and examination submission reminder to the message reminding object; under the operation scene, determining the operation notification time according to the task creation time, determining the message reminding object according to the task submission state, extracting the operation attribute characteristics, determining the operation difficulty level by using a random forest algorithm, determining the operation submission reminding time according to the operation difficulty level, and sending the operation notification and the operation submission reminding to the message reminding object. The invention considers two application scenes of on-line examination and operation at the same time, and avoids 'one tool' of message reminding.
Description
Technical Field
The invention relates to the field of online teaching management, in particular to a method and a system for autonomously sending messages in a teaching management platform.
Background
The more fields come into widespread use of the internet, and the educational field is no exception. In the field of education, teaching management is very important, the learning effect of students is a main basis for judging whether teaching management is successful, and monitoring of a learning process is a premise for ensuring the learning effect. In the learning process, homework and examination are the basis which can reflect the learning effect of students, and the teaching management can be helped to check the defects and mend the leaks by mastering the data of the two parts. At present, more and more teaching tasks are performed on the Internet, so are the operations and the exams, and the difficulty in an online teaching management platform is solved by lacking artificial on-site monitoring and how to realize online monitoring.
The online management in the existing teaching management platform basically uses the traditional teaching mode to prompt a teacher or a staff to call, and QQ or WeChat artificial management students to complete the homework or the examination in time, and no very reasonable online monitoring method exists, so that heavy workload of the teacher or the staff is caused, and the workload is mainly used for contacting and reminding the students to take the examination in time or complete the homework, so that if a method for independently sending homework or examination message reminding in the platform can be designed, the working efficiency of the teaching management can be greatly improved.
The problems of the message reminding method in the existing teaching management are as follows: (1) the number of the considered automatic message reminding scenes is small, the flexibility is lacking, for example, whether the scenes are exams or jobs in courses is poor, the message reminding time of different scenes is different, and if all the scenes are cut at one time, two extremes of untimely message reminding or too many messages can occur; (2) many online education platforms do not have the function of message reminding, are unfavorable for teaching management, because the profound influence of traditional teaching mode, many students still rely on learning through the on-the-spot supervision of teacher, and this is unfavorable for training student's autonomous learning ability, and under the internet education mode, teacher's on-the-spot supervision effect can weaken, adds the message and reminds, can on-line supervision student to cultivate student's consciousness of independently learning.
Disclosure of Invention
In view of the above, the invention provides a method and a device for automatically sending message reminding of online operation or examination in a teaching management platform, which are used for solving the extreme situations of untimely message reminding or excessive message reminding in the existing teaching management platform.
The first aspect of the present invention provides a method for autonomous sending of message alerts in a teaching management platform, the method comprising:
s1, acquiring a task scene type, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene type comprises an examination scene and an homework scene;
s2, under the examination scene, determining examination notification time according to the created task time, determining a message reminding object according to the task submitting state, determining examination advance notice and examination submitting reminding time according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination notification, the examination advance notice and the examination submitting reminding to the message reminding object;
s3, under the operation scene, determining operation notification time according to the task creation time, determining a message reminding object according to the task submission state, extracting operation attribute characteristics, determining the operation difficulty level by using an algorithm of a random forest, determining the operation submission reminding time according to the operation difficulty level, and sending the operation notification and the operation submission reminding to the message reminding object.
Preferably, in the test scenario, the corresponding task parameters include a test question, a test start time, a test end time and a test duration; in the job scene, the corresponding task parameters include job title, job content and commit time.
Preferably, the step S2 specifically includes:
taking the time for creating the test as the time for notifying the test, and issuing test notification information to each student needing to participate in the test, wherein the specific contents of the message prompt are the test title, the starting time, the ending time and the test duration;
acquiring start time starttime of an examination and setting a time length t 0 If starttime-t 0 Later than the time of creating the test, the student who needs to take the test is issued with the test advance notice information;
and acquiring the task submission state of each student before the test ending time, wherein the object of the test submission reminding is the student which does not submit the test yet, and the test submission reminding time is duration 15 minutes before the test ending time.
Preferably, the step S3 specifically includes:
when the homework is created, a homework notification is issued to each student, wherein the notification content is a homework title, homework content and submitting time;
acquiring a task submission state of each student before the job submission time, wherein the object of the job submission reminding is a student without submitting the job; determining a difficulty level value of the current operation, wherein the time of the operation submission reminding is level 30 minutes before the operation submission time;
the method for determining the difficulty level value of the current operation comprises the following steps: dividing the difficulty of the operation into three difficulty levels, namely difficulty, medium difficulty and easiness, and extracting operation attribute characteristics, wherein the corresponding level value is 3, 2 and 1, and the attribute characteristics comprise the number X of knowledge points contained in the operation 1 Duration X of video resource of chapter corresponding to current operation 2 Average result X of last operation 3 Class X of students 4 Degree of importance X of chapter 5 The method comprises the steps of carrying out a first treatment on the surface of the And sequentially using the five attribute features as root nodes of the decision tree to construct the decision tree, judging the decision tree by each student on the five attribute features, obtaining 5 decision results through five rounds of decision tree judgment, and obtaining the result with the highest ticket number as the value of the difficulty level.
Preferably, in the step S3, the constructing a decision tree by using the five attribute features in sequence as the root node of the decision tree specifically includes:
(1) first use of knowledge Point number X 1 As the root node of the decision tree, the judgment basis is as follows:
when X is 1 >When 5, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when X is not less than 3 1 When the number of the students is less than or equal to 5, continuously judging the class X of the students 4 If the student class is a family student, the difficulty is middle, and if the student class is a special lift or a special student, the difficulty is easy;
when X is 1 <When 3, continuously judging the average result X of the last operation 3 If X 3 If the score is more than or equal to 60 minutes, the difficulty is middle, if X 3 <60 minutes, the difficulty is easy; (2) duration X of video resource corresponding to second usage chapter 2 As the root node of the decision tree, the judgment basis is as follows:
when X is 2 >When 20 minutes, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when 10 is less than or equal to X 2 When the time is less than or equal to 20 minutes, continuously judging the class X of the students 4 If the student class is a family student, the difficulty is middle, and if the student class is a special lift or a special student, the difficulty is easy;
when X is 2 <When 10 minutes, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(3) average result X of last operation of third use 3 As the root node of the decision tree, the judgment basis is as follows:
when X is 3 >80 time sharing, continue to judge student category X 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when 60 is less than or equal to X 3 When the importance degree of the chapter is less than or equal to 80, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is medium, the chapter is general or unimportant, and the difficulty level is easy;
when X is 3 <60, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(4) fourth use student class X 4 As the root node of the decision tree, the judgment basis is as follows:
when the class of students is the family, continuously judging the importance degree X of the chapters 5 If the chapter is heavyIf so, the difficulty is difficult, the chapter is general or unimportant, and the difficulty is medium;
when the student category is the special script, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes;
when the student category is a special student, continuously judging the number X of knowledge points contained in the homework or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(5) importance degree X of fifth used chapter 5 As the root node of the decision tree, the judgment basis is as follows:
when the importance degree of the chapter is important, continuously judging the class X of the students 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when the importance degree of the chapter is general, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
when the importance degree of the chapter is unimportant, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes.
In a second aspect of the present invention, there is provided a system for autonomous transmission of message alerts in a teaching management platform, the system comprising:
parameter acquisition module: the task scene type is used for acquiring task scene types, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene types comprise examination scenes and homework scenes;
examination reminding module: the method comprises the steps of determining examination notification time according to creation task time under an examination scene, and sending examination notification to all objects; determining a message reminding object according to the task submitting state, determining the time of examination advance notice and examination submitting reminding according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination advance notice and examination submitting reminding to the message reminding object;
the operation reminding module is used for: the method comprises the steps of determining job notification time according to creation task time under a job scene, and sending job notification to all objects; determining a message reminding object according to the task submitting state, extracting the attribute characteristics of the job, sequentially using the attribute characteristics as a root node of a decision tree to construct the decision tree, determining the job difficulty level by using a random forest algorithm, determining the time of job submitting reminding according to the job difficulty level, and sending the job submitting reminding to the message reminding object.
