CN111415089B - Online flat learning result early warning method based on learning degree analysis - Google Patents
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Abstract
The invention discloses an online tablet learning result early warning method based on learning degree analysis, which comprises the following steps: the system comprises a data collector for collecting learning degree characteristic parameters in the whole learning course process, a memory for the learning degree characteristic parameters, a processor for processing data, a control center for performing remote control, and a communication module for communicating the control center and the processor.
Description
Technical Field
The invention relates to the technical field of online learning, in particular to an online tablet learning result early warning method based on learning degree analysis.
Background
In recent years, with the continuous popularization of broadband internet in common families and education institutions, teaching and learning can be free from the limitation of time, space and place conditions, and knowledge acquisition channels are flexible and diversified; in the online education mode, the courseware and teaching materials in the offline learning mode are electronized, visualized and carried to the Internet, so that the learning convenience is improved; however, the quality of the corresponding learning result of the learning course is often difficult to estimate, and the method of simply taking the score as the characteristic of evaluation also lacks scientificity due to different courses and different content requirements under the same course; therefore, the online learning result of the course in the flat teaching process needs to be pre-judged and known so as to achieve the high-quality online learning effect. .
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an online flat learning result early warning method based on learning degree analysis, which comprises the following steps:
have and be used for gathering the data collection station of complete study course process learning degree characteristic parameter for learning degree characteristic parameter's memory, a treater for carrying out data processing, a control center for carrying out remote control, a communication module for control center and treater carry out communication, its characterized in that: the method comprises the following early warning control steps:
1) dividing the online learning course into a plurality of time stages, wherein each time stage has corresponding learning degree characteristic parameters which can be compared, and the data acquisition unit acquires the corresponding learning degree characteristic parameters of each time stage;
2) the processor determines a regression curve of the learning degree of the course by a linear regression method according to a large number of historical numerical values of the course by taking characteristic parameters of the learning degree as input and learning results as output, determines confidence intervals (a, b) of the regression curve, and infers that the learning degree of the region is not abnormal according to historical data, wherein the measured values in the confidence intervals (a, b) indicate that the region is not overproof;
3) determining a characteristic parameter acquisition period for carrying out learning result early warning, and measuring a corresponding learning result sliding average value for the course in the determined acquisition period;
4) setting N measurement periods, generating a learning result sliding average value measurement value of the course based on the time sequence, and storing the learning result sliding average value measurement value in a memory;
5) comparing, by a processor, the time series based learning result moving average measurement to the regression curve, wherein measurements outside the confidence interval (a, b) indicate learning result anomalies for the corresponding lesson;
6) and if the measured value is outside the trust interval, triggering an alarm and sending the alarm center to the control center.
Preferably, the learning result is expressed as an evaluation score in the measurement period corresponding to the course;
preferably, the test question set of the evaluation score is obtained in a mode that:
1) the method comprises the following steps of firstly, carrying out incremental learning by using a cognitive diagnosis model to obtain the skill mastery degree of a student, and calculating a first matching degree of the skill information examined by the test questions and the skill mastery degree of a user:
2) performing incremental learning by using the IRT model to obtain test question difficulty, test question discrimination and section or whole course learning capacity of a student, and calculating a second matching degree of the test question difficulty and the discrimination and the whole capacity of the user;
3) constructing a candidate recommended test question set according to the first matching degree and the second matching degree;
4) and the processor is in a spindle-shaped structure according to the difficulty distribution of the screened test question set, and the screened test question set covers as many examination skills as possible to obtain the final test questions.
Preferably, the measurement period is each class hour of the corresponding course of the tablet online learning system.
Preferably, the lessons can be lessons in different classes, or different content partitions in the same subject.
Preferably, the feature of the learning degree of the face region is represented by: the learning degree characteristic parameters comprise the completion degree of stage learning content, the interaction frequency of stage learning and the completion degree of exercises after stage learning class.
Preferably, the distance between the trust intervals a and b and the regression curve can be the same, and the distance between the trust intervals a and b and the regression curve H can also be automatically adjusted according to the learning degree conditions of different areas; furthermore, the regression curve in a particular example may be a straight line or a hyperplane. The historical data is formed by adopting corresponding learning degree characteristic data under the condition of normal learning results of students of different sexes and ages.
The completion degree of the learning content is expressed as the completion degree of playing in a normal learning state of a student in a course corresponding to an online learning course; the stage learning interaction frequency is expressed as the feedback frequency aiming at the course content, and the exercise completion degree after the stage learning course is expressed as the ratio of the completed exercise to the total exercise.
Compared with the prior art, the invention has the beneficial technical effects that:
according to the requirement of course learning result early warning in the online flat-bed learning process, learning degree data are collected in real time in different course stages and contents, a trust range can be selected according to the characteristics and requirements of different courses or course branch contents by establishing a rough regression curve trust interval, the limitation of taking appraisal as the characteristic of single evaluation is overcome, and the adaptability and the scientificity of learning result monitoring early warning are improved.
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FIG. 1 is a block diagram of the structure of the present invention.
FIG. 2 is a flow chart of the steps of the present invention.
Detailed Description
Have and be used for gathering the data collection station of complete study course process learning degree characteristic parameter for learning degree characteristic parameter's memory, a treater for carrying out data processing, a control center for carrying out remote control, a communication module for control center and treater carry out communication, its characterized in that: the method comprises the following early warning control steps:
1) dividing the online learning course into a plurality of time stages, wherein each time stage has corresponding learning degree characteristic parameters which can be compared, and the data acquisition unit acquires the corresponding learning degree characteristic parameters of each time stage;
2) the processor determines a regression curve of the learning degree of the course by a linear regression method according to a large number of historical numerical values of the course by taking characteristic parameters of the learning degree as input and learning results as output, determines confidence intervals (a, b) of the regression curve, and infers that the learning degree of the region is not abnormal according to historical data, wherein the measured values in the confidence intervals (a, b) indicate that the region is not overproof;
3) determining a characteristic parameter acquisition period for carrying out learning result early warning, and measuring a corresponding learning result sliding average value for the course in the determined acquisition period;
4) setting N measurement periods, generating a learning result sliding average value measurement value of the course based on the time sequence, and storing the learning result sliding average value measurement value in a memory;
5) comparing, by a processor, the time series based learning result moving average measurement to the regression curve, wherein measurements outside the confidence interval (a, b) indicate learning result anomalies for the corresponding lesson;
6) and if the measured value is outside the trust interval, triggering an alarm and sending the alarm center to the control center.
Preferably, the learning result is expressed as an evaluation score in the measurement period corresponding to the lesson.
Preferably, the test question set of the evaluation score is obtained in a mode that:
1) the method comprises the following steps of firstly, carrying out incremental learning by using a cognitive diagnosis model to obtain the skill mastery degree of a student, and calculating a first matching degree of the skill information examined by the test questions and the skill mastery degree of a user:
2) performing incremental learning by using the IRT model to obtain test question difficulty, test question discrimination and section or whole course learning capacity of a student, and calculating a second matching degree of the test question difficulty and the discrimination and the whole capacity of the user;
3) constructing a candidate recommended test question set according to the first matching degree and the second matching degree;
4) and the processor is in a spindle-shaped structure according to the difficulty distribution of the screened test question set, and the screened test question set covers as many examination skills as possible to obtain the final test questions.
Among them, the DINA model is one of the cognitive diagnosis models widely used at present, and the model is relatively simple and has high diagnosis accuracy. The DINA model mainly comprises two project parameters, namely a guess parameter (g) and a fault parameter(s), wherein the g is the probability that the tested item does not know all attributes of the item examination but answers the item; s is the probability of the trial mastering all the attributes of the project assessment but the wrong answer. The parameters s and g reflect to some extent the noise in the diagnosis. In cognitive diagnosis, it is generally considered that if a subject does not grasp all attributes of item assessment, the subject tends to answer the item;
the difficulty of the test questions, the degree of distinction and the overall ability of the user can be obtained by incremental learning according to a traditional IRT (Item Response Theory) model, and the difficulty of the test questions and the degree of distinction can also be given by field experts;
the specific calculation process of the first matching degree is as follows: first, the sum of the difference between the grasping degree of each skill of the user for examination and the grasping degree of the intermediate level is calculated as a first calculation value. For example, when the user grasp degree is expressed by a value between 0 and 1, the intermediate level grasp degree may be expressed by 0.5. The first calculated value is then divided by the total skill in the lesson exam to produce a second calculated value. Finally, the degree of grasp of the user for the examination skill of the test question is expressed by the inverse number of the second calculated value as the degree of grasp at the intermediate level.
The above calculation process can be described by the following formula (1)
Wherein Hi is the degree of closeness between the mastery degree of the skill of the user for examination of the test question i and the mastery degree of the intermediate level; qijFor the examination condition of the examination question i on the skill j, if the examination question i examines the skill j, Qij1, otherwise Qij0; uj is the mastery degree of the user on the skill j, for example, the value is Uj ∈ [0,1]0 is completely not mastered, 1 is completely mastered; σ is a value at which the user's mastery degree of skill is at an intermediate level, and it takes, for example, 0.5.
According to the IRT theory, the larger the information quantity provided by the test questions to the user is, the more suitable the test questions are recommended to the user, the more the calculation of the information quantity of the test questions can be specifically, after the correct answer probability of the test questions to be recommended by the user is calculated according to the overall capacity value of the user, the information quantity of the test questions to be recommended is calculated by using the correct answer probability of the user, and the formula (2) is as follows:
wherein, Ii (θ) represents the information amount provided by the ith test question to the user with the overall physical ability value θ, Pi (θ) represents the correct answering probability of the user with the overall physical ability value θ on the test question i, and the specific calculation formula is as follows:
where θ is the user's overall physical strength value, biIs the difficulty coefficient of the ith test question, aiThe discrimination coefficient is the ith test question.
And constructing a candidate recommended test question set according to the first matching degree and the second matching degree.
Preferably, the measurement period is each class hour of the corresponding course of the tablet online learning system.
Preferably, the lessons can be lessons in different classes, or different content partitions in the same subject.
Preferably, the feature of the learning degree of the face region is represented by: the learning degree characteristic parameters comprise the completion degree of stage learning content, the interaction frequency of stage learning and the completion degree of exercises after stage learning class.
Preferably, the distance between the trust intervals a and b and the regression curve can be the same, and the distance between the trust intervals a and b and the regression curve H can also be automatically adjusted according to the learning degree conditions of different areas; furthermore, the regression curve in a particular example may be a straight line or a hyperplane. The historical data is formed by adopting corresponding learning degree characteristic data under the condition of normal learning results of students of different sexes and ages.
The completion degree of the learning content is expressed as the completion degree of playing in a normal learning state of a student in a course corresponding to an online learning course; the stage learning interaction frequency is expressed as the feedback frequency aiming at the course content, and the exercise completion degree after the stage learning course is expressed as the ratio of the completed exercise to the total exercise.
