CN110533336A - A kind of instructional decisions matching process and device - Google Patents
A kind of instructional decisions matching process and device Download PDFInfo
- Publication number
- CN110533336A CN110533336A CN201910826587.4A CN201910826587A CN110533336A CN 110533336 A CN110533336 A CN 110533336A CN 201910826587 A CN201910826587 A CN 201910826587A CN 110533336 A CN110533336 A CN 110533336A
- Authority
- CN
- China
- Prior art keywords
- decision
- value
- learning state
- teacher
- model
- 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.)
- Pending
Links
Classifications
-
- 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Abstract
The embodiment of the present application provides a kind of instructional decisions matching process and device, is related to technical field of data processing.The instructional decisions matching process and device can be according to the global learning states of multiple target learners, objectively calculate the teaching state of teacher, and then objective comprehensive instructional decisions are made according to the global learning state of multiple target learners and the teaching state of teacher, the learning state of all target learners is taken into account, and then body teaching affairs are known from for teacher, it formulates teaching improving strategy and effective support is provided.
Description
Technical field
This application involves technical field of data processing, in particular to a kind of instructional decisions matching process and device.
Background technique
With the propulsion of information technology and teaching depth integration, classroom has become the battle position of the reform in education.Currently, in class
In hall education activities, teacher, which is mainly seen using eye, understands the study condition of student by the way of ear is listened, and instructional decisions after class are also only
Can be using the teaching scene of operation or memory as foundation, information is not comprehensive enough, and is susceptible to the subjective feeling of teacher at that time
It influences, it is not objective enough and stable, teaching improving strategy can not being formulated for teacher, effective support be provided.
Summary of the invention
The embodiment of the present application is designed to provide a kind of instructional decisions matching process and device, can be living in classroom instruction
In dynamic, objective comprehensive instructional decisions made, and then be known from body teaching affairs for teacher, formulate teaching improving strategy and be provided with
The support of effect.
The embodiment of the present application first aspect provides a kind of instructional decisions matching process, comprising:
The global learning state set of all target learners of upper class hour is obtained from database；
The teaching state value of teacher is calculated according to the global learning state set；
According to the decision parameters obtained in advance, decision parameters quantization set is calculated, and according to the global learning state set
It closes, the teaching state value and the decision parameters of the teacher, calculates the decision index system value for being used for instructional decisions；
According to preset decision scheme database and decision parameters quantization set, determine and the decision index system value
The instructional decisions scheme to match.
During above-mentioned realization, according to the global learning state of multiple target learners, objectively calculate teacher's
Teaching state, and then objective comprehensive religion is made according to the global learning state of multiple target learners and the teaching state of teacher
Decision is learned, the learning state of all target learners has been taken into account, and then be known from body teaching affairs for teacher, has formulated teaching improving
Strategy provides effective support.
As an alternative embodiment, the teaching state value of teacher is calculated according to the global learning state set,
Include:
Obtain the total number of persons of multiple target learners；
According to the global learning state set, calculating individual learning state in multiple target learners is target
The target number of habit state；
According to the total number of persons of multiple target learners and the target number, the teaching state value of teacher is calculated.
During above-mentioned realization, teacher teaching state value by all target learners global learning state set
What conjunction was calculated, obtained teaching state value is more objective.
As an alternative embodiment, decision parameters quantization set is calculated according to the decision parameters obtained in advance, and
According to the global learning state set, the teaching state value and the decision parameters of the teacher, calculates and determine for imparting knowledge to students
The decision index system value of plan, comprising:
Obtain decision parameters；
Quantification treatment is carried out to the decision parameters according to preset decision quantitative model and obtains decision parameters quantization set；
According to the global learning state set, the decision model for calculating decision index system value is constructed；
By the decision model to the teaching state value of the global learning state set, the teacher and it is described certainly
Plan parameter is handled, and decision index system value is obtained.
During above-mentioned realization, decision model is first constructed, corresponding decision is then calculated by decision model again and is referred to
Scale value, meanwhile, quantification treatment is carried out according to decision parameters of the preset decision quantitative model to input, obtains decision parameters quantization
Set.
As an alternative embodiment, being constructed according to the global learning state set for calculating decision index system
The decision model of value, comprising:
According to the global learning state set, the model parameter value of decision model is determined；
According to the model parameter value and preset original decision model, the decision model for calculating decision index system value is constructed
Type.
During above-mentioned realization, the model parameter of decision model is first calculated, further according to model parameter value and preset original
Beginning decision model can construct decision model.
The embodiment of the present application second aspect provides a kind of instructional decisions coalignment, comprising:
Module is obtained, for obtaining the global learning state set of all target learners of upper class hour from database；
First computing module, for calculating the teaching state value of teacher according to the global learning state set；
Second computing module, for calculating decision parameters quantization set according to the decision parameters obtained in advance, and according to institute
The teaching state value and the decision parameters of global learning state set, the teacher are stated, determining for instructional decisions is calculated
Plan index value；
Decision determining module, for determining according to preset decision scheme database and decision parameters quantization set
The instructional decisions scheme to match out with the decision index system value.
During above-mentioned realization, the first computing module is according to the global learning state of multiple target learners, objectively
The teaching state of teacher is calculated, decision determining module is further according to the global learning state of multiple target learners and the religion of teacher
State makes objective comprehensive instructional decisions, has taken into account the learning state of all target learners, and then is known from for teacher
Body teaching affairs formulate teaching improving strategy and provide effective support.
As an alternative embodiment, first computing module includes:
Number acquisition submodule, for obtaining the total number of persons of multiple target learners；
Number computational submodule, for calculating in multiple target learners according to the global learning state set
Individual learning state is the target number of target learning state；
State value computational submodule, for according to multiple target learners total number of persons and the target number, meter
Calculate the teaching state value of teacher.
During above-mentioned realization, teacher teaching state value by all target learners global learning state set
What conjunction was calculated, obtained teaching state value is more objective.
As an alternative embodiment, second computing module includes:
Parameter acquisition submodule, for obtaining decision parameters；
Quantify submodule, is determined for carrying out quantification treatment to the decision parameters according to preset decision quantitative model
Plan parameter quantization set；
Submodule is constructed, for constructing the decision for calculating decision index system value according to the global learning state set
Model；
Index value computational submodule is used for through the decision model to the global learning state set, the teacher
Teaching state value and the decision parameters handled, obtain decision index system value.
During above-mentioned realization, after quantization submodule obtains decision parameters quantization set, submodule elder generation structure is constructed
Decision model is built, then index value computational submodule passes through decision model again and calculates corresponding decision index system value.
As an alternative embodiment, the building submodule includes:
Parameter determination unit, for determining the model parameter value of decision model according to the global learning state set；
Construction unit, for constructing for calculating decision according to the model parameter value and preset original decision model
The decision model of index value.
During above-mentioned realization, parameter determination unit first calculates the model parameter of decision model, construction unit further according to
Model parameter value and preset original decision model, can construct decision model.
Third aspect present invention discloses a kind of computer equipment, including memory and processor, and the memory is used for
Computer program is stored, the processor runs the computer program so that the computer equipment executes first aspect and discloses
The some or all of instructional decisions matching process.
Fourth aspect present invention discloses a kind of computer readable storage medium, is stored with computer described in the third aspect
The computer program used in equipment.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application will make below to required in the embodiment of the present application
Attached drawing is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore should not be seen
Work is the restriction to range, for those of ordinary skill in the art, without creative efforts, can be with
Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 is the structural block diagram of a kind of instructional decisions matching process and device provided by the embodiments of the present application；
Fig. 2 is the structural block diagram of another instructional decisions matching process and device provided by the embodiments of the present application；
Fig. 3 is the structural block diagram of another instructional decisions matching process and device provided by the embodiments of the present application；
Fig. 4 is the structural block diagram of another instructional decisions matching process and device provided by the embodiments of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application is described.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile the application's
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
Embodiment 1
Fig. 1 is please referred to, Fig. 1 is a kind of schematic process flow diagram of instructional decisions matching process provided by the embodiments of the present application.
As shown in Figure 1, the instructional decisions matching process includes:
S101, the global learning state set that all target learners of upper class hour are obtained from database.
S102, the teaching state value that teacher is calculated according to global learning state set.
The decision parameters that S103, basis obtain in advance calculate decision parameters quantization set, and according to global learning state set
It closes, the teaching state value and decision parameters of teacher, calculates the decision index system value for being used for instructional decisions.
As an alternative embodiment, calculating decision parameters quantization set, also according to the decision parameters obtained in advance
It may comprise steps of:
Obtain the decision parameters B of input；
Quantification treatment is carried out to decision parameters B according to preset decision parameters quantitative model, obtains decision parameters quantization collection
It closes.
In the above-described embodiment, preset decision parameters quantitative model include a variety of decision schemes mark and with it is each
Decision scheme identifies corresponding decision original threshold.When being quantified by decision parameters quantitative model to decision parameters, root
The decision original threshold of decision parameters quantitative model is updated according to decision parameters, obtains multiple decision-making values, and then obtain
Decision parameters quantization set.For example, decision parameters quantitative model includes that the corresponding decision original threshold of decision one isThe corresponding decision original threshold of decision two isThe corresponding decision original threshold of decision three isThe corresponding decision original threshold of decision four isWherein, N is the total number of student attended class, when N is determined
When, the corresponding decision original threshold of decision four also determines.
S104, gathered according to preset decision scheme database and decision parameters quantization, determined and decision index system value phase
Matched instructional decisions scheme.
In the embodiment of the present application, first quantifies set according to decision parameters and determine decision scheme mark corresponding with decision index system value
Know, then obtains decision scheme from preset decision scheme database again and identify corresponding decision scheme, as refer to decision
The instructional decisions scheme that scale value matches.
As it can be seen that implementing instructional decisions matching process described in Fig. 1, can be made objective complete in Activities for Teaching
The instructional decisions in face, and then body teaching affairs are known from for teacher, it formulates teaching improving strategy and effective support is provided.
Embodiment 2
Fig. 2 is please referred to, Fig. 2 is a kind of schematic process flow diagram of instructional decisions matching process provided by the embodiments of the present application.
As shown in Fig. 2, the instructional decisions matching process includes:
S201, the global learning state set that all target learners of upper class hour are obtained from database.
In the embodiment of the present application, global learning state set includes the individual learning state of each target learner；Wherein,
Individual learning state is one or more of active learning state, negative learning state, other learning states.
As an alternative embodiment, in the global learning for obtaining all target learners of upper class hour from database
Before state set, can with the following steps are included:
Obtained the facial expression video of multiple target learners upper class hour；
According to the facial expression video, the corresponding expression video of each target learner is determined；
Expression Recognition processing is carried out to the corresponding expression video of each target learner, obtains the table of each target learner
Feelings information；
Each target learner individual learning state accordingly is determined according to the expression information of each target learner；
According to the individual learning state of each target learner, the global learning state of all target learners is determined.
S202, the teaching state value that teacher is calculated according to global learning state set.
As an alternative embodiment, calculating the teaching state value of teacher according to global learning state set, comprising:
Obtain the total number of persons of multiple target learners；
According to global learning state set, calculating individual learning state in multiple target learners is target learning state
Target number；
According to the total number of persons and target number of multiple target learners, the teaching state value of teacher is calculated.
In the embodiment of the present application, the teaching state value E of teacher jjCalculation formula are as follows:
Wherein, k1=1, SiFor global learning state set, N is the total number of persons of multiple target learners, Ej∈ (0,1)；Si
∈ { passive states } indicates global learning state set SiThe quantity of middle passive states.
In the embodiment of the present application, work as EjWhen value meets default qualified threshold value, then show the teaching affairs of teacher for qualification, Ej
It is worth and smaller illustrates that teachers ' teaching effect is better.
It is further comprising the steps of after step S202:
S203, decision parameters are obtained.
In the embodiment of the present application, by obtaining decision parameters, and decision parameters are participated in the matching of instructional decisions, energy
Enough realize instructional decisions obtain mobilism, can dynamic control decision condition, can dynamically determine to make a policy in what state.
S204, decision parameters quantization collection is obtained to decision parameters progress quantification treatment according to preset decision quantitative model
It closes.
In the present embodiment, preset decision parameters quantitative model include a variety of decision schemes mark and with each decision-making party
The corresponding decision original threshold of pattern identification.When being quantified by decision parameters quantitative model to decision parameters, according to decision
The decision original threshold of decision parameters quantitative model is updated by parameter, obtains multiple decision-making values, and then obtains decision ginseng
Quantification set.
For example, decision parameters quantitative model includes that the corresponding decision original threshold of decision one isCertainly
The corresponding decision original threshold of plan two isThe corresponding decision original threshold of decision three isDecision
Four corresponding decision original thresholds areWherein N is the total number of persons of multiple target learners.Work as B=0.5, N=600
When, the decision parameters quantization set that quantification treatment obtains is carried out to decision parameters by decision parameters quantitative model are as follows: decision one
Corresponding decision original threshold isThe corresponding decision original threshold of decision two isDecision three is corresponding
Decision original threshold beThe corresponding decision original threshold of decision four is
S205, according to global learning state set, construct the decision model for calculating decision index system value.
As an alternative embodiment, being constructed according to global learning state set for calculating decision index system value
Decision model, comprising:
According to global learning state set, the model parameter value of decision model is determined；
According to model parameter value and preset original decision model, the decision model for calculating decision index system value is constructed.
In the above-described embodiment, the model parameter value of decision model includes A, α, beta, gamma, when instructional decisions are disappeared with being in
When the number of pole state is main, decision model be may be expressed as:
Wherein, C indicates the quantity of passive states 1 in global learning state set, and D indicates to disappear in global learning state set
The quantity of pole state 2, the value of F are to indicate the quantity of passive states 3 in global learning state set.
It should be noted that the above-mentioned decision model provided is only a kind of representation, because passive states are not necessarily only
Comprising three kinds of passive states, also may include a kind, 2 kinds, 4 kinds it is even more, in actual use, can be disappeared according to different
Pole state status carries out corresponding decision model building, when passive states include four kinds or more, then passive states 1, passive shape
One of them in state 2, passive states 3 will correspond to two kinds of passive states, when passive states include two kinds, then in C, D, F wherein
The value of one parameter is zero, other situations can be obtained similarly.
In the above-described embodiment, the formula of calculating parameter A are as follows:
Wherein, EjFor the teaching state value of teacher j, B is decision parameters；When A is -1, expression does not need decision；
In the above-described embodiment, the formula of α is calculated are as follows:
Wherein, m1=1, N are the total number of persons of multiple target learners.
In the above-described embodiment, the formula of β is calculated are as follows:
Wherein, m2=1.
In the above-described embodiment, the formula of γ is calculated are as follows:
Wherein, m3=1.
It is further comprising the steps of after step S205:
S206, it is carried out by teaching state value and decision parameters of the decision model to global learning state set, teacher
Processing, obtains decision index system value.
For example, work as N=100, the decision parameters B=0.5 of input, calculate the teaching state value E of teacherj=0.5
When, if the quantity that global learning state set includes positive state is 50, passive states include three passive states, wherein are disappeared
The quantity of pole state 1 is 25, and the quantity of passive states 2 is 15, and the quantity of passive states 3 is 10, then C=25, D=15, F=
10；A=1, α=1, β=0, γ=0 can be calculated according to above-mentioned formula；And then decision index system value P can be calculated are as follows:
In the embodiment of the present application, implement above-mentioned steps S203~step S206, can according to the decision parameters obtained in advance,
Decision parameters quantization set is calculated, and according to global learning state set, the teaching state value and decision parameters of teacher, is calculated
Decision index system value for instructional decisions.
S207, gathered according to preset decision scheme database and decision parameters quantization, determined and decision index system value phase
Matched instructional decisions scheme.
In the embodiment of the present application, preset decision scheme database includes multiple and decision parameters quantization set matches
Decision-making value and decision scheme corresponding with each decision-making value.
For example, when the decision parameters amount obtained by decision parameters quantitative model to decision parameters progress quantification treatment
Change set are as follows: the corresponding decision original threshold of decision one isThe corresponding decision original threshold of decision two isThe corresponding decision original threshold of decision three isThe corresponding decision original threshold of decision four isWhen, then decision scheme database includes:
Decision scheme one: decision index system valueIllustrate that this content of courses has certain difficulty, at this time
Classmates cannot grasp this content of courses.Content of policy decision are as follows: it is theoretical according to the programmed instruction of learning, this content of courses is split, point
It is appropriate to increase class hour at different Small objects, and constantly repeat said content.Programmed instruction of learning theory thinks that teaching first has to son
Carefully consider that planning the content of teaching in the specific time is what, the most effective arrangement of reinforcing, i.e., teacher is very multiple
Miscellaneous behavior pattern is made into small unit or step gradually exquisitely, that is, instructional objective is specifically decomposed, and determines every
The intensity of a kept behavior of step, so that the effect strengthened can improve to the limit.
Decision scheme two: decision index system valueIt is certain anti-to illustrate that student has current content
Sense, teacher-student relationship need to be improved.Content of policy decision are as follows: Hierarchy theory according to demand, decision are that teacher will build easily to student
Academic environment respects fully student, catches the bright spot of student, mostly encourages to student, targetedly motivated, be student's
Intimate friend.Human demand is divided into five kinds by level from low to high as ladder by need hierarchy theory, is respectively: physiology needs
It asks, demand for security, social demand, Esteem Needs and Self-actualization Needs, when Lower-Order Need obtains meeting, people can just be chased after
Seek higher level demand.
Decision scheme three: decision index system valueIllustrate that student does not generate interest to current study.
Content of policy decision are as follows: according to structuralism didactics, decision method is to change teaching method, classroom is given to student, by student
It is grouped, allows student oneself to go that learning knowledge is discussed, encourage and guide student oneself to go to understand the content of courses, teacher is auxiliary
It leads and answers questions.Teacher will improve the instructional decisions ability of oneself according to the actual situation, improve owned many-sided knowledge and
Technical ability improves to the control ability of instructional objective and to the cognitive ability of student, while reinforcing the ideological and moral education of student, mentions
High student learns consciousness.Structuralism didactics emphasized development students intelligence payes attention to logical thinking and independent acquisition knowledge
Ability, emphasize reform teaching, allow student oneself thinking, probe into and find rule.
Decision scheme four: decision index system valueWhen, illustrate to be now in the total ratio of active learning state classmate compared with
Height, the purpose of decision are sought to the remaining part classmate in negative learning state with reaching whole outstanding
Purpose.Content of policy decision are as follows: according to didactics is grasped, carry out one strategy of a band, make the one-to-one drive of outstanding classmate subsequent
Classmate's study, outstanding classmate take time to give guidance in study difficult classmate, and teacher is primarily upon the classmate of difficulty of learning, reaches whole
The target that body can be learnt well.As long as grasping didactics to think to give time enough and teaching appropriate, almost all of
The raw degree (assessment item for generally reaching completion 80%~90%) that can reach grasp to almost all of content, is learned
The difference of raw learning ability cannot determine the quality for the content and study that can he learn, and it is more to determine that he will spend
Few time can be only achieved the Grasping level of the content.In other words the strong student of learning ability can reach in a relatively short period of time
It is horizontal to the grasp of the content, and the student of learning ability difference then will spend longer time to can be only achieved same Grasping level.
For example, when calculated decision parameters quantify to gather are as follows: the corresponding decision original threshold of decision one isThe corresponding decision original threshold of decision two isThe corresponding decision original threshold of decision three isThe corresponding decision original threshold of decision four isCalculated decision index system valueWhen,
It can be concluded that decision index system valueAnd then obtaining the decision scheme to match with decision index system value P is decision-making party
Case one: this content of courses is split, and is divided into different Small objects, appropriate to increase class hour, and constantly repeats said content.
As it can be seen that implementing instructional decisions matching process described in Fig. 2, can be made objective complete in Activities for Teaching
The instructional decisions in face, and then body teaching affairs are known from for teacher, it formulates teaching improving strategy and effective support is provided.
Embodiment 3
Fig. 3 is please referred to, Fig. 3 is a kind of structural schematic diagram of instructional decisions coalignment provided by the embodiments of the present application.Such as
Shown in Fig. 3, which includes:
Module 310 is obtained, for obtaining the global learning state set of all target learners of upper class hour from database.
First computing module 320, for calculating the teaching state value of teacher according to global learning state set.
Second computing module 330, for calculating decision parameters quantization set, and root according to the decision parameters obtained in advance
According to global learning state set, the teaching state value and decision parameters of teacher, the decision index system value for being used for instructional decisions is calculated.
Decision determining module 340, for determining according to preset decision scheme database and decision parameters quantization set
The instructional decisions scheme to match with decision index system value.
As an alternative embodiment, referring to Fig. 4, Fig. 4 is a kind of instructional decisions provided by the embodiments of the present application
The structural schematic block diagram of coalignment.Instructional decisions coalignment shown in Fig. 4 is instructional decisions coalignment as shown in Figure 3
It optimizes, as shown in figure 4, the first computing module 320 includes:
Number acquisition submodule 321, for obtaining the total number of persons of multiple target learners.
Number computational submodule 322, for according to global learning state set, calculating individual in multiple target learners
Habit state is the target number of target learning state.
State value computational submodule 323 calculates teacher for the total number of persons and target number according to multiple target learners
Teaching state value.
As an alternative embodiment, the second computing module 330 includes:
Parameter acquisition submodule 331, for obtaining decision parameters.
Quantify submodule 332, is determined for carrying out quantification treatment to decision parameters according to preset decision quantitative model
Plan parameter quantization set.
Submodule 333 is constructed, for constructing the decision model for calculating decision index system value according to global learning state set
Type.
Index value computational submodule 334, for the teaching state by decision model to global learning state set, teacher
Value and decision parameters are handled, and decision index system value is obtained.
As an alternative embodiment, building submodule includes:
Parameter determination unit, for determining the model parameter value of decision model according to global learning state set.
Construction unit, for constructing for calculating decision index system according to model parameter value and preset original decision model
The decision model of value.
As it can be seen that instructional decisions coalignment described in implementing Fig. 3, can make objective complete in Activities for Teaching
The instructional decisions in face, and then body teaching affairs are known from for teacher, it formulates teaching improving strategy and effective support is provided.
In addition, the present invention also provides a kind of computer equipments.The computer equipment includes memory and processor, storage
Device can be used for storing computer program, and processor is by operation computer program, so that the computer equipment be made to execute above-mentioned side
The function of method or the modules in above-mentioned instructional decisions coalignment.
Memory may include storing program area and storage data area, wherein storing program area can storage program area, at least
Application program needed for one function (such as sound-playing function, image player function etc.) etc.；Storage data area can store root
Created data (such as audio data, phone directory etc.) etc. are used according to mobile terminal.In addition, memory may include high speed
Random access memory, can also include nonvolatile memory, a for example, at least disk memory, flush memory device or
Other volatile solid-state parts.
The present embodiment additionally provides a kind of computer storage medium, for storing calculating used in above-mentioned computer equipment
Machine program.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the application, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part of the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that function marked in the box can also be to be different from some implementations as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each functional module in each embodiment of the application can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) execute each embodiment the method for the application all or part of the steps.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
The above description is only an example of the present application, the protection scope being not intended to limit this application, for ability
For the technical staff in domain, various changes and changes are possible in this application.Within the spirit and principles of this application, made
Any modification, equivalent substitution, improvement and etc. should be included within the scope of protection of this application.It should also be noted that similar label and
Letter indicates similar terms in following attached drawing, therefore, once it is defined in a certain Xiang Yi attached drawing, then in subsequent attached drawing
In do not need that it is further defined and explained.
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Those familiar with the art within the technical scope of the present application, can easily think of the change or the replacement, and should all contain
Lid is within the scope of protection of this application.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
Claims (10)
1. a kind of instructional decisions matching process characterized by comprising
The global learning state set of all target learners of upper class hour is obtained from database；
The teaching state value of teacher is calculated according to the global learning state set；
According to the decision parameters obtained in advance, decision parameters quantization set is calculated, and according to the global learning state set, institute
The teaching state value and the decision parameters of teacher are stated, the decision index system value for being used for instructional decisions is calculated；
According to preset decision scheme database and decision parameters quantization set, determine and the decision index system value phase
The instructional decisions scheme matched.
2. instructional decisions matching process according to claim 1, which is characterized in that according to the global learning state set
Calculate the teaching state value of teacher, comprising:
Obtain the total number of persons of multiple target learners；
According to the global learning state set, calculating individual learning state in multiple target learners is that target learns shape
The target number of state；
According to the total number of persons of multiple target learners and the target number, the teaching state value of teacher is calculated.
3. instructional decisions matching process according to claim 1, which is characterized in that according to the decision parameters obtained in advance,
Calculate decision parameters quantization set, and according to the global learning state set, the teaching state value of the teacher and described
Decision parameters calculate the decision index system value for being used for instructional decisions, comprising:
Obtain decision parameters；
Quantification treatment is carried out to the decision parameters according to preset decision quantitative model and obtains decision parameters quantization set；
According to the global learning state set, the decision model for calculating decision index system value is constructed；
The teaching state value of the global learning state set, the teacher and the decision are joined by the decision model
Number is handled, and decision index system value is obtained.
4. instructional decisions matching process according to claim 3, which is characterized in that according to the global learning state set
It closes, constructs the decision model for calculating decision index system value, comprising:
According to the global learning state set, the model parameter value of decision model is determined；
According to the model parameter value and preset original decision model, the decision model for calculating decision index system value is constructed.
5. a kind of instructional decisions coalignment characterized by comprising
Module is obtained, for obtaining the global learning state set of all target learners of upper class hour from database；
First computing module, for calculating the teaching state value of teacher according to the global learning state set；
Second computing module, for calculating decision parameters quantization set according to the decision parameters obtained in advance, and according to described whole
Body learning state set, the teacher teaching state value and the decision parameters, calculate and refer to for the decisions of instructional decisions
Scale value；
Decision determining module, for according to preset decision scheme database and the decision parameters quantization set, determine with
The instructional decisions scheme that the decision index system value matches.
6. instructional decisions coalignment according to claim 5, which is characterized in that first computing module includes:
Number acquisition submodule, for obtaining the total number of persons of multiple target learners；
Number computational submodule, for calculating individual in multiple target learners according to the global learning state set
Learning state is the target number of target learning state；
State value computational submodule, for according to multiple target learners total number of persons and the target number, calculate religion
The teaching state value of teacher.
7. instructional decisions coalignment according to claim 5, which is characterized in that second computing module includes:
Parameter acquisition submodule, for obtaining decision parameters；
Quantify submodule, obtains decision ginseng for carrying out quantification treatment to the decision parameters according to preset decision quantitative model
Quantification set；
Submodule is constructed, for constructing the decision model for calculating decision index system value according to the global learning state set；
Index value computational submodule, for the religion by the decision model to the global learning state set, the teacher
It learns state value and the decision parameters is handled, obtain decision index system value.
8. instructional decisions coalignment according to claim 7, which is characterized in that the building submodule includes:
Parameter determination unit, for determining the model parameter value of decision model according to the global learning state set；
Construction unit, for constructing for calculating decision index system according to the model parameter value and preset original decision model
The decision model of value.
9. a kind of computer equipment, which is characterized in that including memory and processor, the memory is for storing computer
Program, the processor runs the computer program so that the computer equipment perform claim requires any one of 1 to 4 institute
The instructional decisions matching process stated.
10. a kind of computer readable storage medium, which is characterized in that it is stored in computer equipment as claimed in claim 9
The used computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910826587.4A CN110533336A (en) | 2019-09-02 | 2019-09-02 | A kind of instructional decisions matching process and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910826587.4A CN110533336A (en) | 2019-09-02 | 2019-09-02 | A kind of instructional decisions matching process and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110533336A true CN110533336A (en) | 2019-12-03 |
Family
ID=68666385
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910826587.4A Pending CN110533336A (en) | 2019-09-02 | 2019-09-02 | A kind of instructional decisions matching process and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110533336A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695695A (en) * | 2020-06-09 | 2020-09-22 | 北京百度网讯科技有限公司 | Quantitative analysis method and device for user decision behaviors |
| CN112380263A (en) * | 2020-11-11 | 2021-02-19 | 北京爱论答科技有限公司 | Teaching data recommendation method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017021760A1 (en) * | 2015-08-06 | 2017-02-09 | Institucion Universitaria Salazar Y Herrera | Smart teaching system for a classroom |
| CN108074203A (en) * | 2016-11-10 | 2018-05-25 | 中国移动通信集团公司 | A kind of teaching readjustment method and apparatus |
| CN109657529A (en) * | 2018-07-26 | 2019-04-19 | 台州学院 | Classroom teaching effect evaluation system based on human facial expression recognition |
| CN109711371A (en) * | 2018-12-29 | 2019-05-03 | 山东旭兴网络科技有限公司 | A kind of Estimating System of Classroom Teaching based on human facial expression recognition |
| CN110059614A (en) * | 2019-04-16 | 2019-07-26 | 广州大学 | A kind of intelligent assistant teaching method and system based on face Emotion identification |
| CN110070295A (en) * | 2019-04-25 | 2019-07-30 | 平安科技（深圳）有限公司 | The evaluation and analysis method, apparatus and computer equipment of Classroom Teaching |
-
2019
- 2019-09-02 CN CN201910826587.4A patent/CN110533336A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017021760A1 (en) * | 2015-08-06 | 2017-02-09 | Institucion Universitaria Salazar Y Herrera | Smart teaching system for a classroom |
| CN108074203A (en) * | 2016-11-10 | 2018-05-25 | 中国移动通信集团公司 | A kind of teaching readjustment method and apparatus |
| CN109657529A (en) * | 2018-07-26 | 2019-04-19 | 台州学院 | Classroom teaching effect evaluation system based on human facial expression recognition |
| CN109711371A (en) * | 2018-12-29 | 2019-05-03 | 山东旭兴网络科技有限公司 | A kind of Estimating System of Classroom Teaching based on human facial expression recognition |
| CN110059614A (en) * | 2019-04-16 | 2019-07-26 | 广州大学 | A kind of intelligent assistant teaching method and system based on face Emotion identification |
| CN110070295A (en) * | 2019-04-25 | 2019-07-30 | 平安科技（深圳）有限公司 | The evaluation and analysis method, apparatus and computer equipment of Classroom Teaching |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695695A (en) * | 2020-06-09 | 2020-09-22 | 北京百度网讯科技有限公司 | Quantitative analysis method and device for user decision behaviors |
| CN111695695B (en) * | 2020-06-09 | 2023-08-08 | 北京百度网讯科技有限公司 | Quantitative analysis method and device for user decision behaviors |
| CN112380263A (en) * | 2020-11-11 | 2021-02-19 | 北京爱论答科技有限公司 | Teaching data recommendation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107862970A (en) | A kind of teaching quality evaluation model for being used to overturn classroom | |
| CN110533336A (en) | A kind of instructional decisions matching process and device | |
| CN113763212A (en) | College teaching quality assessment method based on CDBN | |
| Qiu et al. | Effectiveness of artificial intelligence (AI) in improving pupils’ deep learning in primary school mathematics teaching in Fujian Province | |
| Jenhani et al. | Course learning outcome performance improvement: a remedial action classification based approach | |
| Malikin et al. | Research of Methods for Practical Educational Tasks Generation Based on Various Difficulty Levels. | |
| Kamath | Design and development of soft computing model for teaching staff performance evaluation | |
| AbuKhousa et al. | Big learning data analytics support for engineering career readiness | |
| Li et al. | Study on the evaluation of exercise effect in physical education teaching under the application of Random Forest Model | |
| Petković et al. | Neuro-fuzzy assessment of pupil performance based on e-learning platform implementation | |
| Balakrishnan | On modeling the affective effect on learning | |
| Veresné et al. | Training based change management problem for social innovation | |
| Rajagopal et al. | Machine Learning Methods for Online Education Case | |
| Tajali et al. | The relationship between organizational culture and creativity in physical education experts in university of applied science and technology | |
| Shana et al. | Educational data mining: An intelligent system to predict student graduation AGPA | |
| Olmos-López et al. | Artificial Intelligence as a Way to Improve Educational Practices | |
| Zhao | Innovative Research on the Teaching Mode of Preschool Education Courses under the Background of Wireless Communication and Big Data | |
| Ishak et al. | Research on the Relationship between the Development of Artificial Intelligence Technology and the Change of Education Goals | |
| Nayak et al. | Enhancing Student Performance through Adaptive Knowledge Assessment: A Bayesian Optimization Approach | |
| Van Thuy | Developing students metacognitive skills in mathematics classroom | |
| JiméNez-Pitre et al. | INFLUENCE OF ARTIFICIAL INTELLIGENCE ON HIGHER EDUCATION VIRTUAL CLASSROOMS | |
| Kickmeier-Rust et al. | Support Teachers' Predictions of Learning Success by Structural Competence Modelling. | |
| Mamon Saleem Alzboun et al. | The Effect of a proposed IA-Based Educational Software on Teaching Algorithms and Programming and Measuring Its Efficacy on the Levels of Creative Thinking of 10th-Grade Students in Jordan | |
| Akhtar et al. | Analyze the Impact of Digital Transformation on Learning using Soft Computing | |
| Zhang et al. | Machine learning based teaching quality evaluation |
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 |