Compared with the prior art, the invention has the following beneficial effects:
the operation difficulty level is automatically evaluated by adopting a random forest algorithm, and then the operation submission reminding is carried out at different times according to different operation difficulty levels, so that the actual demands of students are fully considered.
Meanwhile, two application scenes of online examination and operation are considered, and one tool of message reminding is avoided, so that the situation that the message reminding is not timely or the message is too much is reasonably avoided, the pertinence is stronger, a plurality of parameters are considered, and the method is flexibly applied to different teaching scenes;
the method reduces the dependence of human supervision, and the mode of self-service transmission of the online message reminding enables students to learn to check the message in time and complete tasks, so that the independent learning consciousness of the students is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for autonomous sending of message reminding in a teaching management platform according to an embodiment of the present invention;
fig. 2 is a flow chart of autonomous message reminding transmission in an examination scenario provided by an embodiment of the present invention;
fig. 3 is a message alert autonomous sending flowchart in a job scenario provided by an embodiment of the present invention;
FIG. 4 is a diagram showing the number X of points of knowledge 1 As a root node decision tree graph;
FIG. 5 is a schematic diagram of a duration X of a video asset corresponding to a usage chapter according to an embodiment of the present invention 2 A decision tree graph as a root node;
FIG. 6 shows the average result X of the last job according to the embodiment of the present invention 3 As a root node decision tree graph;
FIG. 7 shows the use of student class X according to an embodiment of the present invention 4 A node decision tree graph as a root node;
FIG. 8 shows the importance level X of the usage section according to the embodiment of the present invention 5 A decision tree graph as a root node.
Description of the embodiments
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1, the invention provides a method for autonomous sending of message reminder in a teaching management platform, which comprises the following steps:
s1, acquiring a task scene type, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene type comprises an examination scene and an homework scene;
s2, under the examination scene, determining examination notification time according to the created task time, determining a message reminding object according to the task submitting state, determining examination advance notice and examination submitting reminding time according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination notification, the examination advance notice and the examination submitting reminding to the message reminding object;
s3, under the operation scene, determining operation notification time according to the task creation time, determining a message reminding object according to the task submission state, extracting operation attribute characteristics, determining the operation difficulty level by using an algorithm of a random forest, determining the operation submission reminding time according to the operation difficulty level, and sending the operation notification and the operation submission reminding to the message reminding object.
In the embodiment, the job represents the task scene type, jobCreatetime represents the task creation time, starttime represents the task start time, endtime represents the task end time, duration represents the task duration (unit hour), level represents the task difficulty, nontice represents the task forecast time, content represents the specific content of message reminding, isSubmit is the task submission state of students and is used for representing whether the students submit tasks.
After a teacher creates a specific task, the values of the parameters are determined, and specific message reminding times, time, objects and contents are obtained according to the message reminding autonomous sending method provided by the invention.
(1) Assuming that the teacher created an examination, referring to fig. 2, the task scenario type job is assigned a value of 1. Task parameters such as the test title, the start time, the end time, the test duration and the like are set in the test creation process, and the test creation time is recorded, so that the parameters starttime, endtime, duration and jobCreateTime can be determined, and the message reminding time and the message content can be determined according to the task scene type and the corresponding task parameters:
1) Examination notification: at the first time of successful creation of the test, the student to be tested is issued with test notification information, namely, the jobCreatetime is issued with test notification reminding information, and the content is information such as test questions, test starting time, test ending time, test duration and the like.
2) Examination forecast: acquiring start time (unit is hour) of examination and setting duration t 0 Starttime-t 0 Assigning a task forecast time of notify, i.e. notify, if notify is later than the time at which the test was created>jobCreatetime, at task advance notice time, notify to each student who needs to take test to issue the advance notice information of the test; if the time is earlier than jobCreateTime, no test advance notice information is issued. If t 0 The content is "one hour from the start of the test, please participate in time".
3) Examination submission reminding: the method comprises the steps that task submitting states of students are obtained, the students who do not submit examination yet are the objects of examination submitting reminders, namely, the students with issubmit=fast, notification time is determined according to examination duration, the duration is obtained when an online examination is established, the unit is hours, and the examination submitting reminders are 15 minutes in advance of examination ending time endtime; for example, the examination duration is 2 hours, and the examination is reminded to be submitted half an hour before the examination ending time endtime, the content is "15 minutes from the end of the examination and duration, please be submitted in time", and the expression duration 15 should be a specific time.
(2) Assume that a teacher creates a job, such as a pre-learning job, a post-class job, a classroom job, and the like corresponding to a chapter, and referring to fig. 3, the task scenario type job is assigned as 2. In the process of creating the job, the job title, the job content and the submitting time are set, and the job creating time is recorded, so that the parameters jobCreateTime, content, endtime are all determined, and the time and the content of the message reminding are determined as follows:
1) And (3) job notification: the homework is different from examination, and the creation time of the homework is the start time of the homework, namely jobCreatetime=starttime, so that when the homework is created, homework notification should be issued to each student, and content is information such as homework title, homework content, submission time and the like.
2) Job submission reminding: the notification object is a student who has not submitted the homework, namely, the student with the isSubmit of fast, the notification time of the homework submission reminder is determined according to the difficulty level of the homework,
the method for determining the difficulty level value of the current operation comprises the following steps: the difficulty of operation is divided into three difficulty levels of difficulty, medium difficulty and easiness, the corresponding level value is 3, 2 and 1, and the greater the difficulty is, the greater the level value is; the invention adopts a random forest model to calculate the difficulty of the operation, wherein the random forest is a model for randomly selecting k characteristics in a decision tree with m characteristics to form n decision trees, and then selecting a prediction result mode, firstly extracting the attribute characteristics of the operation, wherein the attribute characteristics comprise the number X of knowledge points contained in the operation 1 Duration X of video resource of chapter corresponding to current operation 2 Average result X of last operation 3 Class X of students 4 Degree of importance X of chapter 5 . When using the random forest algorithm, assuming a class with n students, each student will have the above five attribute profile data. Each student makes decision tree decisions on the above five attribute features, and nodes of the decision tree use the above five features in turn. The five attribute features are sequentially used as root nodes of the decision tree to construct the decision tree, specifically:
(1) first use of knowledge Point number X 1 As a root node of the decision tree, as shown in fig. 4, the judgment basis is as follows:
when X is 1 >When 5, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when X is not less than 3 1 When the number of the students is less than or equal to 5, continuously judging the class X of the students 4 If the student class is a family student, the difficulty is middle, and if the student class is a special lift or a special student, the difficulty is easy;
when X is 1 <When 3, continuously judging the average result X of the last operation 3 If X 3 If the score is more than or equal to 60 minutes, the difficulty is middle, if X 3 <60 minutes, the difficulty is easy; wherein the fractions are 100 minutes.
(2) Duration X of video resource corresponding to second usage chapter 2 As a root node of the decision tree, as shown in fig. 5, the judgment basis is as follows:
when X is 2 >When 20 minutes, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when 10 is less than or equal to X 2 When the time is less than or equal to 20 minutes, continuously judging the class X of the students 4 If the student class is a family student, the difficulty is middle, and if the student class is a special lift or a special student, the difficulty is easy;
when X is 2 <When 10 minutes, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(3) average result X of last operation of third use 3 As a root node of the decision tree, as shown in fig. 6, the judgment basis is as follows:
when X is 3 >80 time sharing, continue to judge student category X 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when 60 is less than or equal to X 3 When the importance degree of the chapter is less than or equal to 80, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is medium, the chapter is general or unimportant, and the difficulty level is easy;
when X is 3 <60, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(4) fourth use student class X 4 As a root node of the decision tree, as shown in fig. 7, the judgment basis is as follows:
when the class of students is the family, continuously judging the importance degree X of the chapters 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
as studentsIf the video resource is not the special script, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes;
when the student category is a special student, continuously judging the number X of knowledge points contained in the homework or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(5) importance degree X of fifth used chapter 5 As a root node of the decision tree, as shown in fig. 8, the judgment basis is as follows:
when the importance degree of the chapter is important, continuously judging the class X of the students 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when the importance degree of the chapter is general, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
when the importance degree of the chapter is unimportant, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes.
And 5 decision results are obtained through the five rounds of decision tree judgment, and the result with the highest ticket number is the value of the difficulty level.
