CN111985282A - Learning ability training and evaluating system - Google Patents

Learning ability training and evaluating system Download PDF

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CN111985282A
CN111985282A CN201910441028.1A CN201910441028A CN111985282A CN 111985282 A CN111985282 A CN 111985282A CN 201910441028 A CN201910441028 A CN 201910441028A CN 111985282 A CN111985282 A CN 111985282A
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刘军
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Shenzhen Scope Co ltd
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Abstract

The invention provides a learning ability training and evaluating system, which comprises the following program modules: a training course content unit; a data processing center; a training model content matching unit; a key information base unit; the capability training model unit is used for generating personalized model training content; the method comprises a reading training model, a word dictation training model, an application question training model, a voice training model, a picture training model, a 'friend finding' training model and a graph association training model; a model training content unit; an answer result judgment unit; the capability evaluation center is used for giving a corresponding evaluation result according to the evaluation standard prestored in the system and by combining the judgment result given by the answer result judgment unit; wherein, the learning terminal is provided with an auxiliary device for assisting the evaluation of the attention-concentrating ability of the students. Can be organically combined with the personalized learning of students, and comprehensively and tracelessly realize nine-strength ability training and cultivation.

Description

Learning ability training and evaluating system
Technical Field
The invention relates to learning ability cultivation, in particular to learning ability training and evaluation.
Background
Education increasingly attaches importance to nine abilities of training and culturing students such as observation, concentration, memory, imagination, thinking, reading comprehension, extendibility, thinking and distinguishing analysis, problem solving and the like, but most academic ability evaluation systems are realized in a mode of manually or manually combining a computer, the students manually complete evaluation items or complete the evaluation items on a computer display interface, test results of the evaluation items are collected manually or by software, and an evaluator manually judges the test level of the students according to the test results. Since students at different ages have great difference in cognitive ability and evaluation and training items are not comprehensive enough, the fixed and standardized evaluation content, item and mode cannot be intelligently adapted to different types of students.
The existing ability training and training aiming at the nine forces is mainly to test and train students through an additional training system and items irrelevant to the learning contents of the students, the students need to take additional time to train and evaluate after completing the homework, most of the students are difficult to actively finish the purpose of set training and evaluating items under the condition of no supervision, most of the evaluating contents are not only separated from the learning contents of each student, but also are identical in training and evaluating contents aiming at the students with the same age and gender, and relevant ability training and evaluating cannot be carried out according to the personalized learning of each student.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a learning ability training and evaluating system aiming at the defects in the prior art, which can be organically combined with the personalized learning of students to comprehensively and tracelessly realize nine-strength ability training and cultivation.
The technical scheme adopted by the invention for solving the technical problems is as follows: a learning ability training and evaluating system is provided, which comprises the following program modules:
the training course content unit is used for providing electronic version learning content for students to independently learn on the learning terminal;
the data processing center is used for calculating and processing the related information;
the training model content matching unit is used for identifying and inputting the format and the parameterization of the content in the input text provided by the training course content unit;
the key information base unit stores the character and picture information in the file which is extracted from the training model content matching unit and can be identified by all systems;
the capability training model unit is used for generating personalized model training content; the method comprises a reading training model, a word dictation training model, an application question training model, a voice training model, a picture training model, a 'friend finding' training model and a graph association training model;
The model training content unit is used for presenting the capability training model unit with personalized model training content on the interface of the learning terminal and forming interaction with students;
the answer result judging unit is used for judging the question-answer interaction of the students in the model training content unit; and
the capability evaluation center is used for giving a corresponding evaluation result according to the evaluation standard prestored in the system and by combining the judgment result given by the answer result judgment unit;
wherein, this study terminal disposes auxiliary device for the evaluation of the ability of supplementary student's special attention.
In some embodiments, the assist device comprises: the eye tracker is used for detecting the eyeball rotation condition of the student; and the camera is used for identifying the facial features of the students.
In some embodiments, the e-version learning content includes pre-learning and exercise exercises, courseware uploaded by teachers, and learning content pre-stored or downloaded by parents to the learning terminal.
In some embodiments, the training model content matching unit performs normalized format processing on the e-version learning content to extract all file information capable of being converted.
In some embodiments, the key information library unit is composed of a key text library unit and a key picture library unit.
In some embodiments, the keyword library element includes a textbook word and idiom library, a math application topic, and a text passage chapter; the key picture library unit comprises a figure picture, an animal picture and a real object picture.
In some embodiments, the system performs the building of the input text of the course content unit and all models of the ability training model unit through a game model, a language model, a convolutional neural network model, and a hidden markov model.
In some embodiments, the capability training model unit fills and combines the elements in the key information base unit in the game, the drawing frame, the interactive interface and the learning course model to generate personalized model training contents through program operation.
In some embodiments, the model training content elements include reading training content, word dictation training content, application topic training content, speech training content, pictorial training content, "find friends" training content, and graphical associated training content.
In some embodiments, the competency assessment center includes a reading comprehension assessment, a memory assessment, a problem solving assessment, an analytical thinking ability assessment, a thinking ability assessment, an observation ability assessment, a imagination assessment, an extendibility assessment, and a concentration assessment.
The invention has the advantages that the training course content unit, the data processing center, the training model content matching unit, the key information base unit, the capability training model unit and the model training content unit are adopted; the intelligent cooperation of the answering result judging unit, the ability evaluation center and the auxiliary device configured on the learning terminal can be organically combined with the personalized learning of students, and the nine-strength ability training and cultivation can be comprehensively realized without traces.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 illustrates the framework of the learning ability training and evaluation system of the present invention.
Figure 2 illustrates a more detailed framework of some parts of the system of the invention.
Figure 3 illustrates a further partial more detailed framework of the system of the invention.
FIG. 4 illustrates the process of the system of the present invention for deriving key information content from workout content.
Wherein the reference numerals are as follows: 100. system 200, student 10, training course content unit 20, data processing center 30, training model content matching 40, key information library unit 41, keyword library unit 411, textbook words and idiom library 412, math application topic 413, text passage chapter 42, key image library unit 421, character picture 422, animal picture 423, physical picture 50, ability training model unit 501, content fill and combine 502, game model 503, language model 504, convolutional neural network model 505, hidden Markov model 51, reading training model 52, word typographical training model 53, application topic model training model 54, voice training model 55, picture training model 56, "friend finding" training model 57, graphic association training model 60, ability training content unit 61, word typographical training model 53, word and word model training model 54, ability training model 61, and text model, The learning system comprises reading training content 62, word merwrite training content 63, application topic model training content 64, voice training content 65, picture training content 56, "find friends" training content 57, graph association training content 70, capability evaluation center 71, reading comprehension evaluation 72, memory evaluation 73, problem solving evaluation 74, analysis thinking discrimination evaluation 75, thinking evaluation 76, imagination evaluation 77, observation evaluation 78, extendibility evaluation 79, concentration evaluation 80, answer result evaluation unit 90, learning terminal 91, auxiliary device 95 and answer feedback information.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1 to 4, fig. 1 illustrates a framework structure of the learning ability training and evaluating system of the present invention. Figure 2 illustrates a more detailed framework of some parts of the system of the invention. Figure 3 illustrates a further partial more detailed framework of the system of the invention. FIG. 4 illustrates the process of the system of the present invention for deriving key information content from workout content. The learning ability training and evaluating system 100 of the present invention comprises a learning terminal 90, wherein the learning terminal 90 comprises a plurality of processors; program modules running on these processors include: training course content unit 10, data processing center 20, training model content matching unit 30, key information base unit 40, ability training model unit 50, model training content unit 60, answer result judgment unit 70 and ability evaluation center 80. Among them, the learning terminal 90 is provided with an auxiliary device 91 such as an eye tracker, a camera, or the like.
A training course content unit 10 for providing the electronic version learning content which is self-learnt by the student on the learning terminal 90. The method specifically comprises the following steps: pre-study and exercise exercises, courseware and the like uploaded by teachers; the learning content pre-stored or downloaded to the learning terminal 90 by the parent may include files such as pictures, words, videos, and audios. The lesson content unit 10 takes these learning contents, which are derived from the teacher and the parents, as input texts of the present system 100.
And the data processing center 20 is used for calculating and processing the related information. It relates to a processor, a storage unit, data upload and download, etc., performing parameter calculation, file storage, transmission of calculation parameters for the above-mentioned input text of the lesson content unit 10, and performing analysis calculation for the data of the auxiliary device 91. The data processing center 20 performs feedback information formed by capability training solutions to the following student users: the response feedback information 95 is processed, and the processing result is sent to the response result judgment unit 70 described below. For example, the data processing center 20 is composed of hardware such as a processor and a memory on the learning terminal 90 and a part of software running on the processor.
The training model content matching unit 30 is used for carrying out format and parameterization recognition and entry on the content in the input text provided by the training course content unit 10. Since the input text contains files in various forms, the system 100 needs to normalize the format processing, such as: the audio information is converted into a text form and then recognized; converting the video information into picture information and then identifying; then, after the identification processing, all file information capable of being converted is extracted.
And the key information base unit 40 is used for storing the character and picture information in the file which is extracted from the training model content matching unit 30 and can be recognized by all systems. Which is composed of a keyword library unit 41 and a key picture library unit 42.
The keyword library unit 41 includes a text-book word and phrase library 411, a math application topic 412, and a text passage chapter 413; the key picture library unit 42 includes a person picture 421, an animal picture 422, and a real object picture 423.
A capability training model unit 50, comprising: reading training model 51, word dictation training model 52, application question training model 53, speech training model 54, picture training model 55, "find friends" training model 56, and graphical association training model 57.
It is worth mentioning that, for all the models 51 to 57 described above, the system 100 mainly establishes the input text and the corresponding ability training models 51 to 57 through the game model 502, the language model 503, the convolutional neural network model 504 and the hidden markov model 505.
Specifically, the elements 411 to 413 and 421 to 423 in the key information library unit 40 are filled and combined 501 with the contents of the program framework in the models such as games, frames, interactive interfaces, learning courses, etc., and personalized model training contents are generated by program operations and are transmitted to the model training content unit 60.
The model training content unit 60 is used for displaying the personalized model training content provided by the ability training model unit 50 on the interface of the learning terminal 90 to interact with the student 200. It includes: reading training content 61, word dictation training content 62, application exercise content 63, phonetic training content 64, pictorial training content 65, "find friends" training content 66, and graphical associated training content 67.
The reading training content 61 includes: in the module, students read, set the number of words on the page, and record the stay time and the page turning interval time.
The word dictation, phonetic training content 62, 64 includes: words are called from the models 51 to 57, voice broadcasting is carried out, students answer on an operation interface, the system judges and judges the voice of the users.
The application topic training content 63 includes: words are retrieved from the above models 51 to 57, and matching of the mathematical application problem or thought problem model.
The drawing and graphic associated training content 65, 67 comprises: and calling pictures from the models 51 to 57 for matching, so that students write corresponding words according to the pictures, and judging the types of the pictures.
The "find friends" training content 66 includes: a certain number of pictures are called from the models 51 to 57, keywords are given, students can find corresponding pictures in the pictures, and the system judges and records time.
And an answer result judging unit 70 for judging the question-answer interaction of the student in the model training content unit 60. The student 200 performs the feedback information 95 of the training content at the learning terminal 90, performs the parameter matching of the big data on the capability training content (stored in the capability training content unit 60) generated by the capability training model unit 50, and performs the result matching by searching the relevant answers in the key information base unit 40 or traversing the answers by linking with the extranet cloud database.
And the ability evaluation center 80 is used for giving a corresponding evaluation result according to the evaluation standard prestored in the system and by combining the judgment result given by the answer result judgment unit. The method comprises the following steps: reading comprehension evaluation 81, memory evaluation 82, problem solving evaluation 83, analytical thinking power evaluation 84, thinking power evaluation 85, observation power evaluation 86, imagination evaluation 86, extendibility evaluation 88, and concentration power evaluation 89.
It should be noted that the learning terminal 90 includes a mobile terminal of a mobile phone, a PC terminal, a tablet computer, and other learning devices with an intelligent display. On the interface of learning terminal 90, the student when carrying out above-mentioned ability training, with the help of auxiliary device 91, can in time snatch the attention concentration region when the student studied, and then through data analysis, can be to the appraisal of the concentration of the whole training process of student, and this evaluation mode can reach foretell concentration evaluation 89 completely or partly.
The system 100 of the present invention is described in more detail below with several examples.
Referring to fig. 1, a user (i.e., a student 200) performs learning of school course homework at a learning terminal 90, a teacher issues an electronic out-of-class homework in a system 100 via a server (e.g., a cloud server) in an APP manner, and the student 200 performs registration and login in the system 100, i.e., downloading and importing of learning content (which is achieved by a training course content unit 10); then, the data processing center 20 uploads the above-mentioned teacher to the course, and performs corresponding downloading and data storage analysis processing.
Referring to fig. 2, taking the training model 56 of "finding friends" as an example, to train and evaluate the ability of the student 200, the system 100 may preset 30 pictures as a group, and randomly capture a text corresponding to one of the pictures for feedback. It will be appreciated that the ability training model unit 50 contains the programming and logic architecture of the "find friends" training model 56 described above.
The model training content matching unit 30 captures the character and image features of the data captured by the data processing center 20, identifies the format of the captured data, and identifies the content of the captured data. For example, the student 200 may learn all courseware including 100 pictures, including mathematical figures, illustrations of chinese paragraphs, illustrations of english paragraphs, and so on; the data processing center 20 extracts all 100 pictures by format recognition and screens the pictures by size format, and 80 pictures are obtained by recognition.
Referring to fig. 3, the model training content matching unit 30 performs feature recognition on key features of people, numbers, animals, things, and the like in the 80 pictures, performs picture feature matching through an extranet link server and cloud data, and determines the graphic content in each picture.
Referring to fig. 4, a process for deriving the content saved by the key information base unit 40 from the input lesson provided by the workout content unit 10 is illustrated. Inputting a course in step S401; in step S402, to obtain key information of the first layer, for example: key segment chapter characteristics or key picture characteristics; in step S403, to obtain key information of the second layer, for example: keyword word and sentence characteristics or key element picture characteristics; the resulting output text or picture is processed in step S404.
For example, the system 100 randomly extracts one of the 80 pictures for recognition, such as recognizing that the picture contains a character avatar, the capability training model unit 50 performs high-level feature recognition and matching of the neural convolutional network on the 80 pictures, and fills all the pictures containing the character avatar with the contents of the "find friends" training model 56, thereby generating a new 50-picture set (assuming that each picture contains a character avatar).
The system 100 gives the pictures containing the head portrait on the learning terminal 90, the student 200 clicks a button such as 'start' on an interface of the learning terminal 90, the system 100 starts to present 50 pictures and starts to time, and after the student 200 selects one of the interfaces, the system 100 automatically submits and records the selection time.
The student 200 sends the response feedback information 95 (in this case, the selected picture) to the data processing center 20 for picture parameter calculation and recognition, and counts the time parameter of the elapsed time. The answer result judging unit 70 compares the recognition result with the image containing the portrait given by the system 100 on the interface of the learning terminal 90.
The answer result judgment unit 70 supplies the judgment result and the time parameter to the ability evaluation center 80. It is understood that the capability evaluation center 80 contains a segmentation level for the length of time 50 pictures are searched, which is preset by the system 100. When the judgment of the answer result judgment unit 70 and the given picture feature repetition threshold exceed the set value, the selection of the student 200 can be judged to be correct, and further the observation force evaluation of the student 200 can be achieved by comparing the time parameter for completing the task with the segmentation grade preset by the system, and a corresponding evaluation conclusion can be given.
It is worth mentioning that the user can set the given picture by himself, for example, the picture can be a picture containing elements such as a certain formula, a certain word and a certain word, and the observation level of the student is improved by means of the training and evaluation of finding friends.
For example, the learning terminal 90 is provided with an eye tracker and a camera (i.e., the auxiliary device 91), the student 200 performs a training and answering process of a "find friends" training model, and when the student 200 clicks a "start" button or the like on the interface of the learning terminal 90, the eye tracker detects the eyeball rotation of the student 200 and identifies the facial features of the student by combining with the camera.
The data processing center 20 calculates the parameters collected by the eye tracker and the camera, and determines the time length T1 that the eyes of the student 200 stay on the operation interface of the learning terminal 90 during the training and evaluating process.
Suppose that: in the foregoing example, the time for the student 200 to perform the picture search is T0, and the data processing center 20 can calculate the value β = T1/T0 according to T1 and T0. The system 100 can preset different beta ratios and corresponding concentration level segments, and the system 100 matches the beta values in the competence assessment center 80 to give the concentration assessment result of the student 200.
The learning ability training and evaluating system 100 of the present invention has the following beneficial effects:
1. through a program framework based on a game model, a language model, a convolutional neural network model and a hidden Markov model, feature extraction is carried out on learning contents carried in the system 100 by the student 200, parameter filling and content generation are carried out according to a training frame and an evaluation frame built in the program, completely personalized training and evaluation can be achieved, and the student 200 can be attached to the specific student 200 more efficiently and accurately.
2. By setting the reading training model 51, the word typographical training model 52, the application question training model 53, the voice training model 54, the picture training model 55, the "friend finding" training model 56, the graphic association training model 57, and the auxiliary device 91, nine abilities of reading comprehension, memory, problem solving, analytic thinking and distinguishing training, thinking, observation, imagination, extendibility, and concentration can be trained.
3. The data processing center 20 calculates and matches and judges the parameters of the nine types of ability training items, the system presets the grade threshold values of various models and the division of the ability grade, and the answer result judging unit 70 and the ability evaluation center 80 can evaluate the ability of the student 200.
In conclusion, the learning ability training and evaluating system 100 of the present invention can comprehensively realize nine strength ability training and cultivation, such as observation ability, concentration ability, memory ability, imagination, thinking ability, reading comprehension, extendibility, analysis thinking ability, problem solving ability, etc.; moreover, the content of the nine-strength ability training and the training does not deviate from the learning content of the students, so that the students can realize the ability training and evaluation without traces in the process of personalized learning (completing homework and reviewing homework independently). It is understood that the training and evaluation performed by the system 100 can improve the ability of the student; in other words, the training and evaluation are organically combined in the system 100 to form the student's enhanced ability, mutually supportive legs.
It will be appreciated that the program modules 10, 20, 30, 40, 50, 60, 70 and 80 described above may be flexibly configured on the learning terminal 90 and the server (if any) according to the needs of the actual application. For example, in a situation where the program modules 10, 20, 30, 40, 50, 60, 70 and 80 are configured on the learning terminal 90, the system 100 can be considered to be completely implemented by the learning terminal 90 and software thereon, regardless of the server. In another case, the program modules 10, 20, 30, 40, 50, 60, 70 and 80 are distributed on the learning terminal 90 and the server (not shown), and the system 100 can be regarded as being implemented by the learning terminal 90 and the server and the software thereof.
It should be understood that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, and those skilled in the art can modify the technical solutions described in the above embodiments, or make equivalent substitutions for some technical features; and such modifications and substitutions are intended to be included within the scope of the appended claims.

Claims (10)

1. A learning ability training and evaluation system, comprising the following program modules:
the training course content unit is used for providing electronic version learning content for students to independently learn on the learning terminal;
the data processing center is used for calculating and processing the related information;
the training model content matching unit is used for identifying and inputting the format and the parameterization of the content in the input text provided by the training course content unit;
the key information base unit stores the character and picture information in the file which is extracted from the training model content matching unit and can be identified by all systems;
the capability training model unit is used for generating personalized model training content; the method comprises a reading training model, a word dictation training model, an application question training model, a voice training model, a picture training model, a 'friend finding' training model and a graph association training model;
The model training content unit is used for presenting the capability training model unit with personalized model training content on the interface of the learning terminal and forming interaction with students;
the answer result judging unit is used for judging the question-answer interaction of the students in the model training content unit; and
the capability evaluation center is used for giving a corresponding evaluation result according to the evaluation standard prestored in the system and by combining the judgment result given by the answer result judgment unit;
wherein, this study terminal disposes auxiliary device for the evaluation of the ability of supplementary student's special attention.
2. A learning ability training and evaluation system according to claim 1, wherein: the auxiliary device includes: the eye tracker is used for detecting the eyeball rotation condition of the student; and the camera is used for identifying the facial features of the students.
3. A learning ability training and evaluation system according to claim 1, wherein: the electronic version learning content comprises pre-learning and exercise exercises and courseware uploaded by teachers and learning content prestored or downloaded to the learning terminal by parents.
4. A learning ability training and evaluation system according to claim 1, wherein: the training model content matching unit performs normalized format processing on the electronic version learning content and extracts all file information capable of being converted.
5. A learning ability training and evaluation system according to claim 1, wherein: the key information library unit is composed of a key character library unit and a key picture library unit.
6. A learning ability training and evaluation system according to claim 5, wherein: the keyword library unit comprises a textbook word and phrase library, a math application question and a text paragraph chapter; the key picture library unit comprises a figure picture, an animal picture and a real object picture.
7. A learning ability training and evaluation system according to claim 1, wherein: the system establishes the input text of the training course content unit and all models of the ability training model unit through a game model, a language model, a convolutional neural network model and a hidden Markov model.
8. A learning ability training and evaluation system according to claim 7, wherein: the ability training model unit fills and combines the elements in the key information base unit in the game, the drawing frame, the interactive interface and the learning course model, and generates personalized model training content through program operation.
9. A learning ability training and evaluation system according to claim 1, wherein: the model training content unit comprises reading training content, word dictation training content, application question training content, voice training content, picture training content, friend finding training content and graph association training content.
10. A learning ability training and evaluation system according to any one of claims 1 to 9, characterized in that: the ability evaluation center comprises reading comprehension evaluation, memory evaluation, problem solving evaluation, analysis thinking ability evaluation, observation ability evaluation, imagination evaluation, extendibility evaluation and concentration evaluation.
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