Online textbook matching method and system based on personalized learning

Abstract

The invention discloses an online textbook matching method and system based on personalized learning, and the method comprises the steps: obtaining the personalized feature information of a student based on a preset questionnaire; according to the personalized feature information of the trainee, determining a personalized teaching material setting proportion of the corresponding trainee; obtaining a target of the student; dividing the targets of the trainees according to stages to obtain sub-targets of different stages; according to the stage of the sub-target, matching the personalized textbook database of the corresponding stage; and according to the personalized textbook setting proportion of the corresponding student and the personalized textbook database of the corresponding stage, obtaining the personalized textbook of the corresponding student in the current stage. The learning enthusiasm of students is improved by matching personalized teaching materials.

Description

Technical Field

[0001] The present invention relates to the field of online education, and more specifically, to an online teaching material matching method and system based on personalized learning. Background Art

[0002] With the rapid development of technologies such as the Internet, big data, and artificial intelligence, it has become possible to collect, analyze, and process large amounts of student learning data. These technologies can accurately understand each student's learning characteristics, progress, and needs, thereby providing data support for the generation of personalized teaching materials.

[0003] In traditional education, the teaching model is teacher-centered, imparting knowledge to students through fixed teaching methods, which is often difficult to meet the personalized learning needs of each student. In addition, the fixed teaching model may inhibit students' innovative thinking and critical thinking ability. Students are more likely to passively accept knowledge and lack opportunities for active exploration and innovation. How to improve students' enthusiasm and creativity in learning, and find happiness in the learning process, is an urgent problem to be solved in the current learning process. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present invention provides an online teaching material matching method and system based on personalized learning, which can match personalized teaching materials and improve students' enthusiasm for learning.

[0005] A first aspect of the present invention provides an online teaching material matching method based on personalized learning, comprising:

[0006] Based on the preset questionnaire, obtain the personalized characteristic information of the students;

[0007] According to the individual characteristic information of the students, determine the proportion of the personalized teaching materials for the corresponding students;

[0008] Get the students' goals;

[0009] Divide the students' goals into stages and obtain sub-goals for different stages;

[0010] According to the stage of the sub-goal, match the personalized teaching material database of the corresponding stage;

[0011] According to the setting ratio of the personalized teaching materials for the corresponding students and the personalized teaching materials database for the corresponding stage, the personalized teaching materials for the corresponding students at the current stage are obtained.

[0012] In this solution, the step of determining the proportion of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students specifically includes:

[0013] Extracting personalized feature keywords from personalized feature information;

[0014] Classify personalized feature keywords to obtain personalized keywords of different learning style types;

[0015] Get the number of personalized keywords in each learning style type and the total number of all personalized keywords;

[0016] Divide the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain the teaching material setting ratio of the corresponding learning style type;

[0017] The teaching material setting ratios of all learning style types are combined to obtain the personalized teaching material setting ratios for the corresponding students.

[0018] In this solution, after obtaining the teaching material setting ratio corresponding to the learning style type, it also includes:

[0019] Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, mark the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type;

[0020] Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type;

[0021] Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types;

[0022] Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value;

[0023] When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type;

[0024] Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio;

[0025] Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type;

[0026] Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type;

[0027] If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated;

[0028] The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

[0029] This plan also includes:

[0030] Get the test score of the current stage;

[0031] Extract the target score of the sub-goal of the current stage;

[0032] Subtract the corresponding target score from the test score of the current stage to get the score difference of the current stage;

[0033] If the score difference of the current stage is within the preset first score range, the proportion of personalized teaching materials set for the next stage will remain unchanged;

[0034] If the score difference of the current stage is greater than the maximum value in the preset first score range, then based on the preset first adjustment coefficient, the teaching material setting ratio of the interest learning style type in the next stage is adjusted;

[0035] If the score difference in the current stage is less than the minimum value in the preset first score range, the personalized teaching material setting ratio of the corresponding student is adjusted based on the preset second adjustment coefficient and the second adjustment ratio of the next stage.

[0036] In this solution, the step of adjusting the teaching material setting ratio of the interest learning style type in the next stage based on the preset first adjustment coefficient specifically includes:

[0037] The teaching material setting ratio of the interest learning style type in the current stage is multiplied by the preset first adjustment coefficient to obtain a revised value of the teaching material setting ratio corresponding to the interest learning style type in the next stage;

[0038] Accumulate the teaching material setting ratio of the interest learning style type in the current stage and the revised value of the teaching material setting ratio of the corresponding interest learning style type in the next stage to obtain the teaching material setting ratio of the corresponding interest learning style type in the next stage;

[0039] Accumulate the revised values ​​of the teaching material setting ratios of all interested learning style types in the next stage to obtain the total adjusted values ​​of the teaching material setting ratios of all non-interest learning style types in the next stage;

[0040] Accumulating the proportion of teaching materials set for non-interest learning style types in the current stage to obtain the total value of the proportion of teaching materials set for non-interest learning style types in the current stage;

[0041] Divide the proportion of teaching materials for non-interest learning style types in the current stage by the total proportion of teaching materials for non-interest learning style types in the current stage, and then multiply by the total adjusted proportion of teaching materials for all non-interest learning style types in the next stage to obtain the adjusted proportion of teaching materials for non-interest learning style types in the next stage;

[0042] The teaching material setting ratio of non-interest learning style types in the current stage is subtracted from the teaching material setting ratio adjustment value of non-interest learning style types in the next stage to obtain the teaching material setting ratio of non-interest learning style types in the next stage.

[0043] In this solution, the step of adjusting the setting ratio of personalized teaching materials for the corresponding students based on the preset second adjustment coefficient and the preset second adjustment ratio of the next stage specifically includes:

[0044] Extracting the teaching material setting ratio of the learning style type from the preset second adjustment ratio of the next stage, and setting it as the teaching material setting second adjustment ratio of the corresponding learning style type;

[0045] The second adjustment ratio of the teaching material setting for the learning style type is subtracted from the teaching material setting ratio for the corresponding learning style type in the current stage to obtain a second difference value of the teaching material setting ratio for the corresponding learning style type;

[0046] The second difference value of the teaching material setting ratio corresponding to the learning style type is multiplied by a preset second adjustment coefficient to obtain an adjustment value of the teaching material setting ratio corresponding to the learning style type in the next stage;

[0047] The teaching material setting ratio corresponding to the learning style type in the current stage and the teaching material setting ratio corresponding to the learning style type in the previous stage are adjusted to obtain the teaching material setting ratio corresponding to the learning style type in the next stage.

[0048] In this solution, the step of obtaining the second adjustment ratio in the next stage specifically includes:

[0049] Get the number value n of the next stage, then the corresponding stage is the nth stage;

[0050] Obtaining historical learning data information of the nth stage, wherein the historical learning data information of the nth stage at least includes sub-goals of m other students in the nth stage, the proportion of personalized teaching materials set corresponding to the other students, and the test scores of the other students in the nth stage, where m is greater than or equal to 2;

[0051] Subtract the target scores of the sub-goals of other students at stage n from the test scores of other students at stage n to obtain the target deviation values ​​of other students;

[0052] If the target deviation value of other students is less than or equal to the preset first target deviation threshold, the historical learning data information of the corresponding other students will be deleted;

[0053] Calculate the difference between the target score of the sub-goal of the current student at stage n and the target score of the sub-goal of the current student at stage n to obtain a second target deviation value;

[0054] If the second target deviation value is greater than the preset second target deviation threshold, the corresponding historical learning data information of other students will be deleted;

[0055] After traversing the target scores of all other students' sub-goals in the nth stage, the historical learning data information of the remaining other students is obtained;

[0056] The average value of the personalized teaching material setting ratios in the historical learning data information of the remaining other students is calculated to obtain the second adjustment ratio of the nth stage.

[0057] A second aspect of the present invention provides an online teaching material matching system based on personalized learning, comprising a memory and a processor, wherein the memory stores an online teaching material matching method program based on personalized learning, and when the online teaching material matching method program based on personalized learning is executed by the processor, the following steps are implemented:

[0058] Based on the preset questionnaire, obtain the personalized characteristic information of the students;

[0059] According to the individual characteristic information of the students, determine the proportion of the personalized teaching materials for the corresponding students;

[0060] Get the students' goals;

[0061] Divide the students' goals into stages and obtain sub-goals for different stages;

[0062] According to the stage of the sub-goal, match the personalized teaching material database of the corresponding stage;

[0063] According to the setting ratio of the personalized teaching materials for the corresponding students and the personalized teaching materials database for the corresponding stage, the personalized teaching materials for the corresponding students at the current stage are obtained.

[0064] In this solution, the step of determining the setting ratio of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students specifically includes:

[0065] Extracting personalized feature keywords from personalized feature information;

[0066] Classify personalized feature keywords to obtain personalized keywords of different learning style types;

[0067] Get the number of personalized keywords in each learning style type and the total number of all personalized keywords;

[0068] Divide the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain the teaching material setting ratio of the corresponding learning style type;

[0069] The teaching material setting ratios of all learning style types are combined to obtain the personalized teaching material setting ratios for the corresponding students.

[0070] In this solution, after obtaining the teaching material setting ratio corresponding to the learning style type, it also includes:

[0071] Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, mark the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type;

[0072] Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type;

[0073] Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types;

[0074] Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value;

[0075] When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type;

[0076] Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio;

[0077] Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type;

[0078] Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type;

[0079] If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated;

[0080] The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

[0081] The present invention discloses an online teaching material matching method and system based on personalized learning, which improves students' learning enthusiasm by matching personalized teaching materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A flowchart of an online teaching material matching method based on personalized learning of the present invention is shown;

[0083] Figure 2 A flow chart showing the present invention for determining the proportion of personalized teaching materials for students;

[0084] Figure 3 A block diagram of an online teaching material matching system based on personalized learning according to the present invention is shown. DETAILED DESCRIPTION

[0085] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0086] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0087] Figure 1 A flow chart of an online teaching material matching method based on personalized learning of the present invention is shown.

[0088] like Figure 1 As shown, the present invention discloses an online teaching material matching method based on personalized learning, comprising:

[0089] S101, obtaining personalized characteristic information of the trainees based on a preset questionnaire;

[0090] S102, determining the setting ratio of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students;

[0091] S103, obtaining the students’ goals;

[0092] S104, dividing the students' goals according to stages to obtain sub-goals of different stages;

[0093] S105, matching the personalized teaching material database of the corresponding stage according to the stage where the sub-goal is located;

[0094] S106, obtaining the personalized teaching materials for the corresponding students at the current stage according to the personalized teaching materials setting ratio of the corresponding students and the personalized teaching materials database of the corresponding stage.

[0095] According to an embodiment of the present invention, the preset questionnaire table stores questionnaire questions of various personalized characteristics, and the personalized characteristic information of the students is obtained according to the students' answers to the questionnaire questions of personalized characteristics; different learning stages have personalized teaching material databases of different learning stages, and the personalized teaching material database stores personalized teaching material versions of all knowledge points of the corresponding learning stages, wherein there are at least two or more personalized teaching material versions for the same knowledge point; all knowledge points contained in the corresponding stage are extracted, and arranged in sequence according to the order in which they appear in the textbook, the serial numbers of the corresponding knowledge points are determined, and a personalized teaching material version of the same knowledge point is randomly extracted, and the personalized teaching material versions corresponding to different knowledge points are combined according to the serial number order of the knowledge points to obtain the initial personalized teaching material set of the current stage; the personalized teaching material version of each knowledge point has at least one learning style type label, such as visual type, auditory type, kinesthetic type, visual and auditory mixed type, etc.; the initial personalized teaching material for the current stage is extracted The label of the learning style type in any initial personalized teaching material in the set, and record the number of times the label of each learning style type in the corresponding initial personalized teaching material appears, accumulate the number of times the label of each learning style type in the initial personalized teaching material appears, and obtain the total number of labels, and then divide the number of times the label of the learning style type in the initial personalized teaching material by the total number of labels to obtain the proportion of the learning style type in the corresponding initial personalized teaching material; based on the same learning style type, calculate the difference between the proportion of the learning style type in the corresponding initial personalized teaching material and the teaching material setting ratio of the corresponding learning style type, and take the absolute value to obtain the first proportion difference of the corresponding learning style type, accumulate the first proportion differences of different learning style types to obtain the total error ratio of the initial personalized teaching material, extract the initialized personalized teaching material whose total error ratio is less than the preset error ratio threshold, and send the initialized personalized teaching material whose total error ratio is less than the preset error ratio threshold to the student end for selection to obtain the personalized teaching material of the current stage.

[0096] Figure 2 A flow chart of determining the ratio of personalized teaching materials for students according to the present invention is shown.

[0097] like Figure 2 As shown, the step of determining the setting ratio of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students specifically includes:

[0098] S201, extracting personalized feature keywords from personalized feature information;

[0099] S202, classifying personalized feature keywords to obtain personalized keywords of different learning style types;

[0100] S203, obtaining the number of personalized keywords in each learning style type and the total number of all personalized keywords;

[0101] S204, dividing the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain a teaching material setting ratio corresponding to the learning style type;

[0102] S205, combining the teaching material setting ratios of all learning style types to obtain a personalized teaching material setting ratio for the corresponding students.

[0103] It should be noted that the learning style types include auditory, visual, kinesthetic, etc. Different learning style types include multiple personalized feature keywords. For example, auditory learners are more inclined to acquire new knowledge through listening, and their personalized feature keywords include sound, recitation, etc.

[0104] According to an embodiment of the present invention, after obtaining the teaching material setting ratio corresponding to the learning style type, the method further includes:

[0105] Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, mark the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type;

[0106] Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type;

[0107] Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types;

[0108] Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value;

[0109] When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type;

[0110] Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio;

[0111] Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type;

[0112] Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type;

[0113] If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated;

[0114] The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

[0115] It should be noted that personalized teaching materials are not all matched according to the students' wishes. For example, if a student only likes personalized teaching materials for kinesthetic types, it is impossible to directly give up personalized teaching materials for other learning style types; therefore, a minimum preset first ratio threshold is set for the ratio of teaching materials for different learning style types.

[0116] According to an embodiment of the present invention, it also includes:

[0117] Get the test score of the current stage;

[0118] Extract the target score of the sub-goal of the current stage;

[0119] Subtract the corresponding target score from the test score of the current stage to get the score difference of the current stage;

[0120] If the score difference of the current stage is within the preset first score range, the proportion of personalized teaching materials set for the next stage will remain unchanged;

[0121] If the score difference of the current stage is greater than the maximum value in the preset first score range, then based on the preset first adjustment coefficient, the teaching material setting ratio of the interest learning style type in the next stage is adjusted;

[0122] If the score difference in the current stage is less than the minimum value in the preset first score range, the personalized teaching material setting ratio of the corresponding student is adjusted based on the preset second adjustment coefficient and the second adjustment ratio of the next stage.

[0123] It should be noted that the test scores of students at each stage will affect the adjustment of the proportion of personalized teaching materials for the corresponding students in the next stage, prompting students to learn knowledge related to setting goals while enjoying personalized teaching materials.

[0124] According to an embodiment of the present invention, the step of adjusting the teaching material setting ratio of the interest learning style type in the next stage based on the preset first adjustment coefficient specifically includes:

[0125] The teaching material setting ratio of the interest learning style type in the current stage is multiplied by the preset first adjustment coefficient to obtain a revised value of the teaching material setting ratio corresponding to the interest learning style type in the next stage;

[0126] Accumulate the teaching material setting ratio of the interest learning style type in the current stage and the revised value of the teaching material setting ratio of the corresponding interest learning style type in the next stage to obtain the teaching material setting ratio of the corresponding interest learning style type in the next stage;

[0127] Accumulate the revised values ​​of the teaching material setting ratios of all interested learning style types in the next stage to obtain the total adjusted values ​​of the teaching material setting ratios of all non-interest learning style types in the next stage;

[0128] Accumulating the proportion of teaching materials set for non-interest learning style types in the current stage to obtain the total value of the proportion of teaching materials set for non-interest learning style types in the current stage;

[0129] Divide the proportion of teaching materials for non-interest learning style types in the current stage by the total proportion of teaching materials for non-interest learning style types in the current stage, and then multiply by the total adjusted proportion of teaching materials for all non-interest learning style types in the next stage to obtain the adjusted proportion of teaching materials for non-interest learning style types in the next stage;

[0130] The teaching material setting ratio of non-interest learning style types in the current stage is subtracted from the teaching material setting ratio adjustment value of non-interest learning style types in the next stage to obtain the teaching material setting ratio of non-interest learning style types in the next stage.

[0131] It should be noted that when the score difference of a student in the current stage is greater than the maximum value in the preset first score range, it means that the student has completed the set sub-goal in the current stage. Therefore, the proportion of teaching materials for interest learning style types in the next stage is adjusted by the preset first adjustment coefficient as a reward, and in the process of adjustment, the proportion of teaching materials for non-interest learning style types is not subject to the preset first proportion threshold.

[0132] According to an embodiment of the present invention, the step of adjusting the setting ratio of personalized teaching materials for the corresponding students based on the preset second adjustment coefficient and the preset second adjustment ratio of the next stage specifically includes:

[0133] Extracting the teaching material setting ratio of the learning style type from the preset second adjustment ratio of the next stage, and setting it as the teaching material setting second adjustment ratio of the corresponding learning style type;

[0134] The second adjustment ratio of the teaching material setting for the learning style type is subtracted from the teaching material setting ratio for the corresponding learning style type in the current stage to obtain a second difference value of the teaching material setting ratio for the corresponding learning style type;

[0135] The second difference value of the teaching material setting ratio corresponding to the learning style type is multiplied by a preset second adjustment coefficient to obtain an adjustment value of the teaching material setting ratio corresponding to the learning style type in the next stage;

[0136] The teaching material setting ratio corresponding to the learning style type in the current stage and the teaching material setting ratio corresponding to the learning style type in the previous stage are adjusted to obtain the teaching material setting ratio corresponding to the learning style type in the next stage.

[0137] It should be noted that if the score difference in the current stage is less than the minimum value in the preset first score range, it means that the sub-goal of the current stage has not been completed. Therefore, the personalized teaching material setting ratio of the corresponding students is adjusted step by step by presetting the second adjustment coefficient and the preset second adjustment ratio of the next stage.

[0138] According to an embodiment of the present invention, the step of obtaining the second adjustment ratio in the next stage specifically includes:

[0139] Get the number value n of the next stage, then the corresponding stage is the nth stage;

[0140] Obtaining historical learning data information of the nth stage, wherein the historical learning data information of the nth stage at least includes sub-goals of m other students in the nth stage, the proportion of personalized teaching materials set corresponding to the other students, and the test scores of the other students in the nth stage, where m is greater than or equal to 2;

[0141] Subtract the target scores of other students' sub-goals in stage n from their test scores in stage n to obtain the target deviation values ​​of other students;

[0142] If the target deviation value of other students is less than or equal to the preset first target deviation threshold, the historical learning data information of the corresponding other students will be deleted;

[0143] Calculate the difference between the target score of the sub-goal of the current student at stage n and the target score of the sub-goal of the current student at stage n to obtain a second target deviation value;

[0144] If the second target deviation value is greater than the preset second target deviation threshold, the corresponding historical learning data information of other students will be deleted;

[0145] After traversing the target scores of all other students' sub-goals in the nth stage, the historical learning data information of the remaining other students is obtained;

[0146] The average value of the personalized teaching material setting ratios in the historical learning data information of the remaining other students is calculated to obtain the second adjustment ratio of the nth stage.

[0147] It should be noted that different stages are numbered in chronological order, namely 1, 2, ..., n; the historical learning data information of the remaining other students includes at least the personalized teaching material setting ratios of two other students in the nth stage.

[0148] Figure 3 A block diagram of an online teaching material matching system based on personalized learning according to the present invention is shown.

[0149] like Figure 3 As shown, the second aspect of the present invention provides an online teaching material matching system 3 based on personalized learning, including a memory 31 and a processor 32, wherein the memory stores an online teaching material matching method program based on personalized learning, and when the online teaching material matching method program based on personalized learning is executed by the processor, the following steps are implemented:

[0150] Based on the preset questionnaire, obtain the personalized characteristic information of the students;

[0151] According to the individual characteristic information of the students, determine the proportion of the personalized teaching materials for the corresponding students;

[0152] Get the students' goals;

[0153] Divide the students' goals into stages and obtain sub-goals for different stages;

[0154] According to the stage of the sub-goal, match the personalized teaching material database of the corresponding stage;

[0155] Determine the personalized teaching materials for the corresponding students at the current stage according to the personalized teaching materials setting ratio of the corresponding students and the personalized teaching materials database of the corresponding stage;

[0156] The personalized teaching materials for the student at the current stage are sent to the corresponding student terminal for display.

[0157] According to an embodiment of the present invention, the step of determining the setting ratio of personalized teaching materials for the corresponding student according to the personalized characteristic information of the student specifically includes:

[0158] Extracting personalized feature keywords from personalized feature information;

[0159] Classify personalized feature keywords to obtain personalized keywords of different learning style types;

[0160] Get the number of personalized keywords in each learning style type and the total number of all personalized keywords;

[0161] Divide the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain the teaching material setting ratio of the corresponding learning style type;

[0162] The teaching material setting ratios of all learning style types are combined to obtain the personalized teaching material setting ratios for the corresponding students.

[0163] According to an embodiment of the present invention, after obtaining the teaching material setting ratio corresponding to the learning style type, the method further includes:

[0164] Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, mark the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type;

[0165] Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type;

[0166] Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types;

[0167] Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value;

[0168] When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type;

[0169] Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio;

[0170] Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type;

[0171] Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type;

[0172] If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated;

[0173] The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

[0174] The present invention discloses an online teaching material matching method and system based on personalized learning, which improves students' learning enthusiasm by matching personalized teaching materials.

[0175] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0176] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0177] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0178] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0179] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An online teaching material matching method based on personalized learning, characterized in that: include: Based on the preset questionnaire, obtain the personalized characteristic information of the students; According to the individual characteristic information of the students, determine the proportion of the personalized teaching materials for the corresponding students; Get the students' goals; Divide the students' goals into stages and obtain sub-goals for different stages; According to the stage of the sub-goal, match the personalized teaching material database of the corresponding stage; According to the setting ratio of the personalized teaching materials for the corresponding students and the personalized teaching materials database for the corresponding stage, the personalized teaching materials for the corresponding students at the current stage are obtained.

2. According to claim 1, an online teaching material matching method based on personalized learning is characterized in that: The step of determining the setting ratio of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students specifically includes: Extracting personalized feature keywords from personalized feature information; Classify personalized feature keywords to obtain personalized keywords of different learning style types; Get the number of personalized keywords in each learning style type and the total number of all personalized keywords; Divide the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain the teaching material setting ratio of the corresponding learning style type; The teaching material setting ratios of all learning style types are combined to obtain the personalized teaching material setting ratios for the corresponding students.

3. According to claim 2, the online teaching material matching method based on personalized learning is characterized in that: After obtaining the teaching material setting ratio corresponding to the learning style type, the method further includes: Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, identify the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type; Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type; Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types; Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value; When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type; Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio; Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type; Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type; If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated; The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

4. According to claim 3, the online teaching material matching method based on personalized learning is characterized in that: Also includes: Get the test score of the current stage; Extract the target score of the sub-goal of the current stage; Subtract the corresponding target score from the test score of the current stage to get the score difference of the current stage; If the score difference of the current stage is within the preset first score range, the proportion of personalized teaching materials set for the next stage will remain unchanged; If the score difference of the current stage is greater than the maximum value in the preset first score range, then based on the preset first adjustment coefficient, the teaching material setting ratio of the interest learning style type in the next stage is adjusted; If the score difference in the current stage is less than the minimum value in the preset first score range, the personalized teaching material setting ratio of the corresponding student is adjusted based on the preset second adjustment coefficient and the second adjustment ratio of the next stage.

5. The online teaching material matching method based on personalized learning according to claim 4 is characterized in that: The step of adjusting the teaching material setting ratio of the interest learning style type in the next stage based on the preset first adjustment coefficient specifically includes: The teaching material setting ratio of the interest learning style type in the current stage is multiplied by the preset first adjustment coefficient to obtain a revised value of the teaching material setting ratio corresponding to the interest learning style type in the next stage; Accumulate the teaching material setting ratio of the interest learning style type in the current stage and the revised value of the teaching material setting ratio of the corresponding interest learning style type in the next stage to obtain the teaching material setting ratio of the corresponding interest learning style type in the next stage; Accumulate the revised values ​​of the teaching material setting ratios of all interested learning style types in the next stage to obtain the total adjusted values ​​of the teaching material setting ratios of all non-interest learning style types in the next stage; Accumulating the proportion of teaching materials set for non-interest learning style types in the current stage to obtain the total value of the proportion of teaching materials set for non-interest learning style types in the current stage; Divide the proportion of teaching materials for non-interest learning style types in the current stage by the total proportion of teaching materials for non-interest learning style types in the current stage, and then multiply by the total adjusted proportion of teaching materials for all non-interest learning style types in the next stage to obtain the adjusted proportion of teaching materials for non-interest learning style types in the next stage; The teaching material setting ratio of non-interest learning style types in the current stage is subtracted from the teaching material setting ratio adjustment value of non-interest learning style types in the next stage to obtain the teaching material setting ratio of non-interest learning style types in the next stage.

6. The online teaching material matching method based on personalized learning according to claim 4, characterized in that: The step of adjusting the setting ratio of the personalized teaching materials for the corresponding students based on the preset second adjustment coefficient and the preset second adjustment ratio of the next stage specifically includes: Extracting the teaching material setting ratio of the learning style type from the preset second adjustment ratio of the next stage, and setting it as the teaching material setting second adjustment ratio of the corresponding learning style type; The second adjustment ratio of the teaching material setting for the learning style type is subtracted from the teaching material setting ratio for the corresponding learning style type in the current stage to obtain a second difference value of the teaching material setting ratio for the corresponding learning style type; The second difference value of the teaching material setting ratio corresponding to the learning style type is multiplied by a preset second adjustment coefficient to obtain an adjustment value of the teaching material setting ratio corresponding to the learning style type in the next stage; The teaching material setting ratio corresponding to the learning style type in the current stage and the teaching material setting ratio corresponding to the learning style type in the previous stage are adjusted to obtain the teaching material setting ratio corresponding to the learning style type in the next stage.

7. The online teaching material matching method based on personalized learning according to claim 4 is characterized in that: The step of obtaining the second adjustment ratio in the next stage specifically includes: Get the number value n of the next stage, then the corresponding stage is the nth stage; Obtaining historical learning data information of the nth stage, wherein the historical learning data information of the nth stage at least includes sub-goals of m other students in the nth stage, the proportion of personalized teaching materials set corresponding to the other students, and the test scores of the other students in the nth stage, where m is greater than or equal to 2; Subtract the target scores of the sub-goals of other students at stage n from the test scores of other students at stage n to obtain the target deviation values ​​of other students; If the target deviation value of other students is less than or equal to the preset first target deviation threshold, the historical learning data information of the corresponding other students will be deleted; Calculate the difference between the target score of the sub-goal of the current student at stage n and the target score of the sub-goal of the current student at stage n to obtain a second target deviation value; If the second target deviation value is greater than the preset second target deviation threshold, the corresponding historical learning data information of other students will be deleted; After traversing the target scores of all other students' sub-goals in stage n, the historical learning data information of the remaining students is obtained; The average value of the personalized teaching material setting ratios in the historical learning data information of the remaining other students is calculated to obtain the second adjustment ratio of the nth stage.

8. An online teaching material matching system based on personalized learning, characterized in that: The invention comprises a memory and a processor, wherein the memory stores an online teaching material matching method program based on personalized learning, and when the online teaching material matching method program based on personalized learning is executed by the processor, the following steps are implemented: Based on the preset questionnaire, obtain the personalized characteristic information of the students; According to the individual characteristic information of the students, determine the proportion of the personalized teaching materials for the corresponding students; Get the students' goals; Divide the students' goals into stages and obtain sub-goals for different stages; According to the stage of the sub-goal, match the personalized teaching material database of the corresponding stage; According to the setting ratio of the personalized teaching materials for the corresponding students and the personalized teaching materials database for the corresponding stage, the personalized teaching materials for the corresponding students at the current stage are obtained.

9. The online teaching material matching system based on personalized learning according to claim 8, characterized in that: The step of determining the setting ratio of personalized teaching materials for the corresponding students according to the personalized characteristic information of the students specifically includes: Extracting personalized feature keywords from personalized feature information; Classify personalized feature keywords to obtain personalized keywords of different learning style types; Get the number of personalized keywords in each learning style type and the total number of all personalized keywords; Divide the number of personalized keywords in the learning style type by the total number of all personalized keywords to obtain the teaching material setting ratio of the corresponding learning style type; The teaching material setting ratios of all learning style types are combined to obtain the personalized teaching material setting ratios for the corresponding students.

10. The online teaching material matching system based on personalized learning according to claim 9, characterized in that: After obtaining the teaching material setting ratio corresponding to the learning style type, the method further includes: Determine whether the teaching material setting ratio of the learning style type is less than a preset first ratio threshold of the corresponding learning style type, and if so, identify the corresponding learning style type as a general learning style type, and replace the teaching material setting ratio of the general learning style type with the preset first ratio threshold of the corresponding learning style type; Subtract the teaching material setting ratio corresponding to the general learning style type from the preset first ratio threshold of the general learning style type to obtain the teaching material setting ratio difference of the general learning style type; Traverse the teaching material setting ratios of all learning style types to obtain a set of teaching material setting ratio differences of general learning style types; Accumulating the teaching material setting ratio differences in the teaching material setting ratio difference set of the general learning style type to obtain a first teaching material setting ratio adjustment total value; When the teaching material setting ratio of the learning style type is greater than a preset first ratio threshold of the corresponding learning style type, the corresponding learning style type is identified as an interest learning style type; Accumulate the teaching material setting ratios of interest learning style types to obtain a total value of the first teaching material setting ratio; Divide the teaching material setting ratio of the interest learning style type by the total value of the first teaching material setting ratio, and then multiply by the total value of the first teaching material setting ratio adjustment to obtain a first adjustment value of the teaching material setting ratio corresponding to the interest learning style type; Subtract the first adjustment value of the teaching material setting ratio of the corresponding interest learning style type from the teaching material setting ratio of the interest learning style type to obtain the revised teaching material setting ratio of the corresponding interest learning style type; If the revised teaching material setting ratio of the corresponding interest learning style type is less than the preset first ratio threshold of the corresponding learning style type, the teaching material setting ratio of the corresponding interest learning style type is subtracted from the preset first ratio threshold of the corresponding learning style type to obtain a second teaching material setting ratio adjustment value, and the interest identifier of the corresponding interest learning style type is cancelled, and the total value of the first teaching material setting ratio is recalculated; The total value of the first textbook setting ratio adjustment is subtracted from the second textbook setting ratio adjustment value to obtain the revised total value of the first textbook setting ratio adjustment value.

Images