The time of job submission reminding is level 30 minutes before the submission time, for example, the difficulty is medium, and the reminding message is sent 60 minutes before the submission time. The content of the reminder is "30 minutes from the time of submitting, please complete the job and submit", wherein the expression level 30 should be a specific time.
According to the invention, the difficulty degree of each operation is evaluated by adopting a random forest model, the time for submitting and reminding the operation is determined according to the difficulty degree level of the operation, the actual situation of each student in the process of writing the operation is fully considered, the untimely message reminding and too much message reminding or too early message reminding time is avoided, the actual learning situation of the students can be effectively followed, and the students are timely supervised and promoted to complete the learning task on time.
Corresponding to the embodiment of the method, the invention also provides a message reminding autonomous sending system in the same teaching management platform, which comprises the following steps:
parameter acquisition module: the task scene type is used for acquiring task scene types, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene types comprise examination scenes and homework scenes;
examination reminding module: the method comprises the steps of determining examination notification time according to creation task time under an examination scene, and sending examination notification to all objects; determining a message reminding object according to the task submitting state, determining the time of examination advance notice and examination submitting reminding according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination advance notice and examination submitting reminding to the message reminding object;
the operation reminding module is used for: the method comprises the steps of determining job notification time according to creation task time under a job scene, and sending job notification to all objects; determining a message reminding object according to the task submitting state, extracting the attribute characteristics of the job, sequentially using the attribute characteristics as a root node of a decision tree to construct the decision tree, determining the job difficulty level by using a random forest algorithm, determining the time of job submitting reminding according to the job difficulty level, and sending the job submitting reminding to the message reminding object.
The method embodiments and the system embodiments are in one-to-one correspondence, and the system embodiments are briefly described with reference to the method embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. The method for automatically sending the message reminder in the teaching management platform is characterized by comprising the following steps:
s1, acquiring a task scene type, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene type comprises an examination scene and an homework scene;
s2, under the examination scene, determining examination notification time according to the created task time, determining a message reminding object according to the task submitting state, determining examination advance notice and examination submitting reminding time according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination notification, the examination advance notice and the examination submitting reminding to the message reminding object;
s3, under the operation scene, determining operation notification time according to the task creation time, determining a message reminding object according to the task submission state, extracting operation attribute characteristics, determining the operation difficulty level by using an algorithm of a random forest, determining the operation submission reminding time according to the operation difficulty level, and sending an operation notification and an operation submission reminding to the message reminding object;
in the examination scene, the corresponding task parameters comprise examination questions, examination starting time, examination ending time and examination duration; under a job scene, the corresponding task parameters comprise a job title, job content and submission time;
the step S2 specifically comprises the following steps:
taking the time for creating the test as the time for notifying the test, and issuing test notification information to each student needing to participate in the test, wherein the specific contents of the message prompt are the test title, the starting time, the ending time and the test duration;
acquiring start time starttime of an examination and setting a time length t 0 If starttime-t 0 Later than the time of creating the test, the student who needs to take the test is issued with the test advance notice information;
acquiring task submission states of each student before the examination ending time, wherein the object of the examination submission reminding is the student which does not submit the examination yet, and determining the time of the examination submission reminding according to the examination duration, wherein the time of the examination submission reminding is 15 minutes before the examination ending time;
the step S3 specifically comprises the following steps:
when the homework is created, a homework notification is issued to each student, and the notification content is a homework title and a submitting time;
acquiring a task submission state of each student before the job submission time, wherein the object of the job submission reminding is a student without submitting the job; determining a difficulty level value of the current operation, wherein the time of the operation submission reminding is level 30 minutes before the operation submission time;
the method for determining the difficulty level value of the current operation comprises the following steps: dividing the difficulty of the operation into three difficulty levels, namely difficulty, medium difficulty and easiness, and extracting operation attribute characteristics, wherein the corresponding level value is 3, 2 and 1, and the attribute characteristics comprise the number X of knowledge points contained in the operation 1 Duration X of video resource of chapter corresponding to current operation 2 Average result X of last operation 3 Class X of students 4 Degree of importance X of chapter 5 The method comprises the steps of carrying out a first treatment on the surface of the And sequentially using the five attribute features as root nodes of the decision tree to construct the decision tree, judging the decision tree by each student on the five attribute features, obtaining 5 decision results through five rounds of decision tree judgment, and obtaining the result with the highest ticket number as the value of the difficulty level.
2. The autonomous message alert sending method in the teaching management platform according to claim 1, wherein in the step S3, the step of constructing a decision tree by sequentially using the five attribute features as root nodes of the decision tree is specifically:
(1) first use of knowledge Point number X 1 As the root node of the decision tree, the judgment basis is as follows:
when X is 1 >When 5, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when X is not less than 3 1 When the number of the students is less than or equal to 5, continuously judging the class X of the students 4 If the class of students is the family, the difficulty is that the class of students is the special lift orThe difficulty is easy for the students in the special department;
when X is 1 <When 3, continuously judging the average result X of the last operation 3 If X 3 If the score is more than or equal to 60 minutes, the difficulty is middle, if X 3 <60 minutes, the difficulty is easy;
(2) duration X of video resource corresponding to second usage chapter 2 As the root node of the decision tree, the judgment basis is as follows:
when X is 2 >When 20 minutes, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when 10 is less than or equal to X 2 When the time is less than or equal to 20 minutes, continuously judging the class X of the students 4 If the student class is a family student, the difficulty is middle, and if the student class is a special lift or a special student, the difficulty is easy;
when X is 2 <When 10 minutes, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(3) average result X of last operation of third use 3 As the root node of the decision tree, the judgment basis is as follows:
when X is 3 >80 time sharing, continue to judge student category X 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when 60 is less than or equal to X 3 When the importance degree of the chapter is less than or equal to 80, continuously judging the importance degree X of the chapter 5 If the chapter is important, the difficulty level is medium, the chapter is general or unimportant, and the difficulty level is easy;
when X is 3 <60, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(4) fourth use student class X 4 As the root node of the decision tree, the judgment basis is as follows:
when the class of students is the family, continuously judging the importance degree X of the chapters 5 If the chapter is important, the difficulty level is difficult, the chapter is general or unimportant, and the difficulty level is medium;
when the student category is the special script, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes;
when the student category is a special student, continuously judging the number X of knowledge points contained in the homework or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
(5) importance degree X of fifth used chapter 5 As the root node of the decision tree, the judgment basis is as follows:
when the importance degree of the chapter is important, continuously judging the class X of the students 4 If the class of students is a family student, the difficulty is difficult, and if the class of students is a special lift or a special student, the difficulty is medium;
when the importance degree of the chapter is general, continuously judging the number X of knowledge points contained in the operation or examination 1 If X 1 More than or equal to 3, the difficulty is that of the middle, X 1 <The difficulty is easy for 3;
when the importance degree of the chapter is unimportant, continuing to judge the duration X of the video resource 2 If X 2 Not less than 10 minutes, the difficulty is middle, X 2 <The difficulty is easy after 10 minutes.
3. An autonomous message alert transmission system in a teaching management platform using the method of any of claims 1-2, the system comprising:
parameter acquisition module: the task scene type is used for acquiring task scene types, task creation time and corresponding task parameters created by a teacher, and monitoring task submission states of students in real time, wherein the task scene types comprise examination scenes and homework scenes;
examination reminding module: the method comprises the steps of determining examination notification time according to creation task time under an examination scene, and sending examination notification to all objects; determining a message reminding object according to the task submitting state, determining the time of examination advance notice and examination submitting reminding according to the corresponding task parameters, determining the specific content of the message reminding according to the task scene type and the message reminding time, and sending the examination advance notice and examination submitting reminding to the message reminding object;
the operation reminding module is used for: the method comprises the steps of determining job notification time according to creation task time under a job scene, and sending job notification to all objects; determining a message reminding object according to the task submitting state, extracting the attribute characteristics of the job, sequentially using the attribute characteristics as a root node of a decision tree to construct the decision tree, determining the job difficulty level by using a random forest algorithm, determining the time of job submitting reminding according to the job difficulty level, and sending the job submitting reminding to the message reminding object.
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