CN107665473A - Learning path planning method and device - Google Patents
Learning path planning method and device Download PDFInfo
- Publication number
- CN107665473A CN107665473A CN201610600597.2A CN201610600597A CN107665473A CN 107665473 A CN107665473 A CN 107665473A CN 201610600597 A CN201610600597 A CN 201610600597A CN 107665473 A CN107665473 A CN 107665473A
- Authority
- CN
- China
- Prior art keywords
- student
- knowledge point
- grasping level
- knowledge
- node
- 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
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
- 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 application proposes a kind of learning path planning method and device, and the learning path planing method includes：Collect student and topic record is done to each knowledge point；According to the knowledge mapping for doing topic record, building Students ' Learning；According to the knowledge mapping, obtain each student to each knowledge point should Grasping level and actual Grasping level；According to it is described should Grasping level and the actual Grasping level, plan the individualized learning path using knowledge point as elementary cell corresponding to each student.This method can be planned learning path using knowledge point as granularity, so as to ensure that the learning sequence of student from easy to difficult, more effectively lifts the learning ability of student.Further, the individual demand of student can also be met.
Description
Technical field
The application is related to online education technical field, more particularly to a kind of learning path planning method and device.
Background technology
With the popularization of internet and the extensive use of computer technology, traditional educational mode is also gradually to online religion
Direction transformation is educated, so that one-to-many educational mode has obtained more deep application, " one " refers to online education platform,
" more " refer to the user or student of online education platform；This online education pattern can cause " one " corresponding hundreds of thousands even to hundred
Ten thousand user or student；How this educational pattern accomplishes to enter with reference to user itself study situation because the number of study is numerous
Row learning path is planned, is improved self-study ability and is particularly important.However, existing online education platform generally be directed to
Student's does topic situation, recommends some topics do not grasped to allow student to strengthen exercise, the topic do not grasped is inscribed as wrong, this
Method typically with entitled recommendation granularity, recommends granularity thicker, student may be caused not do the topic recommended always, not only unrestrained
Time-consuming, the learning ability of student can not also get a promotion.
The content of the invention
The application is intended to one of technical problem at least solving in correlation technique to a certain extent.
Therefore, the purpose of the application is to propose a kind of learning path planing method, this method can be with knowledge point
For granularity, learning path is planned, so as to ensure that the learning sequence of student from easy to difficult, more effectively lifts student's
Learning ability.Further, this can correspond to the planning that different students realize different learning paths, meet the personalization of student
Demand.
Further object is to propose a kind of learning path device for planning.
To reach above-mentioned purpose, learning path planing method that the application first aspect embodiment proposes, including：Collect and learn
It is raw that topic record is done to each knowledge point；According to the knowledge mapping for doing topic record, building Students ' Learning；According to the knowledge
Collection of illustrative plates, obtain each student to each knowledge point should Grasping level and actual Grasping level；Journey should be grasped according to described
Degree and the actual Grasping level, plan the individualized learning path using knowledge point as elementary cell corresponding to each student.
The learning path planing method that the application first aspect embodiment proposes, is elementary cell by being planned to knowledge point
Learning path, learning path can be planned using knowledge point as granularity, so as to ensure that the study of student from easy to difficult
Sequentially, the learning ability of student is more effectively lifted.Further, by obtaining each student should slap to each knowledge point
Degree and actual Grasping level are held, and carries out the planning in individualized learning path, the individual demand of student can be met.
To reach above-mentioned purpose, learning path device for planning that the application second aspect embodiment proposes, including：Collect mould
Block, topic record is done to each knowledge point for collecting student；Module is built, for doing topic record according to, builds student
The knowledge mapping of study；Acquisition module, for according to the knowledge mapping, obtaining each student should slap to each knowledge point
Hold degree and actual Grasping level；Planning module, for according to described in should Grasping level and the actual Grasping level, planning
Individualized learning path corresponding to each student using knowledge point as elementary cell.
The learning path device for planning that the application second aspect embodiment proposes, is elementary cell by being planned to knowledge point
Learning path, learning path can be planned using knowledge point as granularity, so as to ensure that the study of student from easy to difficult
Sequentially, the learning ability of student is more effectively lifted.Further, by obtaining each student should slap to each knowledge point
Degree and actual Grasping level are held, and carries out the planning in individualized learning path, the individual demand of student can be met.
The aspect and advantage that the application adds will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the application.
Brief description of the drawings
The above-mentioned and/or additional aspect of the application and advantage will become from the following description of the accompanying drawings of embodiments
Substantially and it is readily appreciated that, wherein：
Fig. 1 is the schematic flow sheet for the learning path planing method that the application one embodiment proposes；
Fig. 2 is the knowledge done topic record, build Students ' Learning according to student to each knowledge point in the embodiment of the present application
The schematic flow sheet of collection of illustrative plates；
Fig. 3 is the stream for building the knowledge mapping of Students ' Learning in the embodiment of the present application according to knowledge point Grasping level matrix
Journey schematic diagram；
Fig. 4 is that one built in the embodiment of the present application according to the knowledge point Grasping level matrix of a specific example is specific
The schematic diagram of the knowledge mapping of example；
Fig. 5 is to obtain each student should slap to each knowledge point according to the knowledge mapping in the embodiment of the present application
Hold the schematic flow sheet of degree and actual Grasping level；
Fig. 6 be the knowledge mapping shown in Fig. 4 is carried out it is regular after the obtained schematic diagram of knowledge mapping；
Fig. 7 be in the embodiment of the present application according to should Grasping level and the actual Grasping level, plan each learn
Schematic flow sheet corresponding to raw using knowledge point as the individualized learning path of elementary cell；
Fig. 8 is the structural representation for the learning path device for planning that the application one embodiment proposes；
Fig. 9 is the structural representation of the learning path device for planning of the application another embodiment proposition.
Embodiment
Embodiments herein is described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar module or the module with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the application, and it is not intended that limitation to the application.On the contrary, this
All changes that the embodiment of application includes falling into the range of the spirit and intension of attached claims, modification and equivalent
Thing.
