CN112885172B - Network teaching method and system - Google Patents
Network teaching method and system Download PDFInfo
- Publication number
- CN112885172B CN112885172B CN202110462894.6A CN202110462894A CN112885172B CN 112885172 B CN112885172 B CN 112885172B CN 202110462894 A CN202110462894 A CN 202110462894A CN 112885172 B CN112885172 B CN 112885172B
- Authority
- CN
- China
- Prior art keywords
- knowledge
- knowledge point
- student
- difficulty
- students
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012360 testing method Methods 0.000 claims abstract description 159
- 230000005540 biological transmission Effects 0.000 claims description 55
- 238000012545 processing Methods 0.000 claims description 15
- 238000012163 sequencing technique Methods 0.000 claims description 13
- 241000239290 Araneae Species 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 4
- 238000010295 mobile communication Methods 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 2
- 238000009826 distribution Methods 0.000 description 28
- 206010063659 Aversion Diseases 0.000 description 13
- 230000008569 process Effects 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 11
- 239000013598 vector Substances 0.000 description 10
- 238000005457 optimization Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000012216 screening Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 206010051602 Laziness Diseases 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002650 habitual effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G09B5/12—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Marketing (AREA)
- Computational Linguistics (AREA)
- Human Resources & Organizations (AREA)
- General Health & Medical Sciences (AREA)
- Economics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention discloses a network teaching method and a system, which can more truly and accurately master the learning mastery degree of each student by carrying out difficulty grading on the associated test questions, adjust the learning scheme according to the test results of the students, carry out online learning on the knowledge which can be mastered by the network teaching by an autonomous learning method so as to reduce the learning cost, provide online real-time online teaching for the knowledge points which cannot be mastered by the students by the network teaching better, and accurately guide the learning difficulty points of the students; meanwhile, for students with partial departments, the corresponding learning scheme is automatically adjusted according to the test results of the students, so that the adjusted learning scheme is more matched with the learning ability and progress of the students, and the education cost is not additionally increased.
Description
Technical Field
The invention relates to a network education technology, in particular to a network teaching method and a system.
Background
Since 2020, internet education began to explode, and many schools and education institutions began to perform internet education, which breaks through the limitation of education in region and time and is helpful for the averaging of education resources. However, the existing internet education adopts unified courseware to teach, or a teacher aims at a plurality of students, and adopts the same teaching materials and lesson preparation notes to teach, but because the learning ability and the mastered knowledge level of different students have obvious difference, the existing non-differentiated teaching mode inevitably causes that part of students can not go up more teaching progress, and the other part of students can not learn the eager knowledge, if one-to-one teaching is adopted, the labor cost is large, the education cost is increased in geometric multiples, and the family education burden is greatly increased.
In view of the above, a network teaching method is provided, which is low in cost and can make a learning scheme according to the learning ability and the learning situation of different students.
Disclosure of Invention
The invention aims to overcome the technical defects and provides a network teaching method and a network teaching system, so that the beneficial effects of low cost and pertinence making of a learning scheme according to the learning ability and the learning condition of different students can be realized.
The invention provides a network teaching method, which comprises the following steps:
establishing a knowledge classification data question bank, wherein each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree;
establishing association between a knowledge classification data question bank and a teaching courseware according to the classification of the knowledge points, and randomly extracting test questions corresponding to the corresponding knowledge points in different difficulty grades from the knowledge classification data question bank after the teaching courseware is played;
counting the test results of the students for mastering the knowledge points, and comprehensively judging the mastering degree of the students for the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention;
and (4) counting the test results of different disciplines of the student, and comprehensively judging the mastering degree of the student on the different disciplines according to the test results so as to determine whether the teaching content needs to be adjusted.
In one possible implementation, the creating of the knowledge classification database includes:
determining all knowledge points involved in network teaching;
acquiring a historical knowledge list recommended to a historical user in historical network teaching, and dividing the historical knowledge list based on courses of the network teaching to obtain a plurality of sub-historical lists;
calling a knowledge point processed by each history user and a backspacing knowledge point from the sub-history list, and acquiring a backspacing reason pushed to a display end of the history user when the knowledge point is backspacing;
acquiring the processing condition of the historical user on the processed knowledge points;
determining the difficulty G of each knowledge point according to the following formula and the reason of return and the processing condition;
wherein n represents the number of the historical users, and m represents the number of problem solving steps corresponding to the same knowledge points; t is ti,jShowing the problem solving time of the jth problem solving step in the ith historical user corresponding to the same knowledge point; t is ti,j+1Showing the problem solving time of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user; chi shapei,j,j+1The consistency of the processing of the ith historical user to the adjacent jth and j +1 problem solving steps in the same knowledge point is shown, and the value range is [0, 1%]；βi,jThe problem solving correct probability of the jth problem solving step in the ith historical user corresponding to the same knowledge point is represented; beta is ai,j+1The problem solving correct probability of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user is represented; deltaiThe comprehensive problem solving correct probability of the ith historical user corresponding to the same knowledge point is represented, and the value range is [0, 1 ]]Wherein, β j represents the problem solving correct probability of the ith historical user corresponding to the jth problem solving step in the same knowledge point; gamma rayjRepresenting the jth solution in corresponding same knowledge pointsWeighted value of question step, and
(ii) a y represents the backspacing quantity of the n historical user pairs corresponding to the same knowledge point; phi is apA backoff factor representing a backoff reason corresponding to the p-th backoff, wherein the value of the backoff factor is 0 when the backoff factor corresponds to the same knowledge point and is 1 when the backoff factor corresponds to the same knowledge point and cannot be solved;
determining the difficulty level of each knowledge point based on the difficulty level G of each knowledge point and a preset difficulty level threshold, and constructing a first knowledge base;
acquiring geographical knowledge styles of different historical users, extracting geographical knowledge from the first knowledge base, and acquiring a second knowledge base;
and constructing a knowledge classification data question bank according to the first knowledge base and the second knowledge base.
In a possible implementation manner, after the knowledge classification database is built, the method further includes:
establishing a traction data list of each knowledge point in the knowledge classification data question bank, determining transmission time of each corresponding knowledge point based on the same transmission network according to the data volume of each knowledge point in the traction data list, and simultaneously determining a first weight value corresponding to each knowledge point and a second weight value corresponding to the traction data list;
determining the delay transmission time of each knowledge point according to the transmission time, the first weight value and the second weight value, and calibrating the delay transmission time on the corresponding knowledge point;
when a corresponding knowledge set is pushed to the student, determining the corresponding delay transmission time of each knowledge point in the knowledge set, and further determining the initial transmission time corresponding to each knowledge point;
acquiring a network pushing environment for pushing corresponding knowledge to the students, correcting initial transmission time corresponding to the knowledge points to be transmitted according to the network pushing environment, and performing priority transmission sequencing on the knowledge points to be transmitted according to a correction result;
and transmitting the sequencing result according to the priority, and correspondingly pushing the sequencing result to the student end of the student.
In one possible implementation manner, the randomly extracting test questions with different difficulty ratings corresponding to the corresponding knowledge points from the knowledge classification database includes:
and after the teaching courseware is played, the teaching courseware extracts the testing subjects related to the teaching courseware from the knowledge classification data question bank according to the matching between the courseware keywords and the knowledge point keywords and automatically classifies the testing subjects according to the difficulty grade classification marks of the testing subjects.
In one possible implementation manner, the randomly extracting test questions with different difficulty ratings corresponding to the corresponding knowledge points from the knowledge classification database includes:
in the same round of testing, for two times of tests with the same difficulty and difficulty grading on the same knowledge point, the test questions of the two times of tests with the same difficulty and difficulty grading are different; the method specifically comprises the following steps:
classifying and labeling each test question according to the knowledge points and the difficulty and easiness grades;
in the test question extraction process of the same round-the-clock test, counting and extracting test question labels corresponding to knowledge points, and randomly extracting test questions according to difficulty and easiness grades;
and deleting the test question label which is extracted.
In a possible implementation manner, the counting test results of the students on the knowledge points, and comprehensively judging the degree of the students on the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention includes:
testing from the lowest level according to the difficulty level, and judging the testing difficulty level of the next stage of the student according to the testing result;
after the student completes the highest difficulty test, comprehensively judging the mastery degree of the student on the knowledge points, entering the learning of the next knowledge point after judging the complete mastery of the student, and otherwise, testing one round;
and after the student fails the minimum difficulty test, sending an artificial teaching reminder to the mobile communication terminals of the student and the student guardian.
In a possible implementation manner, the comprehensively judging the mastery degree of the knowledge points by the students comprises:
respectively setting the answering time, the answering error rate and the influence factors of the repeated watching times of students on the same knowledge point relative to the test result;
respectively counting the answering time, the answering error rate and the repeated watching times, obtaining corresponding influence factors according to the counting result, and performing secondary accounting on the test result according to the influence factors;
and comparing the accounting result with a passing threshold value, and comprehensively judging the mastering degree of the students.
In a possible implementation manner, the counting test results of different disciplines of the student, and comprehensively judging how well the student mastery the different disciplines according to the test results to determine whether the teaching content needs to be adjusted includes:
setting influence factors of knowledge point mastering rate and answer error rate relative to average passing score;
respectively counting the knowledge point mastering rate, the answer error rate and the passing score of each knowledge point of each subject, obtaining corresponding influence factors according to the counting result, and carrying out secondary accounting on the average passing score of the corresponding subject according to the influence factors;
making a spider web statistical chart of the student comprehensive disciplines according to the accounting result, and judging the mastery degree of the student on different disciplines according to the spider web statistical chart;
the study course of the subject which is more partial than other subjects is increased, and the study course of the subject with better mastering degree is correspondingly reduced.
In a possible implementation manner, after determining that the teaching content needs to be adjusted, the method further includes:
comprehensively judging the mastery degree of the student on different disciplines according to the test result, and constructing knowledge mastery distribution of knowledge points of the different disciplines;
acquiring historical learning information of the student, wherein the historical learning information comprises valid knowledge listing information, invalid knowledge listing information, valid calculation information and invalid calculation information of the student on a corresponding knowledge point;
acquiring an optimization factor and a recommendation factor of the knowledge point according to the historical learning information;
dividing the knowledge mastering distribution of the students into effective knowledge distribution and ineffective knowledge distribution according to the optimization factors and recommendation factors;
meanwhile, effective knowledge distribution is kept, and the preference degree and the aversion degree of the students to each knowledge point in the ineffective knowledge distribution are obtained;
dividing the invalid knowledge distribution according to the preference degree and the aversion degree to obtain to-be-processed knowledge distribution;
the factors are acquired according to the historical learning information of students, the knowledge mastering distribution is divided, meanwhile, the preference and the aversion of invalid knowledge distribution are acquired, the distribution to be processed is divided again, the necessary mastering conditions are acquired through processing, the point of interest teaching accessories are extracted through combining habit characteristics, the addition is carried out, and the learning efficiency is improved.
Meanwhile, screening the knowledge points of which the aversion degree of the student is greater than a first preset degree, and acquiring aversion information, and simultaneously screening the knowledge points of which the preference degree of the student is greater than a second preset degree, and acquiring preference information;
setting the necessary grasping conditions of each screened knowledge point according to the aversion information and the preference information;
acquiring the learning habits of the students, constructing habit vectors, matching the habit vectors with each row of vectors in a preset matrix, acquiring preset vectors in the preset matrix with the matching degree higher than the preset degree, and extracting corresponding habit features;
and extracting interest point teaching accessories from an interest database according to the habit characteristics and the necessary mastering conditions, adding to corresponding knowledge points in the to-be-processed knowledge distribution, and pushing to the student end of the student for display.
The invention provides a network teaching system, which comprises the following functional modules:
the question bank establishing module is used for establishing a knowledge classification data question bank, each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree;
the test question extraction module is used for establishing the association between the knowledge classification data question bank and the teaching courseware according to the classification of the knowledge points, and randomly extracting test questions with different difficulty grades corresponding to the corresponding knowledge points from the knowledge classification data question bank after the teaching courseware is played;
the knowledge statistics and checking module is used for counting the test results of the students on the knowledge points and judging the mastery degree of the students on the knowledge points according to the test results so as to determine whether to continue autonomous learning or manual intervention;
and the subject counting and checking module is used for counting the testing results of different subjects of the student and judging the mastery degree of the student on the different subjects according to the testing results so as to determine whether the teaching content needs to be adjusted.
Compared with the prior art, the network teaching method and system, the server and the medium can more truly and accurately master the learning mastery degree of each student by easily grading the associated test questions, adjust the learning scheme according to the test results of the students, perform online learning on the knowledge which can be mastered by the network teaching by an autonomous learning method so as to reduce the learning cost, provide online real-time online teaching for the students which cannot better master knowledge points by the network teaching, and accurately guide the learning difficulty points of the students; meanwhile, for students with partial departments, the corresponding learning scheme is automatically adjusted according to the test results of the students, so that the adjusted learning scheme is more matched with the learning ability and progress of the students, and the education cost is not additionally increased.
Drawings
FIG. 1 is a flow chart of a network teaching method according to an embodiment of the present invention;
fig. 2 is a block diagram of a network teaching system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, an embodiment of the present invention provides a network teaching method, where as shown in the figure, the network teaching method includes the following steps:
s1, establishing a knowledge classification data question bank, wherein each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree.
And in the knowledge classification data question bank, classifying each knowledge point by a plurality of difficulty degrees, wherein each classification corresponds to a plurality of test questions with the same difficulty degree, and each difficulty degree corresponds to a passing threshold. For example, a primary, middle and high level difficulty rating is assigned to each knowledge point, which is required to be mastered by students, and a secondary, or an Olympic rating is assigned to each knowledge point, such that the high difficulty rating is selectable and is selected autonomously according to the wishes of the students.
Through the difficulty grading of refining the knowledge points, the mastering degree of students and the learning difficulty and difficulty of the students can be more accurately known, so that the learning scheme more suitable for each student is adjusted.
S2, establishing the association between the knowledge classification database and the teaching courseware according to the knowledge point classification, and randomly extracting test questions with different difficulty grades corresponding to the corresponding knowledge points from the knowledge classification database after the teaching courseware is played.
And (3) corresponding the classified test questions to the teaching courseware according to knowledge point classification, obtaining an in-class test stage after the teaching courseware is played, and automatically randomly extracting the test questions with different difficulty classifications corresponding to the corresponding knowledge points from the knowledge classification data question bank by the system to test the learning mastery degree of students.
Specifically, each test question is provided with a plurality of knowledge point keywords and difficulty level classification marks in the knowledge classification database, each teaching courseware is provided with at least one courseware keyword, after the teaching courseware is played, the teaching courseware extracts the test questions related to the teaching courseware from the knowledge classification database according to matching between the courseware keywords and the knowledge point keywords, and automatically classifies the test questions according to the difficulty level classification marks of the test questions.
However, in the same round of testing, for two tests with the same difficulty and difficulty grading appearing at the same knowledge point, the test questions of the two tests with the same difficulty and difficulty grading are different. Specifically, each test question can be classified and labeled according to knowledge points and difficulty and easiness grading; each classification label information comprises a knowledge point name, a difficulty grade and an arrangement serial number in the grade, and each test question has an independent classification label.
In the test question extraction process of the same round-robin test, counting and extracting test question labels corresponding to knowledge points, storing the extracted labels in a temporary database, and randomly extracting the test questions according to difficulty and easiness grades; for example, when the test is performed from the primary level, all test question labels in the primary level are extracted into the temporary database, the test question labels are randomly extracted from the temporary database, corresponding test questions are extracted from the knowledge classification database according to the labels, and the extracted test question labels are deleted from the temporary database. And setting a corresponding number of temporary databases according to the corresponding difficulty and difficulty grading number.
If the student does not pass the intermediate test and falls into the primary test, and then the primary test is finished and the intermediate test is started, the system randomly extracts the test question label from the temporary database with the label deleted before, so that the problem that the mastery degree of the student on the knowledge point cannot be really known due to the repeated test questions in the test process is avoided.
Preferably, a replay key of the teaching video of the corresponding knowledge point is arranged in the student answering page. Namely, in the process of answering the questions, if the students forget or do not understand the knowledge points, the students can watch the teaching video repeatedly to help solve the questions.
And S3, counting the test results of the knowledge points mastered by the students, and comprehensively judging the mastered degree of the knowledge points by the students according to the test results to determine whether to continue autonomous learning or manually intervene.
The test is carried out from the lowest level according to the difficulty level, and the test difficulty level of the next stage of the student is judged according to the test result; for example: if the student completes the primary test and the test result is higher than the pass threshold, the secondary test can be entered, and if the student fails the pass threshold of the secondary test, the primary test is returned again.
After the student completes the highest difficulty test, in order to more comprehensively and truly know the mastery degree of the student, the mastery degree of the student on the knowledge point needs to be comprehensively judged. Specifically, setting the answering time, answering error rate, and influence factors of repeated watching times of students on the same knowledge point relative to the test result; respectively counting the answer time, answer error rate and repeated watching times of students, obtaining corresponding influence factors according to the counting results, carrying out secondary accounting on the test results according to the influence factors, judging that the students enter the learning of the next knowledge point after being completely mastered if the final accounting result exceeds a preset complete mastery threshold, otherwise, adding a test round if the final accounting result does not exceed the preset complete mastery threshold, randomly extracting test questions from all test questions of the knowledge point, judging that the students completely master the knowledge point if the test result exceeds the preset complete mastery threshold, and playing the teaching video to the students again if the test result does not exceed the preset complete mastery threshold.
After the student fails to pass the lowest difficulty test, an artificial teaching reminder is sent to the mobile communication terminals of the student and the student guardian, namely, an artificial teaching help message is sent, the student is taught on line one-to-one by the corresponding teacher, the problem of the child is known more visually, and the problem of the child is solved in real time in an online manner.
So, can carry out online study through the method of independently studying through the knowledge that the network teaching can master to the student, can't provide online real-time online teaching again through the better knowledge point of mastering of network teaching to the student, solve and impart knowledge to the knowledge point that the student can't master to not only can reduce the cost of learning, can also carry out accurate guidance to student's difficult point of learning.
And S4, counting the test results of different disciplines of the student, and comprehensively judging the mastering degree of the student on the different disciplines according to the test results so as to determine whether the teaching content needs to be adjusted.
Specifically, setting the influence factors of knowledge point mastering rate and answer error rate relative to average passing score; respectively counting the knowledge point mastery rate, the answer error rate and the passing score of each knowledge point of each subject, obtaining corresponding influence factors according to the statistical results of the knowledge point mastery rate and the answer error rate, and carrying out secondary accounting on the average passing score of the corresponding subject according to the influence factors; the spider web statistical chart of the student comprehensive discipline is made according to the accounting result, the mastering degree of the student to different disciplines can be visually known through the spider web statistical chart, so that the learning class time of the discipline which is more partial than other disciplines is increased according to the spider web statistical chart, the learning class time of the discipline with better mastering degree is correspondingly reduced, and therefore the corresponding learning scheme is adjusted according to the learning capacity and the learning progress of different students, and the adjusted learning scheme is matched with the learning progress of the student.
According to the online teaching method, difficulty and easiness in grading are carried out on the associated test questions, so that the learning mastery degree of each student can be mastered more truly and accurately, the learning scheme is adjusted according to the test results of the students, the students can learn on line by an autonomous learning method according to knowledge mastered by online teaching, the learning cost is reduced, online real-time online teaching is provided for the students who cannot master knowledge points better by online teaching, and accurate guidance is carried out on the learning difficulty points of the students; meanwhile, for students with partial departments, the corresponding learning scheme is automatically adjusted according to the test results of the students, so that the adjusted learning scheme is more matched with the learning ability and progress of the students, and the education cost is not additionally increased.
The problem library is generally established through manual operation, difficulty degrees are only carried out on the contents of the problems, or the problems are simply made by students through a questionnaire mode or the difficulty degrees are divided on different knowledge points through suggestions given by expert groups, however, the division result has universality, and due to the fact that the independent learning capacity of each student is different or the learning habits of the students such as laziness, dragging and the like (but the knowledge points cannot be shown to be unknown), the following mode is adopted when the data problem library is constructed:
the network education method comprises the following steps of when a knowledge classification data question bank is established:
determining all knowledge points involved in network teaching;
acquiring a historical knowledge list recommended to a historical user in historical network teaching, and dividing the historical knowledge list based on courses of the network teaching to obtain a plurality of sub-historical lists;
calling a knowledge point processed by each history user and a backspacing knowledge point from the sub-history list, and acquiring a backspacing reason pushed to a display end of the history user when the knowledge point is backspacing;
acquiring the processing condition of the historical user on the processed knowledge points;
determining the difficulty G of each knowledge point according to the following formula and the reason of return and the processing condition;
wherein n represents the number of the historical users, and m represents the number of problem solving steps corresponding to the same knowledge points; t is ti,jShowing the problem solving time of the jth problem solving step in the ith historical user corresponding to the same knowledge point; t is ti,j+1Showing the problem solving time of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user; chi shapei,j,j+1The consistency of the processing of the ith historical user to the adjacent jth and j +1 problem solving steps in the same knowledge point is shown, and the value range is [0, 1%]；βi,jThe problem solving correct probability of the jth problem solving step in the ith historical user corresponding to the same knowledge point is represented; beta is ai,j+1The problem solving correct probability of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user is represented; deltaiThe comprehensive problem solving correct probability of the ith historical user corresponding to the same knowledge point is represented, and the value range is [0, 1 ]]Wherein, β j represents the problem solving correct probability of the ith historical user corresponding to the jth problem solving step in the same knowledge point; gamma rayjRepresents the weight value corresponding to the jth problem solving step in the same knowledge point, and
(ii) a y represents the backspacing quantity of the n historical user pairs corresponding to the same knowledge point; phi is apA backoff factor representing a backoff reason corresponding to the p-th backoff, wherein the value of the backoff factor is 0 when the backoff factor corresponds to the same knowledge point and is 1 when the backoff factor corresponds to the same knowledge point and cannot be solved;
determining the difficulty level of each knowledge point based on the difficulty level G of each knowledge point and a preset difficulty level threshold, and constructing a first knowledge base;
acquiring geographical knowledge styles of different historical users, extracting geographical knowledge from the first knowledge base, and acquiring a second knowledge base;
and constructing a knowledge classification data question bank according to the first knowledge base and the second knowledge base.
In this embodiment, the historical user refers to a user who processes the same knowledge point, and the historical user is representative and may be a solution result of students in different levels to the knowledge point.
In this embodiment, the courses of the network teaching are divided because the courses are different and the corresponding knowledge points are also different, each sub-history list corresponds to one course, and each sub-history list includes a plurality of knowledge points of the course.
In this embodiment, the knowledge points processed by the historical user refer to the user that solved the problem corresponding to the knowledge point, and the backspacing knowledge points refer to the user that did not solve the problem corresponding to the knowledge point.
In this embodiment, the reason for the return is acquired so that the degree of grasp of the knowledge point by the user can be preliminarily determined.
In this embodiment, the difficulty level thresholds are pre-divided, and each difficulty level corresponds to two difficulty level thresholds, that is, the difficulty level is determined by performing screening in a threshold interval.
In this embodiment, the geographical knowledge style refers to a characteristic knowledge point of the place where the user is located, for example, the characteristic knowledge point in the geographical area a is a1, and the characteristic knowledge point in the area B is B2.
The beneficial effects of the above technical scheme are: through acquireing historical knowledge list, be convenient for acquire effectual knowledge data, through dividing the course, be convenient for effectively distinguish, obtain a plurality of sub-historical lists, through the knowledge of transferring processing and backspacing, be convenient for effectively master different users to the operation result of the same knowledge point, and through according to the formula, calculate the degree of difficulty of knowledge point, be convenient for effectually divide the degree of difficulty of knowledge point, and simultaneously, draw regional knowledge from first database, be convenient for pertinence is recommended, guarantee to construct rationality and the validity of data knowledge base, study for the follow-up, and convenience is provided.
Wherein, after the construction of the knowledge classification data question bank, the method further comprises the following steps:
establishing a traction data list of each knowledge point in the knowledge classification data question bank, determining transmission time of each corresponding knowledge point based on the same transmission network according to the data volume of each knowledge point in the traction data list, and simultaneously determining a first weight value corresponding to each knowledge point and a second weight value corresponding to the traction data list;
determining the delay transmission time of each knowledge point according to the transmission time, the first weight value and the second weight value, and calibrating the delay transmission time on the corresponding knowledge point;
when a corresponding knowledge set is pushed to the student, determining the corresponding delay transmission time of each knowledge point in the knowledge set, and further determining the initial transmission time corresponding to each knowledge point;
acquiring a network pushing environment for pushing corresponding knowledge to the students, correcting initial transmission time corresponding to the knowledge points to be transmitted according to the network pushing environment, and performing priority transmission sequencing on the knowledge points to be transmitted according to a correction result;
and transmitting the sequencing result according to the priority, and correspondingly pushing the sequencing result to the student end of the student.
In this embodiment, the relation data list refers to other knowledge points related to the knowledge point, for example, the relation data points related to the knowledge point B include B1, B2, B3, etc., in this case, a relation data list may be constructed according to B1, B2, B3, etc., and the relation of the knowledge points in the list may be sorted according to the relation of B1, B2, B3 to the knowledge point.
In this embodiment, the data amount refers to the capacity of each knowledge point in the list, and since the capacities are different, and the corresponding transmission times are also different in the transmission process, the transmission time can be obtained based on the same transmission network, but since the transmission times are different, the last transmission of a knowledge point with a large capacity is often finished in the transmission process, and since the weight value of each knowledge point is not clear, the display screen is often uneven in the transmission display process, and a good experience effect cannot be brought to the user.
In this embodiment, the initial transmission time refers to, for example, that the corresponding delay time of the knowledge point a is delayed by 0.3 seconds, and at this time, the corresponding initial transmission time is the 0.3 th second after the first knowledge point starts to transmit the knowledge point a.
In this embodiment, the network push environment is related to the communication quality of the network, and if the communication quality is poor, for example, the transmission of the knowledge point a may be performed 0.2 seconds ahead, at this time, 0.2 seconds ahead is a delay corresponding to 0.1 seconds based on the initial transmission time, so as to implement the sequencing of priority transmission.
The beneficial effects of the above technical scheme are: the delay transmission time of each knowledge point is determined by determining the transmission time, the knowledge points and the weighted values of the list, and the corresponding calibration is carried out, but the initial transmission time is corrected because the delay transmission time is related to the network environment in the transmission process, so that the transmission priorities of the related knowledge points are sequenced, the effective learning of a user is ensured, and the learning effect of the user is improved.
Wherein, after determining that the teaching content needs to be adjusted, the method further comprises:
comprehensively judging the mastery degree of the student on different disciplines according to the test result, and constructing knowledge mastery distribution of knowledge points of the different disciplines;
acquiring historical learning information of the student, wherein the historical learning information comprises valid knowledge listing information, invalid knowledge listing information, valid calculation information and invalid calculation information of the student on a corresponding knowledge point;
acquiring an optimization factor and a recommendation factor of the knowledge point according to the historical learning information;
dividing the knowledge mastering distribution of the students into effective knowledge distribution and ineffective knowledge distribution according to the optimization factors and recommendation factors;
meanwhile, effective knowledge distribution is kept, and the preference degree and the aversion degree of the students to each knowledge point in the ineffective knowledge distribution are obtained;
dividing the invalid knowledge distribution according to the preference degree and the aversion degree to obtain to-be-processed knowledge distribution;
the factors are acquired according to the historical learning information of students, the knowledge mastering distribution is divided, meanwhile, the preference and the aversion of invalid knowledge distribution are acquired, the distribution to be processed is divided again, the necessary mastering conditions are acquired through processing, the point of interest teaching accessories are extracted through combining habit characteristics, the addition is carried out, and the learning efficiency is improved.
Meanwhile, screening the knowledge points of which the aversion degree of the student is greater than a first preset degree, and acquiring aversion information, and simultaneously screening the knowledge points of which the preference degree of the student is greater than a second preset degree, and acquiring preference information;
setting the necessary grasping conditions of each screened knowledge point according to the aversion information and the preference information;
acquiring the learning habits of the students, constructing habit vectors, matching the habit vectors with each row of vectors in a preset matrix, acquiring preset vectors in the preset matrix with the matching degree higher than the preset degree, and extracting corresponding habit features;
and extracting interest point teaching accessories from an interest database according to the habit characteristics and the necessary mastering conditions, adding to corresponding knowledge points in the to-be-processed knowledge distribution, and pushing to the student end of the student for display.
In this embodiment, although a certain knowledge point is easy to understand, for example, the more complicated the step combing process and the more likely the calculation result is wrong, the more dislike of the knowledge point is caused, for example, the more complicated the step combing process and the more likely the calculation result is wrong, and the larger the dislike degree is at this time.
Also, the preference degrees are opposite to the above.
In this embodiment, the valid knowledge listing information refers to knowledge points with a degree of mastery greater than 60%, the invalid knowledge listing information refers to knowledge points with a degree of mastery less than 60%, the valid calculation information refers to the calculation capability for the knowledge points greater than 60%, and the invalid calculation information refers to the calculation capability for the knowledge points less than 60%.
In this embodiment, an optimization factor and a recommendation factor for a knowledge point are obtained according to historical learning information, where the optimization factor refers to strong calculation capability, strong mastery capability, high mastery degree, and the like, and the recommendation factor is opposite to the optimization factor.
In this embodiment, the to-be-processed knowledge distribution refers to knowledge points that the student needs to learn and grasp.
In this embodiment, the habit vector includes various learning habit parameters, such as learning ability, computing ability, and habitual skills in the learning process, and the predetermined vector is pre-constructed.
In this embodiment, the necessary grasping conditions, such as the most critical point of a certain knowledge point, and the corresponding answering skills, are all required to be grasped, and are regarded as the necessary grasping conditions.
In this embodiment, the additional interest points are to improve the learning efficiency of the student and to improve the mastery degree of the knowledge points.
The beneficial effects of the above technical scheme are: the factors are acquired according to the historical learning information of students, the knowledge mastering distribution is divided, meanwhile, the preference and the aversion of invalid knowledge distribution are acquired, the distribution to be processed is divided again, the necessary mastering conditions are acquired through processing, the point of interest teaching accessories are extracted through combining habit characteristics, the addition is carried out, and the learning efficiency is improved.
The invention also provides a network teaching system, as shown in fig. 2, which comprises the following functional modules:
the question bank establishing module 10 is used for establishing a knowledge classification data question bank, each knowledge point in the knowledge classification data question bank is classified according to the difficulty level, and each classification corresponds to a test question of the corresponding difficulty level;
the test question extraction module 20 is used for establishing the association between the knowledge classification database and the teaching courseware according to the knowledge point classification, and randomly extracting test questions with different difficulty classifications corresponding to the corresponding knowledge points from the knowledge classification database after the teaching courseware is played;
the knowledge statistics and checking module 30 is used for counting the test results of the students on the knowledge points, and comprehensively judging the mastery degree of the students on the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention;
and the subject statistics and checking module 40 is used for counting the test results of different subjects of the student, and comprehensively judging the mastery degree of the student on the different subjects according to the test results so as to determine whether the teaching content needs to be adjusted.
The execution mode of the network teaching system of this embodiment is basically the same as that of the network teaching method, and therefore, detailed description thereof is omitted.
The server in this embodiment is a device for providing computing services, and generally refers to a computer with high computing power, which is provided to a plurality of consumers via a network. The server of this embodiment includes: a memory including an executable program stored thereon, a processor, and a system bus, it will be understood by those skilled in the art that the terminal device structure of the present embodiment does not constitute a limitation of the terminal device, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The memory may be used to store software programs and modules, and the processor may execute various functional applications of the terminal and data processing by operating the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the terminal, etc. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The executable program of the network teaching method is contained in a memory, and can be divided into one or more modules/sub-modules, wherein the one or more modules/sub-modules are stored in the memory and executed by a processor to complete the information acquisition and implementation process, and the one or more modules/sub-modules can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used for describing the execution process of the computer program in the server. For example, the computer program can be divided into a question bank establishing module, a test question extracting module, a knowledge statistics checking module and a subject statistics checking module.
The processor is a control center of the server, connects various parts of the whole terminal equipment by various interfaces and lines, and executes various functions of the terminal and processes data by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, thereby performing overall monitoring of the terminal. Optionally, the processor may include one or more processing sub-modules; preferably, the processor may integrate an application processor, which mainly handles operating systems, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The system bus is used to connect functional units in the computer, and can transmit data information, address information and control information, and the types of the functional units can be PCI bus, ISA bus, VESA bus, etc. The system bus is responsible for data and instruction interaction between the processor and the memory. Of course, the system bus may also access other devices such as network interfaces, display devices, etc.
The server at least includes a CPU, a chipset, a memory, a disk system, and the like, and other components are not described herein again.
In the embodiment of the present invention, the executable program executed by the processor included in the terminal specifically includes: a network teaching method comprises the following steps:
establishing a knowledge classification data question bank, wherein each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree;
establishing association between a knowledge classification data question bank and a teaching courseware according to the classification of the knowledge points, and randomly extracting test questions corresponding to the corresponding knowledge points in different difficulty grades from the knowledge classification data question bank after the teaching courseware is played;
counting the test results of the students for mastering the knowledge points, and comprehensively judging the mastering degree of the students for the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention;
and (4) counting the test results of different disciplines of the student, and comprehensively judging the mastering degree of the student on the different disciplines according to the test results so as to determine whether the teaching content needs to be adjusted.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and sub-modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the modules, sub-modules, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A network teaching method is characterized in that the network teaching method comprises the following steps:
establishing a knowledge classification data question bank, wherein each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree;
establishing association between a knowledge classification data question bank and a teaching courseware according to the classification of the knowledge points, and randomly extracting test questions corresponding to the corresponding knowledge points in different difficulty grades from the knowledge classification data question bank after the teaching courseware is played;
counting the test results of the students for mastering the knowledge points, and comprehensively judging the mastering degree of the students for the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention;
counting the test results of different disciplines of the student, comprehensively judging the mastering degree of the student on the different disciplines according to the test results so as to determine whether the teaching content needs to be adjusted or not;
after the knowledge classification data question bank is constructed, the method further comprises the following steps:
establishing a traction data list of each knowledge point in the knowledge classification data question bank, determining transmission time of each corresponding knowledge point based on the same transmission network according to the data volume of each knowledge point in the traction data list, and simultaneously determining a first weight value corresponding to each knowledge point and a second weight value corresponding to the traction data list;
determining the delay transmission time of each knowledge point according to the transmission time, the first weight value and the second weight value, and calibrating the delay transmission time on the corresponding knowledge point;
when a corresponding knowledge set is pushed to the student, determining the corresponding delay transmission time of each knowledge point in the knowledge set, and further determining the initial transmission time corresponding to each knowledge point;
acquiring a network pushing environment for pushing corresponding knowledge to the students, correcting initial transmission time corresponding to the knowledge points to be transmitted according to the network pushing environment, and performing priority transmission sequencing on the knowledge points to be transmitted according to a correction result;
and transmitting the sequencing result according to the priority, and correspondingly pushing the sequencing result to the student end of the student.
2. The network teaching method of claim 1, wherein the creating of the knowledge classification database comprises:
determining all knowledge points involved in network teaching;
acquiring a historical knowledge list recommended to a historical user in historical network teaching, and dividing the historical knowledge list based on courses of the network teaching to obtain a plurality of sub-historical lists;
calling a knowledge point processed by each history user and a backspacing knowledge point from the sub-history list, and acquiring a backspacing reason pushed to a display end of the history user when the knowledge point is backspacing;
acquiring the processing condition of the historical user on the processed knowledge points;
determining the difficulty G of each knowledge point according to the following formula and the reason of return and the processing condition;
wherein n represents the number of the historical users, and m represents the number of problem solving steps corresponding to the same knowledge points; t is ti,jShowing the problem solving time of the jth problem solving step in the ith historical user corresponding to the same knowledge point; t is ti,j+1Showing the problem solving time of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user; chi shapei,j,j+1The consistency of the processing of the ith historical user to the adjacent jth and j +1 problem solving steps in the same knowledge point is shown, and the value range is [0, 1%]；βi,jThe problem solving correct probability of the jth problem solving step in the ith historical user corresponding to the same knowledge point is represented; beta is ai,j+1The problem solving correct probability of the (j + 1) th problem solving step in the same knowledge point corresponding to the ith historical user is represented; deltaiThe comprehensive problem solving correct probability of the ith historical user corresponding to the same knowledge point is represented, and the value range is [0, 1 ]]Wherein, β j represents the problem solving correct probability of the ith historical user corresponding to the jth problem solving step in the same knowledge point; gamma rayjRepresents the weight value corresponding to the jth problem solving step in the same knowledge point, and
(ii) a y represents the backspacing quantity of the n historical user pairs corresponding to the same knowledge point; phi is apA backoff factor representing a backoff reason corresponding to the p-th backoff, wherein the value of the backoff factor is 0 when the backoff factor corresponds to the same knowledge point and is 1 when the backoff factor corresponds to the same knowledge point and cannot be solved;
determining the difficulty level of each knowledge point based on the difficulty level G of each knowledge point and a preset difficulty level threshold, and constructing a first knowledge base;
acquiring geographical knowledge styles of different historical users, extracting geographical knowledge from the first knowledge base, and acquiring a second knowledge base;
and constructing a knowledge classification data question bank according to the first knowledge base and the second knowledge base.
3. The network teaching method of claim 1, wherein randomly extracting test questions with different difficulty ratings corresponding to corresponding knowledge points from the knowledge classification database comprises:
and after the teaching courseware is played, the teaching courseware extracts the testing subjects related to the teaching courseware from the knowledge classification data question bank according to the matching between the courseware keywords and the knowledge point keywords and automatically classifies the testing subjects according to the difficulty grade classification marks of the testing subjects.
4. The network teaching method of claim 1, wherein randomly extracting test questions with different difficulty ratings corresponding to corresponding knowledge points from the knowledge classification database comprises:
in the same round of testing, for two times of tests with the same difficulty and difficulty grading on the same knowledge point, the test questions of the two times of tests with the same difficulty and difficulty grading are different; the method specifically comprises the following steps:
classifying and labeling each test question according to the knowledge points and the difficulty and easiness grades;
in the test question extraction process of the same round-the-clock test, counting and extracting test question labels corresponding to knowledge points, and randomly extracting test questions according to difficulty and easiness grades;
and deleting the test question label which is extracted.
5. The network teaching method as claimed in claim 1, wherein the step of counting the test results of the students' mastery of the knowledge points and comprehensively judging the mastery degree of the students to the knowledge points according to the test results to determine whether to continue autonomous learning or manual intervention comprises:
testing from the lowest level according to the difficulty level, and judging the testing difficulty level of the next stage of the student according to the testing result;
after the student completes the highest difficulty test, comprehensively judging the mastery degree of the student on the knowledge points, entering the learning of the next knowledge point after judging the complete mastery of the student, and otherwise, testing one round;
and after the student fails the minimum difficulty test, sending an artificial teaching reminder to the mobile communication terminals of the student and the student guardian.
6. The network teaching method as claimed in claim 1, wherein said comprehensively judging the mastery degree of the knowledge points by the students comprises:
respectively setting the answering time, the answering error rate and the influence factors of the repeated watching times of students on the same knowledge point relative to the test result;
respectively counting the answering time, the answering error rate and the repeated watching times, obtaining corresponding influence factors according to the counting result, and performing secondary accounting on the test result according to the influence factors;
and comparing the accounting result with a passing threshold value, and comprehensively judging the mastering degree of the students.
7. The network teaching method of claim 1, wherein the step of counting the test results of different disciplines of the student, and comprehensively judging how well the student mastery of the different disciplines is performed according to the test results to determine whether the teaching content needs to be adjusted comprises:
setting influence factors of knowledge point mastering rate and answer error rate relative to average passing score;
respectively counting the knowledge point mastering rate, the answer error rate and the passing score of each knowledge point of each subject, obtaining corresponding influence factors according to the counting result, and carrying out secondary accounting on the average passing score of the corresponding subject according to the influence factors;
making a spider web statistical chart of the student comprehensive disciplines according to the accounting result, and judging the mastery degree of the student on different disciplines according to the spider web statistical chart;
the study course of the subject which is more partial than other subjects is increased, and the study course of the subject with better mastering degree is correspondingly reduced.
8. The network teaching system is characterized by comprising the following functional modules:
the question bank establishing module is used for establishing a knowledge classification data question bank, each knowledge point in the knowledge classification data question bank is classified according to the difficulty degree, and each classification corresponds to a test question of the corresponding difficulty degree;
the test question extraction module is used for establishing the association between the knowledge classification data question bank and the teaching courseware according to the classification of the knowledge points, and randomly extracting test questions with different difficulty grades corresponding to the corresponding knowledge points from the knowledge classification data question bank after the teaching courseware is played;
the knowledge statistics and checking module is used for counting the test results of the students on the knowledge points and judging the mastery degree of the students on the knowledge points according to the test results so as to determine whether to continue autonomous learning or manual intervention;
the subject counting and checking module is used for counting the testing results of different subjects of the student and judging the mastery degree of the student on the different subjects according to the testing results so as to determine whether the teaching content needs to be adjusted or not;
after the knowledge classification data question bank is constructed, the method further comprises the following steps:
establishing a traction data list of each knowledge point in the knowledge classification data question bank, determining transmission time of each corresponding knowledge point based on the same transmission network according to the data volume of each knowledge point in the traction data list, and simultaneously determining a first weight value corresponding to each knowledge point and a second weight value corresponding to the traction data list;
determining the delay transmission time of each knowledge point according to the transmission time, the first weight value and the second weight value, and calibrating the delay transmission time on the corresponding knowledge point;
when a corresponding knowledge set is pushed to the student, determining the corresponding delay transmission time of each knowledge point in the knowledge set, and further determining the initial transmission time corresponding to each knowledge point;
acquiring a network pushing environment for pushing corresponding knowledge to the students, correcting initial transmission time corresponding to the knowledge points to be transmitted according to the network pushing environment, and performing priority transmission sequencing on the knowledge points to be transmitted according to a correction result;
and transmitting the sequencing result according to the priority, and correspondingly pushing the sequencing result to the student end of the student.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110462894.6A CN112885172B (en) | 2021-04-28 | 2021-04-28 | Network teaching method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110462894.6A CN112885172B (en) | 2021-04-28 | 2021-04-28 | Network teaching method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112885172A CN112885172A (en) | 2021-06-01 |
| CN112885172B true CN112885172B (en) | 2021-08-06 |
Family
ID=76040682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110462894.6A Active CN112885172B (en) | 2021-04-28 | 2021-04-28 | Network teaching method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112885172B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239185B (en) * | 2021-07-13 | 2021-10-29 | 深圳市创能亿科科技开发有限公司 | Method and device for making teaching courseware and computer readable storage medium |
| CN114647721B (en) * | 2022-05-23 | 2022-09-16 | 风林科技(深圳)有限公司 | Educational intelligent robot control method, device and medium |
| CN117252461B (en) * | 2023-08-29 | 2024-06-21 | 深圳市国华在线教育科技有限公司 | Online training multimode teaching method and system based on big data |
| CN118153929A (en) * | 2024-05-13 | 2024-06-07 | 山东爱书人家庭教育科技有限公司 | Learning resource allocation method, system, device and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1422085A (en) * | 2001-11-24 | 2003-06-04 | Lg电子株式会社 | Block transmission dispatching technique |
| CN108090852A (en) * | 2016-11-23 | 2018-05-29 | 西安学析网络科技有限公司 | The feelings data analysis of basic education middle school and adaptively solve scheme generation system |
| CN109214962A (en) * | 2017-06-29 | 2019-01-15 | 北京易教阳光教育科技有限公司 | Web education learning method, system, terminal and its storage medium |
| CN110287293A (en) * | 2019-07-08 | 2019-09-27 | 上海乂学教育科技有限公司 | Automatically topic management system is pushed away |
| CN112419812A (en) * | 2020-11-30 | 2021-02-26 | 北京爱论答科技有限公司 | Exercise correction method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07307759A (en) * | 1994-05-16 | 1995-11-21 | Matsushita Electric Ind Co Ltd | Inter-network connection device |
| US8366449B2 (en) * | 2008-08-13 | 2013-02-05 | Chi Wang | Method and system for knowledge diagnosis and tutoring |
| CN104765842B (en) * | 2015-04-15 | 2018-05-01 | 中山大学 | A kind of optimal Learning Scheme method for pushing and system |
-
2021
- 2021-04-28 CN CN202110462894.6A patent/CN112885172B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1422085A (en) * | 2001-11-24 | 2003-06-04 | Lg电子株式会社 | Block transmission dispatching technique |
| CN108090852A (en) * | 2016-11-23 | 2018-05-29 | 西安学析网络科技有限公司 | The feelings data analysis of basic education middle school and adaptively solve scheme generation system |
| CN109214962A (en) * | 2017-06-29 | 2019-01-15 | 北京易教阳光教育科技有限公司 | Web education learning method, system, terminal and its storage medium |
| CN110287293A (en) * | 2019-07-08 | 2019-09-27 | 上海乂学教育科技有限公司 | Automatically topic management system is pushed away |
| CN112419812A (en) * | 2020-11-30 | 2021-02-26 | 北京爱论答科技有限公司 | Exercise correction method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112885172A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112885172B (en) | Network teaching method and system | |
| CN109903617B (en) | Personalized exercise method and system | |
| CN107230174B (en) | Online interactive learning system and method based on network | |
| WO2021253480A1 (en) | Intelligent exercise recommendation method and apparatus, computer device and storage medium | |
| US20160005323A1 (en) | Adaptive e-learning system and method | |
| Van Overschelde et al. | US Every Student Succeeds Act: Negative impacts on teaching out-of-field | |
| CN109816265B (en) | Knowledge characteristic mastery degree evaluation method, question recommendation method and electronic equipment | |
| CN110473438B (en) | Word auxiliary learning system and method based on quantitative analysis | |
| Shepherd | The impact of teacher subject knowledge on learner performance in South Africa: A within-pupil across-subject approach | |
| CN111125640A (en) | Knowledge point learning path recommendation method and device | |
| Shepherd | Learn to teach, teach to learn: A within-pupil across-subject approach to estimating the impact of teacher subject knowledge on South African grade 6 performance | |
| CN110543995A (en) | student cognitive level testing and evaluating system based on fuzzy algorithm | |
| CN112650918B (en) | Personalized recommendation method and system strongly related to user knowledge model | |
| CN112348725A (en) | Knowledge point difficulty grading method based on big data | |
| CN113657723A (en) | Knowledge point mastering condition diagnosis method, test question recommendation method and device | |
| CN110929020A (en) | Knowledge point mastery degree analysis method based on test achievement | |
| CN110765362A (en) | Collaborative filtering personalized learning recommendation method based on learning condition similarity | |
| CN116777698A (en) | Intelligent teaching method and system based on AI (advanced technology attachment) intelligence | |
| CN114885216A (en) | Exercise pushing method and system, electronic equipment and storage medium | |
| CN110991943A (en) | Teaching quality evaluation system based on cloud computing | |
| CN113506481A (en) | Computer teaching system based on cloud computing and big data | |
| CN109800880B (en) | Self-adaptive learning feature extraction system based on dynamic learning style information and application | |
| JP2006072122A (en) | Learner acquisition characteristic analysis system, its method, and program | |
| US11482127B2 (en) | System and method for behavioral analysis and recommendations | |
| CN112507792A (en) | Online video key frame positioning method, positioning system, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |