CN115660913A - System and method for customizing learning content for user - Google Patents
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Abstract
The invention relates to the field of data processing, in particular to a system and a method for customizing learning content for a user, wherein the system comprises a first receiving module, a second receiving module, a third receiving module and a fourth receiving module, wherein the first receiving module receives a first keyword in a user request; a first obtaining module obtains first learning content according to the first keyword; the second receiving module receives selection information of the second keyword, and the second keyword corresponding to the selection information is a target second keyword; the second acquisition module acquires second learning content according to the target second keyword; the analysis module analyzes any learning unit content according to the historical error rate of the feedback information corresponding to the target second keyword, the actual error rate of the information to be fed back corresponding to the learning unit content and a preset error rate to obtain an analysis result; and the third acquisition module acquires third learning content from the second learning content according to the analysis result. Personalized and accurate learning content is continuously generated through the request of the user and the learning result, and the accuracy of the customized learning content is improved.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a system and method for customizing learning content for a user.
Background
With the appearance of artificial intelligence, the artificial intelligence brings infinite possibility for education transformation, and under the energization of the artificial intelligence, personalized teaching and learning is realized, and the learning quality of students is broken through.
One method disclosed in patent application No. 202210044382.2 for providing user-customized learning content includes: a step of configuring a multiple choice question database including one or more multiple choice questions and collecting option selection data of a user for the multiple choice questions; a step of calculating an option selection probability of the user with respect to the modeling vector based on the modeling vector of the user and the modeling vector of the multiple choice question; a step of calculating a problem solving accuracy of the multiple choice problems based on the option selection probability; a step of calculating an understanding index of the user for the multiple choice questions based on at least one of the degree of understanding of the concept, the configuration of the concept, the solution question correctness rate, and the option selection probability; and a step of recommending user-customized learning content with respect to the understanding index for the multiple choice questions based on the user.
In the prior art, the option selection probability, the problem solving accuracy and the understanding index of a user are calculated through a modeling vector to recommend the customized learning content for the user, and the method can recommend too much learning content which is possibly not in line with the requirements of the user, so that the accuracy of the learning content customized for the user is low.
Disclosure of Invention
Therefore, the invention provides a system and a method for customizing the learning content for the user, which can solve the problem of low accuracy of the learning content customized for the user.
To achieve the above object, one aspect of the present invention provides a system for customizing learning content for a user, the system comprising:
the system comprises a first receiving module, a second receiving module and a display module, wherein the first receiving module is used for receiving a user request, and the user request comprises a first keyword;
the first acquisition module is used for acquiring first learning content in a database according to the first keyword;
the second receiving module is used for receiving the receiving result of the first learning content from the user, if the receiving result is negative, the first keywords comprise a plurality of second keywords, the second keywords are displayed to the user according to the first keywords, and the selection information of the second keywords is received, wherein the second keywords corresponding to the selection information are target second keywords;
the second obtaining module is used for obtaining second learning content from the first learning content according to the target second keyword, the second learning content comprises a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to the plurality of information to be fed back;
the analysis module is used for analyzing any learning unit content according to the historical error rate of the feedback information corresponding to the target second keyword, the actual error rate of the information to be fed back corresponding to the learning unit content and a preset error rate to obtain an analysis result;
and the third acquisition module is used for acquiring third learning content from the second learning content according to the analysis result.
Further, when the first obtaining module obtains the first learning content, the database stores the learning content and a matching relationship in advance, the matching relationship is that the first keyword is matched with the learning content, and the first obtaining unit matches the learning content in the database according to the first keyword to obtain the first learning content.
Further, when the second obtaining module obtains the second learning content, the target second keyword is matched with any learning content in the first learning content, and the second obtaining module obtains the second learning content by matching in the first learning content according to the target second keyword.
Further, the analysis module comprises a dividing unit, a setting unit, an obtaining unit, a comparing unit and a determining unit, and the analysis module comprises:
the classification unit is used for carrying out grade classification on the content of the learning unit to obtain a grade classification result;
the setting unit is used for setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
the acquisition unit is used for acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
the comparison unit is used for comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and the determining unit is used for determining the analysis result of the content of the learning unit according to the comparison result.
Further, when the dividing unit divides the content of the learning unit into levels, the user request further includes a user history learning record, the user history learning record includes a plurality of history learning unit contents and corresponding keywords, the target second keyword of the learning unit content is matched with the keyword corresponding to the history learning unit content, if the matching is successful, the history error rate a of the history learning unit content corresponding to the successful matching is obtained, the history error rate a is compared with a preset first standard error rate A1 and a preset second standard error rate A2, if a is less than A1, the level of the learning unit content is determined to be level i, if A1 is less than or equal to A2, the level of the learning unit content is determined to be level ii, if a is greater than A2, the level of the learning unit content is determined to be level iii, wherein the preset standard error rate is greater than A1 and less than A2, and the level i is greater than level ii;
if the matching fails, the grade of the content of the learning unit is directly judged as I grade.
Further, when the setting unit sets a preset error rate for the information to be fed back corresponding to the content of the learning unit, if the level of the content of the learning unit is level i, the preset error rate for the information to be fed back is set to a1, if the level of the content of the learning unit is level ii, the preset error rate for the information to be fed back is set to a2, and if the level of the content of the learning unit is level iii, the preset error rate for the information to be fed back is set to a3, where a1 > a2 > a3.
Further, when the comparing unit compares the actual error rate E with a preset error rate of information to be fed back corresponding to the content of the learning unit, if the level of the content of the learning unit corresponding to the information to be fed back is level i, the comparing unit compares the actual error rate E with a preset error rate a1, if E > a1, it is determined that the error rate of the user is high, and if E is less than or equal to a1, it is determined that the error rate of the user is low; if the grade of the content of the learning unit corresponding to the information to be fed back is II grade or III grade, comparing the actual error rate E with the preset error rate a2 or a3 to obtain a comparison result;
when the determining unit determines that the error rate of the user is high, analyzing an error entry in feedback information, wherein the feedback information comprises a plurality of key points, the key points correspond to entries, counting the number of the key points corresponding to the error entry, calculating the key point proportion rate P corresponding to the error entry in the feedback information corresponding to the content of the learning unit, determining an analysis result A '=1-P of the content of the learning unit, and associating the analysis result A' with the key points corresponding to the error entry.
Further, when acquiring the third learning content, the third acquiring module does not acquire the third learning content if it is determined that the error rate of the user is low, and when it is determined that the error rate of the user is low, acquires the third learning content from the second learning content according to the analysis result a 'of the determined learning unit content, acquires a keypoint corresponding to an error entry according to the analysis result a' and the error entry, where keypoints of all feedback information in the learning unit content are used as keypoints of the learning unit content, matches the acquired keypoint with the keypoint of the learning unit content in the second learning content, acquires the second learning content of the matched keypoint related to the second learning content according to the matched keypoint, and generates the third learning content.
In another aspect, the present invention further provides a method for customizing learning content for a user, where the method includes:
receiving a first user request, wherein the first user request comprises a first keyword;
acquiring first learning content in a database according to the first keyword;
receiving an acceptance result of the user for the first learning content, if the acceptance result is negative, the first keywords comprise a plurality of second keywords, displaying the second keywords to the user according to the first keywords, and receiving selection information of the second keywords, wherein the second keywords corresponding to the selection information are target second keywords;
acquiring second learning content from the first learning content according to the target second keyword, wherein the second learning content comprises a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to a plurality of information to be fed back;
analyzing any learning unit content according to the historical error rate of the target second keyword and the actual error rate and preset error rate of the information to be fed back corresponding to the learning unit content to obtain an analysis result;
and acquiring third learning content from the second learning content according to the analysis result.
Further, the analyzing any of the learning unit contents includes:
grading the content of the learning unit to obtain a grading result;
setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and determining the analysis result of the content of the learning unit according to the comparison result.
Compared with the prior art, the method has the advantages that the first obtaining module obtains first learning content for the user for the first time through the first keyword in the user request received by the first receiving module, the second obtaining module can continuously receive the selected target second keyword through the second receiving module according to the selection of the user to obtain second learning content, the analyzing module judges the mastering degree of the learning content by the user according to the difficulty degree of information to be fed back in the second learning content and the actual error rate corresponding to the difficulty degree, the third obtaining module customizes more accurate third learning content for the user according to the mastering degree, and generates personalized and accurate learning content continuously through the request and the learning result of the user, so that the learning content is few and accurate, and the accuracy of the customized learning content is improved.
Particularly, the first acquisition module acquires the first learning content according to the matching relation between the first keyword list and the learning content in the first keyword matching database, so that the learning content of the user is acquired for the first time, the learning content required by the user is screened out, and the accuracy of the learning content is improved.
Particularly, the second obtaining module obtains the second learning content by matching in the first learning content according to the target second keyword and any matching relation of the first learning content and the target second keyword, so that the learning content of the user is finely screened, more specific learning content required by the user is screened, and the accuracy of the learning content is improved.
Particularly, the dividing unit judges whether the user has learned the content of the learning unit through the historical learning record of the user and divides the grade of the content of the learning unit according to the historical error rate, so that the standard error rate can be conveniently set according to the grade, the user mastering degree is comprehensively analyzed according to the error rate, more personalized and accurate learning content is customized for the user, and the accuracy rate of the customized learning content is improved.
Particularly, the third obtaining module obtains the third learning content from the second learning content according to the determined analysis result of the learning unit content, obtains the corresponding third learning content according to the associated key point according to the analysis result of the learning unit content, and obtains the learning content through the knowledge point corresponding to the error entry in the determined analysis result of the learning unit content, so that the learning content is more detailed, and the accuracy rate of the customized learning content is improved.
Drawings
Fig. 1 is a schematic structural diagram of a system for customizing learning content for a user according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an analysis module according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for customizing learning content for a user according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of an analysis process according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a system for customizing learning content for a user according to an embodiment of the present invention includes:
a first receiving module 110, configured to receive a user request, where the user request includes a first keyword;
a first obtaining module 120, configured to obtain a first learning content in a database according to the first keyword;
a second receiving module 130, configured to receive an acceptance result of the first learning content from the user, if the acceptance result is negative, the first keyword includes a plurality of second keywords, display the second keywords to the user according to the first keyword, and receive selection information of the second keywords, where the second keywords corresponding to the selection information are target second keywords;
a second obtaining module 140, configured to obtain second learning content from the first learning content according to the target second keyword, where the second learning content includes a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to a plurality of information to be fed back;
the analysis module 150 is configured to analyze any learning unit content according to the historical error rate of the feedback information corresponding to the target second keyword, the actual error rate of the information to be fed back corresponding to the learning unit content, and a preset error rate, so as to obtain an analysis result;
a third obtaining module 160, configured to obtain a third learning content from the second learning content according to the analysis result.
Specifically, the information to be fed back is exercises which are set according to learning contents; the second keyword is a lower concept of the first keyword; after the first learning content is obtained, if the first learning content does not have the learning content desired by the user, a second keyword which is a related lower concept of the first keyword is displayed for the user to select, and a target second keyword is obtained, wherein if the first keyword is a function, the second keyword is a linear function, a quadratic function, a proportional function, an inverse proportional function and the like. .
Specifically, in the embodiment of the present invention, a first obtaining module obtains a first learning content for a user for the first time through a first keyword in a user request received by a first receiving module, a second obtaining module can continue to receive a selected target second keyword through a second receiving module according to a selection of the user to obtain a second learning content, an analysis module determines a mastering degree of the learning content by the user according to a difficulty level of information to be fed back in the second learning content and an actual error rate corresponding to the difficulty level, a third obtaining module customizes a more accurate third learning content for the user according to the mastering degree, and generates an individualized and accurate learning content continuously through the request of the user and a learning result, so that the learning content is fewer and accurate, and the accuracy of the customized learning content is improved.
Specifically, when the first obtaining module obtains the first learning content, the database stores the learning content and a matching relationship in advance, the matching relationship is that the first keyword is matched with the learning content, and the first obtaining unit obtains the first learning content by matching the learning content in the database according to the first keyword.
Specifically, in the embodiment of the present invention, the first obtaining module obtains the first learning content according to the matching relationship between the first keyword list and the learning content and according to the learning content in the first keyword matching database, and performs initial obtaining on the learning content of the user, screens out the learning content required by the user, and improves the accuracy of the learning content.
Specifically, when the second obtaining module obtains the second learning content, the target second keyword is matched with any learning content in the first learning content, and the second obtaining module obtains the second learning content by matching in the first learning content according to the target second keyword.
Specifically, since the first learning content is relatively wide, the second learning content is obtained by screening out the learning content matched with the first learning content according to the target second keyword.
Specifically, in the embodiment of the present invention, the second obtaining module obtains the second learning content by matching in the first learning content according to the target second keyword and the matching relationship between the target second keyword and any learning content in the first learning content, performs fine screening on the learning content of the user, screens out a more specific learning content required by the user, and improves the accuracy of the learning content.
Referring to fig. 2, the analysis module includes a dividing unit 151, a setting unit 152, an obtaining unit 153, a comparing unit 154, and a determining unit 155, and the analysis module includes:
the classification unit is used for performing grade classification on the content of the learning unit to obtain a grade classification result;
the setting unit is used for setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
the acquisition unit is used for acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
the comparison unit is used for comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and the determining unit is used for determining the analysis result of the content of the learning unit according to the comparison result.
Specifically, the information to be fed back is a problem, the problem form is a selection problem, and the information to be fed back after the user finishes doing the problem is changed into feedback information; wherein the grade is divided into difficulty grades for dividing the learning unit.
Specifically, in the embodiment of the present invention, the analysis module analyzes the level of any learning unit content to set a corresponding preset error rate, determines an analysis result according to the relationship between the obtained actual error rate of the feedback information corresponding to the learning unit content by the user and the corresponding preset error rate, that is, the mastery degree of the learning unit content by the user, and compares the actual learning detection result with the standard result of the corresponding level, so that the mastery degree of the user is analyzed more accurately, instead of analyzing the mastery degree unilaterally according to the error rate, customizing more personalized and accurate learning content for the user according to the comprehensive analysis mastery degree, and improving the accuracy rate of the customized learning content.
Specifically, when the classification unit classifies the content of the learning unit in a grade, the user request further includes a user history learning record, the user history learning record includes a plurality of history learning unit contents and corresponding keywords, the target second keyword of the learning unit content is matched with the keyword corresponding to the history learning unit content, if the matching is successful, a history error rate a of the history learning unit content corresponding to the successful matching is obtained, the history error rate a is compared with a preset first standard error rate A1 and a preset second standard error rate A2, if a is less than A1, the grade of the learning unit content is determined to be level i, if A1 is less than or equal to A2, the grade of the learning unit content is determined to be level ii, if a is greater than A2, the grade of the learning unit content is determined to be level iii, wherein the preset standard error rate is greater than A1 and less than A2, and the grade i is greater than level ii;
if the matching fails, the grade of the content of the learning unit is directly judged as I grade.
Specifically, the historical error rate a is detected after the user has learned for the last time, and when the target second keyword of the content of the learning unit is matched with the keyword corresponding to the historical learning unit, if the matching is successful, it indicates that the user has learned the content of the learning unit, and if the matching is unsuccessful, it indicates that the user has not learned the content of the learning unit.
Specifically, the dividing unit in the embodiment of the invention judges whether the user has learned the content of the learning unit or not through the user historical learning record, and then divides the grade of the content of the learning unit according to the historical error rate, so that the standard error rate can be conveniently set according to the grade, the user mastering degree can be comprehensively analyzed according to the error rate, more personalized and accurate learning content can be customized for the user, and the accuracy rate of the customized learning content can be improved.
Specifically, when the setting unit sets a preset error rate for the information to be fed back corresponding to the content of the learning unit, if the level of the content of the learning unit is level i, the preset error rate for the information to be fed back is set to a1, if the level of the content of the learning unit is level ii, the preset error rate for the information to be fed back is set to a2, if the level of the content of the learning unit is level iii, the preset error rate for the information to be fed back is set to a3, where the preset error rate is a1 > a2 > a3;
when the actual error rate E is compared with a preset error rate of information to be fed back corresponding to the content of the corresponding learning unit, if the level of the content of the learning unit corresponding to the information to be fed back is I level, the comparison unit compares the actual error rate E with a preset error rate a1, if E is greater than a1, the error rate of the user is determined to be high, and if E is less than or equal to a1, the error rate of the user is determined to be low; if the grade of the content of the learning unit corresponding to the information to be fed back is II grade or III grade, comparing the actual error rate E with the preset error rate a2 or a3 to obtain a comparison result;
when the determining unit determines that the error rate of the user is high, analyzing an error entry in feedback information, wherein the feedback information comprises a plurality of key points, the key points correspond to entries, counting the number of the key points corresponding to the error entry, calculating the key point proportion rate P corresponding to the error entry in the feedback information corresponding to the content of the learning unit, determining an analysis result A '=1-P of the content of the learning unit, and associating the analysis result A' with the key points corresponding to the error entry.
Specifically, the information to be fed back is a single item choice question or a plurality of item choices, the error item is an error item, the key point in the choice question is a knowledge point, if the error item relates to one knowledge point for many times, only one knowledge point is recorded when the number of the knowledge points is counted, and the analysis result can be the mastery degree of the user on the content of the learning unit.
Specifically, when acquiring the third learning content, the third acquiring module does not acquire the third learning content if it is determined that the error rate of the user is low, acquires the third learning content from the second learning content based on the analysis result a 'of the specified learning unit content if it is determined that the error rate of the user is low, acquires a key point corresponding to an error entry based on the analysis result a' and the error entry, sets a key point of all feedback information in the learning unit content as a key point of the learning unit content, matches the acquired key point with a key point of the learning unit content in the second learning content, acquires the second learning content of the matched key point related to the second learning content based on the matched key point, and generates the third learning content.
Specifically, the knowledge points that are key points in the problem are the knowledge points related to the content of the learning unit in the second learning content.
Specifically, in the embodiment of the present invention, the third obtaining module obtains the third learning content from the second learning content according to the determined analysis result of the learning unit content, obtains the associated key point according to the analysis result of the learning unit content, matches the key point of the learning unit content in the second learning content according to the obtained key point, further obtains the corresponding third learning content according to the key point, and obtains the learning content through the knowledge point corresponding to the wrong entry in the determined analysis result of the learning unit content, so that the learning content is more detailed, and the accuracy rate of customizing the learning content is improved.
Referring to fig. 3, a method for customizing learning content for a user according to an embodiment of the present invention includes:
step S210, receiving a first user request, wherein the first user request comprises a first keyword;
step S220, acquiring first learning content in a database according to the first keyword;
step S230, receiving an acceptance result of the user for the first learning content, if the acceptance result is negative, displaying the second keywords to the user according to the first keywords, and receiving selection information of the second keywords, wherein the second keywords corresponding to the selection information are target second keywords;
step S240, acquiring second learning content from the first learning content according to the target second keyword, wherein the second learning content comprises a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to a plurality of information to be fed back;
step S250, analyzing any learning unit content according to the historical error rate of the target second keyword, the actual error rate of the information to be fed back corresponding to the learning unit content and a preset error rate to obtain an analysis result;
and step S260, acquiring third learning content in the second learning content according to the analysis result.
Specifically, the embodiment of the invention obtains the first learning content for the user for the first time through the first keyword in the user request, can continuously receive the target second keyword selected by the user according to the selection of the user to obtain the second learning content, judges the mastering degree of the user on the learning content according to the difficulty level of the information to be fed back in the second learning content and the actual error rate corresponding to the difficulty level, customizes more accurate third learning content for the user according to the mastering degree, continuously generates personalized and accurate learning content through the request of the user and the learning result, makes the learning content less and more precise, and improves the efficiency of customizing the learning content for the user and the accuracy of the learning content.
Referring to fig. 4, when analyzing the content of any learning unit, the method includes:
step S251, grade division is carried out on the content of the learning unit to obtain a grade division result;
step S252, setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
step S253, acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
step S254, comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and step S255, determining the analysis result of the content of the learning unit according to the comparison result.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can be within the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A system for customizing learning content for a user, comprising:
the system comprises a first receiving module, a first processing module and a second receiving module, wherein the first receiving module is used for receiving a user request which comprises a first keyword;
the first acquisition module is used for acquiring first learning content in a database according to the first keyword;
the second receiving module is used for receiving the receiving result of the first learning content from the user, if the receiving result is negative, the first keywords comprise a plurality of second keywords, the second keywords are displayed to the user according to the first keywords, and the selection information of the second keywords is received, wherein the second keywords corresponding to the selection information are target second keywords;
the second obtaining module is used for obtaining second learning content from the first learning content according to the target second keyword, the second learning content comprises a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to the plurality of information to be fed back;
the analysis module is used for analyzing any learning unit content according to the historical error rate of the feedback information corresponding to the target second keyword, the actual error rate of the information to be fed back corresponding to the learning unit content and a preset error rate to obtain an analysis result;
and the third acquisition module is used for acquiring third learning content from the second learning content according to the analysis result.
2. The system according to claim 1, wherein the database stores the learning content and the matching relationship in advance when the first obtaining module obtains the first learning content, the matching relationship is that the first keyword matches the learning content, and the first obtaining unit obtains the first learning content by matching the learning content in the database according to the first keyword.
3. The system according to claim 2, wherein the second obtaining module matches the target second keyword with any learning content in the first learning content when obtaining the second learning content, and the second obtaining module obtains the second learning content by matching in the first learning content according to the target second keyword.
4. The system according to claim 3, wherein the analysis module comprises a dividing unit, a setting unit, an obtaining unit, a comparing unit and a determining unit, and the analysis module analyzes any learning unit content comprises:
the classification unit is used for performing grade classification on the content of the learning unit to obtain a grade classification result;
the setting unit is used for setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
the acquisition unit is used for acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
the comparison unit is used for comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and the determining unit is used for determining the analysis result of the content of the learning unit according to the comparison result.
5. The system for customizing learning content for a user according to claim 4, wherein the dividing unit performs level division on the learning unit content, the user request further includes a user history learning record, the user history learning record includes a plurality of history learning unit contents and corresponding keywords, the target second keyword of the learning unit content is matched with the keyword corresponding to the history learning unit content, if the matching is successful, a history error rate a of the history learning unit content corresponding to the matching is obtained, the history error rate a is compared with a preset first standard error rate A1 and a preset second standard error rate A2, if a is less than A1, the level of the learning unit content is determined as level i, if A1 is less than or equal to A2, the level of the learning unit content is determined as level ii, and if a is greater than A2, the level of the learning unit content is determined as level iii, wherein the preset standard error rate is greater than A1 and greater than A2, and the level of the level ii is greater than level iii;
if the matching fails, the grade of the content of the learning unit is directly judged as I grade.
6. The system according to claim 5, wherein the setting unit sets the preset error rate of the information to be fed back to a1 if the level of the content of the learning unit is level i, sets the preset error rate of the information to be fed back to a2 if the level of the content of the learning unit is level ii, and sets the preset error rate of the information to be fed back to a3 if the level of the content of the learning unit is level iii, where a1 > a2 > a3.
7. The system according to claim 6, wherein the comparing unit compares the actual error rate E with a preset error rate of the information to be fed back corresponding to the content of the learning unit, if the level of the content of the learning unit corresponding to the information to be fed back is level i, the actual error rate E is compared with a preset error rate a1, if E > a1, the error rate of the user is determined to be high, and if E ≦ a1, the error rate of the user is determined to be low; if the grade of the content of the learning unit corresponding to the information to be fed back is II grade or III grade, comparing the actual error rate E with a preset error rate a2 or a3 to obtain a comparison result;
when the determining unit determines that the error rate of the user is high, analyzing an error entry in feedback information, wherein the feedback information comprises a plurality of key points, the key points correspond to entries, counting the number of the key points corresponding to the error entry, calculating the key point proportion rate P corresponding to the error entry in the feedback information corresponding to the content of the learning unit, determining an analysis result A '=1-P of the content of the learning unit, and associating the analysis result A' with the key points corresponding to the error entry.
8. The system according to claim 7, wherein the third acquiring module, when acquiring the third learning content, does not acquire the third learning content if it is determined that the error rate of the user is low, acquires the third learning content from the second learning content based on the analysis result a 'of the determined learning unit content if it is determined that the error rate of the user is low, acquires a keypoint corresponding to an error entry based on the analysis result a' and the error entry, matches the acquired keypoint with a keypoint of the learning unit content in the second learning content using a keypoint of all feedback information in the learning unit content as the keypoint of the learning unit content, acquires the second learning content of the matched keypoint to which the second learning content relates based on the matched keypoint, and generates the third learning content.
9. A method of customizing learning content for a user applying the system for customizing learning content for a user according to any one of claims 1 to 8, comprising:
receiving a first user request, wherein the first user request comprises a first keyword;
acquiring first learning content in a database according to the first keyword;
receiving an acceptance result of the user for the first learning content, if the acceptance result is negative, the first keywords comprise a plurality of second keywords, displaying the second keywords to the user according to the first keywords, and receiving selection information of the second keywords, wherein the second keywords corresponding to the selection information are target second keywords;
acquiring second learning content from the first learning content according to the target second keyword, wherein the second learning content comprises a plurality of learning unit contents and a plurality of information to be fed back, and any learning unit content corresponds to a plurality of information to be fed back;
analyzing any learning unit content according to the historical error rate of the target second keyword and the actual error rate and preset error rate of the information to be fed back corresponding to the learning unit content to obtain an analysis result;
and acquiring third learning content from the second learning content according to the analysis result.
10. The method of customizing learning content for a user according to claim 9, wherein analyzing any of the learning unit content comprises:
grading the content of the learning unit to obtain a grading result;
setting a preset error rate for the information to be fed back corresponding to the content of the learning unit according to the grade division result;
acquiring the actual error rate of the feedback information corresponding to the content of the learning unit by the user;
comparing the actual error rate with a preset error rate of the information to be fed back corresponding to the content of the corresponding learning unit to obtain a comparison result;
and determining the analysis result of the content of the learning unit according to the comparison result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738036A (en) * | 2023-02-03 | 2023-09-12 | 上海陟明信息技术有限责任公司 | User behavior analysis method and system based on big data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100005413A1 (en) * | 2008-07-07 | 2010-01-07 | Changnian Liang | User Interface for Individualized Education |
| CN109299399A (en) * | 2018-12-17 | 2019-02-01 | 广东小天才科技有限公司 | Learning content recommendation method and terminal equipment |
| CN109522420A (en) * | 2018-11-16 | 2019-03-26 | 广东小天才科技有限公司 | Method and system for acquiring learning demand |
| CN111540244A (en) * | 2020-05-11 | 2020-08-14 | 镇江纵陌阡横信息科技有限公司 | Multi-terminal online teaching system based on big data analysis |
| CN112052686A (en) * | 2020-09-02 | 2020-12-08 | 合肥分贝工场科技有限公司 | Voice learning resource pushing method for user interactive education |
| CN113851021A (en) * | 2021-09-18 | 2021-12-28 | 读书郎教育科技有限公司 | Wrong-answer-book-based recommended learning system and method |
| CN114202978A (en) * | 2021-12-15 | 2022-03-18 | 潘浩栋 | Intelligent training method and system based on big data |
-
2022
- 2022-11-09 CN CN202211402493.2A patent/CN115660913A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100005413A1 (en) * | 2008-07-07 | 2010-01-07 | Changnian Liang | User Interface for Individualized Education |
| CN109522420A (en) * | 2018-11-16 | 2019-03-26 | 广东小天才科技有限公司 | Method and system for acquiring learning demand |
| CN109299399A (en) * | 2018-12-17 | 2019-02-01 | 广东小天才科技有限公司 | Learning content recommendation method and terminal equipment |
| CN111540244A (en) * | 2020-05-11 | 2020-08-14 | 镇江纵陌阡横信息科技有限公司 | Multi-terminal online teaching system based on big data analysis |
| CN112052686A (en) * | 2020-09-02 | 2020-12-08 | 合肥分贝工场科技有限公司 | Voice learning resource pushing method for user interactive education |
| CN113851021A (en) * | 2021-09-18 | 2021-12-28 | 读书郎教育科技有限公司 | Wrong-answer-book-based recommended learning system and method |
| CN114202978A (en) * | 2021-12-15 | 2022-03-18 | 潘浩栋 | Intelligent training method and system based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 匡容;杨振国;刘文印;: ""基于多重因素的个性化学习推荐系统"", 《计算机应用研究》, no. 01 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738036A (en) * | 2023-02-03 | 2023-09-12 | 上海陟明信息技术有限责任公司 | User behavior analysis method and system based on big data |
| CN116738036B (en) * | 2023-02-03 | 2024-03-22 | 上海陟明信息技术有限责任公司 | User behavior analysis method and system based on big data |
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