CN116306863A - Collaborative knowledge tracking modeling method and system based on contrast learning - Google Patents

Collaborative knowledge tracking modeling method and system based on contrast learning Download PDF

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CN116306863A
CN116306863A CN202310019414.8A CN202310019414A CN116306863A CN 116306863 A CN116306863 A CN 116306863A CN 202310019414 A CN202310019414 A CN 202310019414A CN 116306863 A CN116306863 A CN 116306863A
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崔超然
张春云
马何博
姚羽墨
许浩然
马玉玲
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Abstract

The collaborative knowledge tracking modeling method based on contrast learning comprises collaborative prediction: before each question is made, the students determine the similarity between the historical answer sequences of the students and the historical answer sequences of other students by calculating the intersection ratio, finally, K most similar students are searched, the state of common knowledge in the students and the answer performance at the next moment are extracted, and the historical answer sequences of the original students are cooperated to perform corresponding prediction; contrast study: in the retrieved similar students, the knowledge states are similar when the same item is made, namely, a pair of positive examples is formed by the knowledge states of the similar students and the knowledge states of the original students at the moment, and the knowledge states of the similar students at all other moments and the knowledge states of the original students at the moment form negative examples, so that the representation of each knowledge state is more accurate after the loss function is compared; the invention has the beneficial effects that: the teaching machine can improve the teaching quality and realize personalized teaching.

Description

Collaborative knowledge tracking modeling method and system based on contrast learning
Technical Field
The invention belongs to the field of intelligent education and education big data mining, and particularly relates to a collaborative knowledge tracking modeling method and system based on contrast learning.
Background
Online education is evolving on an unprecedented scale.
Meanwhile, according to the actual situation of each student, a learning plan is formulated, educational resources can be efficiently utilized, learning efficiency and educational output are improved, and the educational means of "teaching in accordance with the material" is deeply concerned by educational workers. In the conventional educational outcome evaluation mechanism, merely evaluating the knowledge mastery level of a student by means of the final score of an examination cannot embody the mastery condition of each knowledge concept by the student. For example, even if the first and second students obtain the same score in the same test question, the two students cannot simply consider that the level of knowledge mastered by the two students is the same, because there may be a case where the student first answers the wrong test question and the student second answers the right. Even if two students make mistakes in the same topic, it is possible that two students make mistakes at different knowledge points examined in the topic. The modeling of the mastering level of students on each knowledge concept is a basic premise for realizing teaching based on the material, implementing personalized teaching and improving the teaching working quality.
Knowledge tracking (Knowledge Tracing, KT) models knowledge mastering conditions of students according to past answering conditions of the students, so that the knowledge mastering degree of the students on knowledge points can be accurately predicted. The knowledge tracking problem may be formed as a group of students S and a group of exercises E in one student answer interaction, where different students are required to answer different exercises to reach a grasp of the relevant knowledge. Each exercise is associated with a specific knowledge concept. The learning order of the student is denoted i= (e_1, a_1), (e_2, a_2), …, (e_n, a_n), where e_t represents the t-th exercise the student answers, a_t represents an error-positive tag (i.e. 1 represents a correct answer, 0 represents a wrong answer), and N represents the sequence length of the learning interaction. The research content of knowledge tracking is to model the answer sequence of the student to obtain the knowledge state of the student (the mastering degree of the student on each knowledge point) at each moment, so that the probability of answering the question by the student can be predicted according to the knowledge state of the student before the student makes the next question.
Most of methods based on deep neural networks only use the student's own historical answer sequences to predict and use simple sequence models to represent knowledge states at each moment. This approach is too simple, does not fully exploit the information, and does not distinguish well between representations of knowledge states, which greatly limits further improvement of model performance. In a real environment, when a teacher judges whether a student can answer a current question, the teacher can refer to the historical answer performance of the student, meanwhile, the performance of similar students on the question can be considered, and multiple information is synthesized to perform corresponding judgment.
Disclosure of Invention
In order to solve the defects in the prior art, improve the teaching work quality and realize personalized teaching, the invention discloses a collaborative knowledge tracking modeling method and system based on contrast learning; according to the invention, the sequence modeling is performed by using the historical answer sequences of the students and the historical answer sequences of similar students, the obtained two knowledge state representations and the question making representation of the similar students on the problem are fused, and the representation of the further accurate knowledge state is used for comparison learning, so that the comprehensive representation is finally obtained to predict the answer representation of the students, and the accuracy of model prediction is improved.
The application provides a collaborative knowledge tracking modeling method based on contrast learning, which comprises collaborative prediction: before each question is made, the students search all the students who have made the question or the concept, the similarity between the historical answer sequences of the students and the historical answer sequences of the original students is determined through calculating the intersection ratio, finally K most similar students are searched, the common knowledge states of the students and the answer expression at the next moment are extracted, and the historical answer sequences of the original students are cooperated to perform corresponding prediction;
contrast study: in the retrieved similar students, the knowledge states are similar when the same item is made, namely, a pair of positive examples is formed by the knowledge states of the similar students and the knowledge states of the original students at the moment, and the knowledge states of the similar students at all other moments and the knowledge states of the original students at the moment form negative examples, so that the representation of each knowledge state is more accurate after the loss function is compared;
the method also comprises the following steps:
(1) Problem definition and data preprocessing;
(2) Retrieving similar students;
(3) Modeling a gating unit LSTM;
(4) Contrast learning;
(5) Collaborative prediction, loss calculation and optimization functions.
Preferably, in the problem definition and the data preprocessing, in the two data sets, namely, the ASSIST2009 and the ASSIST2017, the actual student answer sequences often have the situations of short answer sequences (the number of the answers is less than 2), blank values (the questions lack knowledge points or the knowledge points do not have corresponding questions), and the like. For shorter sequences of student sample data, and for information with incomplete data, removal is required during the preprocessing stage. For the EdNet dataset, since the original data is too huge (about 78 thousands of students data), 5000 students were randomly selected from the original data, and then the processing manner is the same as the two datasets. In order to intuitively show the answer sequences of students, the answer sequence of each student in the data set is represented by four rows: the number of answers of the first behavior answer sequence; question numbers of the second behavior answer sequence; knowledge points corresponding to the third behavior question numbers; and fourth, student answer pair or not (1 is answer pair, 0 is answer mistake).
Preferably, the similar students are searched, namely before each question is made by the students, all the students who have made the question or the concept are searched, the similarity between the historical answer sequences of the students and the historical answer sequences of the original students is determined by calculating the intersection ratio, and finally the K most similar students are searched.
Preferably, the gating unit LSTM modeling includes:
Figure SMS_1
Figure SMS_2
Figure SMS_3
Figure SMS_4
Figure SMS_5
h t ＝o t *tanh(C t )
preferably, the collaborative prediction, loss calculation and optimization function includes:
(1) Obtaining knowledge state h of original student at time t in LSTM t Knowledge state c of the same student before doing the same question t And the performance r of similar students on the current questions t+1 And splice the threeObtaining v t . Next, v t And weight matrix W yv Bias b y Calculating to obtain y after sigmoid function sigma (& gt) t+1 ，y t+1 Is a matrix containing N number of questions, the probability of each question corresponding to a pair (between 0 and 1):
y t+1 ＝σ(W yv v t +b y )
will y t+1 A thinking answer to the underlying question (i.e., q) with a predictive value of 0.5 or more t+1 =1), consider the solution to be wrong (q t+1 =0). And true (q t+1 ，a t+1 ) Comparing, calculating loss
Figure SMS_6
Figure SMS_7
(2) In a learning sequence of N students in a minimatch, there are n×l knowledge states, and then there are n×l similar knowledge states correspondingly, we will know knowledge state h of a student at time t t And similar knowledge state c at this time t As a pair of positive examples, and the remaining 2 (N. Times.L-1) knowledge states as negative examples, a contrast loss was calculated
Figure SMS_8
Figure SMS_9
Figure SMS_10
Figure SMS_11
Figure SMS_12
And optimizing the total loss of the function by Adam
Figure SMS_13
Figure SMS_14
The collaborative knowledge tracking modeling system based on contrast learning comprises a terminal device and a server, wherein the server comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor realizes all the methods when executing the program.
Compared with the prior art, the invention has the following beneficial effects:
1. by searching out similar students to conduct collaborative prediction, compared with the traditional method for predicting by only using own historical answer sequences, the method provided by the invention has the advantage that the related information of the similar students is added, so that the knowledge state of the students can be expressed more accurately.
2. The method can obtain a better knowledge state representation by utilizing the relationship between the knowledge states of similar students compared with the traditional method of modeling by using a simple sequence model only. Compared with the existing method, the accuracy of the knowledge tracking model can be remarkably improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is an AUC index value for each of the methods of the present invention.
FIG. 2 is a schematic representation of the process of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: collaborative knowledge tracking modeling method based on contrast learning
The specific implementation steps are as follows:
(1) Problem definition and data preprocessing
In the prior art, no matter how to optimize a sequence model and add auxiliary information, similar student information is not considered for collaborative prediction, but actually, in a real environment, when a teacher judges whether a student can answer a current question, the teacher can refer to the historical answer performance of the student, meanwhile, the performance of the similar student is considered, and multiple kinds of information are synthesized for corresponding judgment.
The invention is mainly used for experiments on three published data sets commonly used for knowledge tracking problems. In two data sets, namely an ASSIST2009 and an ASSIST2017, the actual student answer sequences often have the situations of short answer sequences (the number of the answers is less than 2), blank values (the test questions lack of knowledge points or the knowledge points do not have corresponding test questions) and the like. For shorter sequences of student sample data, and for information with incomplete data, removal is required during the preprocessing stage. For the EdNet dataset, since the original data is too huge (about 78 thousands of students data), 5000 students were randomly selected from the original data, and then the processing manner is the same as the two datasets. In order to intuitively show the answer sequences of students, the answer sequence of each student in the data set is represented by four rows: the first line is the number of answers of the answer sequence; the second line is the question number of the answer sequence; the third row is the knowledge point corresponding to the question number; the fourth line is the student answer pair or not (1 is answer pair, 0 is answer error).
(2) Retrieving similar students
Interaction at time i for any one student u
Figure SMS_15
Historical sequence
Figure SMS_16
We retrieve all records from the dataset that meet the following conditions as candidate records
Figure SMS_17
(1) The two interaction records come from different students, namely
Figure SMS_18
(2) The two interactions have the same concept or problem, i.e. c i ＝c j or e i ＝e j
All candidate recorded students are similar students, and their history sequence is
Figure SMS_19
Figure SMS_20
To further obtain the K students that have the most similar information at student u's time i, we use the intersection ratio to calculate the similarity between the current student and the candidate students, and select the K students with the largest intersection ratio as similar students.
Figure SMS_21
(3) Gated loop unit LSTM modeling
The method solves the problem that the number of answers of each student is different, the step length is set to be 50 (less than 50 is filled and supplemented by zero), each 50 questions are in a group, each interaction record is initialized and expressed in the following mode, and then the interaction records are input into a gating circulation unit LSTM, and the obtained output is mapped into N-dimensional representation to obtain the probability of each question answer pair.
Figure SMS_22
Figure SMS_23
Figure SMS_24
Figure SMS_25
Figure SMS_26
Figure SMS_27
h t ＝o t *tanh(C t )
(4) Contrast learning
In a learning sequence of N students in a minimatch, there are n×l knowledge states, and correspondingly n×l similar knowledge states exist, we will know knowledge state h at time t of a student t And similar knowledge state c at this time t As a pair of positive examples, the remaining 2 (N x L-1) knowledge states are taken as negative examples.
Figure SMS_28
Figure SMS_29
sim(u,v)＝u T v/|u||v|
(5) Collaborative prediction, loss calculation and optimization function
Obtaining knowledge state h of original student at time t in LSTM t Knowledge state c of the same student before doing the same question t Representation r of similar students on the current question t+1 And splicing the three to obtain v t . Next, v t And weight matrix W yv Bias b y Calculating to obtain y after sigmoid function sigma (& gt) t+1 ，y t+1 Is a matrix containing N number of questions, the probability of each question corresponding to a pair (between 0 and 1):
y t+1 ＝σ(W yv v t +b y )
will y t+1 A thinking answer to the underlying question (i.e., q) with a predictive value of 0.5 or more t+1 =1), consider the solution to be wrong (q t+1 =0). And true (q t+1 ，a t+1 ) Comparing, calculating loss
Figure SMS_30
Figure SMS_31
Calculation of contrast loss
Figure SMS_32
Figure SMS_33
And optimizing the total loss of the function by Adam
Figure SMS_34
Figure SMS_35
Simulation verification is carried out on the implementation method, and fig. 1 shows the performance of the method on the public data sets ASSIST2009, ASSIST2017 and EdNet, and compared with the existing 4 depth knowledge tracking modeling methods (respectively marked as DKT, DKVMN, SAKT, AKT), the method has better performance. The invention utilizes AUC index to measure the performance of each method, wherein AUC (Area Under Curve) is a common index for measuring the performance of a model method, and the meaning of the AUC index is the area of an area surrounded by an ROC curve and a coordinate axis.
Embodiment two: collaborative knowledge tracking modeling system based on contrast learning
In one or more embodiments, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the two-channel graph neural network-based knowledge tracking modeling method of embodiment one when executing the program. For brevity, the description is omitted here. It should be understood that the processor in this embodiment may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The knowledge tracking modeling method based on the dual-channel graph neural network in the first embodiment can be directly embodied as the execution completion of a hardware processor or the execution completion of the combination execution of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Finally, it should be noted that: those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware; whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution; skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application; while the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.

Claims (6)

1. The collaborative knowledge tracking modeling method based on contrast learning is characterized by comprising the following steps of: including collaborative prediction: before each question is made, the students search all the students who have made the question or the concept, the similarity between the historical answer sequences of the students and the historical answer sequences of the original students is determined through calculating the intersection ratio, finally K most similar students are searched, the common knowledge states of the students and the answer expression at the next moment are extracted, and the historical answer sequences of the original students are cooperated to perform corresponding prediction;
contrast study: the knowledge states of similar students are similar when doing the same item, namely, the knowledge states of the similar students and the knowledge states of the original students at the moment form a pair of positive examples, the knowledge states of the corresponding other moment and the knowledge states of the original students at the moment form negative examples, and the representation of each knowledge state is more accurate after the loss function is compared;
the method also comprises the following steps:
(1) Problem definition and data preprocessing;
(2) Retrieving similar students;
(3) Modeling a gating unit LSTM;
(4) Contrast learning;
(5) Collaborative prediction, loss calculation and optimization functions.
2. The collaborative knowledge tracking modeling method based on contrast learning according to claim 1, wherein: the problem definition and the data preprocessing are that in two data sets, namely an ASSIST2009 and an ASSIST2017, in order to intuitively embody the answer sequences of students, the answer sequences of each student in the data sets are represented by four rows: the number of answers of the first behavior answer sequence; question numbers of the second behavior answer sequence; knowledge points corresponding to the third behavior question numbers; fourth behavior students answer or not.
3. The collaborative knowledge tracking modeling method based on contrast learning according to claim 1, wherein: before the students do each question, the similar students are searched, all the students doing the question or the concept are searched, the similarity between the historical answer sequences of the students and the historical answer sequences of the original students is determined through calculating the intersection ratio, and finally the K most similar students are searched.
4. The collaborative knowledge tracking modeling method based on contrast learning according to claim 1, wherein: the gating cell LSTM modeling includes:
Figure FDA0004041906730000021
Figure FDA0004041906730000022
Figure FDA0004041906730000023
Figure FDA0004041906730000024
Figure FDA0004041906730000025
h t ＝o t *tanh(C t )。
5. the collaborative knowledge tracking modeling method based on contrast learning according to claim 1, wherein: the collaborative prediction, loss calculation and optimization function includes:
(1) Obtaining knowledge state h of original student at time t in LSTM t Knowledge state c of the same student before doing the same question t And the performance r of similar students on the current questions t+1 And splicing the three to obtain v t . Next, v t And weight matrix W yv Bias b y Calculating to obtain y after sigmoid function sigma (& gt) t+1 ，y t+1 Is a matrix containing N number of questions, the probability of each question corresponding to a pair (between 0 and 1):
y t+1 ＝σ(W yv v t +b y )
will y t+1 A thinking answer to the underlying question (i.e., q) with a predictive value of 0.5 or more t+1 =1), consider the solution to be wrong (q t+1 =0). And true (q t+1 ，a t+1 ) Comparing, calculating loss
Figure FDA0004041906730000031
Figure FDA0004041906730000032
(2) N students in a minibandIn the learning sequence of (a), there are n×l knowledge states, so there are n×l similar knowledge states correspondingly, we will be a knowledge state h of student at time t t And similar knowledge state c at this time t As a pair of positive examples, and the remaining 2 (N. Times.L-1) knowledge states as negative examples, a contrast loss is calculated
Figure FDA0004041906730000033
Figure FDA0004041906730000034
Figure FDA0004041906730000035
Figure FDA0004041906730000036
Figure FDA0004041906730000037
And optimizing the total loss of the function by Adam
Figure FDA0004041906730000038
Figure FDA0004041906730000039
6. Collaborative knowledge tracking modeling system based on contrast learning, including a terminal equipment, its characterized in that: the collaborative knowledge tracking modeling method based on the contrast learning comprises the server, wherein the server comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and the processor realizes the collaborative knowledge tracking modeling method based on the contrast learning when executing the program.
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Publication number Priority date Publication date Assignee Title
CN117077737A (en) * 2023-08-22 2023-11-17 长江大学 Knowledge tracking system for dynamic collaboration of knowledge points
CN117077737B (en) * 2023-08-22 2024-03-15 长江大学 Knowledge tracking system for dynamic collaboration of knowledge points
CN117744783A (en) * 2024-01-29 2024-03-22 暨南大学 Knowledge tracking method and system based on man-in-the-loop

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