CN101452464B - System and method for generating learning element snapshot - Google Patents
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Abstract
The invention provides a system for generating a study element snapshot. The system comprises an interface which receives an analysis object element and a user identification code, wherein the analysis object element corresponds to a class and comprises a plurality of sentences and multimedia data, and the plurality of the sentences comprise at least one keyword. The system also comprises a study element library, a character preview unit, a multimedia preview unit and a snapshot generating unit, wherein the study element library comprises a plurality of study elements and a user study course record; each study element corresponds to at least one class and comprises at least one keyword; the user study course record comprises a record for using the plurality of the study elements corresponding to the user identification code; the character preview unit selects at least one sentence from the plurality of the sentences of the analysis object element to be a preview sentence according to the user study course record corresponding to the user identification code; the multimedia preview unit finds out multimedia with high correlation degree with the preview sentence from the multimedia data of the analysis object element and takes the multimedia as preview multimedia; and the snapshot generating unit is combined with the preview sentences and the preview multimedia to generate a snapshot corresponding to the analysis object element and displays the snapshot on a display device.
Description
Technical field
The present invention is relevant for data processing, particularly relevant for a kind of System and method for (SYSTEM AND METHOD FOR GENERATING SNAPSHOTS OF LEARNING OBJECTS) that produces learning element snapshot.
Background technology
In recent years; (Application Programming Institute API), provides other query site to have increasing education resource storehouse that open application interface is arranged; And come defining interface according to SQI (Simple Query Interface), conveniently to stride the station retrieval.
But, strides the station retrieval and tend to the big learning element resource of the amount of searching, and the description of learning element is difficult to provide the information of user's needs usually.
According to a traditional method, the supplier of element resources can write the Summary file of element resources content voluntarily and describe and the upload pictures file, to form " snapshot " of this element resources.
Yet this kind snapshot is Summary file description and the picture that the supplier (author) of element resources sets up the element resources content on their own, wastes time and energy very much in the making.And the content of this kind snapshot is (being provided by the author) of fixing, and it writes summary with author's angle, and literal wherein and image content often make the user is not easy to judge whether this element meets the needs of itself, and is therefore, also inconvenient in the use.
Therefore, the method and system that need a kind of automatic generation learning element snapshot are to overcome the problems referred to above.
Summary of the invention
The present invention provides the system that produces learning element snapshot, and it comprises:
One interface, it is in order to receive: an analytic target element and user's identification code, wherein this analytic target element is corresponding to an analytic target part classification, and comprises plural sentence and multimedia document, and should comprise at least one analytic target keyword by the plural number sentence; And user's identification code; One learning element storehouse, it comprises: Learning of Complex Number element and user's learning process record, wherein each this learning element is classified corresponding at least one learning element, and each this learning element comprises at least one learning element keyword; And, and this user learning process record, it comprises the record corresponding to this Learning of Complex Number element of use of this user's identification code; One literal preview unit; It is to link with this interface and this learning element storehouse; In order to this user's learning process record according to this user's identification code correspondence; From this plural number sentence of this analytic target element, choose at least one sentence as preview sentences, it is in order to choose the relatively large sentence of message amount in this plural number sentence that comprises an associative key and this analytic target element as preview sentences; Wherein, said associative key is according to the corresponding said user's learning process record of said user's identification code, from said Learning of Complex Number element, finds out at least one keyword relevant with said analytic target element; One multimedia preview unit, its its be and this interface and this learning element storehouse link, in order to it from this multimedia document of this analytic target element, find out and the high person of this preview sentences degree of correlation as preview multimedia; An and snapshot generating unit; Its its be to link with this literal preview unit and this multimedia preview unit; In order to combining this preview sentences and this preview multimedia producing a snapshot, and this snapshot is shown on the display device corresponding to this analytic target element.
The present invention also provides a kind of method that produces learning element snapshot.This method at first receives an analytic target element, and it is classified corresponding to one, and comprises plural sentence and multi-medium data, and should comprise at least one keyword by the plural number sentence.And receive user's identification code.And a learning element storehouse is provided, and wherein this learning element storehouse comprises: the Learning of Complex Number element, and wherein each this learning element is corresponding at least one classification, and each this learning element comprises at least one keyword; And user's learning process record, it comprises the record corresponding to this Learning of Complex Number element of use of this user's identification code.According to this corresponding user's learning process record of this user's identification code; From the plural sentence of this analytic target element, choose at least one sentence, promptly choose the relatively large sentence of quantity of information in the said plural sentence that comprises an associative key and said analytic target element as preview sentences as preview sentences; Wherein, said associative key is according to the corresponding said user's learning process record of said user's identification code, from said Learning of Complex Number element, finds out at least one keyword relevant with said analytic target element.And from this multi-medium data of this analytic target element, find out and the high person of this preview sentences degree of correlation as preview multimedia.Combine this preview sentences and this preview multimedia with the snapshot of generation again, and show this snapshot corresponding to this analytic target element.
Description of drawings
Fig. 1 shows the synoptic diagram according to the system of the generation learning element snapshot of one embodiment of the invention.
Fig. 2 shows the method flow diagram according to the generation learning element snapshot of the embodiment of the invention.
The process flow diagram of Fig. 3 displayed map 2 Chinese words previewers.
One example of the incidence matrix that Fig. 4 step display S301 produces.
The process flow diagram of the individualized associative key step of decision in Fig. 5 displayed map 3.
The process flow diagram of multimedia preview program in Fig. 6 displayed map 2.
Interface～11; Analytic target element～101; User's identification code～103; Literal preview unit～131; Preview sentences～132; Multimedia preview unit～133; Preview multimedia～134; Snapshot generating unit～135; Snapshot～136; Keyword～105; Processor～130; Display device～15; Learning element storehouse～17; Learning element～171; User's learning process record～173.
Embodiment
For let the object of the invention, characteristic, and advantage can be more obviously understandable, hereinafter is special lifts preferred embodiment, and cooperates appended diagram, does detailed explanation.
Instructions of the present invention provides various embodiment that the technical characterictic of the different embodiments of the present invention is described.Wherein, the usefulness that is configured to explanation of each element among the embodiment is not in order to restriction the present invention.And the part of reference numerals repeats among the embodiment, for the purpose of simplifying the description, is not the relevance that means between the different embodiment.
Fig. 1 shows the synoptic diagram according to the system of the generation learning element snapshot of one embodiment of the invention.The system that produces learning element snapshot can be any electronic installation with calculation function, like personal computer etc.Present embodiment is the system that example explanation produces learning element snapshot with the personal computer, and it comprises: interface 11, processor 130, display device 15, and learning element storehouse 17.
Wherein, each learning element 171 is corresponding at least one classification, and each learning element 171 comprises at least one keyword.
The record that each user's learning process record 173 comprises corresponding to this Learning of Complex Number element of use of specific user's identification code.For example, use a certain specific user's identification code to login, and the learning element title of using, its corresponding classification with and the keyword that comprises.
If user's desire to the analytic target element 101 of above-mentioned input, produces the snapshot about a certain particular keywords, also can see through interface 11 input keywords 105, produce the usefulness of snapshot for subsequent analysis.
Fig. 2 shows the method flow diagram according to the generation learning element snapshot of the embodiment of the invention.The method that produces learning element snapshot can be implemented in the system of generation learning element snapshot as shown in Figure 1, also can be rendered in any electronic installation with calculation function, like personal computer etc.
In the method for Fig. 2, user's identification code and keyword according to user's input are produced as the personalized learning element snapshot that this user makes to measure automatically.
In step S200, input user identification code, for example user's title and password etc.
In step S201, the analytic target element is provided.The analytic target element person of being to use wants the object analyzed, and it can be learning element, corresponding to a classification, and comprises plural sentence and multi-medium data, and should comprise at least one keyword by the plural number sentence.Wherein the sentence data are made up of literal, and multi-medium data can comprise like film, sound, picture, form etc.
In step S202, carry out pre-process to the analytic target element.According to the pre-process of present embodiment, sentence data and multi-medium data in the analytic target element are separated, for subsequent treatment.
In step S203, carry out the literal previewer, according to this user's identification code, from the plural sentence of this analytic target element, choose at least one sentence as preview sentences.The details of the literal previewer that step S203 carries out as after state.
In step S204, carry out the multimedia preview program, its from this multi-medium data of this analytic target element, find out and the high person of this preview sentences degree of correlation as preview multimedia.The details of the multimedia preview program that step S204 carries out as after state.
In step S205, in conjunction with this preview sentences and this preview multimedia to produce a snapshot corresponding to this analytic target element.
In step S206, the snapshot that step S205 is produced is shown on the display device.
The process flow diagram of Fig. 3 displayed map 2 Chinese words previewers.
Among the step S300, the learning element storehouse is provided, it comprises Learning of Complex Number element and user's learning process record.Wherein each learning element is corresponding at least one classification, and each this learning element comprises at least one keyword.User's learning process record comprises the record corresponding to this Learning of Complex Number element of use of at least one user's identification code.
The degree of association matrix of this plural number sentence of computational analysis object elements in step S301, it comprises the degree of association R between any two of this plural number sentence.Wherein, the degree of association of sentence Si and sentence Si is according to computes:
Wherein Ui and Uj are the binary speech that neither comprises with sentence Si and Sj in this analytic target element, and Ui ∪ Uj is the summation of the speech number of binary speech among Ui and the Uj, and Ui ∩ Uj is the speech number of the binary speech that comprises jointly among Ui and the Uj.
The molecule of above-mentioned formula 1 multiply by 2, is because calculating is to be the basis with the binary speech.Therefore, the numerical value of the molecule multiplier of formula 1 can be different according to adopting as calculating basic words and phrases character.
Fig. 4 representes an example of the incidence matrix that step S301 produces.Wherein the analytic target element comprises sentence S1～S7.The incidence matrix of Fig. 4 comprises the degree of association of any two sentences among sentence S1～S7.
And in step S302, calculate among sentence S1～S7 the summation of each and other sentence degree of association.The incidence matrix of Fig. 4 comprise among sentence S1～S7 between any one and other sentence the degree of association with.
In step S303, receive the keyword A of user's input.
In step S305, the individualized associative key of decision.The decision of individualized associative key according to analytic target element and this corresponding user's learning process record of user's identification code, is found out at least one keyword B relevant with this analytic target element in the Learning of Complex Number element from the learning element storehouse.The details of the individualized associative key of step S305 decision as after state.
Step S307; The degree of association of calculating sentence S1～S7 and other sentence according to step S302 with, and the keyword A of user's input of receiving of step S303, and the keyword B of step S305 decision; Choose that to comprise data volume more (promptly; The degree of association and bigger), and comprises the sentence of keyword A and/or B, as preview sentences.
Referring to Fig. 4, among sentence S1～S7, the degree of association and the highest sentence are in regular turn: sentence S1, S6, S3, S4.When desiring to choose 3 sentences as preview sentences, if taking into account critical speech not, the sentence of then choosing is S1, S6, S3; If taking into account critical speech (for example keyword A and/or B) then filters out the sentence that comprises keyword A and/or B earlier, again from wherein picking out the degree of association and relatively large person as preview sentences.
The process flow diagram of the individualized associative key step of decision in Fig. 5 displayed map 3.The step of the individualized associative key of decision; Be the information such as classification and keyword of utilizing each learning element in the learning element storehouse; And the user's learning process record in the learning element storehouse; In user's learning process record of query learning component library, interested other keyword of user's most probable (that is individualized associative key).
In step S500, user's identification code of acquisition user input.
In step S501, obtain the keyword and the pairing classification thereof that comprise in the analytic target element.At this, the analytic target element is belonged to classification 1 and is classified 2, and the analytic target element comprises keyword 1 and keyword 2.
At step S503, according to this user's learning process record, from this Learning of Complex Number element, find out and this analytic target element corresponding to same category and the used learning element of user once logined with this user's identification code.For example, from the learning element storehouse, find out earlier and belong to classification 1 or 2 the learning element set of classifying (being referred to as learning element set 1), from learning element set 1, found out again once by the used learning element set of this user (being referred to as learning element set 2).
Step S505 from the learning element that step S503 finds out, finds out the learning element that comprises the keyword that this analytic target element had.For example, from learning element set 2, find out the learning element set (being referred to as learning element set 3) that comprises above-mentioned keyword 1 or keyword 2.
Among the step S507, search the keyword be contained in the learning element that step S505 finds out, and with this keyword of not being contained in this analytic target element as keyword B.For example, learning element set 3 comprises a plurality of learning elements, and in these learning elements, except keyword 1 and keyword 2, still comprises keyword 3, keyword 4, reaches keyword 5.Keyword 3, keyword 4, and keyword 5 be the individualized associative key that tentatively determines.
Because if the quantity of keyword is too much; Can cause difficulty and analysis result out of true in the processing; Therefore, execution in step S509 further filters out more important individualized associative key according to pre-defined rule from the keyword that step S507 finds out again.For example, can from keyword 3, keyword 4, and keyword 5, find out the keyword that comprises in the nearest used learning element, as the individualized associative key of decision.For example, the keyword that nearest used learning element comprises is a keyword 3, promptly chooses keyword 3 as individualized associative key, as the usefulness that produces the preview literal.
The process flow diagram of multimedia preview program in Fig. 6 displayed map 2.
Among the step S601, calculate the degree of association between any two of plural sentence in this analytic target element.For example; Can directly capture (that is
of formula 1, of the degree of association between any two of plural sentence in the analytic target element that calculates among the step S301
Among the step S603, and with the distance of this degree of association divided by each sentence in each this multi-medium data in this analytic target element and this analytic target element.For example, with " number of characters of being separated by " unit of account as the distance of multi-medium data and sentence.Wherein the distance table of sentence Si and multi-medium data Gj be shown d (Si, Gj).
Among the step S605, the R that obtains according to step S601 (i, j) and the d that obtains of step S603 (Si Gj), calculates the degree of association of each sentence in each this multi-medium data and this analytic target element, its basis following formula:
Wherein
wherein Ui and Uj are the binary speech that neither comprises with sentence Si and Sj in this analytic target element; And Ui ∪ Uj is the summation of the speech number of binary speech among Ui and the Uj; Ui ∩ Uj is the speech number of the binary speech that comprises jointly among Ui and the Uj; (Si Gj) is the physical distance of multi-medium data Gj and sentence Si to d.
Step S605 calculates the degree of association of each sentence in each this multi-medium data and this analytic target element, can be expressed as a degree of association matrix.
Step S607 according to above-mentioned multimedia degree of association matrix, chooses and this shortest multi-medium data of this preview sentences distance, as the preview multimedia data.
Though the present invention discloses as above with preferred embodiment; Right its is not in order to limiting the present invention, anyly has the knack of this art, do not breaking away from the spirit and scope of the present invention; When can doing a little change and retouching, thus protection scope of the present invention when with the claim scope the person of being defined be as the criterion.
Claims (14)
1. a system that produces learning element snapshot is characterized in that, said system comprises:
One interface; It is in order to receive an analytic target element and user's identification code; Wherein said analytic target element is corresponding to an analytic target part classification, and comprises plural sentence and multi-medium data, and said plural sentence comprises at least one analytic target keyword;
One learning element storehouse; It comprises Learning of Complex Number element and user's learning process record; Wherein each said learning element is classified corresponding at least one learning element; And each said learning element comprises at least one learning element keyword, and this user's learning process record comprises the record corresponding to the said Learning of Complex Number element of the use of said user's identification code;
One literal preview unit; Link with said interface and learning element storehouse; In order to said user's learning process record according to said user's identification code correspondence; From the said plural sentence of said analytic target element, choose at least one sentence, promptly choose the relatively large sentence of quantity of information in the said plural sentence that comprises an associative key and said analytic target element as preview sentences as preview sentences; Wherein, said associative key is according to the corresponding said user's learning process record of said user's identification code, from said Learning of Complex Number element, finds out at least one keyword relevant with said analytic target element;
One multimedia preview unit is connected with said interface and learning element storehouse, in order to from the said multi-medium data of said analytic target element, find out with the high person of the said preview sentences degree of correlation as preview multimedia; And
One snapshot generating unit is connected with said literal preview unit and multimedia preview unit, in order to combining said preview sentences and said preview multimedia producing the snapshot corresponding to said analytic target element, and said snapshot is shown on the display device.
2. according to claim 1 the system of generation learning element snapshot is characterized in that,
Said interface also comprises in order to receive user's keyword of user input;
Said literal preview unit also comprise in order to:
Calculate the quantity of information of the said plural sentence of said analytic target element;
Find out at least one said associative key relevant with said analytic target element;
According to quantity of information and the said user's keyword and the said associative key of said plural sentence, choose the relatively large and sentence that comprises said user's keyword and/or associative key of quantity of information as said preview sentences.
3. the system of generation learning element snapshot as claimed in claim 2; It is characterized in that; Said literal preview unit more comprises the degree of association matrix in order to the said plural sentence that calculates said analytic target element, and it comprises the degree of association between any two of said plural sentence; And
Calculate the variable of the degree of association summation of each sentence and other sentence in the said plural sentence as its quantity of information.
4. the system of generation learning element snapshot as claimed in claim 3 is characterized in that, said literal preview unit more comprises in order to the degree of association between any two according to the said plural sentence of computes:
wherein Ui is the binary speech that the sentence Si in the said analytic target element is comprised; Uj is the binary speech that the sentence Sj in the said analytic target element comprises; And Ui ∪ Uj is the summation of the speech number of binary speech among Ui and the Uj, and Ui ∩ Uj is the speech number of the binary speech that comprises jointly among Ui and the Uj.
5. the system of generation learning element snapshot as claimed in claim 2 is characterized in that, said literal preview unit more comprises to be carried out when in said Learning of Complex Number element, finding out at least one associative key relevant with said analytic target element:
(A), from said Learning of Complex Number element, find out the used said learning element of user that the learning element classification is same as the analytic target part classification and once logined with said user's identification code according to said user's learning process record;
(B) from the learning element that step (A) is found out, find out the learning element that comprises said analytic target keyword; And
(C) search the learning element keyword be contained in the learning element that step (B) finds out, and with the said learning element keyword that is not contained in said analytic target element as said associative key.
6. the system of generation learning element snapshot as claimed in claim 2 is characterized in that, said multimedia preview unit also comprises in order to carry out:
Calculate the multimedia distance matrix, calculate the distance of each sentence in each said multi-medium data and the said analytic target element in the said analytic target element; And
Calculate multimedia degree of association matrix, calculate the degree of association of each sentence in each said multi-medium data and the said analytic target element, to obtain and the shortest said multi-medium data of said preview sentences distance.
7. the system of generation learning element snapshot as claimed in claim 6 is characterized in that, carries out when said multimedia preview unit more is included in the degree of association of calculating each sentence in each said multi-medium data and the said analytic target element:
Calculate the degree of association between any two of plural sentence in the said analytic target element; And
With the distance of the said degree of association, to obtain the degree of association of each sentence in each said multi-medium data and the said analytic target element divided by each sentence in each said multi-medium data in the said analytic target element and the said analytic target element.
8. method that produces learning element snapshot, this method comprises:
Receive an analytic target element, it is corresponding to an analytic target part classification, and comprises plural sentence and multi-medium data, and said plural sentence comprises at least one analytic target keyword;
Receive user's identification code;
One learning element storehouse is provided, and wherein said learning element storehouse comprises: the Learning of Complex Number element, and wherein each said learning element is classified corresponding at least one learning element, and each said learning element comprises at least one learning element keyword; One user's learning process record, it comprises the record corresponding to the said Learning of Complex Number element of the use of said user's identification code;
According to the corresponding said user's learning process record of said user's identification code; From the plural sentence of said analytic target element, choose at least one sentence, promptly choose the relatively large sentence of quantity of information in the said plural sentence that comprises an associative key and said analytic target element as preview sentences as preview sentences; Wherein, said associative key is according to the corresponding said user's learning process record of said user's identification code, from said Learning of Complex Number element, finds out at least one keyword relevant with said analytic target element;
From the said multi-medium data of said analytic target element, find out with the high person of the said preview sentences degree of correlation as preview multimedia; And
Producing a snapshot, and show said snapshot in conjunction with said preview sentences and said preview multimedia corresponding to said analytic target element.
9. the method for generation learning element snapshot as claimed in claim 8 is characterized in that, said method also comprises:
Receive user's keyword;
Calculate the quantity of information of the said plural sentence of said analytic target element;
Find out at least one said associative key relevant with said analytic target element;
According to quantity of information and the said user's keyword and the said associative key of said plural sentence, choose the relatively large and sentence that comprises said user's keyword and/or associative key of quantity of information as said preview sentences.
10. the method for generation learning element snapshot as claimed in claim 9 is characterized in that, the step of quantity of information of calculating the said plural sentence of said analytic target element also comprises:
Calculate the degree of association matrix of the said plural sentence of said analytic target element, it comprises the degree of association between any two of said plural sentence; And
Calculate the variable of the degree of association summation of each sentence and other sentence in the said plural sentence as its quantity of information.
11. the method for generation learning element snapshot as claimed in claim 10 is characterized in that, the degree of association between any two of said plural sentence is according to computes:
12. the method for generation learning element snapshot as claimed in claim 9 is characterized in that, the step of from said Learning of Complex Number element, finding out at least one associative key relevant with said analytic target element also comprises:
(A), from said Learning of Complex Number element, find out the used said learning element of user that the learning element classification is same as the analytic target part classification and once logined with said user's identification code according to said user's learning process record;
(B) from the learning element that step (A) is found out, find out the learning element that comprises said analytic target keyword; And
(C) search the learning element keyword be contained in the learning element that step (B) finds out, and with the said learning element keyword that is not contained in said analytic target element as associative key.
13. the method for generation learning element snapshot as claimed in claim 9 is characterized in that, said method also comprises:
Calculate the multimedia distance matrix, calculate the distance of preview sentences in each the said multi-medium data that is contained in the said analytic target element and the said analytic target element;
Calculate multimedia degree of association matrix, calculate the degree of association of preview sentences in each said multi-medium data and the said analytic target element, to obtain and the shortest said multi-medium data of said preview sentences distance.
14. the method for generation learning element snapshot as claimed in claim 13 is characterized in that, the step of calculating the degree of association of each sentence in each said multi-medium data and the said analytic target element comprises:
Calculate the degree of association between any two of plural sentence in the said analytic target element; And
And with the distance of the said degree of association, to obtain the degree of association of each sentence in each said multi-medium data and the said analytic target element divided by each sentence in each said multi-medium data in the said analytic target element and the said analytic target element.
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