CN110349062A - Preparatory test method based on artificial intelligence - Google Patents
Preparatory test method based on artificial intelligence Download PDFInfo
- Publication number
- CN110349062A CN110349062A CN201910636313.9A CN201910636313A CN110349062A CN 110349062 A CN110349062 A CN 110349062A CN 201910636313 A CN201910636313 A CN 201910636313A CN 110349062 A CN110349062 A CN 110349062A
- Authority
- CN
- China
- Prior art keywords
- preparatory
- knowledge
- test
- topic
- point
- 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.)
- Granted
Links
- 238000010998 test method Methods 0.000 title claims abstract description 18
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 10
- 238000012360 testing method Methods 0.000 claims abstract description 108
- 238000012552 review Methods 0.000 claims abstract description 7
- 238000013507 mapping Methods 0.000 claims description 13
- 230000009897 systematic effect Effects 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000007596 consolidation process Methods 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Educational Technology (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Electrically Operated Instructional Devices (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The preparatory test method based on artificial intelligence that this application involves a kind of when student enters systematic learning, before first step test, first passes through preparatory test, finds situation of the student on the knowledge point of this class, whether determination was learned on the knowledge point.If learning the knowledge point, subsequent study can be entered, reviews or preview the stage if not learning the knowledge point and can enter.The first step of test can find most suitable topic in exam pool based on big data first first to be surveyed, and guarantee that the result of test is accurate, suitable path is selected further according to the result of test.
Description
Technical field
This application involves education sector more particularly to a kind of preparatory test methods based on artificial intelligence.
Background technique
In teaching process, it is often necessary to test to student, be evaluated with the level to student.It has tested whether
Effect, it is of great importance that the result of test will have enough identifications.The identification and test content of test have important relationship,
Only it is suitble to the test content of measurand that could make test result that there is enough identifications.If in primary test,
Tested student all does pair or all does wrong, and such test result does not have effective identification, can not carry out effective
Evaluation.Therefore, suitable test content is selected to be necessary according to the case where measurand.It is same under traditional education mode
The object of a test is usually the student that a batch has similar learning experiences, to can also arrange corresponding test content
The test for having identification is carried out with the level to student.
Summary of the invention
Inventor is by long-term observation and experiment discovery, in modern education field, especially with internet and manually
Under the emerging educational pattern that the development of intelligence occurs, before being tested to student, often due to whether not knowing student
The knowledge point was learned, leads to not decide whether to be tested or test whether enough identifications, this is traditional
Such issues that hardly occur in teaching pattern.In traditional teaching pattern, the student that teacher is faced usually has
Similar learning experiences, such as the student of the same grade, knowledge point that big learning handed down in a family is crossed and the knowledge point that do not learned are all similar,
So teacher can relatively accurately estimate the study situation of student, thus also just reasonably judge which content needs to test,
Which content does not need test (for example all learning certainly, or all do not learned certainly), and can design suitable content and topic
Mesh tests student.Therefore, under traditional education mode, due to the influence of overall situation, such issues that people are unaware of.
And the purpose for testing script is to efficiently be evaluated and (for example more saved relative to other evaluation methods
Save time), but since student does not learn this partial knowledge, almost all is wrong during the test, such test
Without what meaning, not only without saving the time, many times are wasted instead.So under emerging educational pattern, inventor
By largely observing and testing, creatively propose, before being tested to student, using preparatory testing process to student
Whether learned the course content to be prejudged, then decides whether to carry out the test that this section is held within the class period.Due to the accurate of anticipation with
No to influence whether subsequent study, so finding, the topic suitably tested in advance is critically important, and system can be found according to big data
Most there is the topic of identification, the case where to test student.And this mode tested in advance may be simultaneously under traditional education mode
Without too big necessity, therefore such issues that nobody can appreciate that, also just it is even more impossible to expect similar solution, and
There is no the support of big data, it is also difficult to realize.
In view of the above drawbacks of the prior art, the application provides a kind of preparatory test method based on artificial intelligence, learns
When life enters systematic learning, before first step test, preparatory test is first passed through, finds student's knowing in this class
Know the situation on point, whether determination was learned on the knowledge point.If learning the knowledge point, subsequent study can be entered, such as
Fruit, which did not learn the knowledge point, can enter review or preview the stage.The first step of test can be first based on big data in exam pool
Most suitable topic is found first to be surveyed, guarantees that the result of test is accurate, suitable road is selected further according to the result of test
Diameter.
