CN111368182A - Individualized self-adaptive learning recommendation method based on big data analysis of education platform - Google Patents

Individualized self-adaptive learning recommendation method based on big data analysis of education platform Download PDF

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CN111368182A
CN111368182A CN202010097373.0A CN202010097373A CN111368182A CN 111368182 A CN111368182 A CN 111368182A CN 202010097373 A CN202010097373 A CN 202010097373A CN 111368182 A CN111368182 A CN 111368182A
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郑洪涛
江华清
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Hebei Canglan Education Technology Group Co ltd
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Abstract

The invention provides an individualized self-adaptive learning recommendation method based on big data analysis of an education platform, which comprises the following steps: step 1: obtaining historical learning record data of learners from the big data of the education platform; step 2: analyzing historical learning record data based on knowledge point classification to obtain a comprehensive knowledge map of the learner; calculating the learning efficiency of the learner according to historical learning record data; and step 3: analyzing the comprehensive knowledge map, and making a learning plan based on the learning time and the learning efficiency of the learner; the learning plan is recommended to the learner. The invention discloses an individualized self-adaptive learning recommendation method based on education platform big data analysis.

Description

Individualized self-adaptive learning recommendation method based on big data analysis of education platform
Technical Field
The invention relates to the technical field of recommendation methods, in particular to a personalized self-adaptive learning recommendation method based on education platform big data analysis.
Background
Currently, big data is a technology for mining and analyzing a large amount of data to find out rules and solutions.
With the gradual development of big data technology, big data of an education platform applied to the education field is increasingly completed, all data of each student from the last school are required to be stored in the big data of the education platform, and with the mining of the big data of the education platform, the research work of online learning personalized recommendation of learners is still insufficient. The existing learning effect evaluation method is to perform simple grade division, and needs to establish a knowledge graph for each student urgently, so as to provide personalized learning plan recommendation for each student (learner).
Disclosure of Invention
The invention aims to provide a personalized self-adaptive learning recommendation method based on the analysis of big data of an education platform.
The embodiment of the invention provides an individualized adaptive learning recommendation method based on big data analysis of an education platform, which comprises the following steps:
step 1: obtaining historical learning record data of learners from the big data of the education platform;
step 2: analyzing historical learning record data based on knowledge point classification to obtain a comprehensive knowledge map of the learner; calculating the learning efficiency of the learner according to historical learning record data;
and step 3: analyzing the comprehensive knowledge map, and making a learning plan based on the learning time and the learning efficiency of the learner; the learning plan is recommended to the learner.
Preferably, the personalized adaptive learning recommendation method based on the big data analysis of the education platform further comprises the following steps:
and 4, step 4: receiving an instruction that a learner agrees to execute a learning plan; acquiring corresponding learning materials from the big data of the education platform according to the learning plan, and playing the learning materials;
and 5: after the learning material is played, acquiring a training exercise question set corresponding to the learning material from the big data of the education platform and displaying the training exercise question set;
step 6: obtaining the answer of the learner to the exercise problem set and judging the score, and analyzing the learning efficiency of the learning and the knowledge mastery degree evaluation of the learner based on the learning time, the learning materials and the judgment score of the learner to the learning plan;
and 7: and receiving the wrong question analysis of the learner on the exercise problem centralized exercise problems and sending the wrong question analysis to the big data of the education platform.
And 8: and receiving confirmation of the big data of the education platform on wrong question analysis and displaying the confirmation to the learner.
Preferably, step 8: receiving the confirmation of the big data of the education platform on the wrong question analysis and displaying the confirmation to the learner, wherein the method specifically comprises the following steps:
when the learner analyzes the error of the exercise to be unclear error points, the learner directly obtains correct analysis materials of the exercise from the big data of the education platform and plays the materials;
when the wrong question analysis is correct, the output analysis is correct;
when the wrong question is analyzed to be wrong, the analysis mistake is output, and correct analysis materials of the practice questions are played.