The present invention has been described in detail, and the principle and embodiments of the present invention are explained herein by using specific examples, which are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (2)
1. The utility model provides an online flat board learning result early warning method based on learning degree analysis, has the data collection station who is used for gathering complete study course process learning degree characteristic parameter, is used for learning degree characteristic parameter's memory, is used for carrying out data processing's treater, is used for carrying out remote control's control center, is used for control center and treater to carry out the communication module of communication, its characterized in that: the method comprises the following early warning control steps:
1) dividing the online learning course into a plurality of time stages, wherein each time stage has corresponding learning degree characteristic parameters which can be compared, and the data acquisition unit acquires the corresponding learning degree characteristic parameters of each time stage;
2) the processor determines a regression curve of the learning degree of the course by a linear regression method according to a large number of historical numerical values of the course by taking characteristic parameters of the learning degree as input and learning results as output, determines confidence intervals (a, b) of the regression curve, and infers that the learning degree of the intervals is not abnormal according to historical data, wherein measured values in the confidence intervals (a, b) indicate that the learning degree does not exceed the standard;
3) determining a characteristic parameter measurement period for carrying out learning result early warning, and measuring a corresponding learning result sliding average value for the course in the determined measurement period;
4) setting N measurement periods, generating a learning result sliding average value measurement value of the course based on the time sequence, and storing the learning result sliding average value measurement value in a memory;
5) comparing, by a processor, the time series based learning result moving average measurement to the regression curve, wherein measurements outside the confidence interval (a, b) indicate learning result anomalies for the corresponding lesson;
6) if the measured value is outside the trust interval, triggering an alarm and sending the alarm center to a control center;
the learning result is expressed as an evaluation score in the measuring period of the corresponding course;
the evaluation score is obtained by the test question set acquisition mode as follows:
1) the method comprises the following steps of firstly, carrying out incremental learning by using a cognitive diagnosis model to obtain the skill mastery degree of a student, and calculating a first matching degree of the skill information examined by the test questions and the skill mastery degree of a user:
calculating a first matching degree of the skill information of the test examination and the skill mastering degree of the user as follows:
wherein HiA first matching degree of skill information examined by the user for the test question i and the skill mastering degree of the user; qijFor the examination condition of the examination question i on the skill j, if the examination question i examines the skill j, QijIf the test question i has no examination skill j, Q is equal to 1ij＝0；UjFor the user's mastery of skill j, UjIs taken as Uj∈[0,1]When U is formedjA value of 0 indicates that the user has not mastered the skill j at all, and U is set tojA value of 1 indicates that the user has fully mastered the skill j; sigma is a value of the skill mastery degree of the user at a middle level, the value of sigma is 0.5, and k is the total skill number examined in the course;
2) performing incremental learning by using the IRT model to obtain test question difficulty, test question discrimination and section or whole course learning capacity of a student, and calculating a second matching degree of the test question difficulty and the discrimination and the whole capacity of the user;
calculating the correct answer probability of the user to the to-be-recommended test questions according to the overall capacity value of the user, wherein the correct answer probability is as follows:
wherein, Pi(theta) represents the probability of correct answer on the test question i by the user with the overall physical ability value theta, theta is the user's overall physical ability value, biIs the difficulty coefficient of the ith test question, aiThe discrimination coefficient of the ith test question is obtained;
calculating a second matching degree of the difficulty and the discrimination of the test questions and the overall ability of the user according to the correct answer probability of the test questions to be recommended by the user, and the following steps are performed:
wherein, Ii(θ)A second matching degree representing the difficulty and the discrimination degree of the ith test question and the overall capability of the user;
3) constructing a candidate recommended test question set according to the first matching degree and the second matching degree;
4) the processor obtains final test questions according to the screening principle that the difficulty distribution of the screened test question set is in a spindle-shaped structure and the screened test question set covers as many examination skills as possible;
the measurement period is each class time of the corresponding course of the flat-panel online learning system;
the characteristic of the learning degree of the face area is represented as: the learning degree characteristic parameters comprise stage learning content completion degree, stage learning interaction frequency and exercise completion degree after a stage learning class, and the learning content completion degree is expressed as the completion degree of on-line learning course corresponding class and playing under the normal learning state of students; the stage learning interaction frequency is expressed as the feedback frequency aiming at the course content, and the exercise completion degree after the stage learning course is expressed as the ratio of the completed exercise to the total exercise;
the trust intervals a and b can have the same distance with the regression curve, and can also adjust the distance with the regression curve H according to the learning degree conditions of different areas; furthermore, the regression curve can be a straight line or a hyperplane, and historical data is formed by adopting corresponding learning degree characteristic data under the condition of normal learning results of students with different sexes and ages.
2. The method of claim 1, wherein: the lessons may be actually different classes of lessons or may be partitions of different content in the same subject.
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| CN106407237A (en) * | 2015-08-03 | 2017-02-15 | 科大讯飞股份有限公司 | An online study test question recommendation method and system |
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| CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
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