Knowledge mapping: all knowledge points in study stage and before are put together in the current study stage, are fabricated to tool
There is the knowledge mapping of preposition successor relationship.Preposition successor relationship: referring to that knowledge point b can not learn is can not learn because of knowledge point a, can
Claiming a is the preposition knowledge point of b, and b is the subsequent knowledge point of a.And knowledge mapping is exactly the preposition successor relationship between knowledge point
Mark the clear structure of knowledge.The knowledge point in each subject each study stage is combined in the way of big data,
And preposition successor relationship is marked to each knowledge point, be formed by knowledge mapping can promote significantly student study,
Understand and remembers.And it is this by big data generate knowledge mapping in the way of, traditional education sector be difficult to imagine also without
What method was realized.It is not had in traditional education sector due to the influence of integrated environment and the restriction of objective condition in other words
People wants in this way to go that all relevant knowledge points are associated and are marked.
Ability value: student grasps situation to the whole of some knowledge point.It is a kind of " latent that item response theory assumes that subject has
In speciality ", latent trait is a kind of statistics conception proposed on the basis of observation analysis is test and reacted, in test, potential spy
Matter generally refers to potential ability, and the estimation through common test total score as this potentiality.Item response theory is thought to be tested
Reaction and achievement and their latent trait on test item have special relationship.Latent trait is exactly our ability
Value.It is acquired using artificial intelligence technology and analyzes a large amount of students to the learning process of each knowledge point, thus to each knowledge point
Set and simultaneously dynamically adjust corresponding ability value, can the study situation effectively to student accurately evaluated.It is based on
This evaluation method that can quantify is conducive to intelligently push personalized education resource to student, and intelligently adjusts
The process of whole student's study.And it is this by artificial intelligence technology student is evaluated in the way of, in traditional education sector
Being difficult to imagine also cannot achieve.In other words in traditional education sector, due to the influence and objective condition of integrated environment
It restricts, never someone wants to go to be adjusted the Grasping level of each knowledge point for each student in this way.
In this application, for some specific student, it is set with corresponding ability value for each knowledge point,
Student can be judged to the grasp situation of knowledge point (such as: up to standard or not up to standard) according to ability value.In student's study or again
During habit, the case where learning or review according to student, ability value can be dynamically adjusted.
The application provides a kind of preparatory test method based on artificial intelligence, comprising the following steps: in be tested
Hold, filters out test topic in advance；And preparatory test question purpose answer situation is judged whether to survey according to measurand
Examination.
In some embodiments, optionally, according to content to be tested, preparatory knowledge on testing point is selected；And according to institute
The preparatory knowledge on testing point of selection filters out test topic in advance.
In some embodiments, optionally, it according to the difficulty of knowledge point relevant to content to be tested, selects to survey in advance
Try knowledge point.
In some embodiments, optionally, according to knowledge mapping relevant to content to be tested, test in advance is selected to know
Know point.
In some embodiments, optionally, based on the mode of big data, from topic relevant to preparatory knowledge on testing point
Filter out test topic in advance.
In some embodiments, optionally, test topic in advance is filtered out according to the identification of topic, wherein survey in advance
Examination question mesh is included in exam pool the highest one or multi-channel topic of identification in topic corresponding with preparatory knowledge on testing point.
In some embodiments, optionally, if measurand answers preparatory test question purpose accuracy and reaches threshold value,
It is tested.
In some embodiments, optionally, if measurand answers preparatory test question purpose accuracy and is not up to threshold value,
Whether grasp the knowledge point then learnt before judgement.
In some embodiments, optionally, if the knowledge point that measurand learns before having grasped, carries out new knowledge
The study of point.
In some embodiments, optionally, if the knowledge point that measurand learns before not grasping, to learning before
Knowledge point reviewed.
Compared with prior art, the technical solution of the application include at least following improvement and the utility model has the advantages that
First, testing efficiency is promoted, separates which student needs to test, which student does not need to test；
Second, it needs classmate to be tested and is not required to classmate to be tested can be arranged into respectively in a variety of modes of learning.
Third can be saved effectively the testing time for student by testing in advance, more efficient.
It is described further below with reference to technical effect of the attached drawing to the design of the application, specific structure and generation, with
It is fully understood from the purpose, feature and effect of the application.
Detailed description of the invention
When following detailed description is read in conjunction with the figure, the application will be become more fully understood from, throughout the drawings, identical
Appended drawing reference represent identical part, in which:
Fig. 1 is the flow chart of one embodiment of the preparatory test method of the application.
Specific embodiment
Below by the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described implementation
Example is a part of the embodiment of the application, rather than whole embodiments.Based on the embodiment in the application, ordinary skill
Personnel's every other embodiment obtained without making creative work all should belong to the model of the application protection
It encloses.
The application provides a kind of preparatory test method based on artificial intelligence, comprising the following steps: first according to be tested
Content filters out test topic in advance；Further according to student to the preparatory test question purpose answer situation, judge whether to survey
Examination.In screening in advance test topic step, preparatory knowledge on testing point can be selected first according to content to be tested；Further according to
Selected preparatory knowledge on testing point filters out test topic in advance.The quantity for the knowledge point tested in advance is relative to subsequent
It is less for official testing, it based on less knowledge point and takes less time, efficiently to tested before official testing
Object carries out preparatory assessment, in order to reasonable arrangement official testing, to improve the efficiency and effect of official testing.
When selecting preparatory knowledge on testing point, it can be selected according to the difficulty of knowledge point relevant to content to be tested
It selects.There may be a series of relevant knowledge points around content to be tested, the study of these knowledge points and understand that difficulty is usually
Different, it can arrange or select corresponding knowledge point to be tested in advance according to the difficulty of knowledge point and the purpose of test.
It is tested for example, can choose the smallest knowledge point of difficulty, whether has been learnt before in order to assess measurand in advance
Related content is crossed, to more effectively judge whether to subsequent official testing.It can also select that difficulty is smaller, difficulty respectively
The biggish several knowledge points of moderate and/or difficulty are combined to be tested in advance, in order to more comprehensively or more there is identification
Study and the degree of understanding of the assessment measurand in degree ground to related content, so as to be mentioned for the subsequent official testing of reasonable arrangement
For reference.
In some embodiments, preparatory knowledge on testing can be selected according to knowledge mapping relevant to content to be tested
Point.There may be a series of relevant knowledge points around content to be tested, the usual of these knowledge points is phase on knowledge mapping
Connection, there is certain preposition successor relationship from each other, can according to preposition successor relationship of the knowledge point in knowledge mapping,
Arrange or select corresponding knowledge point to be tested in advance.It is tested in advance for example, can choose preposition knowledge point, in order to
Whether related content was learnt before assessment measurand, to more effectively judge whether to subsequent formal survey
Examination.Preposition and/or subsequent knowledge point can also be selected to combine respectively to be tested in advance, in order to more comprehensively or
Assess study and the degree of understanding of the measurand to related content with more having identification, so as to for reasonable arrangement it is subsequent just
Formula test provides reference.
In some embodiments, it is also possible to by the difficulty of knowledge point relevant to content to be tested and knowledge mapping
Preposition successor relationship combine, to select preparatory knowledge on testing point.
It, can be based on the mode of big data, from topic relevant to preparatory knowledge on testing point when topic is tested in screening in advance
In filter out in advance test topic.The case where being learnt in exam pool according to a large amount of students marks corresponding identification to each topic
Degree, can filter out identification most from topic corresponding with preparatory knowledge on testing point in exam pool according to the identification of topic
High one or multi-channel topic is as test topic in advance.For example, the statistical analysis by big data, specifically knows for one
Know point, most student does before study not to some topic, but can then do after study pair, then the topic is to this
Identification is higher for knowledge point, whether has learnt the knowledge point before capable of being effectively tested out measurand.
After filtering out test topic in advance, answered by measurand.If measurand answers test topic in advance
Accuracy reach threshold value, then carry out subsequent official testing.The threshold value of accuracy can be set in advance, such as 100%,
90%, 80%, 75%, 70%, 60%, 50% etc..In some embodiments, at least accuracy is needed to reach 50% or more,
It at least does to half topic, just can be carried out official testing.Such as: if test topic only together, needs to do pair in advance
It just can be carried out official testing；If in advance test topic have twice, need entirely to or at least to together, just can be carried out formal survey
Examination.
In the case that if the preparatory test question purpose accuracy of measurand answer is not up to threshold value or does wrong entirely, into
Whether grasp the knowledge point (or upper unit) learnt before the judgement of one step.Before if measurand has been grasped
The knowledge point of study can then carry out the study of new knowledge point.It, can be with if the knowledge point that student learns before not yet grasping
First the knowledge point learnt before is reviewed, carries out the study of new knowledge point again after review.In some embodiments, may be used
To be tested accordingly by the knowledge point learnt for before, to judge to grasp situation.Before studying new knowledge knows point first
The knowledge point learnt before is tested or reviewed, first " reviewing knowledge already acquired " again " know new ", can effectively can promote learning effect.
In some embodiments, the case where can also being tested in advance according to measurand, tested to dynamically adjust
Ability value of the object on preparatory knowledge on testing point.The height of ability value can reflect measurand to the understanding of the knowledge point and
Grasp situation.During measurand is learnt using system, measurand is commented in real time or dynamically
Valence, and be reflected in the adjustment to ability value, so that the ability value recorded in system can more precisely reflect
The true horizon of measurand.Ability value can be used as important reference index auxiliary measurand study, and can be according to energy
Force value is that measurand formulates personalized study or testing scheme.It, can be according to being predicted for example, when carrying out official testing
Know the ability value design of point or the topic of appropriate level of difficulty is arranged to be tested, effectively improves the identification of test.Otherwise, if
Ability value is relatively high, but test item difficulty is lower, even if complete right, is also unable to test out the true horizon of measurand；
Or ability value is also relatively low, but test item difficulty is higher, it may be completely wrong, but can not accurately assess measurand
Grasping level.
Method and system provided by the present application is illustrated in more detail with specific embodiment below.Following table
It is the difficult parameters of a class or the unit knowledge point and knowledge point to be learnt.
| Label | Title | Difficulty |
| c090101 | The concept of secondary radical | 0.2 |
| c090102 | The significant condition of secondary radical | 0.2 |
| c090103 | The property and abbreviation of secondary radical | 0.3 |
| c090203 | The multiplication of secondary radical | 0.4 |
| c090204 | The division of secondary radical | 0.4 |
| c090205 | The multiplication and division of secondary radical | 0.5 |
| c090201 | Most simple secondary radical | 0.6 |
| c090202 | Mould parting design | 0.8 |
| c090301 | Similar secondary radical | 0.6 |
| c090302 | The addition and subtraction of secondary radical | 0.7 |
| c090303 | The hybrid operation of secondary radical | 0.8 |
| c090304 | The abbreviation evaluation of secondary radical | 0.8 |
| c090305 | The application of secondary radical | 0.8 |
Before starting the study of this class, it is the general of secondary radical that system, which first selects most suitable knowledge point inside this class,
It reads, then according to the data on backstage, is analyzed by data, find the highest topic of current identification, can accurately judge to learn
Whether life learns the knowledge point, and topic is then pushed to student, subsequent of student is judged according to the answer result of student
Practise path.2 topics can be generally selected, if 2 topics are entirely right, or have 1 topic correct, can all enter test rank
Section.If two topics are completely wrong, the knowledge point of upper class can be judged, if upper class is all grasped, be advanced to new
The study of class.If upper class knowledge point advances to the review and consolidation stage there are also what is do not grasped.
In some embodiments, it is also possible to select suitable knowledge point according to knowledge mapping.Following table is and secondary radical
The preposition successor relationship between each knowledge point is marked in the knowledge mapping of relevant knowledge point.System is according to preposition subsequent in this way
Relationship map, to select suitable knowledge point to be tested in advance.
Before the study for starting this unit, system first selects preposition knowledge point " concept of secondary radical " inside this unit
As preparatory knowledge on testing point.Then it according to the data on backstage, is analyzed, is found highest with the knowledge point identification by data
Topic can accurately judge whether student learns the knowledge point.Then topic is pushed to student, according to the answer knot of student
Fruit judges the subsequent learning path of student.It can choose 3 topics, if can all enter test phase to 2 or 3.
If three topics are completely wrong, the knowledge point of upper class can be judged, if upper class is all grasped, advance to new class
Study.If upper class knowledge point advances to the review and consolidation stage there are also what is do not grasped.
In some embodiments, above-mentioned various methods, module, device or system can be in one or more processing units
(for example, digital processing unit, analog processor, be designed to processing information digital circuit, be designed to processing letter
Analog circuit, state machine and/or other mechanisms for electronically handling information of breath) in be implemented.This or more
A processing unit may include the instruction in response to being electronically stored on electronic storage medium to execute some of method
Or one or more devices of all operations.The one or more processing unit may include by hardware, firmware and/or software
It is configured and is designed specifically for use in one or more devices of one or more operations of execution method.The above, only
The preferable specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any to be familiar with the art
Technical staff is subject to equivalent replacement within the technical scope of the present application, according to the technical solution of the application and its inventive concept
Or change, should all it cover within the scope of protection of this application.
Presently filed embodiment can carry out in hardware, firmware, software or its various combination.It is also used as storing
On a machine-readable medium and the instruction that one or more processing units read and execute can be used to realize the application.?
In one embodiment, machine readable media may include readable in machine (for example, computing device) for storing and/or transmitting
The various mechanisms of the information of form.For example, machine readable storage medium may include read-only memory, random access memory,
Magnetic disk storage medium, optical storage media, flash memory device and other media for storing information, and it is machine readable
Transmission medium may include the transmitting signal (including carrier wave, infrared signal, digital signal) of diversified forms and be used for transmission letter
Other media of breath.Although in terms of the particular exemplary for executing certain movements and the angle of embodiment can be in above disclosure
It describes firmware, software, routine or instruction in content, but will be apparent that, this kind of description is merely for facilitating purpose and this kind of dynamic
Make actually by computing device, processing unit, processor, controller or other dresses for executing firmware, software, routine or instruction
It sets or machine generates.
This specification uses examples to disclose the application, and one or more of examples are described or are illustrated in explanation
Among book and its attached drawing.Each example is provided to explanation the application and provides, rather than in order to limit the application.In fact,
It is obvious to the skilled person that can be to this Shen in the case where not departing from scope of the present application or spirit
Row various modifications and variations that come in.For example, the diagram of a part as one embodiment or description feature can with it is another
One embodiment is used together, to obtain further embodiment.Therefore, it is intended that the application and covers and wanted in appended right
Seek the modifications and variations carried out in the range of book and its equivalent.
Claims (10)
1. a kind of preparatory test method based on artificial intelligence, which comprises the following steps:
According to content to be tested, test topic in advance is filtered out；And
According to measurand to the preparatory test question purpose answer situation, judge whether to test.
2. preparatory test method according to claim 1, it is characterised in that:
According to content to be tested, preparatory knowledge on testing point is selected；And
According to selected preparatory knowledge on testing point, the preparatory test topic is filtered out.
3. preparatory test method according to any one of the preceding claims, it is characterised in that:
According to the difficulty of knowledge point relevant to content to be tested, the preparatory knowledge on testing point is selected.
4. preparatory test method according to any one of the preceding claims, it is characterised in that:
According to knowledge mapping relevant to content to be tested, the preparatory knowledge on testing point is selected.
5. preparatory test method according to any one of the preceding claims, it is characterised in that:
Mode based on big data filters out the preparatory test question from topic relevant to the preparatory knowledge on testing point
Mesh.
6. preparatory test method according to any one of the preceding claims, it is characterised in that:
The preparatory test topic is filtered out according to the identification of topic, wherein the preparatory test topic is included in exam pool
The highest one or multi-channel topic of identification in topic corresponding with the preparatory knowledge on testing point.
7. preparatory test method according to any one of the preceding claims, it is characterised in that:
If measurand, which answers the preparatory test question purpose accuracy, reaches threshold value, tested.
8. preparatory test method according to any one of the preceding claims, it is characterised in that:
If measurand answers the preparatory test question purpose accuracy and is not up to threshold value, the knowledge point learnt before judgement
Whether grasp.
9. preparatory test method according to any one of the preceding claims, it is characterised in that:
If the knowledge point that measurand learns before having grasped, carries out the study of new knowledge point.
10. preparatory test method according to any one of the preceding claims, it is characterised in that:
If the knowledge point that measurand learns before not grasping, reviews the knowledge point learnt before.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910636313.9A CN110349062B (en) | 2019-07-15 | 2019-07-15 | Artificial intelligence based pre-testing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910636313.9A CN110349062B (en) | 2019-07-15 | 2019-07-15 | Artificial intelligence based pre-testing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110349062A true CN110349062A (en) | 2019-10-18 |
| CN110349062B CN110349062B (en) | 2020-11-24 |
Family
ID=68175321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910636313.9A Active CN110349062B (en) | 2019-07-15 | 2019-07-15 | Artificial intelligence based pre-testing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110349062B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090091370A (en) * | 2008-02-25 | 2009-08-28 | 박종배 | Method for producing knowledge-type data using standard condition in selection and unfixed data in answer |
| CN105070130A (en) * | 2015-08-04 | 2015-11-18 | 北京优宇通教育科技有限公司 | Level assessment method and level assessment system |
| CN109993453A (en) * | 2019-04-10 | 2019-07-09 | 沈阳哲航信息科技有限公司 | A kind of Inquiry evaluation system and method |
| CN110009957A (en) * | 2019-04-10 | 2019-07-12 | 上海乂学教育科技有限公司 | The big knowledge mapping test macro of mathematics and method in adaptive learning |
-
2019
- 2019-07-15 CN CN201910636313.9A patent/CN110349062B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090091370A (en) * | 2008-02-25 | 2009-08-28 | 박종배 | Method for producing knowledge-type data using standard condition in selection and unfixed data in answer |
| CN105070130A (en) * | 2015-08-04 | 2015-11-18 | 北京优宇通教育科技有限公司 | Level assessment method and level assessment system |
| CN109993453A (en) * | 2019-04-10 | 2019-07-09 | 沈阳哲航信息科技有限公司 | A kind of Inquiry evaluation system and method |
| CN110009957A (en) * | 2019-04-10 | 2019-07-12 | 上海乂学教育科技有限公司 | The big knowledge mapping test macro of mathematics and method in adaptive learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110349062B (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bright et al. | Graphical representations: Helping students interpret data | |
| Niemi | Assessing conceptual understanding in mathematics: Representations, problem solutions, justifications, and explanations | |
| CN110246072B (en) | Review method executed by machine equipment | |
| CN109388746A (en) | A kind of education resource intelligent recommendation method based on learner model | |
| Kendeou et al. | Reading comprehension theories: A view from the top down | |
| Kafipour et al. | Vocabulary levels and vocabulary learning strategies of Iranian undergraduate students | |
| Kuschmierz et al. | Development and evaluation of the knowledge about evolution 2.0 instrument (KAEVO 2.0) | |
| CN108763588A (en) | A kind of knowledge point quantitative analysis method and device | |
| CN105224920A (en) | A kind ofly will proceed to the learning test diagnostic system on line under line | |
| CN108665394A (en) | Adaptive learning method and system | |
| US10643489B2 (en) | Constructed response scoring mechanism | |
| CN110111223A (en) | Adaptive educational method and system based on artificial intelligence | |
| CN110288270A (en) | Learning effect detection method based on artificial intelligence | |
| CN113657723A (en) | Knowledge point mastering condition diagnosis method, test question recommendation method and device | |
| CN116340624A (en) | Self-adaptive learning information recommendation method, device, equipment and storage medium | |
| Segedy et al. | Coherence over time: understanding day-to-day changes in students’ open-ended problem solving behaviors | |
| CN110349062A (en) | Preparatory test method based on artificial intelligence | |
| CN111008916A (en) | Knowledge point grasp inference method, knowledge point grasp inference system, computer device, and storage medium | |
| TWI640951B (en) | Method and system for analyzing learning status of learner and selecting complementary problem corresponding to learning status | |
| CN109657190A (en) | A kind of student individuality study coach algorithm | |
| TW201428666A (en) | Method for evaluating learning outcomes of individual concept and computer readable media thereof | |
| TW202025074A (en) | System for maintaining quality of question bank based on answer result and method thereof | |
| Ho et al. | A multiple aspects quantitative indicator for ability of English vocabulary: vocabulary quotient | |
| Tracy et al. | Mindfulness, structure, and effectiveness | |
| Abdullah et al. | Behaviours of Students When Solving the Assessment of Programme for International Student Assessment (PISA) Mathematical Problems |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 10th Floor, Building B7, Huaxin Tiandi, 188 Yizhou Road, Xuhui District, Shanghai, 2003 Applicant after: Shanghai squirrel classroom Artificial Intelligence Technology Co., Ltd Address before: 10th Floor, Building B7, Huaxin Tiandi, 188 Yizhou Road, Xuhui District, Shanghai, 2003 Applicant before: SHANGHAI YIXUE EDUCATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |