CN112052760A - Method and device for judging learning effectiveness aiming at different article types - Google Patents
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Abstract
The invention relates to a method and a device for judging learning effectiveness aiming at different article types, which comprises the steps of obtaining articles; judging the type of the article; and carrying out corresponding learning effectiveness judgment according to article types, wherein the article types comprise text articles, picture articles and text and picture mixed articles. The invention can calculate the learning progress of different training article types, thereby judging whether each article is effectively learned and the learning progress of students, better knowing the learning condition of the students and supervising and urging the learning according to the learning condition of the students.
Description
Technical Field
The invention belongs to the technical field of video playing, and particularly relates to a method and a device for judging learning effectiveness aiming at different article types.
Background
At present, many training management systems exist, training courses are also diversified, and have formats such as characters, pictures, PDFs (portable document formats) and videos, but an effective calculation method is not available for a while on how to effectively identify whether an article is effectively learned or not and how much learning progress is completed.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for determining learning effectiveness for different article types to solve the problem that whether an article is effectively learned cannot be calculated in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme: a method of learning validity determination for different article types, comprising:
acquiring an article;
judging the type of the article;
and carrying out corresponding learning validity judgment according to the article types.
Further, the article types include text articles, picture articles, and text and picture mixed articles.
Further, when the article type is a text article, the learning validity determination includes:
acquiring a standard reading speed;
obtaining the length of a screen interval according to the product of the standard reading speed and the preset time;
dividing the total number of characters and the length of a screen interval to obtain the number of intervals; wherein the interval number is an integer;
snapping a human face at an initial node of each screen interval in the interval number, and snapping at set time intervals; if the snapshot is effective, the current node is considered to be effective in reading and the effective reading duration is fed back;
and calculating the reading progress according to the effective reading time.
Further, when the article type is a picture article, the learning validity determination includes:
determining the pixel height of each line of characters in the picture, and converting the pixel height into the number of character lines;
determining the total amount of characters in the picture according to the number of characters corresponding to each line of characters in the picture and the number of characters, and acquiring the total amount of characters in each picture;
acquiring a standard reading speed, and acquiring the plan learning duration of all pictures according to the total number of the characters, the number of the pictures and the standard reading speed;
capturing when learning picture articles, feeding back the effective learning duration of each picture, and acquiring the actual learning duration of all pictures;
and acquiring the reading progress according to the planned learning time length and the actual learning time length.
Further, when the article type is a mixed article of characters and pictures, the determining the learning effectiveness according to the article type includes:
acquiring a first reading progress of characters in an article and a second reading progress of pictures in the article;
acquiring a first weight of the total word number of characters in an article and a second weight of the total word number of pictures in the article;
and acquiring the overall learning progress of the text and picture mixed article according to the first reading progress, the second reading progress, the first weight and the second weight.
Further, obtaining a standard reading speed comprises:
and acquiring pre-stored reading speed data, and extracting a median in the reading speed data to be used as a standard reading speed.
Furthermore, a camera is adopted to capture the human face.
Further, the preset time is 50 seconds, and the set time is 20 seconds.
An embodiment of the present application provides an apparatus for determining learning effectiveness for different article types, including:
the acquisition module is used for acquiring articles;
the judging module is used for judging the article types;
and the judging module is used for carrying out corresponding learning validity judgment according to the article types.
Further, the article types include text articles, picture articles, and text and picture mixed articles.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the invention provides a method and a device for judging learning effectiveness aiming at different article types, which comprises the steps of obtaining articles; judging the type of the article; and carrying out corresponding learning effectiveness judgment according to article types, wherein the article types comprise text articles, picture articles and text and picture mixed articles. The invention can calculate the learning progress of different training article types, thereby judging whether each article is effectively learned and the learning progress of students, better knowing the learning condition of the students and supervising and urging the learning according to the learning condition of the students.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating steps of a method for learning validity determination for different article types according to the present invention;
FIG. 2 is a schematic flow chart of a method for determining learning effectiveness of a text article according to the present invention;
FIG. 3 is a flowchart illustrating a method for determining learning effectiveness of a picture article according to the present invention;
FIG. 4 is a flowchart illustrating a method for determining learning effectiveness of a text-image mixture article according to the present invention;
fig. 5 is a schematic structural diagram of an apparatus for learning validity determination for different article types according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
A specific method for determining learning effectiveness for different article types provided in the embodiments of the present application is described below with reference to the accompanying drawings.
As shown in fig. 1, a method for determining learning effectiveness for different article types provided in this embodiment of the present application includes:
s101, acquiring an article;
firstly, articles needing to be subjected to learning effectiveness judgment are acquired in a training management system, wherein the training management system is provided with a file library, and all the articles are stored in the file library.
S102, judging the article type;
the article types comprise text articles, picture articles and text and picture mixed articles. And judging whether the article type is a text article, a picture article or a text and picture mixed article.
S103, performing corresponding learning validity judgment according to the article types.
And obtaining corresponding learning effectiveness judging methods according to different article types for judgment.
The working principle of the method for judging the learning effectiveness aiming at different article types is as follows: the method comprises the steps of firstly, obtaining an article needing learning effectiveness judgment in a training management system, judging whether the article type is a text article, a picture article or a text and picture mixed article, and obtaining corresponding learning effectiveness judgment methods according to different article types for judgment.
In some embodiments, as shown in fig. 2, when the article type is a text article, the learning validity determination includes:
s201, acquiring a standard reading speed;
s202, obtaining the length of a screen interval according to the product of the standard reading speed and the preset time;
s203, dividing the total number of characters of the seal by the length of a screen interval to obtain the number of intervals; wherein the interval number is an integer;
s204, snapping the face of the initial node of each screen interval in the interval number, and snapping at set time intervals; if the snapshot is effective, the current node is considered to be effective in reading and the effective reading duration is fed back;
and S205, calculating the reading progress according to the effective reading time length.
Preferably, the standard reading speed is obtained, including:
and acquiring pre-stored reading speed data, and extracting a median in the reading speed data to be used as a standard reading speed.
The reading speed data is the speed of reading articles by multiple persons, and the median in the data is selected as the standard reading speed.
Preferably, the preset time is 50 seconds, and the set time is 20 seconds.
Then, taking the median 10(w/s) × 50(s) according to the standard reading speed as the length L of a screen interval; according to the total word number W/screen interval length L of the article, wherein the total word number W/screen interval length L is the interval number M, M is an integer, a face is snapshotted at an initial node of each screen interval i, and the value of i is 1 … … n; and then after 20S, carrying out snapshot again, if the snapshots are all effective, considering that the reading of the current node is effective, and finally returning the actual effective reading time length S of each screen intervaliReading progress
If the content exceeds 100%, 100% is selected.
Preferably, the human face is captured by a camera.
In some embodiments, as shown in fig. 3, when the article type is a picture article, the learning validity determination includes:
s301, determining the pixel height of each line of characters in the picture, and converting the pixel height into the number of character lines;
s302, determining the total amount of characters in the picture according to the number of the characters corresponding to each line of characters in the picture and the number of the characters, and acquiring the total amount of the characters in each picture;
s303, acquiring a standard reading speed, and acquiring the plan learning duration of all pictures according to the total number of the characters, the number of the pictures and the standard reading speed;
s304, capturing images when learning the image articles, feeding back the effective learning duration of each image, and acquiring the actual learning durations of all the images;
s305, obtaining the reading progress according to the plan learning time length and the actual learning time length.
Specifically, the pixel height of each line of characters in the picture is converted into the line number R of the characters according to the pixel height H of each line of characters in the picture, and then the number of characters contained in the picture is estimated to be about W ═ R × C according to the number of characters C corresponding to each line of the picture; converting the total number W of characters corresponding to each pictureiAssuming that the number of pictures is N and the normal reading speed of the text is V, the planned learning time of the whole picture is N
When the picture content is learned, the face snapshot is also carried out, and the actual effective learning time length S of each picture is returnediThen the actual learning duration of the whole picture is
Obtaining the whole picture reading progress PP＝At/Pt。
In some embodiments, as shown in fig. 4, when the article type is a text and picture mixed article, the performing the corresponding learning validity determination according to the article type includes:
s401, acquiring a first reading progress of characters in an article and a second reading progress of pictures in the article;
s402, acquiring a first weight of the total word number of characters in the article and a second weight of the total word number of pictures in the article;
and S403, acquiring the overall learning progress of the text and picture mixed article according to the first reading progress, the second reading progress, the first weight and the second weight.
Specifically, if the text contains both characters and pictures, the weights w1 and w2 are set according to the total word count of the entire characters and the total word count of the pictures, respectively, so that the learning progress P of the entire text is (Pw × w1+ Pp × w2)/(w1+ w 2).
As shown in fig. 5, an apparatus for performing learning validity determination for different article types according to an embodiment of the present application includes:
an obtaining module 501, configured to obtain an article;
a judging module 502, configured to judge a type of the article;
and the judging module 503 is configured to perform corresponding learning validity judgment according to the article type.
The working principle of the device for judging the learning effectiveness of different article types provided by the application is that the acquisition module 501 acquires an article, the judgment module 502 judges the article type, and the judgment module 503 judges the learning effectiveness correspondingly according to the article type.
In some embodiments, the article types include text articles, picture articles, and mixed text and picture articles.
The embodiment of the application provides computer equipment, which comprises a processor and a memory connected with the processor;
the memory is used for storing a computer program, and the computer program is used for executing the method for judging the learning effectiveness aiming at different article types provided by any one of the above embodiments;
the processor is used to call and execute the computer program in the memory.
In summary, the present invention provides a method and an apparatus for determining learning effectiveness for different article types, including obtaining an article; judging the type of the article; and carrying out corresponding learning effectiveness judgment according to article types, wherein the article types comprise text articles, picture articles and text and picture mixed articles. The invention can calculate the learning progress of different training article types, thereby judging whether each article is effectively learned and the learning progress of students, better knowing the learning condition of the students and supervising and urging the learning according to the learning condition of the students.
It is to be understood that the embodiments of the method provided above correspond to the embodiments of the apparatus described above, and the corresponding specific contents may be referred to each other, which is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for learning validity determination for different article types, comprising:
acquiring an article;
judging the type of the article;
and carrying out corresponding learning validity judgment according to the article types.
2. The method of claim 1,
the article types comprise text articles, picture articles and text and picture mixed articles.
3. The method of claim 2, wherein when the article type is a text article, the learning validity determination comprises:
acquiring a standard reading speed;
obtaining the length of a screen interval according to the product of the standard reading speed and the preset time;
dividing the total number of characters and the length of a screen interval to obtain the number of intervals; wherein the interval number is an integer;
snapping a human face at an initial node of each screen interval in the interval number, and snapping at set time intervals; if the snapshot is effective, the current node is considered to be effective in reading and the effective reading duration is fed back;
and calculating the reading progress according to the effective reading time.
4. The method of claim 3, wherein when the article type is a picture article, the learning validity determination comprises:
determining the pixel height of each line of characters in the picture, and converting the pixel height into the number of character lines;
determining the total amount of characters in the picture according to the number of characters corresponding to each line of characters in the picture and the number of characters, and acquiring the total amount of characters in each picture;
acquiring a standard reading speed, and acquiring the plan learning duration of all pictures according to the total number of the characters, the number of the pictures and the standard reading speed;
capturing when learning picture articles, feeding back the effective learning duration of each picture, and acquiring the actual learning duration of all pictures;
and acquiring the reading progress according to the planned learning time length and the actual learning time length.
5. The method of claim 4, wherein when the article type is a text and picture mixed article, the determining the learning effectiveness according to the article type comprises:
acquiring a first reading progress of characters in an article and a second reading progress of pictures in the article;
acquiring a first weight of the total word number of characters in an article and a second weight of the total word number of pictures in the article;
and acquiring the overall learning progress of the text and picture mixed article according to the first reading progress, the second reading progress, the first weight and the second weight.
6. The method of claim 3 or 4, wherein obtaining a standard reading speed comprises:
and acquiring pre-stored reading speed data, and extracting a median in the reading speed data to be used as a standard reading speed.
7. The method according to any one of claims 1 to 5,
and a camera is adopted to capture the human face.
8. The method of claim 3,
the preset time is 50 seconds, and the set time is 20 seconds.
9. An apparatus for learning validity determination for different article types, comprising:
the acquisition module is used for acquiring articles;
the judging module is used for judging the article types;
and the judging module is used for carrying out corresponding learning validity judgment according to the article types.
10. The apparatus of claim 9,
the article types comprise text articles, picture articles and text and picture mixed articles.
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