Fig. 1 is the schematic flow sheet for the learning path planing method that the application one embodiment proposes.
As shown in figure 1, the present embodiment comprises the following steps：
S11：Collect student and topic record is done to each knowledge point.
The topic record that does refers to student to grasp correlated knowledge point, the record with knowledge point related topic done, or
The record of the topic related to knowledge point done during student examination；During such as student's on-line study, the record of the exercise done, when
So, or the record for other topics related to knowledge point that student does, specific topic the application are not construed as limiting.
S12：According to the knowledge mapping for doing topic record, building Students ' Learning；
Wherein, knowledge mapping be used to showing sequencing in learning knowledge point of the knowledge point, student of Students ' Learning and
The probability of sequencing.
Specifically, knowledge mapping can be made up of point and side, wherein, each corresponding knowledge point of point, side is used to connect
Two points, and direction sensing be present, to show sequencing of the student in learning knowledge point, and, each edge tool
There is corresponding weight, to show the probability of sequencing.
The flow of specific structure knowledge mapping may refer to subsequent embodiment.
S13：According to the knowledge mapping, each student Grasping level and actual should grasp to each knowledge point is obtained
Degree.
Idiographic flow may refer to follow-up embodiment.
S14：According to it is described should Grasping level and the actual Grasping level, plan corresponding to each student with knowledge point
For the individualized learning path of elementary cell.
Wherein, the learning path using knowledge point as elementary cell is used to demonstrate the need for the knowledge point of study and each knowledge
The priority learning sequence of point.
In addition, corresponding different student, can cook up different learning paths, therefore, can claim the learning path
For individualized learning path.
The flow in specific planning individualized learning path may refer to subsequent embodiment.
, can be right using knowledge point as granularity by being planned to the learning path that knowledge point is elementary cell in the present embodiment
Learning path is planned, so as to ensure that the learning sequence of student from easy to difficult, more effectively lifts the learning ability of student.
According to it is described should Grasping level and the actual Grasping level, plan corresponding to each student using knowledge point as elementary cell
Individualized learning path.
Fig. 2 is the knowledge done topic record, build Students ' Learning according to student to each knowledge point in the embodiment of the present application
The schematic flow sheet of collection of illustrative plates.
As shown in Fig. 2 structure flow includes：
S21：Topic record is done to each knowledge point according to student, builds knowledge point Grasping level matrix.
Wherein, Grasping level matrix in knowledge point is used to show Grasping level of each student to each knowledge point.
Specifically, doing topic record to each knowledge point according to each student, each student is calculated to each knowledge point
Grasping level, and, knowledge point Grasping level matrix is formed to the Grasping level of each knowledge point by each student.
For example, each grasp journey of each student of element representation to each knowledge point in the knowledge point Grasping level matrix
Spend aij, it is assumed that share n student, m knowledge point, then knowledge point Grasping level matrix A can be expressed as：
Wherein, aijRepresent student SiTo knowledge point KjGrasping level.
In Grasping level of each student of calculating to each knowledge point, corresponding each student and each knowledge point, if
The student is less than first threshold to the sum for doing topic record of the knowledge point, it is determined that the student is to the knowledge point
Grasping level is 0, otherwise, if the sum is more than or equal to first threshold, by the average rate for doing topic record
Grasping level as the student to the knowledge point.
Specifically, calculate aijWhen, judge student SiThe topic done and knowledge point KjRelated sum, if the sum is small
In first threshold, then student SiTo knowledge point KjGrasping level be 0；Otherwise, by student SiDo with knowledge point KjRelated topic
Average rate as student SiTo knowledge point KjGrasping level aij, when the average rate calculates, first calculate
Raw SiDo with knowledge point KjThe scoring rate of related each topic, i.e. student SiDo the score and criteria scores of each topic
Ratio；Student S is calculated againiDo with knowledge point KjThe average value of the scoring rate of related all topics, circular
As shown in formula (1):
Wherein, TijRepresent student SiDo with knowledge point KjRelated all topics sum, ScoretRepresent student SiDo
With knowledge point KjThe actual score of t-th related of topic, stdScoretRepresent the criteria scores of t-th of topic.Wherein, each
The criteria scores of topic are the values of known determination, as 10 points or 15 grade.
After all students have been calculated to the Grasping level of each knowledge point, you can obtain knowledge point Grasping level matrix.
Further, it is to be understood that as the constant renewal of data, such as student constantly learn to produce new inscribe
Record, then above-mentioned knowledge point Grasping level matrix can inscribe record according to new doing and be updated.And specific renewal time
It can be timing renewal or be updated according to when predeterminable event occurs.
S22：According to the knowledge point Grasping level matrix, the knowledge mapping of Students ' Learning is built.
The knowledge mapping is directed acyclic graph, is represented using G=(V, E), and wherein V represents the node in figure, and E represents figure
In directed edge.Figure interior joint represents knowledge point, and two knowledge points for having sequencing are connected using a directed edge, directed edge
Direction represent first knowledge point K in rear knowledge point, such as E (s, r) for order formerly knowledge point sensing ordersPoint in rear knowledge
Point KrA directed edge；The weight of every directed edge represents that there is the probability of sequencing two knowledge points of side connection, that is, learns
Raw learn well needs first to learn the probability of first knowledge point well in rear knowledge point, is represented using W, as W (s, r) represents knowledge point KsWith know
Know point KrThere is the weight of the probability of sequencing, i.e. side E (s, r).
Specifically, referring to Fig. 3, according to knowledge point Grasping level matrix, the flow bag of the knowledge mapping of Students ' Learning is built
Include：
S31：According to knowledge point corresponding to the Grasping level matrix of knowledge point, the node in knowledge mapping is determined.
Wherein, the node number in knowledge mapping is identical with the number of knowledge point corresponding to the Grasping level matrix of knowledge point,
For example, knowledge point Grasping level matrix shares m knowledge point, then m node is shared in knowledge mapping, each node is corresponding one
Knowledge point.
S32：Using each node in knowledge mapping successively as present node, and according to knowledge point Grasping level matrix,
It is the probability that present node points to each other nodes to calculate sequencing successively.
When computationally stating probability, it can be calculated current corresponding to each student first according to knowledge point Grasping level matrix
Node points to the confidence level of each other nodes；Present node corresponding to all students is pointed into the credible of each other nodes again
The average of degree points to the probability of each other nodes as present node.
Specifically, assuming that present node is referred to as first node, an other nodes are referred to as section point, then are calculating first
When node points to the probability of section point, the confidence level that first node corresponding to each student points to section point is first calculated, then
The average of above-mentioned confidence level corresponding to all students is defined as to above-mentioned probability.
When calculating the confidence level of first node sensing section point corresponding to each student, according to each student to first
The Grasping level of node (knowledge point) to the Grasping level of section point (knowledge point) with determining.Specifically, corresponding each student,
The first Grasping level and the second Grasping level are obtained from the Grasping level matrix of knowledge point, first Grasping level is
The raw Grasping level to knowledge point corresponding to present node, second Grasping level are to corresponding to each other nodes
The Grasping level of knowledge point；Calculate the difference of first Grasping level and the second Grasping level；If the difference be more than or
Equal to Second Threshold, then the confidence level is set to 1, the confidence level is otherwise set to 0.
Such as：Present node (knowledge point) is Ks, an other nodes are Kr, then K corresponding to some student is calculatedsPoint to
KrConfidence level when, the computational methods of the confidence level is calculate the student to knowledge point KsGrasping level with to knowledge point Kr
Grasping level difference, judge whether the difference is more than or equal to Second Threshold, if it is, confidence level be 1, otherwise,
Confidence level is 0.
After above-mentioned confidence level corresponding to each student is calculated, then by the average of above-mentioned confidence level corresponding to all students
As above-mentioned probability, i.e. the weight of directed edge between two knowledge points, the weight meter of directed edge between described two knowledge points
Shown in calculation method such as formula (2)：
Wherein, W (s, r) represents knowledge point KsAnd KrOrder be " Ks、Kr" (namely KsPoint to Kr) probability, i.e. side E (s,
R) weight, n are pupil load, and f (i, s, r) is to knowledge point K according to each studentsAnd KrGrasping level calculate current two
The reliability function of individual knowledge dot sequency, shown in f (i, s, r) computational methods such as formula (3)：
Wherein, aisRepresent student SiTo knowledge point KsGrasping level, airRepresent student SiTo knowledge point KrGrasp journey
Degree, TH2Represent knowledge point KsAnd KrOrder be " Ks、Kr" probability threshold value, i.e. Second Threshold, its value can be according to specifically should
With or experimental result determine, as value be 0.4, as student SiTo knowledge point KsGrasping level with to knowledge point KrGrasp journey
When difference between degree is more than the threshold value, the value of reliability function is 1, otherwise, value 0.
S33：Whether needed to build the directed edge of each other nodes of present node sensing according to the probabilistic determination, and
When needed, the directed edge, and the weight using the probability as the directed edge are built.
Wherein it is possible to when above-mentioned probability is more than three threshold values, it is determined that need to build above-mentioned directed edge.
The value of 3rd threshold value determines according to application demand or experimental result, if value is 0.7.
For example the probability of first node sensing section point is 0.8, because the probability is more than the 3rd threshold value 0.7, is then built
First node points to the directed edge of the 3rd node, and the weight of the directed edge is 0.8.
Due to egress can be determined according to S31, directed edge and corresponding weight can be constructed according to S32-S33, and
Directed edge of the knowledge mapping by node and with weight forms, and therefore, knowledge mapping can be constructed by S31-S33.
Specifically, the value for assuming Second Threshold is 0.4, when the value of the 3rd threshold value is 0.7, according to knowing for Fig. 4 left sides
The knowledge mapping of the Students ' Learning on the right of Fig. 4 can be constructed by knowing point Grasping level matrix.Specific building process is as follows：
Knowledge mapping G=(V, E), figure interior joint set V={ K1,K2,K3,K4, node set interior joint is taken out successively,
As present node, calculating present node and other nodes successively has the probability of first order, described in detail below：
Node K is taken out first1As present node, K is calculated successively1With K2,K3,K4There is the probability of sequencing, calculate
To W (1,2)=0.75, W (1,3)=0, W (Isosorbide-5-Nitrae)=0.75, because the value of the 3rd threshold value is 0.7, then node K1With K2And K4
Between directed edge be present, side E (1,2) and side E (Isosorbide-5-Nitrae) are added in line set respectively, i.e. E={ E (1,2), E (Isosorbide-5-Nitrae) }；
Further take out node K2As present node, calculate node K successively2With K1,K3,K4There is the probability of sequencing, calculate
W (2,1)=0, W (2,3)=0, W (2,4)=0 is obtained, is not above the weight of the 3rd threshold value, does not meet that the side of condition adds
Into line set；
Further take out node K3As present node, calculate node K successively3With K1,K2,K4There is the probability of sequencing, calculate
W (3,1)=0, W (3,2)=0.75, W (3,4)=1 is obtained, then node K3With K2And K4Between directed edge be present, respectively by side E
(3,2) and side E (3,4) are added in line set, i.e. E={ E (1,2), E (Isosorbide-5-Nitrae), E (3,2), E (3,4) }；
Finally take out node K4As present node, calculate node K successively4With K1,K2,K3There is the probability of sequencing, count
Calculation obtains W (4,1)=0, W (4,2)=0, W (4,3)=0, is not above the weight of the 3rd threshold value, does not meet that the side of condition adds
Enter into line set；
The probability calculation for having sequencing between node terminates, and obtains line set E={ E (1,2), E (Isosorbide-5-Nitrae), E (3,2), E
(3,4) }, the side between node is connected according to line set, that is, the knowledge mapping built.
Fig. 5 is to obtain each student should slap to each knowledge point according to the knowledge mapping in the embodiment of the present application
Hold the schematic flow sheet of degree and actual Grasping level.
As shown in figure 5, including：
S51：The knowledge mapping is carried out it is regular, obtain it is regular after knowledge mapping, and according to the knowledge after regular
Collection of illustrative plates calculate each student to each knowledge point should Grasping level.
Wherein, the regular flow of knowledge mapping is included：
Successively using the node that multiple predecessor nodes in knowledge mapping be present as present node, to present node with it is each before
The weight of directed edge driven between node is normalized, and the knowledge mapping after weight is normalized is as the knowledge graph after regular
Spectrum.
Specifically, normalization refers to between regular preceding weight divided by all predecessor nodes of present node and present node
Directed edge weight and.Shown in specific regular method such as formula (4)：
Wherein, Norm (o, r) is power of o-th of node to r-th of node after regular in the predecessor node of r-th of node
It is heavy, weight of o-th of node to r-th of node before regular in the predecessor node of W (o, r) r-th of node of expression；roRepresent r
The predecessor node of individual node is total, and j-th of node is regular to r-th of node in the predecessor node of W (j, s) r-th of node of expression
Preceding weight.
By taking a specific example as an example, the knowledge shown in Fig. 6 can be obtained after regular to the knowledge mapping progress shown in Fig. 4
Collection of illustrative plates.
To knowledge mapping carry out it is above-mentioned it is regular after, can use following steps calculate should Grasping level：
Each node in corresponding knowledge mapping, if the node has predecessor node, according to each student to institute
The actual Grasping level of knowledge point corresponding to each predecessor node of node is stated, and between the node and each predecessor node
Weight after the normalization of directed edge, calculate each student to knowledge point corresponding to the node should Grasping level.In addition,
If predecessor node is not present in node, can by knowledge point corresponding to the node should Grasping level be set to fixed value, such as
It is set to 1.
, it is necessary to consider that student corresponds to the grasp of knowledge point to the predecessor node of each node in knowledge mapping during specific calculating
Degree calculates each node and corresponds to the degree that knowledge point should grasp, and therefore, what each node corresponded to knowledge point should grasp journey
The actual Grasping level that degree is corresponded to knowledge point by the predecessor node of the node determines；Each node corresponds to should grasping for knowledge point
Degree be all predecessor nodes of the node correspond to knowledge point actual Grasping level and each predecessor node to side between the node
Weight sum of products, specifically calculate as shown in formula (5)：
Wherein, Ability (i, r) represents student SiTo knowledge point KrShould Grasping level；roRepresent r-th node
Predecessor node sum, aioRepresent student SiThe actual grasp of knowledge point is corresponded to o-th of node in the predecessor node of r-th of node
Degree.
S52：Grasping level by each student to each knowledge point corresponding to node in the knowledge mapping, it is defined as every
Actual Grasping level of the individual student to each knowledge point.
Wherein, each student can determine to the Grasping level of each knowledge point according to knowledge point Grasping level matrix, than
Such as, the element of the first row of knowledge point Grasping level matrix represents Grasping level of first student to each knowledge point respectively,
The element of second row of knowledge point Grasping level matrix represents Grasping level of second student to each knowledge point respectively, according to this
Analogize.
Fig. 7 be in the embodiment of the present application according to should Grasping level and the actual Grasping level, plan each learn
Schematic flow sheet corresponding to raw using knowledge point as the individualized learning path of elementary cell.
As shown in fig. 7, specific learning path planing method includes：
S71：Corresponding each student, calculate corresponding to each knowledge point it is described should Grasping level actual grasp journey with described
Difference between degree.
S72：Knowledge point is ranked up according to the difference, obtained corresponding to the student using knowledge point as elementary cell
Individualized learning path.
Wherein, knowledge point can be ranked up according to the order of difference from big to small in sequence.And difference identical
The order of knowledge point can be random.
The degree that should be grasped to each knowledge point according to student and student are to the Grasping level of each knowledge point to student
The path of follow-up learning knowledge point carries out personalized planning, if the degree that should grasp of a knowledge point with student to the knowledge
The actual Grasping level difference of point is bigger, illustrates that the knowledge point more should first learn, then when planning learning path, the knowledge point
It is more forward, shown in the probability calculation mode such as formula (6) that each knowledge point should first learn：
Prec(i, r)=Ability (i, r)-air (6)
Wherein, Prec(i, r) represents student SiThe probability that r nodes correspond to knowledge point, Ability (i, r) should first be learnt
Represent student SiR nodes are corresponded to knowledge point should Grasping level, airRepresent student SiThe palm of knowledge point is corresponded to r nodes
Hold degree；
Student should first be learnt each knowledge point probability sort from big to small after, each probability is corresponded into knowledge point road
Footpath is as the individualized learning path planned student.
Such as：
Student SiTo knowledge point K1、K2、K3、K4Actual Grasping level be followed successively by ai1=0.8, ai2=0.6, ai3=0.5,
ai4=0.3；(numerical value can obtain from the Grasping level matrix of knowledge point, by the member of every a line in the Grasping level of knowledge point
Actual Grasping level to each knowledge point of the element as corresponding student.Knowledge point Grasping level matrix is renewable)；
Student S is calculated by the knowledge mapping after regular in formula (5) and Fig. 6iEach knowledge point should be grasped
Degree is Ability (i, 1)=1, Ability (i, 2)=0.65, Ability (i, 3)=1, Ability (i, 4)=0.629；
Student S can be calculated by formula (7)iThe probability that each knowledge point should first be learnt is Prec(i, 1)=0.2,
Prec(i, 2)=0.05, Prec(i, 3)=0.5, Prec(i, 4)=0.329；
Therefore, to student SiThe individualized learning path of planning is K3→K4→K1→K2。
Further, it is determined that behind individualized learning path, can according to each knowledge point on individualized learning path,
The related topic of corresponding knowledge point is recommended into corresponding student.
Fig. 8 is the structural representation for the learning path device for planning that the application one embodiment proposes.
As shown in figure 8, the device 80 includes：Collection module 81, structure module 82, acquisition module 83 and planning module 84.
Collection module 81, topic record is done to each knowledge point for collecting student；
Module 82 is built, for doing topic record according to, builds the knowledge mapping of Students ' Learning；
Acquisition module 83, for according to the knowledge mapping, journey should to be grasped to each knowledge point by obtaining each student
Degree and actual Grasping level；
Planning module 84, for according to described in should Grasping level and the actual Grasping level, plan each student couple
The individualized learning path using knowledge point as elementary cell answered.
In some embodiments, referring to Fig. 9, the acquisition module 83 includes：
Regular submodule 831, for the knowledge mapping carry out it is regular, obtain it is regular after knowledge mapping, Yi Jigen
According to the knowledge mapping after regular calculate each student to each knowledge point should Grasping level；
Determination sub-module 832, for the palm by each student to each knowledge point corresponding to node in the knowledge mapping
Degree is held, is defined as actual Grasping level of each student to each knowledge point.
In some embodiments, referring to Fig. 9, the planning module 84 includes：
Calculating sub module 841, for corresponding each student, calculate corresponding to each knowledge point it is described should Grasping level with
Difference between the actual Grasping level；
Sorting sub-module 842, for being ranked up according to the difference to knowledge point, obtain corresponding to the student to know
Know individualized learning path of the point for elementary cell.
In some embodiments, the regular submodule be used for the knowledge mapping carry out it is regular, obtain it is regular after know
Know collection of illustrative plates, including：
Successively using the node that multiple predecessor nodes in knowledge mapping be present as present node, to present node with it is each before
The weight of directed edge driven between node is normalized, and the knowledge mapping after weight is normalized is as the knowledge graph after regular
Spectrum.
In some embodiments, the regular submodule is used to calculate each student to each according to the knowledge mapping after regular
Knowledge point should Grasping level, including：
Each node in corresponding knowledge mapping, if the node has predecessor node, according to each student to institute
The actual Grasping level of knowledge point corresponding to each predecessor node of node is stated, and between the node and each predecessor node
Weight after the normalization of directed edge, calculate each student to knowledge point corresponding to the node should Grasping level.
In some embodiments, referring to Fig. 9, the structure module 82 includes：
First structure submodule 821, for doing topic record according to, builds knowledge point Grasping level matrix；
Second structure submodule 822, for according to the knowledge point Grasping level matrix, building the knowledge graph of Students ' Learning
Spectrum.
In some embodiments, the first structure submodule is specifically used for：Each knowledge point is done according to each student
Topic record, calculates Grasping level of each student to each knowledge point, and, by grasp journey of each student to each knowledge point
Degree composition knowledge point Grasping level matrix.
In some embodiments, the first structure submodule is used to do topic note to each knowledge point according to each student
Record, calculates Grasping level of each student to each knowledge point, including：Corresponding each student and each knowledge point, if described
Student is less than first threshold to the sum for doing topic record of the knowledge point, it is determined that grasp of the student to the knowledge point
Degree is 0, otherwise, if the sum is more than or equal to first threshold, using it is described do topic record average rate as
Grasping level of the student to the knowledge point.
In some embodiments, the second structure submodule is specifically used for：
According to knowledge point corresponding to the Grasping level matrix of knowledge point, the node in knowledge mapping is determined；
Using each node in knowledge mapping successively as present node, and according to knowledge point Grasping level matrix, successively
It is the probability that present node points to each other nodes to calculate sequencing；
Whether needed to build the directed edge of each other nodes of present node sensing according to the probabilistic determination, and needed
When wanting, the directed edge, and the weight using the probability as the directed edge are built.
In some embodiments, the second structure submodule is used to, according to knowledge point Grasping level matrix, calculate successively successively suitable
Sequence is the probability that present node points to each other nodes, including：
According to knowledge point Grasping level matrix, calculate that present node corresponding to each student points to each other nodes can
Reliability；
The average that present node corresponding to all students is pointed to the confidence level of each other nodes refers to as present node
To the probability of each other nodes.
In some embodiments, the second structure submodule is used for according to knowledge point Grasping level matrix, calculates each student couple
The present node answered points to the confidence level of each other nodes, including：
Corresponding each student, obtains the first Grasping level and the second Grasping level, institute from the Grasping level matrix of knowledge point
It is Grasping level of the student to knowledge point corresponding to present node to state the first Grasping level, and second Grasping level is pair
The Grasping level of knowledge point corresponding to each other nodes；
Calculate the difference of first Grasping level and the second Grasping level；
If the difference is more than or equal to Second Threshold, the confidence level is set to 1, otherwise put the confidence level
For 0.
In some embodiments, whether the second structure submodule is used to need structure present node to refer to according to the probabilistic determination
To the directed edge of each other nodes, including：
If the probability is more than the 3rd threshold value, it is determined that needs to build the oriented of each other nodes of present node sensing
Side.
It is understood that the device of the present embodiment is corresponding with above method embodiment, particular content may refer to method
The associated description of embodiment, is no longer described in detail herein.
, can be right using knowledge point as granularity by being planned to the learning path that knowledge point is elementary cell in the present embodiment
Learning path is planned, so as to ensure that the learning sequence of student from easy to difficult, more effectively lifts the learning ability of student.
It is understood that same or similar part can mutually refer in the various embodiments described above, in certain embodiments
Unspecified content may refer to same or analogous content in other embodiment.
It should be noted that in the description of the present application, term " first ", " second " etc. are only used for describing purpose, without
It is understood that to indicate or implying relative importance.In addition, in the description of the present application, unless otherwise indicated, the implication of " multiple "
Refer at least two.
Any process or method described otherwise above description in flow chart or herein is construed as, and represents to include
Module, fragment or the portion of the code of the executable instruction of one or more the step of being used to realize specific logical function or process
Point, and the scope of the preferred embodiment of the application includes other realization, wherein can not press shown or discuss suitable
Sequence, including according to involved function by it is basic simultaneously in the way of or in the opposite order, carry out perform function, this should be by the application
Embodiment person of ordinary skill in the field understood.
It should be appreciated that each several part of the application can be realized with hardware, software, firmware or combinations thereof.Above-mentioned
In embodiment, software that multiple steps or method can be performed in memory and by suitable instruction execution system with storage
Or firmware is realized.If, and in another embodiment, can be with well known in the art for example, realized with hardware
Any one of row technology or their combination are realized：With the logic gates for realizing logic function to data-signal
Discrete logic, have suitable combinational logic gate circuit application specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are appreciated that to realize all or part of step that above-described embodiment method carries
Suddenly it is that by program the hardware of correlation can be instructed to complete, described program can be stored in a kind of computer-readable storage medium
In matter, the program upon execution, including one or a combination set of the step of embodiment of the method.
In addition, each functional unit in each embodiment of the application can be integrated in a processing module, can also
That unit is individually physically present, can also two or more units be integrated in a module.Above-mentioned integrated mould
Block can both be realized in the form of hardware, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and as independent production marketing or in use, can also be stored in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only storage, disk or CD etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the application.In this manual, to the schematic representation of above-mentioned term not
Necessarily refer to identical embodiment or example.Moreover, specific features, structure, material or the feature of description can be any
One or more embodiments or example in combine in an appropriate manner.
Although embodiments herein has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is impossible to the limitation to the application is interpreted as, one of ordinary skill in the art within the scope of application can be to above-mentioned
Embodiment is changed, changed, replacing and modification.
Claims (16)
- A kind of 1. learning path planing method, it is characterised in that including：Collect student and topic record is done to each knowledge point；According to the knowledge mapping for doing topic record, building Students ' Learning；According to the knowledge mapping, obtain each student to each knowledge point should Grasping level and actual Grasping level；According to it is described should Grasping level and the actual Grasping level, plan corresponding to each student using knowledge point to be substantially single The individualized learning path of member.
- 2. according to the method for claim 1, it is characterised in that it is described according to the knowledge mapping, obtain each student couple Each knowledge point should Grasping level and actual Grasping level, including：The knowledge mapping is carried out it is regular, obtain it is regular after knowledge mapping, and calculated according to the knowledge mapping after regular Each student to each knowledge point should Grasping level；Grasping level by each student to each knowledge point corresponding to node in the knowledge mapping, is defined as each student couple The actual Grasping level of each knowledge point.
- 3. according to the method for claim 2, it is characterised in that it is described regular to knowledge mapping progress, obtain regular Knowledge mapping afterwards, including：Successively using the node that multiple predecessor nodes in knowledge mapping be present as present node, present node and each forerunner are saved The weight of directed edge between point is normalized, and the knowledge mapping after weight is normalized is as the knowledge mapping after regular.
- 4. according to the method for claim 3, it is characterised in that the knowledge mapping according to after regular calculates each student To each knowledge point should Grasping level, including：Each node in corresponding knowledge mapping, if the node has predecessor node, according to each student to the section The actual Grasping level of knowledge point corresponding to each predecessor node of point, and it is oriented between the node and each predecessor node Weight after the normalization on side, calculate each student to knowledge point corresponding to the node should Grasping level.
- 5. according to the method for claim 1, it is characterised in that should Grasping level and the actual palm described in the basis Degree is held, plans the individualized learning path using knowledge point as elementary cell corresponding to each student, including：Corresponding each student, calculate it is described corresponding to each knowledge point should be between Grasping level and the actual Grasping level Difference；Knowledge point is ranked up according to the difference, obtains the personalization using knowledge point as elementary cell corresponding to the student Learning path.
- 6. according to the method for claim 1, it is characterised in that topic record is done described in the basis, builds Students ' Learning Knowledge mapping, including：Topic record is done according to described, builds knowledge point Grasping level matrix；According to the knowledge point Grasping level matrix, the knowledge mapping of Students ' Learning is built.
- 7. according to the method for claim 6, it is characterised in that topic record is done described in the basis, structure knowledge point is grasped Degree matrix, including：Topic record is done to each knowledge point according to each student, calculates Grasping level of each student to each knowledge point, with And knowledge point Grasping level matrix is formed to the Grasping level of each knowledge point by each student.
- 8. according to the method for claim 7, it is characterised in that each student of basis does topic note to each knowledge point Record, calculates Grasping level of each student to each knowledge point, including：Corresponding each student and each knowledge point, if the student is less than first to the sum for doing topic record of the knowledge point Threshold value, it is determined that the student is 0 to the Grasping level of the knowledge point, otherwise, if the sum is more than or equal to first Threshold value, then using Grasping level of the average rate for doing topic record as the student to the knowledge point.
- 9. according to the method for claim 6, it is characterised in that described according to the knowledge point Grasping level matrix, structure The knowledge mapping of Students ' Learning, including：According to knowledge point corresponding to the Grasping level matrix of knowledge point, the node in knowledge mapping is determined；Using each node in knowledge mapping successively as present node, and according to knowledge point Grasping level matrix, calculate successively Sequencing is the probability that present node points to each other nodes；Whether needed to build the directed edge of each other nodes of present node sensing according to the probabilistic determination, and needed When, build the directed edge, and the weight using the probability as the directed edge.
- 10. according to the method for claim 9, it is characterised in that it is described according to knowledge point Grasping level matrix, calculate successively Sequencing is the probability that present node points to each other nodes, including：According to knowledge point Grasping level matrix, calculate present node corresponding to each student and point to the credible of each other nodes Degree；The average that present node corresponding to all students is pointed to the confidence level of each other nodes is pointed to often as present node The probability of individual other nodes.
- 11. according to the method for claim 10, it is characterised in that it is described according to knowledge point Grasping level matrix, calculate every Present node corresponding to individual student points to the confidence level of each other nodes, including：Corresponding each student, obtains the first Grasping level and the second Grasping level from the Grasping level matrix of knowledge point, and described the One Grasping level is Grasping level of the student to knowledge point corresponding to present node, and second Grasping level is to described The Grasping level of knowledge point corresponding to each other nodes；Calculate the difference of first Grasping level and the second Grasping level；If the difference is more than or equal to Second Threshold, the confidence level is set to 1, the confidence level is otherwise set to 0.
- 12. according to the method for claim 9, it is characterised in that described whether to need structure to work as according to the probabilistic determination Front nodal point points to the directed edge of each other nodes, including：If the probability is more than the 3rd threshold value, it is determined that needs to build the directed edge that present node points to each other nodes.
- A kind of 13. learning path device for planning, it is characterised in that including：Collection module, topic record is done to each knowledge point for collecting student；Module is built, for doing topic record according to, builds the knowledge mapping of Students ' Learning；Acquisition module, for according to the knowledge mapping, obtain each student to each knowledge point should Grasping level and reality Border Grasping level；Planning module, for according to should Grasping level and the actual Grasping level, plan corresponding to each student with Knowledge point is the individualized learning path of elementary cell.
- 14. device according to claim 13, it is characterised in that the acquisition module includes：Regular submodule, it is regular for being carried out to the knowledge mapping, obtain it is regular after knowledge mapping, and according to after regular Knowledge mapping calculate each student to each knowledge point should Grasping level；Determination sub-module, for the Grasping level by each student to each knowledge point corresponding to node in the knowledge mapping, It is defined as actual Grasping level of each student to each knowledge point.
- 15. device according to claim 13, it is characterised in that the planning module includes：Calculating sub module, for corresponding each student, calculate it is described corresponding to each knowledge point should Grasping level and the reality Difference between the Grasping level of border；Sorting sub-module, for being ranked up according to the difference to knowledge point, obtain corresponding to the student using knowledge point as The individualized learning path of elementary cell.
- 16. device according to claim 13, it is characterised in that the structure module includes：First structure submodule, for doing topic record according to, builds knowledge point Grasping level matrix；Second structure submodule, for according to the knowledge point Grasping level matrix, building the knowledge mapping of Students ' Learning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610600597.2A CN107665473B (en) | 2016-07-27 | 2016-07-27 | Learning path planning method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610600597.2A CN107665473B (en) | 2016-07-27 | 2016-07-27 | Learning path planning method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107665473A true CN107665473A (en) | 2018-02-06 |
| CN107665473B CN107665473B (en) | 2021-06-25 |
Family
ID=61115177
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610600597.2A Active CN107665473B (en) | 2016-07-27 | 2016-07-27 | Learning path planning method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107665473B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108647363A (en) * | 2018-05-21 | 2018-10-12 | 安徽知学科技有限公司 | Map construction, display methods, device, equipment and storage medium |
| CN108665394A (en) * | 2018-05-07 | 2018-10-16 | 北京文华在线教育科技股份有限公司 | Adaptive learning method and system |
| CN109191345A (en) * | 2018-09-17 | 2019-01-11 | 合肥工业大学 | A kind of cognitive diagnosis method of Student oriented cognitive process |
| CN109191929A (en) * | 2018-09-25 | 2019-01-11 | 上海优谦智能科技有限公司 | The intellectual education system of knowledge based map |
| CN109816265A (en) * | 2019-02-01 | 2019-05-28 | 北京作业盒子科技有限公司 | Evaluation method, topic recommended method and the electronic equipment of knowledge feature master degree |
| CN109886848A (en) * | 2019-01-30 | 2019-06-14 | 网易（杭州）网络有限公司 | Method, apparatus, medium and the electronic equipment of data processing |
| CN110197450A (en) * | 2019-05-31 | 2019-09-03 | 上海乂学教育科技有限公司 | Ordinal relation acquisition methods, device, equipment and medium between knowledge point |
| CN110263181A (en) * | 2019-06-17 | 2019-09-20 | 北京作业盒子科技有限公司 | The method for digging of the structure of knowledge and the planing method of learning path |
| CN111798138A (en) * | 2020-07-07 | 2020-10-20 | 广州视源电子科技股份有限公司 | Data processing method, computer storage medium and related equipment |
| CN112052342A (en) * | 2020-09-04 | 2020-12-08 | 西南大学 | Learning path recommendation method and system based on online test result big data analysis |
| CN112446523A (en) * | 2019-09-04 | 2021-03-05 | 北京新唐思创教育科技有限公司 | Learning path planning method, electronic device and computer storage medium |
| CN112446009A (en) * | 2019-09-02 | 2021-03-05 | 北京新唐思创教育科技有限公司 | Learning path planning method and device, readable storage medium and server |
| CN112632295A (en) * | 2020-12-31 | 2021-04-09 | 郑雁 | Learning resource dynamic planning method and device based on knowledge graph |
| CN112765406A (en) * | 2019-11-04 | 2021-05-07 | 亿度慧达教育科技（北京）有限公司 | Learning resource planning method, electronic device, and computer storage medium |
| CN112825071A (en) * | 2019-11-21 | 2021-05-21 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN112825147A (en) * | 2019-11-21 | 2021-05-21 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN113361926A (en) * | 2021-06-07 | 2021-09-07 | 江苏师范大学 | Teaching quality evaluation method based on teaching sequence |
| CN113610237A (en) * | 2021-08-19 | 2021-11-05 | 北京京东乾石科技有限公司 | Learning path planning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8155081B1 (en) * | 2007-05-21 | 2012-04-10 | Marvell International Ltd. | Self learning roaming optimization |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105389622A (en) * | 2015-10-20 | 2016-03-09 | 西安交通大学 | Multi-constraint learning path recommendation method based on knowledge map |
| CN105632285A (en) * | 2016-03-14 | 2016-06-01 | 成都爱易佰网络科技有限公司 | Variant improvement system and method meeting repairing of learning process |
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
-
2016
- 2016-07-27 CN CN201610600597.2A patent/CN107665473B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8155081B1 (en) * | 2007-05-21 | 2012-04-10 | Marvell International Ltd. | Self learning roaming optimization |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105389622A (en) * | 2015-10-20 | 2016-03-09 | 西安交通大学 | Multi-constraint learning path recommendation method based on knowledge map |
| CN105632285A (en) * | 2016-03-14 | 2016-06-01 | 成都爱易佰网络科技有限公司 | Variant improvement system and method meeting repairing of learning process |
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
Non-Patent Citations (1)
| Title |
|---|
| 曹荣: "论微课内容之"微"与"不微"", 《中国教育信息化》 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108665394A (en) * | 2018-05-07 | 2018-10-16 | 北京文华在线教育科技股份有限公司 | Adaptive learning method and system |
| CN108665394B (en) * | 2018-05-07 | 2021-11-30 | 北京文华在线教育科技股份有限公司 | Self-adaptive learning method and system |
| CN108647363A (en) * | 2018-05-21 | 2018-10-12 | 安徽知学科技有限公司 | Map construction, display methods, device, equipment and storage medium |
| CN109191345A (en) * | 2018-09-17 | 2019-01-11 | 合肥工业大学 | A kind of cognitive diagnosis method of Student oriented cognitive process |
| CN109191929A (en) * | 2018-09-25 | 2019-01-11 | 上海优谦智能科技有限公司 | The intellectual education system of knowledge based map |
| CN109886848A (en) * | 2019-01-30 | 2019-06-14 | 网易（杭州）网络有限公司 | Method, apparatus, medium and the electronic equipment of data processing |
| CN109886848B (en) * | 2019-01-30 | 2021-08-13 | 网易有道信息技术(杭州)有限公司 | Data processing method, device, medium and electronic equipment |
| CN109816265A (en) * | 2019-02-01 | 2019-05-28 | 北京作业盒子科技有限公司 | Evaluation method, topic recommended method and the electronic equipment of knowledge feature master degree |
| CN110197450A (en) * | 2019-05-31 | 2019-09-03 | 上海乂学教育科技有限公司 | Ordinal relation acquisition methods, device, equipment and medium between knowledge point |
| CN110197450B (en) * | 2019-05-31 | 2024-03-08 | 上海松鼠课堂人工智能科技有限公司 | Method, device, equipment and medium for acquiring sequence relation between knowledge points |
| CN110263181B (en) * | 2019-06-17 | 2021-08-06 | 北京作业盒子科技有限公司 | Knowledge structure mining method and learning path planning method |
| CN110263181A (en) * | 2019-06-17 | 2019-09-20 | 北京作业盒子科技有限公司 | The method for digging of the structure of knowledge and the planing method of learning path |
| CN112446009B (en) * | 2019-09-02 | 2022-06-21 | 北京新唐思创教育科技有限公司 | Learning path planning method and device, readable storage medium and server |
| CN112446009A (en) * | 2019-09-02 | 2021-03-05 | 北京新唐思创教育科技有限公司 | Learning path planning method and device, readable storage medium and server |
| CN112446523A (en) * | 2019-09-04 | 2021-03-05 | 北京新唐思创教育科技有限公司 | Learning path planning method, electronic device and computer storage medium |
| CN112765406B (en) * | 2019-11-04 | 2022-11-11 | 亿度慧达教育科技（北京）有限公司 | Learning resource planning method, electronic device, and computer storage medium |
| CN112765406A (en) * | 2019-11-04 | 2021-05-07 | 亿度慧达教育科技（北京）有限公司 | Learning resource planning method, electronic device, and computer storage medium |
| CN112825071A (en) * | 2019-11-21 | 2021-05-21 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN112825147A (en) * | 2019-11-21 | 2021-05-21 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN112825147B (en) * | 2019-11-21 | 2022-08-26 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN112825071B (en) * | 2019-11-21 | 2022-08-30 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN111798138A (en) * | 2020-07-07 | 2020-10-20 | 广州视源电子科技股份有限公司 | Data processing method, computer storage medium and related equipment |
| CN112052342B (en) * | 2020-09-04 | 2023-05-12 | 西南大学 | Learning path recommendation method and system based on online test result big data analysis |
| CN112052342A (en) * | 2020-09-04 | 2020-12-08 | 西南大学 | Learning path recommendation method and system based on online test result big data analysis |
| CN112632295A (en) * | 2020-12-31 | 2021-04-09 | 郑雁 | Learning resource dynamic planning method and device based on knowledge graph |
| CN113361926B (en) * | 2021-06-07 | 2024-02-02 | 江苏师范大学 | Teaching quality evaluation method based on teaching sequence |
| CN113361926A (en) * | 2021-06-07 | 2021-09-07 | 江苏师范大学 | Teaching quality evaluation method based on teaching sequence |
| CN113610237A (en) * | 2021-08-19 | 2021-11-05 | 北京京东乾石科技有限公司 | Learning path planning method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107665473B (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107665473A (en) | Learning path planning method and device | |
| CN107665472A (en) | Learning path planning method and device | |
| Radwan et al. | Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system | |
| CN105824802B (en) | It is a kind of to obtain the method and device that knowledge mapping vectorization indicates | |
| Lykourentzou et al. | Early and dynamic student achievement prediction in e‐learning courses using neural networks | |
| CN111881342A (en) | Recommendation method based on graph twin network | |
| CN104573669A (en) | Image object detection method | |
| CN103679185A (en) | Convolutional neural network classifier system as well as training method, classifying method and application thereof | |
| CN110991532B (en) | Scene graph generation method based on relational visual attention mechanism | |
| CN106600065A (en) | Individualized learning path extraction and jointing method and system based on directed hypergraph | |
| CN107544973A (en) | A kind of method and apparatus that data are handled | |
| CN111737427B (en) | Method for recommending lesson forum posts by combining forum interaction behaviors and user reading preference | |
| Chen et al. | Education 4.0 using artificial intelligence for students performance analysis | |
| CN114201684A (en) | Knowledge graph-based adaptive learning resource recommendation method and system | |
| CN114021722A (en) | Attention knowledge tracking method integrating cognitive portrayal | |
| Isljamovic et al. | Predicting students’ academic performance using artificial neural network: a case study from faculty of organizational sciences | |
| Prasanalakshmi et al. | Classification and prediction of student academic performance in king khalid university-a machine learning approach | |
| CN112598089B (en) | Image sample screening method, device, equipment and medium | |
| Hagedoorn et al. | Massive open online courses temporal profiling for dropout prediction | |
| AU2020102708A4 (en) | Student participation and performance prediction analysis technique during online classes using data mining | |
| Hoq et al. | Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course. | |
| CN113283488A (en) | Learning behavior-based cognitive diagnosis method and system | |
| Gamulin et al. | Comparing classification models in the final exam performance prediction | |
| CN104391828B (en) | The method and apparatus for determining short text similarity | |
| CN107292464B (en) | Automatic evaluation method and system based on directed acyclic graph and topological sorting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |