CN117556965A - Teaching course optimization method, system and storage medium based on intelligent operation platform - Google Patents
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Abstract
The invention discloses a teaching course optimization method, a system and a storage medium based on an intelligent homework platform, which comprise the steps of obtaining a course teaching plan of target course content, extracting course knowledge points, obtaining interaction characteristics of students on the course knowledge points in the teaching process, generating course homework to push, obtaining homework information uploaded by the students on the intelligent homework platform, intelligently modifying the homework information, identifying the knowledge points, clustering the knowledge points according to modification results, extracting clustering results to evaluate the mastering condition of the students, screening the knowledge points with poor mastering condition, segmenting teaching courses, obtaining preference teaching modes of student groups, and optimizing teaching time and teaching scheme of the course teaching plan. According to the invention, feedback data of students for course teaching is obtained through intelligent arrangement and analysis of homework, knowledge combing is carried out according to the feedback data, and the aim of maximizing teaching quality is achieved through course arrangement optimization.
Description
Technical Field
The invention relates to the technical field of intelligent teaching, in particular to a teaching course optimization method, a system and a storage medium based on an intelligent operation platform.
Background
With the development of informatization technology, various informatization classroom teaching systems are gradually developed and occupy an important position in informatization teaching practice. The intelligent operation platform is based on an informatization teaching platform and a mobile terminal, and changes traditional operation and teaching modes through advanced information technologies such as a network cloud disk, an electronic schoolbag, big data and the like based on a novel teaching form of dynamic learning data analysis. Can give full play to student's main part effect in intelligent homework platform, reinforcing student's independence and autonomy of study, teacher arrange the homework for the student through intelligent homework platform, the student submits through intelligent homework platform on line that homework for the classroom knows student's basic condition, and teacher's teaching is more pointed.
Under the traditional teaching mode of course, students have little interest in learning course knowledge, can not better cultivate students' practice innovation ability, and open and convenient that the transformation of information technology brought is enjoyed, but the phenomenon that teaching efficiency is low appears, only applied informatization teaching means, and teaching content and teaching mode do not take place too big change, are finally restricted by informatization means. At present, the online course teaching mode has the defects that, for example, the assessment and the feedback of the quality of the homework submitted by the students are lacking, the teaching content of the teacher is lack of pertinence, the teaching content is still difficult to accept by the students, the enthusiasm of the students to participate in activities is low, and the classroom effect is poor. Therefore, how to optimize course teaching content by using the intelligent operation platform is a problem to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a teaching course optimization method, a system and a storage medium based on an intelligent operation platform.
The first aspect of the invention provides a teaching course optimization method based on an intelligent operation platform, which comprises the following steps:
acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, cutting the course teaching plan by using the screened course knowledge points, and optimizing the time of the course teaching plan;
and extracting course content corresponding to the time-optimized course teaching plan, acquiring a preference teaching mode of the student group according to the history teaching data, retrieving a teaching example through the preference teaching mode, and optimizing a teaching scheme corresponding to the target course content.
In the scheme, a course teaching plan of target course content is obtained, course knowledge points are extracted according to the course teaching plan, and the method specifically comprises the following steps:
dividing the target course content according to the course teaching plan, generating teaching plan labels through different teaching stages, marking the divided course content, and word segmentation is carried out on the divided course content to obtain word vector sets under different teaching plan labels;
according to the word vector set, a local subset is obtained according to different chapters, word vectors are led into a low-dimensional vector space in the local subset, the word vectors are learned and represented through a graph convolution network, and the position characteristics of the word vectors are obtained;
the position feature is combined with semantic information of the word vector to obtain local feature information, an attention mechanism is introduced to weight the obtained local feature information, the weighted local feature information is imported into a BiLSTM network, and a feature vector containing bidirectional sequence information is generated;
importing the feature vectors into a full-connection layer, acquiring score vectors of the local subsets for all course knowledge categories according to the full-connection layer, carrying out normalization processing on the score vectors to acquire corresponding knowledge categories, and extracting keyword vectors in the local subsets according to the knowledge categories;
And aggregating the keyword vectors to obtain course knowledge points of the word vector sets under the labels of different teaching plans.
In this scheme, acquire the teaching in-process student to the interactive feature of course knowledge point, through interactive feature generates course homework, specifically does:
acquiring video stream information of a teaching process according to video monitoring equipment, acquiring course knowledge points related to the teaching process through a teaching course plan, and acquiring a time stamp of the course knowledge points in the video stream information;
dividing video stream information based on a time stamp, obtaining a video segment corresponding to a course knowledge point, extracting video image frames from the video segment, obtaining face image information of a student group according to the video image frames, preprocessing the face image information, and obtaining face key points of the face image information;
acquiring a position change sequence corresponding to the video segment according to the position characteristics of the facial key points, calculating and identifying the micro-expression of the students according to the similarity through the position change sequence, and counting the micro-expression characteristics of the student groups;
acquiring sound signals of a student group, extracting sound features, combining the sound features with micro expression features to acquire interactive features of the student group on course knowledge points, acquiring interactive scores by utilizing the interactive features, and acquiring the course knowledge points which do not meet preset score standards for marking;
And carrying out proportional gain on the marked course knowledge points, and generating course operation according to the proportion occupied by different course knowledge points.
In the scheme, knowledge points are clustered according to correction results, clustering results are extracted to evaluate student mastering conditions, course knowledge points with mastering conditions not meeting a preset threshold are screened, and the method specifically comprises the following steps:
acquiring standard answer information corresponding to course operation, extracting key steps according to the standard answer information, intelligently correcting the uploaded operation information according to the key steps and scoring standards, acquiring scoring information of key steps corresponding to different operation items, and marking the operation items by using the scoring information;
clustering key steps through course knowledge points, taking the course knowledge points as initial clustering centers, acquiring membership matrixes from each key step in different operation projects to the initial clustering centers, and when the maximum membership is greater than or equal to a preset first threshold, classifying the key steps into class clusters corresponding to the maximum membership;
when the maximum membership is smaller than a preset first threshold, judging whether the maximum membership is larger than a preset second threshold, if so, classifying the key steps into a plurality of class clusters, wherein the first threshold is larger than the second threshold;
Obtaining class clusters corresponding to the course knowledge points by utilizing repeated iterative clustering, obtaining average scores in different class clusters to represent student mastering conditions, screening the course knowledge points with mastering conditions not meeting a preset threshold, and obtaining homework items corresponding to key steps from the class clusters corresponding to the screened course knowledge points;
acquiring course knowledge points related to the operation project, counting occurrence frequencies of other course knowledge points except for the poor course knowledge points, and acquiring high-association course knowledge points of the poor course knowledge points according to the occurrence frequencies;
and generating a focused course knowledge point set according to the poorly mastered course knowledge points and the high-association course knowledge points.
In this scheme, utilize the course knowledge point of screening to cut the course teaching plan, optimize the time of course teaching plan, specifically:
positioning poorly mastered course knowledge points in a set of concerned course knowledge points in the course teaching plan, cutting the related course teaching plan according to positioning results, and screening the related course teaching plan in a future preset time range;
acquiring course knowledge points in preset time steps before and after positioning in the screened course teaching plan, judging whether the course knowledge points are high-association course knowledge points, if so, marking the corresponding course knowledge points, and generating a teaching plan time sequence for marking the course knowledge points;
And obtaining grading information of the high-association course knowledge points in the course operation, setting the priority of the marked course knowledge points according to the grading information, selecting the marked course knowledge points with the highest priority to be combined with the adjacent poorly mastered course knowledge points, and extracting corresponding course content as the next teaching plan.
In the scheme, a preference teaching mode of a student group is obtained according to historical teaching data, a teaching example is searched through the preference teaching mode, and a teaching scheme corresponding to target course content is optimized, specifically:
acquiring historical teaching data of student groups in different courses, extracting interaction feature sequences corresponding to the historical teaching data, acquiring average interaction scores of the historical teaching data according to the interaction feature sequences, and screening the historical teaching data with the average interaction scores meeting preset standards;
extracting teaching modes of a target course through the screened historical teaching data, acquiring teaching modes corresponding to similar course types according to course characteristics of the target course, sequencing according to occurrence frequencies of different teaching modes, and selecting a preset number of teaching modes as preferential teaching modes of student groups;
retrieving a teaching example by utilizing a big data means according to the preference teaching mode, generating training data by the teaching example, constructing a teaching mode migration model, and performing model training by utilizing the training data;
And optimizing the teaching mode of the target course content by using the trained teaching mode migration model, outputting the structured data containing the course content, and generating a corresponding teaching scheme.
The second aspect of the present invention also provides a teaching course optimization system based on an intelligent operation platform, the system comprising: the intelligent operation platform-based teaching course optimization method comprises a memory and a processor, wherein the memory comprises an intelligent operation platform-based teaching course optimization method program, and when the intelligent operation platform-based teaching course optimization method program is executed by the processor, the following steps are realized:
acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, cutting the course teaching plan by using the screened course knowledge points, and optimizing the time of the course teaching plan;
And extracting course content corresponding to the time-optimized course teaching plan, acquiring a preference teaching mode of the student group according to the history teaching data, retrieving a teaching example through the preference teaching mode, and optimizing a teaching scheme corresponding to the target course content.
The third aspect of the present invention also provides a computer readable storage medium, where the computer readable storage medium includes a teaching course optimization method program based on an intelligent operation platform, where the teaching course optimization method program based on the intelligent operation platform implements the steps of the teaching course optimization method based on the intelligent operation platform as described in any one of the above.
The invention discloses a teaching course optimization method, a system and a storage medium based on an intelligent homework platform, which comprise the steps of obtaining a course teaching plan of target course content, extracting course knowledge points, obtaining interaction characteristics of students on the course knowledge points in the teaching process, generating course homework to push, obtaining homework information uploaded by the students on the intelligent homework platform, intelligently modifying the homework information, identifying the knowledge points, clustering the knowledge points according to modification results, extracting clustering results to evaluate the mastering condition of the students, screening the knowledge points with poor mastering condition, segmenting teaching courses, obtaining preference teaching modes of student groups, and optimizing teaching time and teaching scheme of the course teaching plan. According to the invention, feedback data of students for course teaching is obtained through intelligent arrangement and analysis of homework, knowledge combing is carried out according to the feedback data, and the aim of maximizing teaching quality is achieved through course arrangement optimization.
Drawings
FIG. 1 shows a flow chart of a teaching course optimization method based on an intelligent operation platform of the present invention;
FIG. 2 is a flow chart illustrating the selection of points of knowledge for generating a lesson in accordance with the present invention;
FIG. 3 is a flow chart illustrating the assessment of student mastery based on course work feedback in accordance with the present invention;
FIG. 4 shows a block diagram of a teaching course optimization system based on an intelligent operation platform of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a teaching course optimization method based on an intelligent operation platform.
As shown in fig. 1, a first aspect of the present invention provides a teaching course optimization method based on an intelligent operation platform, including:
S102, acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
s104, pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
s106, clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, and cutting the course teaching plan by using the screened course knowledge points to optimize the time of the course teaching plan;
s108, course content corresponding to the time-optimized course teaching plan is extracted, a preference teaching mode of the student group is obtained according to the history teaching data, a teaching example is searched through the preference teaching mode, and a teaching scheme corresponding to the target course content is optimized.
The method comprises the steps of obtaining course content of a target course and a corresponding course teaching plan, dividing the target course content according to the course teaching plan, generating teaching plan labels through different teaching stages, marking the divided course content, and Word2vec model Word segmentation is carried out on sentences in the divided course content to obtain Word vector sets under different teaching plan labels; dividing according to different chapters or sentences in the word vector set to obtain local subsets, introducing word vectors into a low-dimensional vector space in the local subsets, learning and representing the word vectors through a graph convolution network to obtain the position characteristics of the word vectors, wherein in a sentence, two word vectors in one sentence can repeatedly appear, and the position characteristics are used as one of the local characteristics; and acquiring local feature information by combining the position features with semantic information of word vectors, and weighting the acquired local feature information by introducing an attention mechanism so as to increase information weight useful for prediction classification, wherein the local feature information only comprises feature information of adjacent word vectors and lacks overall sequential information. Importing the weighted local feature information into a BiLSTM network to generate a feature vector containing bidirectional sequence information; the feature vector is led into a full connection layer according to full connection The next layer obtains score vectors of local subsets for all curriculum knowledge categories，，/>For parameter information +.>Is local feature information. Normalizing the score vectors to obtain corresponding knowledge categories, obtaining conditional probability distribution of course knowledge categories, and extracting keyword vectors from a local subset according to the knowledge categories; and aggregating the keyword vectors to obtain course knowledge points of the word vector sets under the labels of different teaching plans.
FIG. 2 illustrates a flow chart of the present invention for selecting a point of knowledge for a lesson to generate a lesson operation.
According to the embodiment of the invention, the interactive characteristics of students to the course knowledge points in the teaching process are obtained, and course homework is generated through the interactive characteristics, specifically:
s202, acquiring video stream information of a teaching process according to video monitoring equipment, acquiring course knowledge points related to the teaching process through a teaching course plan, and acquiring time stamps of the course knowledge points in the video stream information;
s204, dividing the video stream information based on the time stamp, obtaining a video segment corresponding to the curriculum knowledge points, extracting video image frames from the video segment, obtaining face image information of the student group according to the video image frames, preprocessing the face image information, and obtaining face key points of the face image information;
S206, acquiring a position change sequence corresponding to the video segment according to the position characteristics of the facial key points, calculating and identifying the micro-expression of the students according to the similarity through the position change sequence, and counting the micro-expression characteristics of the student groups;
s208, acquiring sound signals of a student group, extracting sound features, combining the sound features with micro expression features to acquire interactive features of the student group to course knowledge points, acquiring interactive scores by using the interactive features, and acquiring the course knowledge points which do not meet preset score standards for marking;
s210, proportional gain is carried out on the marked course knowledge points, and course operation is generated through the proportion of different course knowledge points.
It should be noted that, an interested area containing facial key points is obtained from the preprocessed facial image information, the facial key points are positioned in the interested area, facial features are extracted according to the position change sequence of the facial key points, similarity calculation is performed in a preset micro-expression database based on the facial features, and the micro-expressions of the student individuals are identified; and carrying out microexpressive classification in the preset microexpressive database, screening positive microexpressions, and representing students with high acceptance degree and high understanding degree on teaching contents through the positive microexpressions. And counting the micro-expressions of the student individuals to obtain micro-expression characteristics of the student groups. And performing voice separation in the video stream information, performing voice semantic recognition according to voice signals to obtain voice features, combining micro-expression features and the voice features to obtain interactive features of student groups on course knowledge points, constructing an interactive feature scoring system, setting weight information for the voice features and the micro-expression features, obtaining interactive scores according to preset scoring threshold standards and the weight information, screening and mastering poor course knowledge points, and increasing the duty ratio of the course knowledge points in course work.
FIG. 3 shows a flow chart of the present invention for assessing student mastery based on course work feedback.
According to the embodiment of the invention, knowledge points are clustered according to the modification result, the clustering result is extracted to evaluate the mastering condition of students, and course knowledge points with mastering conditions not meeting a preset threshold are screened, specifically:
s302, standard answer information corresponding to course operation is obtained, key steps are extracted according to the standard answer information, intelligent correction is carried out on uploaded operation information according to the key steps and scoring criteria, scoring information of key steps corresponding to different operation items is obtained, and the operation items are marked by the scoring information;
s304, clustering key steps through course knowledge points, taking the course knowledge points as initial clustering centers, acquiring membership matrixes from each key step in different operation projects to the initial clustering centers, and when the maximum membership is greater than or equal to a preset first threshold, classifying the key steps into class clusters corresponding to the maximum membership;
s306, judging whether the maximum membership is larger than a preset second threshold value or not when the maximum membership is smaller than a preset first threshold value, if so, classifying the key step into a plurality of class clusters, wherein the first threshold value is larger than the second threshold value;
S308, obtaining class clusters corresponding to the course knowledge points by utilizing repeated iterative clustering, obtaining average scores in different class clusters to represent student mastering conditions, screening the course knowledge points with mastering conditions not meeting a preset threshold, and obtaining homework items corresponding to key steps in the class clusters corresponding to the screened course knowledge points;
s310, acquiring course knowledge points related to a work project, counting the occurrence frequency of other course knowledge points except for the poor course knowledge points, and acquiring high-association course knowledge points of the poor course knowledge points according to the occurrence frequency;
s312, generating a focused course knowledge point set according to the poorly mastered course knowledge points and the high-association course knowledge points.
The first threshold value is thatU represents the maximum membership degree, c represents the cluster number of the class, and the second threshold is +.>P represents the degree of overlap. The method comprises the steps of screening and mastering bad course knowledge points, and simultaneously obtaining high-correlation knowledge points of the bad course knowledge points, wherein the course knowledge points cannot be used in combination with the bad course knowledge points.
It should be noted that, positioning poorly mastered course knowledge points in the set of concerned course knowledge points in the course teaching plan, cutting the related course teaching plan according to the positioning result, and screening the related course teaching plan in a future preset time range; acquiring course knowledge points in preset time steps before and after positioning points in the screened course teaching plan, judging whether the course knowledge points are high-association course knowledge points, if so, marking the corresponding course knowledge points, and generating a teaching plan time sequence for marking the course knowledge points; and obtaining grading information of the high-association course knowledge points in the course operation, setting the priority of the marked course knowledge points according to the grading information, selecting the marked course knowledge points with the highest priority to be combined with the adjacent poorly mastered course knowledge points, and extracting corresponding course content as the next teaching plan. The teaching content corresponding to the next teaching plan is utilized to consolidate the knowledge points of the poor course mastered by the student groups while updating the teaching progress, the knowledge points of the poor course are updated and mastered through the homework information of the intelligent homework platform, and the proportion of the knowledge points of the poor course mastered by the history in the homework of the course is set by combining with the forgetting curve of the student groups; meanwhile, when the teaching plan of the target course goes to the teaching stage corresponding to the high-association course knowledge point, joint application training of the course knowledge point with poor history mastery is performed.
It is to be noted that, the history teaching data of the student group in different courses is obtained, the interactive feature sequence corresponding to the history teaching data is extracted, the average interactive score of the history teaching data is obtained according to the interactive feature sequence, and the history teaching data with the average interactive score meeting the preset standard is screened; extracting teaching modes of a target course through the screened historical teaching data, acquiring teaching modes corresponding to similar course types according to course characteristics of the target course, sequencing according to occurrence frequencies of different teaching modes, and selecting a preset number of teaching modes as preferential teaching modes of student groups; searching a teaching example by utilizing a big data means according to the preference teaching mode, generating training data by the teaching example, constructing a teaching mode migration model based on a neural network and other deep learning methods, and performing model training by utilizing the training data; and (3) performing teaching mode optimization on the target course content by using the trained teaching mode migration model, applying the preference teaching mode of the student group to the target course content, outputting the structured data containing the course content, and generating a corresponding teaching scheme, thereby improving the attractiveness of the target course and achieving a better intelligent teaching effect.
According to the embodiment of the invention, a personal database of a student is obtained through an intelligent homework platform, the proportion information of knowledge points of a poor course mastered by the student is obtained according to the intelligent homework correction of the course, and the proportion information is used as one of the basis of the course time division of the student; storing knowledge points of a poor course corresponding to students into the personal database, predicting the performance information of the students based on the mastering conditions of the knowledge points of the target course in the personal database, generating early warning information according to comparison of performance thresholds, formulating exclusive self-learning schemes of the students according to the mastering conditions and the current course teaching plan, adding the exclusive self-learning schemes to update course homework on the basis of course homework, acquiring the mastering conditions of the knowledge points of the target course for course homework feedback update, judging whether the mastering conditions are suitable for the knowledge points of the course in the current teaching stage, and modifying the exclusive self-learning scheme to optimize course homework until the performance of the course accords with a preset threshold when the mastering conditions are not suitable for the knowledge points of the course. The image information of the student course mastering condition is generated through the homework feedback of the intelligent platform, the self-learning scheme is generated according to the image information and is automatically added to the course homework, and the unqualified probability of students in the target course examination is greatly reduced through supervising the self-learning.
FIG. 4 shows a block diagram of a teaching course optimization system based on an intelligent operation platform of the present invention.
The second aspect of the present invention also provides a teaching course optimization system 4 based on an intelligent operation platform, the system comprising: the intelligent operation platform based teaching course optimization method program is executed by the processor to realize the following steps:
acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, cutting the course teaching plan by using the screened course knowledge points, and optimizing the time of the course teaching plan;
And extracting course content corresponding to the time-optimized course teaching plan, acquiring a preference teaching mode of the student group according to the history teaching data, retrieving a teaching example through the preference teaching mode, and optimizing a teaching scheme corresponding to the target course content.
According to the embodiment of the invention, the interactive characteristics of students to the course knowledge points in the teaching process are obtained, and course homework is generated through the interactive characteristics, specifically:
acquiring video stream information of a teaching process according to video monitoring equipment, acquiring course knowledge points related to the teaching process through a teaching course plan, and acquiring a time stamp of the course knowledge points in the video stream information;
dividing video stream information based on a time stamp, obtaining a video segment corresponding to a course knowledge point, extracting video image frames from the video segment, obtaining face image information of a student group according to the video image frames, preprocessing the face image information, and obtaining face key points of the face image information;
acquiring a position change sequence corresponding to the video segment according to the position characteristics of the facial key points, calculating and identifying the micro-expression of the students according to the similarity through the position change sequence, and counting the micro-expression characteristics of the student groups;
Acquiring sound signals of a student group, extracting sound features, combining the sound features with micro expression features to acquire interactive features of the student group on course knowledge points, acquiring interactive scores by utilizing the interactive features, and acquiring the course knowledge points which do not meet preset score standards for marking;
and carrying out proportional gain on the marked course knowledge points, and generating course operation according to the proportion occupied by different course knowledge points.
It should be noted that, an interested area containing facial key points is obtained from the preprocessed facial image information, the facial key points are positioned in the interested area, facial features are extracted according to the position change sequence of the facial key points, similarity calculation is performed in a preset micro-expression database based on the facial features, and the micro-expressions of the student individuals are identified; and carrying out microexpressive classification in the preset microexpressive database, screening positive microexpressions, and representing students with high acceptance degree and high understanding degree on teaching contents through the positive microexpressions. And counting the micro-expressions of the student individuals to obtain micro-expression characteristics of the student groups. And performing voice separation in the video stream information, performing voice semantic recognition according to voice signals to obtain voice features, combining micro-expression features and the voice features to obtain interactive features of student groups on course knowledge points, constructing an interactive feature scoring system, setting weight information for the voice features and the micro-expression features, obtaining interactive scores according to preset scoring threshold standards and the weight information, screening and mastering poor course knowledge points, and increasing the duty ratio of the course knowledge points in course work.
According to the embodiment of the invention, knowledge points are clustered according to the modification result, the clustering result is extracted to evaluate the mastering condition of students, and course knowledge points with mastering conditions not meeting a preset threshold are screened, specifically:
acquiring standard answer information corresponding to course operation, extracting key steps according to the standard answer information, intelligently correcting the uploaded operation information according to the key steps and scoring standards, acquiring scoring information of key steps corresponding to different operation items, and marking the operation items by using the scoring information;
clustering key steps through course knowledge points, taking the course knowledge points as initial clustering centers, acquiring membership matrixes from each key step in different operation projects to the initial clustering centers, and when the maximum membership is greater than or equal to a preset first threshold, classifying the key steps into class clusters corresponding to the maximum membership;
when the maximum membership is smaller than a preset first threshold, judging whether the maximum membership is larger than a preset second threshold, if so, classifying the key steps into a plurality of class clusters, wherein the first threshold is larger than the second threshold;
obtaining class clusters corresponding to the course knowledge points by utilizing repeated iterative clustering, obtaining average scores in different class clusters to represent student mastering conditions, screening the course knowledge points with mastering conditions not meeting a preset threshold, and obtaining homework items corresponding to key steps from the class clusters corresponding to the screened course knowledge points;
Acquiring course knowledge points related to the operation project, counting occurrence frequencies of other course knowledge points except for the poor course knowledge points, and acquiring high-association course knowledge points of the poor course knowledge points according to the occurrence frequencies;
and generating a focused course knowledge point set according to the poorly mastered course knowledge points and the high-association course knowledge points.
The first threshold value is thatU represents the maximum membership degree, c represents the cluster number of the class, and the second threshold is +.>P represents the degree of overlap. The method comprises the steps of screening and mastering bad course knowledge points, and simultaneously obtaining high-correlation knowledge points of the bad course knowledge points, wherein the course knowledge points cannot be used in combination with the bad course knowledge points.
The third aspect of the present invention also provides a computer readable storage medium, where the computer readable storage medium includes a teaching course optimization method program based on an intelligent operation platform, where the teaching course optimization method program based on the intelligent operation platform implements the steps of the teaching course optimization method based on the intelligent operation platform as described in any one of the above.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The teaching course optimization method based on the intelligent operation platform is characterized by comprising the following steps of:
acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, cutting the course teaching plan by using the screened course knowledge points, and optimizing the time of the course teaching plan;
and extracting course content corresponding to the time-optimized course teaching plan, acquiring a preference teaching mode of the student group according to the history teaching data, retrieving a teaching example through the preference teaching mode, and optimizing a teaching scheme corresponding to the target course content.
2. The teaching course optimization method based on the intelligent operation platform according to claim 1, wherein course teaching plans of target course contents are obtained, course knowledge points are extracted according to the course teaching plans, and specifically:
Dividing the target course content according to the course teaching plan, generating teaching plan labels through different teaching stages, marking the divided course content, and word segmentation is carried out on the divided course content to obtain word vector sets under different teaching plan labels;
according to the word vector set, a local subset is obtained according to different chapters, word vectors are led into a low-dimensional vector space in the local subset, the word vectors are learned and represented through a graph convolution network, and the position characteristics of the word vectors are obtained;
the position feature is combined with semantic information of the word vector to obtain local feature information, an attention mechanism is introduced to weight the obtained local feature information, the weighted local feature information is imported into a BiLSTM network, and a feature vector containing bidirectional sequence information is generated;
importing the feature vectors into a full-connection layer, acquiring score vectors of the local subsets for all course knowledge categories according to the full-connection layer, carrying out normalization processing on the score vectors to acquire corresponding knowledge categories, and extracting keyword vectors in the local subsets according to the knowledge categories;
and aggregating the keyword vectors to obtain course knowledge points of the word vector sets under the labels of different teaching plans.
3. The teaching course optimization method based on the intelligent homework platform as claimed in claim 1, wherein the interactive features of students to the course knowledge points in the teaching process are obtained, and course homework is generated through the interactive features, specifically:
acquiring video stream information of a teaching process according to video monitoring equipment, acquiring course knowledge points related to the teaching process through a teaching course plan, and acquiring a time stamp of the course knowledge points in the video stream information;
dividing video stream information based on a time stamp, obtaining a video segment corresponding to a course knowledge point, extracting video image frames from the video segment, obtaining face image information of a student group according to the video image frames, preprocessing the face image information, and obtaining face key points of the face image information;
acquiring a position change sequence corresponding to the video segment according to the position characteristics of the facial key points, calculating and identifying the micro-expression of the students according to the similarity through the position change sequence, and counting the micro-expression characteristics of the student groups;
acquiring sound signals of a student group, extracting sound features, combining the sound features with micro expression features to acquire interactive features of the student group on course knowledge points, acquiring interactive scores by utilizing the interactive features, and acquiring the course knowledge points which do not meet preset score standards for marking;
And carrying out proportional gain on the marked course knowledge points, and generating course operation according to the proportion occupied by different course knowledge points.
4. The teaching course optimization method based on the intelligent work platform according to claim 1, wherein the knowledge points are clustered according to the correction result, the clustering result is extracted to evaluate the mastering condition of students, and course knowledge points with mastering conditions not meeting a preset threshold are screened, specifically:
acquiring standard answer information corresponding to course operation, extracting key steps according to the standard answer information, intelligently correcting the uploaded operation information according to the key steps and scoring standards, acquiring scoring information of key steps corresponding to different operation items, and marking the operation items by using the scoring information;
clustering key steps through course knowledge points, taking the course knowledge points as initial clustering centers, acquiring membership matrixes from each key step in different operation projects to the initial clustering centers, and when the maximum membership is greater than or equal to a preset first threshold, classifying the key steps into class clusters corresponding to the maximum membership;
when the maximum membership is smaller than a preset first threshold, judging whether the maximum membership is larger than a preset second threshold, if so, classifying the key steps into a plurality of class clusters, wherein the first threshold is larger than the second threshold;
Obtaining class clusters corresponding to the course knowledge points by utilizing repeated iterative clustering, obtaining average scores in different class clusters to represent student mastering conditions, screening the course knowledge points with mastering conditions not meeting a preset threshold, and obtaining homework items corresponding to key steps from the class clusters corresponding to the screened course knowledge points;
acquiring course knowledge points related to the operation project, counting occurrence frequencies of other course knowledge points except for the poor course knowledge points, and acquiring high-association course knowledge points of the poor course knowledge points according to the occurrence frequencies;
and generating a focused course knowledge point set according to the poorly mastered course knowledge points and the high-association course knowledge points.
5. The teaching course optimization method based on the intelligent operation platform according to claim 1, wherein the course teaching plan is segmented by using the screened course knowledge points, and the time of the course teaching plan is optimized, specifically:
positioning poorly mastered course knowledge points in a set of concerned course knowledge points in the course teaching plan, cutting the related course teaching plan according to positioning results, and screening the related course teaching plan in a future preset time range;
Acquiring course knowledge points in preset time steps before and after positioning in the screened course teaching plan, judging whether the course knowledge points are high-association course knowledge points, if so, marking the corresponding course knowledge points, and generating a teaching plan time sequence for marking the course knowledge points;
and obtaining grading information of the high-association course knowledge points in the course operation, setting the priority of the marked course knowledge points according to the grading information, selecting the marked course knowledge points with the highest priority to be combined with the adjacent poorly mastered course knowledge points, and extracting corresponding course content as the next teaching plan.
6. The teaching course optimization method based on the intelligent homework platform as claimed in claim 1, wherein the method is characterized in that the preferred teaching mode of the student group is obtained according to the history teaching data, the teaching examples are searched through the preferred teaching mode, and the teaching scheme corresponding to the content of the optimized target course is specifically:
acquiring historical teaching data of student groups in different courses, extracting interaction feature sequences corresponding to the historical teaching data, acquiring average interaction scores of the historical teaching data according to the interaction feature sequences, and screening the historical teaching data with the average interaction scores meeting preset standards;
Extracting teaching modes of a target course through the screened historical teaching data, acquiring teaching modes corresponding to similar course types according to course characteristics of the target course, sequencing according to occurrence frequencies of different teaching modes, and selecting a preset number of teaching modes as preferential teaching modes of student groups;
retrieving a teaching example by utilizing a big data means according to the preference teaching mode, generating training data by the teaching example, constructing a teaching mode migration model, and performing model training by utilizing the training data;
and optimizing the teaching mode of the target course content by using the trained teaching mode migration model, outputting the structured data containing the course content, and generating a corresponding teaching scheme.
7. An intelligent operation platform-based teaching course optimization system, which is characterized by comprising: the intelligent operation platform-based teaching course optimization method comprises a memory and a processor, wherein the memory comprises an intelligent operation platform-based teaching course optimization method program, and when the intelligent operation platform-based teaching course optimization method program is executed by the processor, the following steps are realized:
acquiring a course teaching plan of target course content, extracting course knowledge points according to the course teaching plan, acquiring interaction characteristics of students on the course knowledge points in the teaching process, and generating course homework through the interaction characteristics;
Pushing the course homework through an intelligent homework platform, acquiring homework information uploaded by students on the intelligent homework platform, intelligently correcting the homework information and identifying course knowledge points;
clustering knowledge points according to the correction result, extracting a clustering result to evaluate student grasp conditions, screening course knowledge points with grasp conditions not conforming to a preset threshold, cutting the course teaching plan by using the screened course knowledge points, and optimizing the time of the course teaching plan;
and extracting course content corresponding to the time-optimized course teaching plan, acquiring a preference teaching mode of the student group according to the history teaching data, retrieving a teaching example through the preference teaching mode, and optimizing a teaching scheme corresponding to the target course content.
8. The teaching course optimization system based on the intelligent homework platform as claimed in claim 7, wherein the interactive features of the students to the course knowledge points in the teaching process are obtained, and the course homework is generated through the interactive features, specifically:
acquiring video stream information of a teaching process according to video monitoring equipment, acquiring course knowledge points related to the teaching process through a teaching course plan, and acquiring a time stamp of the course knowledge points in the video stream information;
Dividing video stream information based on a time stamp, obtaining a video segment corresponding to a course knowledge point, extracting video image frames from the video segment, obtaining face image information of a student group according to the video image frames, preprocessing the face image information, and obtaining face key points of the face image information;
acquiring a position change sequence corresponding to the video segment according to the position characteristics of the facial key points, calculating and identifying the micro-expression of the students according to the similarity through the position change sequence, and counting the micro-expression characteristics of the student groups;
acquiring sound signals of a student group, extracting sound features, combining the sound features with micro expression features to acquire interactive features of the student group on course knowledge points, acquiring interactive scores by utilizing the interactive features, and acquiring the course knowledge points which do not meet preset score standards for marking;
and carrying out proportional gain on the marked course knowledge points, and generating course operation according to the proportion occupied by different course knowledge points.
9. The teaching course optimization system based on the intelligent work platform according to claim 7, wherein the knowledge points are clustered according to the correction result, the clustering result is extracted to evaluate the mastering condition of the student, and the course knowledge points with the mastering condition not meeting the preset threshold are screened specifically as follows:
Acquiring standard answer information corresponding to course operation, extracting key steps according to the standard answer information, intelligently correcting the uploaded operation information according to the key steps and scoring standards, acquiring scoring information of key steps corresponding to different operation items, and marking the operation items by using the scoring information;
clustering key steps through course knowledge points, taking the course knowledge points as initial clustering centers, acquiring membership matrixes from each key step in different operation projects to the initial clustering centers, and when the maximum membership is greater than or equal to a preset first threshold, classifying the key steps into class clusters corresponding to the maximum membership;
when the maximum membership is smaller than a preset first threshold, judging whether the maximum membership is larger than a preset second threshold, if so, classifying the key steps into a plurality of class clusters, wherein the first threshold is larger than the second threshold;
obtaining class clusters corresponding to the course knowledge points by utilizing repeated iterative clustering, obtaining average scores in different class clusters to represent student mastering conditions, screening the course knowledge points with mastering conditions not meeting a preset threshold, and obtaining homework items corresponding to key steps from the class clusters corresponding to the screened course knowledge points;
Acquiring course knowledge points related to the operation project, counting occurrence frequencies of other course knowledge points except for the poor course knowledge points, and acquiring high-association course knowledge points of the poor course knowledge points according to the occurrence frequencies;
and generating a focused course knowledge point set according to the poorly mastered course knowledge points and the high-association course knowledge points.
10. A computer-readable storage medium, characterized by: the computer readable storage medium includes a teaching course optimization method program based on an intelligent operation platform, and when the teaching course optimization method program based on the intelligent operation platform is executed by a processor, the steps of the teaching course optimization method based on the intelligent operation platform as claimed in any one of claims 1 to 6 are implemented.
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