Preferably, when the learning material is played, the control instruction of the learner is received, and the control instruction comprises one or more of pause, backward, fast forward and skip.
Preferably, the analyzing the historical learning record data based on knowledge point classification to obtain the comprehensive knowledge map of the learner specifically comprises:
obtaining a comprehensive knowledge mapping template,
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
classifying historical learning record data according to knowledge points; calculating the mastery value of each basic knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000031
wherein Z isiA grasp value indicating the ith basic knowledge point; n is a radical ofi0Representing the number of training questions of the ith basic knowledge point in the historical learning record data; n is a radical ofi1Representing the correct number of the trainees to the training questions of the ith basic knowledge point in the historical learning record data; n is a first preset value; n is a radical ofi2Representing the correct number of the trainees to the last N training questions of the ith basic knowledge point in the historical learning record data;
calculating the mastered value of each comprehensive knowledge point based on the degree of relationship between the comprehensive knowledge point and the basic knowledge point and the mastered value of each basic knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000032
wherein Z isjA grasp value representing the jth integrated knowledge point; a. thej0Representing the training question number of the jth comprehensive knowledge point in the historical learning record data; a. thei1Representing the correct number of the trainees to the training questions of the jth integrated knowledge point in the historical learning record data; a is a second preset value; a. thei2Representing the correct number of the trainees to the last A training questions of the jth integrated knowledge point in the historical learning record data; zmA grasp value indicating an mth basic knowledge point associated with the jth integrated knowledge point; a is a preset correction weight; bmA weight representing an association of an mth basic knowledge point associated with a jth integrated knowledge point;
filling the calculated mastery values of the knowledge points into a comprehensive knowledge graph module; forming the comprehensive knowledge map of the learner.
Preferably, the calculating the learning efficiency of the learner according to the historical learning record data specifically includes:
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
calculating the mastery value of each basic knowledge point based on the standard learning time of each basic knowledge point and the learning time of the learner for each basic knowledge point in the historical learning record data; the calculation formula is as follows:
Figure BDA0002385575710000041
wherein, YiRepresenting the learning efficiency of the ith basic knowledge point; t isi0Representing a standard learning time for the ith basic knowledge point; t isi1Representing the learning time of the learner for the ith basic knowledge point in the historical learning record data;
calculating first learning efficiency of each comprehensive knowledge point based on standard learning time of each comprehensive knowledge point and learning time of a learner for each comprehensive knowledge point in historical learning record data, correcting the first learning efficiency based on the relationship degree of the comprehensive knowledge points and the basic knowledge points and the learning efficiency of each basic knowledge point, and calculating the learning efficiency of each comprehensive knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000042
wherein, YjRepresenting the learning efficiency of the jth integrated knowledge point; t isj0Representing a standard learning time for a jth integrated knowledge point; t isj1Representing the learning time of the learner for the jth integrated knowledge point in the historical learning record data; y ismRepresenting the learning efficiency of the mth basic knowledge point associated with the jth integrated knowledge point; d is a preset learning efficiency correction weight; bmRepresenting an association weight of an mth basic knowledge point associated with the jth integrated knowledge point;
calculating the learning efficiency of the learner based on the learning efficiency of all the basic knowledge points and the learning efficiency of all the comprehensive knowledge points, wherein the calculation formula is as follows:
Figure BDA0002385575710000043
wherein Y represents the learning efficiency of the learner αiEfficiency of learning representing ith basic knowledge pointThe weight of (c); y isiIndicating the learning efficiency of the ith basic knowledge point βjA weight representing the learning efficiency of the jth basic knowledge point; y isjThe learning efficiency of the jth basic knowledge point is shown.
Preferably, the comprehensive knowledge map is analyzed, and a learning plan is formulated based on learning time and learning efficiency, and the method specifically comprises the following steps:
comparing the comprehensive knowledge map with a pre-stored standard knowledge map set, and when the comprehensive knowledge map is consistent with one standard knowledge map, acquiring a new knowledge point set corresponding to the standard knowledge map, a knowledge point set needing to be reviewed, a first learning time required for learning the new knowledge point set and knowledge points in the knowledge point set needing to be reviewed;
acquiring the learning time input by the learner as second learning time, and comparing the first learning time with the second learning time;
deleting the knowledge points corresponding to the first learning time with the time value larger than the time value of the second learning time from the new knowledge point set or the knowledge point set needing to be reviewed, and obtaining the new knowledge point set as a first knowledge point set and the knowledge point set needing to be reviewed as a second knowledge point set; the first knowledge point set is a set formed by all the first knowledge points; the second knowledge point set is a set formed by all second knowledge points;
based on the first set of knowledge points and the second set of knowledge points; enumerating a plurality of first learning plans, the first learning plans comprising at least one first knowledge point and at least one second knowledge point;
confirming a third learning time of each first learning plan based on the first learning time, wherein the calculation formula of the third learning time is as follows:
Figure BDA0002385575710000051
wherein t represents a third learning time; t is t1iFirst knowledge point representing ith first knowledge point in first learning planLearning time; t is t2jA first learning time representing a jth second knowledge point within the first learning plan; t is t0Taking a preset time value as the connection time of two knowledge points in the first plan; the connection time is the pause time between two preset knowledge points;
and screening a third time with the time value smaller than that of the second time and selecting the first learning plan with the minimum difference value with the second time to recommend to the learner.
Preferably, the learning efficiency of the learning and the knowledge mastery degree evaluation of the learner are analyzed based on the learning time of the learner on the learning plan, the learning materials and the learning problems; the method specifically comprises the following steps:
acquiring a first score from a first score table stored in advance based on the learning time of the learner on the learning plan;
acquiring a second score from a pre-stored second score table based on the learning materials;
obtaining a third score from a pre-stored third score table based on the scores and the types of the exercise questions;
obtaining knowledge mastery degree evaluation of the learner from a pre-stored knowledge mastery table based on the sum of the scores of the first score, the second score and the third score;
and acquiring the learning efficiency of the learning from a pre-stored learning efficiency evaluation table based on the learning time, the learning materials, the type of the practice problem and the score of the learner on the learning plan.
Preferably, the practice problems analyzed as errors are collected and integrated into a new practice problem set to be recommended to the learner for practice when a preset time elapses or when a preset number is reached.
The preferred individualized adaptive learning recommendation method based on the education platform big data analysis further comprises the following steps:
and step 9: and when the comprehensive knowledge map of the learner reaches a preset and stored perfect learning comprehensive knowledge map, counting the times of wrong exercises of the learner on the exercise questions of each knowledge point, extracting the exercise questions with the preset times of wrong exercises, integrating the exercise questions into a new exercise question set, and recommending the new exercise question set to the learner for exercise.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram of a personalized adaptive learning recommendation method based on education platform big data analysis in the embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a personalized self-adaptive learning recommendation method based on big data analysis of an education platform, which comprises the following steps of:
step 1: obtaining historical learning record data of learners from the big data of the education platform;
step 2: analyzing historical learning record data based on knowledge point classification to obtain a comprehensive knowledge map of the learner; calculating the learning efficiency of the learner according to historical learning record data;
and step 3: analyzing the comprehensive knowledge map, and making a learning plan based on the learning time and the learning efficiency of the learner; the learning plan is recommended to the learner.
The working principle and the beneficial effects of the technical scheme are as follows:
obtaining historical learning record data of learners from the big data of the education platform; analyzing historical learning record data based on knowledge point classification, wherein the knowledge points are mainly divided into basic knowledge points and comprehensive knowledge points, and the comprehensive knowledge points are organic combinations of the basic knowledge points to obtain a comprehensive knowledge map of the learner; the comprehensive knowledge map mainly displays the knowledge point condition currently mastered by the learner; calculating the learning efficiency of the learner according to historical learning record data; analyzing the comprehensive knowledge map, and making a learning plan based on the learning time and the learning efficiency of the learner; the learning plan is recommended to the learner. The education big data comprises big data such as learning materials which are suitable for learning sections of students, learning deviation of each student uploaded by a student terminal, teaching characteristics of each teacher uploaded by a teacher terminal, historical learning record data (mainly appraisal of answers and learning videos) of each student, learning materials corresponding to various knowledge maps and the like. The comprehensive knowledge map is a map which is refined to describe the knowledge system of the student by each knowledge point, and the knowledge points which are not learned by the student and the mastery degree of the learned knowledge points can be directly seen from the map; the learning efficiency mainly reflects the time of receiving new knowledge points by students.
The invention discloses an individualized self-adaptive learning recommendation method based on education platform big data analysis.
In one embodiment, the personalized adaptive learning recommendation method based on the education platform big data analysis further comprises the following steps:
and 4, step 4: receiving an instruction that a learner agrees to execute a learning plan; acquiring corresponding learning materials from the big data of the education platform according to the learning plan, and playing the learning materials;
and 5: after the learning material is played, acquiring a training exercise question set corresponding to the learning material from the big data of the education platform and displaying the training exercise question set;
step 6: obtaining the answer of the learner to the exercise problem set and judging the score, and analyzing the learning efficiency of the learning and the knowledge mastery degree evaluation of the learner based on the learning time, the learning materials and the judgment score of the learner to the learning plan;
and 7: and receiving the wrong question analysis of the learner on the exercise problem centralized exercise problems and sending the wrong question analysis to the big data of the education platform.
And 8: and receiving confirmation of the big data of the education platform on wrong question analysis and displaying the confirmation to the learner.
The working principle and the beneficial effects of the technical scheme are as follows:
acquiring corresponding learning materials from the big data of the education platform based on the learning plan, and playing the learning materials; the learning materials comprise explanation videos, PPT, books and the like; after the exercise of the exercise question set is carried out, the learner carries out wrong question analysis on the wrong question, so that the learner can carry out a retrusive process; this helps the learner to master the knowledge point content.
To provide the learner with a better understanding of the knowledge points contained in the learning material, in one embodiment, step 8: receiving the confirmation of the big data of the education platform on the wrong question analysis and displaying the confirmation to the learner, wherein the method specifically comprises the following steps:
when the learner analyzes the error of the exercise to be unclear error points, the learner directly obtains correct analysis materials of the exercise from the big data of the education platform and plays the materials;
when the wrong question analysis is correct, the output analysis is correct;
when the wrong question is analyzed to be wrong, the analysis mistake is output, and correct analysis materials of the practice questions are played.
To facilitate the learner's autonomous control of the learning material, in one embodiment, learner control instructions are received while playing the learning material, the control instructions including one or more combinations of pause, rewind, fast forward, and skip.
When the learner feels that the knowledge points played by the learning materials are mastered, the fast forwarding can be realized; when the learner feels that the learning materials are not heard, the learning materials can be played again after going backwards; when the learner has other things to interrupt learning, the learner can pause; and the node of the control instruction is a boundary point between the knowledge point and the knowledge point in the learning material. I.e. fast forward starts from the current knowledge point to the next knowledge point; backing off is to start from the current knowledge point to the last knowledge point; a pause is a stop at a point in time between two knowledge points.
In one embodiment, the parsing the historical learning record data based on the knowledge point classification to obtain the comprehensive knowledge map of the learner specifically includes:
obtaining a comprehensive knowledge mapping template,
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
classifying historical learning record data according to knowledge points; calculating the mastery value of each basic knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000091
wherein Z isiA grasp value indicating the ith basic knowledge point; n is a radical ofi0Representing the number of training questions of the ith basic knowledge point in the historical learning record data; n is a radical ofi1Representing the correct number of the trainees to the training questions of the ith basic knowledge point in the historical learning record data; n is a first preset value; n is a radical ofi2Representing the correct number of the trainees to the last N training questions of the ith basic knowledge point in the historical learning record data;
calculating the mastered value of each comprehensive knowledge point based on the degree of relationship between the comprehensive knowledge point and the basic knowledge point and the mastered value of each basic knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000092
wherein Z isjA grasp value representing the jth integrated knowledge point; a. thej0Representing the training question number of the jth comprehensive knowledge point in the historical learning record data; a. thei1Representing the correct number of the trainees to the training questions of the jth integrated knowledge point in the historical learning record data; a is a second preset value; a. thei2Representing the correct number of the trainees to the last A training questions of the jth integrated knowledge point in the historical learning record data; zmA grasp value indicating an mth basic knowledge point associated with the jth integrated knowledge point; a is a preset correction weight; bmA weight representing an association of an mth basic knowledge point associated with a jth integrated knowledge point;
filling the calculated mastery values of the knowledge points into a comprehensive knowledge graph module; forming the comprehensive knowledge map of the learner.
The working principle and the beneficial effects of the technical scheme are as follows:
through the classification analysis of the basic knowledge points and the comprehensive knowledge points, the comprehensive knowledge map of the learner can be better established, and an adaptive learning plan can be conveniently made.
In one embodiment, the calculating the learning efficiency of the learner according to the historical learning record data specifically includes:
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
calculating the mastery value of each basic knowledge point based on the standard learning time of each basic knowledge point and the learning time of the learner for each basic knowledge point in the historical learning record data; the calculation formula is as follows:
Figure BDA0002385575710000101
wherein, YiRepresenting the learning efficiency of the ith basic knowledge point; t isi0Representing a standard learning time for the ith basic knowledge point; t isi1Representing the learning time of the learner for the ith basic knowledge point in the historical learning record data;
calculating first learning efficiency of each comprehensive knowledge point based on standard learning time of each comprehensive knowledge point and learning time of a learner for each comprehensive knowledge point in historical learning record data, correcting the first learning efficiency based on the relationship degree of the comprehensive knowledge points and the basic knowledge points and the learning efficiency of each basic knowledge point, and calculating the learning efficiency of each comprehensive knowledge point; the calculation formula is as follows:
Figure BDA0002385575710000102
wherein, YjRepresenting the learning efficiency of the jth integrated knowledge point; t isj0Representing a standard learning time for a jth integrated knowledge point; t isj1Representing the learning time of the learner for the jth integrated knowledge point in the historical learning record data; y ismRepresenting the learning efficiency of the mth basic knowledge point associated with the jth integrated knowledge point; d is a preset learning efficiency correction weight; bmRepresenting an association weight of an mth basic knowledge point associated with the jth integrated knowledge point;
calculating the learning efficiency of the learner based on the learning efficiency of all the basic knowledge points and the learning efficiency of all the comprehensive knowledge points, wherein the calculation formula is as follows:
Figure BDA0002385575710000111
wherein Y represents the learning efficiency of the learner αiA weight representing the learning efficiency of the ith basic knowledge point; y isiIndicating the learning efficiency of the ith basic knowledge point βjA weight representing the learning efficiency of the jth basic knowledge point; y isjThe learning efficiency of the jth basic knowledge point is shown.
The working principle and the beneficial effects of the technical scheme are as follows:
through the classification processing of the common knowledge points and the comprehensive knowledge points, the learning efficiency of the learner can be more accurately determined, and an adaptive learning plan can be conveniently made.
In one embodiment, analyzing the integrated knowledge map and making a learning plan based on the learning time and the learning efficiency of the learner specifically comprises:
comparing the comprehensive knowledge map with a pre-stored standard knowledge map set, and when the comprehensive knowledge map is consistent with one standard knowledge map, acquiring a new knowledge point set corresponding to the standard knowledge map, a knowledge point set needing to be reviewed, a first learning time required for learning the new knowledge point set and knowledge points in the knowledge point set needing to be reviewed;
acquiring the learning time input by the learner as second learning time, and comparing the first learning time with the second learning time;
deleting the knowledge points corresponding to the first learning time with the time value larger than the time value of the second learning time from the new knowledge point set or the knowledge point set needing to be reviewed, and obtaining the new knowledge point set as a first knowledge point set and the knowledge point set needing to be reviewed as a second knowledge point set; the first knowledge point set is a set formed by all the first knowledge points; the second knowledge point set is a set formed by all second knowledge points;
based on the first set of knowledge points and the second set of knowledge points; enumerating a plurality of first learning plans, the first learning plans comprising at least one first knowledge point and at least one second knowledge point;
confirming a third learning time of each first learning plan based on the first learning time, wherein the calculation formula of the third learning time is as follows:
Figure BDA0002385575710000121
wherein t represents a third learning time; t is t1iA first learning time representing an ith first knowledge point within the first learning plan; t is t2jA first learning time representing a jth second knowledge point within the first learning plan; t is t0Taking a preset time value as the connection time of two knowledge points in the first plan;
the connection time is the pause time between two preset knowledge points;
and screening a third time with the time value smaller than that of the second time and selecting the first learning plan with the minimum difference value with the second time to recommend to the learner.
The working principle and the beneficial effects of the technical scheme are as follows:
and analyzing the comprehensive knowledge map based on the learning time of the learner so as to formulate an individualized and self-adaptive learning plan.
In order to make the learner understand the effect of the learning and provide a data basis for the subsequent learning plan formulation; in one embodiment, the learning efficiency of the learning and the knowledge mastery degree evaluation of the learner are analyzed based on the learning time, the learning materials and the learning problems of the learner on the learning plan; the method specifically comprises the following steps:
acquiring a first score from a first score table stored in advance based on the learning time of the learner on the learning plan;
acquiring a second score from a pre-stored second score table based on the learning materials;
obtaining a third score from a pre-stored third score table based on the scores and the types of the exercise questions;
obtaining knowledge mastery degree evaluation of the learner from a pre-stored knowledge mastery table based on the sum of the scores of the first score, the second score and the third score;
and acquiring the learning efficiency of the learning from a pre-stored learning efficiency evaluation table based on the learning time, the learning materials, the type of the practice problem and the score of the learner on the learning plan.
In one embodiment, the practice problems analyzed as errors are collected and integrated into a new practice problem set to be recommended to the learner for practice when a preset time elapses or when a preset number is reached.
The working principle and the beneficial effects of the technical scheme are as follows:
for the exercise questions with wrong question analysis as errors, complete comprehensive knowledge map barriers are established for the learners, and the learners need to practice again and master the exercise questions.
To further consolidate the learner's knowledge; in one embodiment, the personalized adaptive learning recommendation method based on the big data analysis of the education platform further comprises the following steps:
and step 9: and when the comprehensive knowledge map of the learner reaches a preset and stored perfect learning comprehensive knowledge map, counting the times of wrong exercises of the learner on the exercise questions of each knowledge point, extracting the exercise questions with the preset times of wrong exercises, integrating the exercise questions into a new exercise question set, and recommending the new exercise question set to the learner for exercise.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A personalized self-adaptive learning recommendation method based on education platform big data analysis is characterized by comprising the following steps:
step 1: obtaining historical learning record data of learners from the big data of the education platform;
step 2: analyzing the historical learning record data based on knowledge point classification to obtain a comprehensive knowledge map of the learner; calculating the learning efficiency of the learner according to the historical learning record data;
and step 3: analyzing the comprehensive knowledge map, and making a learning plan based on the learning time of the learner and the learning efficiency; recommending the learning plan to the learner.
2. The method of claim 1, further comprising:
and 4, step 4: receiving instructions that the learner agrees to execute the learning plan; acquiring corresponding learning materials from the big data of the education platform according to the learning plan, and playing the learning materials;
and 5: after the learning material is played, acquiring a training exercise question set corresponding to the learning material from the big data of the education platform and displaying the training exercise question set;
step 6: obtaining answers and appraises of the learner to the exercise problem set, and analyzing learning efficiency of the learning and knowledge mastery degree evaluation of the learner based on learning time, learning materials and appraises of the learner to the learning plan;
and 7: receiving the wrong question analysis of the learner on the exercises in the exercise set and sending the wrong question analysis to the big data of the education platform;
and 8: and receiving confirmation of the education platform big data on the wrong question analysis and displaying the confirmation to the learner.
3. The method for personalized adaptive learning recommendation based on education platform big data analysis according to claim 2, characterized in that step 8: receiving confirmation of the education platform big data on the wrong question analysis and displaying the confirmation to the learner, wherein the confirmation specifically comprises the following steps:
when the learner analyzes the error of the practice problems to be unclear error points, directly obtaining correct analysis materials of the practice problems from the big data of the education platform and playing the materials;
when the wrong question analysis is correct, outputting the correct analysis;
and when the wrong question analysis is wrong, outputting an analysis error and playing correct analysis materials of the practice questions.
4. The method of claim 1, wherein the learner's control instructions including one or more of pause, back, fast forward, skip are received while the learning material is being played.
5. The method as claimed in claim 1, wherein the step of parsing the historical learning record data based on knowledge point classification to obtain the comprehensive knowledge map of the learner comprises:
obtaining a comprehensive knowledge mapping template,
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
classifying the historical learning record data according to knowledge points; calculating the mastery value of each basic knowledge point; the calculation formula is as follows:
Figure FDA0002385575700000021
wherein Z isiA grasp value indicating the ith basic knowledge point; n is a radical ofi0Representing the number of training questions of the ith basic knowledge point in the historical learning record data; n is a radical ofi1Representing the correct number of the learner on the training questions of the ith basic knowledge point in the historical learning record data; n is a first preset value; n is a radical ofi2Representing the correct number of the last N training questions of the learner on the ith basic knowledge point in the historical learning record data;
calculating the mastery value of each comprehensive knowledge point based on the relationship degree of the comprehensive knowledge point and the basic knowledge point and the mastery value of each basic knowledge point; the calculation formula is as follows:
Figure FDA0002385575700000031
wherein Z isjA grasp value representing the jth integrated knowledge point; a. thej0Representing the training question number of the jth comprehensive knowledge point in the historical learning record data; a. thei1Representing the correct number of the learner on the training questions of the jth integrated knowledge point in the historical learning record data; a is a second preset value; a. thei2Representing the correct number of the training questions of the learner on the last A of the jth integrated knowledge point in the historical learning record data; zmA grasp value indicating an mth basic knowledge point associated with the jth integrated knowledge point; a is a preset correction weight; bmA weight representing an association of an mth basic knowledge point associated with a jth integrated knowledge point;
filling the calculated mastery values of the knowledge points into the comprehensive knowledge map module; forming a comprehensive knowledge map of the learner.
6. The method as claimed in claim 1, wherein the calculating learning efficiency of the learner according to the historical learning record data includes:
dividing the knowledge points into basic knowledge points and comprehensive knowledge points, and establishing the association and the degree of association between the comprehensive knowledge points and the basic knowledge points;
calculating a grasp value of each basic knowledge point based on the standard learning time of each basic knowledge point and the learning time of the learner for each basic knowledge point in the historical learning record data; the calculation formula is as follows:
Figure FDA0002385575700000032
wherein, YiRepresenting the learning efficiency of the ith basic knowledge point; t isi0Representing a standard learning time for the ith basic knowledge point; t isi1Representing the learning time of the learner for the ith basic knowledge point in the historical learning record data;
calculating first learning efficiency of each comprehensive knowledge point based on standard learning time of each comprehensive knowledge point and learning time of the learner for each comprehensive knowledge point in the historical learning record data, correcting the first learning efficiency based on the relationship degree of the comprehensive knowledge points and basic knowledge points and the learning efficiency of each basic knowledge point, and calculating the learning efficiency of each comprehensive knowledge point; the calculation formula is as follows:
Figure FDA0002385575700000041
wherein, YjRepresenting the learning efficiency of the jth integrated knowledge point; t isj0Representing a standard learning time for a jth integrated knowledge point; t isj1Representing the learner pairs in the historical learning record dataLearning time at jth integrated knowledge point; y ismRepresenting the learning efficiency of the mth basic knowledge point associated with the jth integrated knowledge point; d is a preset learning efficiency correction weight; bmRepresenting an association weight of an mth basic knowledge point associated with the jth integrated knowledge point;
calculating the learning efficiency of the learner based on the learning efficiency of all the basic knowledge points and the learning efficiency of all the comprehensive knowledge points, wherein the calculation formula is as follows:
Figure FDA0002385575700000042
wherein Y represents the learning efficiency of the learner αiA weight representing the learning efficiency of the ith basic knowledge point; y isiIndicating the learning efficiency of the ith basic knowledge point βjA weight representing the learning efficiency of the jth basic knowledge point; y isjThe learning efficiency of the jth basic knowledge point is shown.
7. The method as claimed in claim 1, wherein the step of analyzing the comprehensive knowledge map and making a learning plan based on learning time and learning efficiency comprises:
comparing the comprehensive knowledge map with a pre-stored standard knowledge map set, and when the comprehensive knowledge map is consistent with one standard knowledge map, acquiring a new knowledge point set corresponding to the standard knowledge map, a knowledge point set needing to be reviewed, a first learning time required by the new knowledge point set and knowledge points in the knowledge point set needing to be reviewed;
acquiring the learning time input by the learner as second learning time, and comparing the first learning time with the second learning time;
deleting the knowledge points corresponding to the first learning time with the time value larger than the time value of the second learning time from the new knowledge point set or the knowledge point set needing to be reviewed, and obtaining the new knowledge point set as a first knowledge point set and the knowledge point set needing to be reviewed as a second knowledge point set; the first knowledge point set is a set formed by all the first knowledge points; the second knowledge point set is a set formed by all second knowledge points;
based on the first set of knowledge points and the second set of knowledge points; enumerating a plurality of first learning plans, the first learning plans comprising at least one of the first knowledge points and at least one second knowledge point;
identifying a third learning time for each of the first learning plans based on the first learning time, the third learning time being calculated by:
Figure FDA0002385575700000051
wherein t represents a third learning time; t is t1iA first learning time representing an ith first knowledge point within the first learning plan; t is t2jA first learning time representing a jth second knowledge point within the first learning plan; t is t0Taking a preset time value as the connection time of two knowledge points in the first plan; the connection time is the pause time between two preset knowledge points;
and screening a third time with a time value smaller than that of the second time and selecting the first learning plan with the minimum difference value with the second time to recommend to the learner.
8. The method as claimed in claim 2, wherein the learning efficiency and knowledge mastery evaluation of the learner are analyzed based on the learning time, learning material and exercise of the learner on the learning plan; the method specifically comprises the following steps:
obtaining a first score from a first pre-stored score table based on a learning time of the learner for the learning plan;
acquiring a second score from a pre-stored second score table based on the learning materials;
obtaining a third score from a pre-stored third score table based on the scores and the types of the exercise questions;
obtaining knowledge mastery degree evaluation of the learner from a pre-stored knowledge mastery table based on a sum of scores of the first score, the second score and the third score;
and acquiring the learning efficiency of the learning from a pre-stored learning efficiency evaluation table based on the learning time of the learner on the learning plan, the learning materials, the type of the exercise and the score.
9. The method as claimed in claim 3, wherein the exercise problem sets analyzed as errors are collected and integrated as new exercise problem sets to be recommended to the learner for exercise when a preset time elapses or when a preset number is reached.
10. The method of claim 2, further comprising:
and step 9: and when the comprehensive knowledge map of the learner reaches a preset and stored perfect learning comprehensive knowledge map, counting the times of wrong exercises of the learner on the exercise questions of each knowledge point, extracting the exercise questions with the preset times of wrong exercises, integrating the exercise questions into a new exercise question set, and recommending the new exercise question set to the learner for exercise.
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