CN108984516B - Online course content evaluation method and system based on bullet screen evaluation cloud data - Google Patents
Online course content evaluation method and system based on bullet screen evaluation cloud data Download PDFInfo
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Abstract
The invention discloses an online course content evaluation method and system based on bullet screen comment cloud data, wherein the method comprises the following steps: 1) The bullet screen comment data of the learner are sent to a bullet screen cloud data system through a student terminal; 2) The bullet screen cloud data system receives, stores and verifies the bullet screen comment data and sends qualified bullet screen comment data to the screen terminal; 3) The screen terminal receives and displays the bullet screen comment data and returns the corresponding big data characteristic value to the bullet screen cloud data system; 4) The data system receives and processes the historical big data characteristic value of the barrage comment, and pushes the result to the teacher terminal; 5) And the teacher terminal receives the processing and analyzing result, judges whether the course content is abnormal or not, and gives information prompt to the teacher for the abnormal part of the course content in a visual form. The online course content is identified and improved in real time and in a full process manner, and the authenticity and the accuracy of online course content evaluation are improved.
Description
Technical Field
The invention relates to the technical field of online course content evaluation, in particular to an online course content evaluation method and system based on bullet screen evaluation cloud data.
Background
The current mainstream techniques for online course content evaluation are three types: expert simulation evaluation, institution self-examination evaluation and learner investigation evaluation. The expert simulation evaluation mainly utilizes experts to simulate the identity experience of students and sense the content of the online courses in the initial stage of online course construction (without putting into formal teaching or learning), so as to evaluate the quality of the online courses. The institution self-examination and evaluation refers to rough review and clearance of online course contents by training institutions, individuals or manufacturers at the initial construction stage (without putting into formal teaching or learning) of the online course contents, and aims to judge which courses meet basic requirements or specifications of network courses. The learner investigation evaluation is to investigate the overall perception or satisfaction of the learner after completing a certain or a series of online courses (putting into formal teaching or learning), and feedback the quality information of the online course content from the user perspective.
However, the above-mentioned collection of the technical data information of online course content evaluation occurs before the online course content formal learning (expert simulation evaluation, institution self-examination evaluation), or after the online course content formal learning (learner investigation), and the online course content perception information data of the learner learning process cannot be collected yet, so that it is difficult to realize the whole-process and normalized evaluation and monitoring of the online course content quality. In addition, the evaluation granularity of the current evaluation technology is large, the whole course, unit or chapter is mostly used as an evaluation unit, the evaluation and result thereof lack pertinence, the design problem of the online course content at the microscopic level can not be identified and diagnosed in time, the opportunity of finding the course content design problem and correcting errors in time is lost, and the optimization and improvement of the online course content quality are not facilitated. In addition, the traditional evaluation technology cannot continuously, accurately, truly and effectively record the evaluation data of the content quality of the online course in the learning process of the learner, and the online course content quality analysis and diagnosis based on the learner perception big data is difficult to develop.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide an online course content evaluation method and system based on bullet screen evaluation cloud data, and aims to solve the problems that the existing evaluation method is lack of pertinence to online course content evaluation and is difficult to realize full-process and normalized monitoring.
The specific technical scheme for realizing the purpose of the invention is as follows:
an online course content evaluation method based on bullet screen evaluation cloud data comprises the following specific steps:
A. the bullet screen comment data of the learner are packaged and sent to a bullet screen cloud data system through the student terminal;
B. the bullet screen cloud data system receives, stores and verifies the bullet screen comment data, and then directly packs and sends the qualified bullet screen comment data to the screen terminal;
C. the screen terminal receives and displays the bullet screen comment data and returns a big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
D. the bullet screen cloud data system receives and processes the historical big data characteristic value of the bullet screen comment, and pushes the processing and analyzing result to a teacher terminal;
E. and the teacher terminal receives the processing and analyzing result, judges whether a part or a segment of the course content is abnormal or not according to the distribution condition of the emotion label marking points, and gives information prompt to the teacher for the abnormal part of the course content in a visual form.
The online course content evaluation method based on bullet screen comment cloud data is characterized in that the student bullet screen comment data in the step A relate to two types: one type is text comment data which comprises text comment contents, the number of text comments and time nodes relative to the course contents; another type is emotion tag data, including emotion tag category, number of emotion tags, and their time node relative to the course content. The emotion labels are emoticons capable of expressing the learning experience of students. Wherein, the setting of mood label relates to the technical transformation to traditional bullet screen expression sign. Specifically, according to the characteristics of online learning, the traditional barrage emoticons are deleted and classified, three types of learning expression parameters including praise, question and spit groove are reserved, and the traditional barrage emoticons are named as learning emotion labels. In addition, the bullet screen sending attribute of the student terminal is set, and the limited comment content can be normally and effectively sent only if the limited comment content contains the text and the emotion label at the same time.
The online course content evaluation method based on bullet screen comment cloud data is characterized in that the verification in the step B refers to logic processing of the bullet screen comment data and aims to verify the integrity and the legality of the data.
The on-line course content evaluation method based on bullet screen comment cloud data, wherein the big data characteristic value corresponding to the bullet screen comment in the step C comprises the following steps: the number of the text comments, the time node of each text comment relative to the course content, the category of the emotion labels, the number of the emotion labels, the time node of the course content corresponding to each emotion label, and the like.
The on-line course content evaluation method based on the bullet screen comment cloud data is characterized in that the processing and analysis in the step D specifically means that historical big data of bullet screen comments are counted and analyzed by means of a mathematical statistical method on the basis of the received big data of bullet screen comments to obtain a course content quality analysis result based on the bullet screen comment cloud data.
In the online course content evaluation method based on bullet screen comment cloud data, the emotion label marking points in the step E are points of different colors used for replacing and identifying emotion label categories and meanings in visual information prompts sent to a teacher end. The green mark points represent praise, the orange mark points represent question, and the red mark points represent spitting grooves. It should be noted that, each annotation point in the visual chart is interactive and is associated with the corresponding online course content and the bullet screen text comment content. When a certain point is clicked, the corresponding online course content and the barrage text comment thereof are automatically presented and played.
An online course content evaluation system based on bullet screen evaluation cloud data, the system comprising:
the student terminal sending module is used for sending the barrage comment data of the students to the barrage cloud data system;
the bullet screen cloud data system receiving, storing and verifying module is used for receiving, storing and verifying the bullet screen comment data;
the bullet screen cloud data system sending module is used for packaging and distributing the bullet screen comment data which are qualified in verification to the screen terminal;
the screen terminal receiving and displaying module is used for receiving and displaying the bullet screen comment data sent by the bullet screen cloud data system;
the screen terminal returning module is used for returning the big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
the bullet screen cloud data system processing and pushing module is used for extracting a big data characteristic value corresponding to bullet screen comment data, performing statistical analysis and pushing a processing and analyzing result to a teacher terminal;
and the teacher terminal receiving and prompting module is used for receiving the historical big data analysis result of the barrage comment and giving information prompt to the teacher in a visual mode according to the distribution condition of the emotion label mark points.
In the online course content evaluation system based on barrage comment cloud data, in a student terminal sending module, barrage comment data of students relate to two types of data including text comment content, text comment quantity, time nodes of each text comment relative to course content, emotion label categories, emotion label quantity, and time nodes of each emotion label relative to course content. The student terminal sending module also relates to the technical transformation of emoticons and sending attributes of the traditional barrage. Traditional barrage expressions are deleted and classified, and three types of package conditions including praise, question and spitting groove are reserved, namely emotion labels are learned. In addition, the student terminal resets the bullet screen sending attribute, and the set comment content can be normally sent only if the comment content contains two types of information, namely a text and an emotion label.
According to the online course content evaluation system based on the bullet screen comment cloud data, in the bullet screen comment cloud data receiving, storing and verifying module, verification refers to logic processing of the bullet screen comment data, and integrity and legality of the data are verified.
On-line course content evaluation system based on bullet screen comment cloud data, wherein, the big data characteristic value in the screen returning module includes: the method comprises the steps of text comment quantity, course content time nodes corresponding to all text comments, emotion label categories, emotion label quantity and course content time nodes corresponding to all emotion labels.
The online course content evaluation system based on the barrage comment cloud data is characterized in that the barrage comment historical big data participating in statistics in the barrage comment processing and pushing module mainly comprises the number of text comments, course content time nodes corresponding to all the text comments, emotion label categories, the number of emotion labels and course content time nodes corresponding to all the emotion labels.
According to the online course content evaluation system based on bullet screen comment cloud data, the emotion label marking points received and prompted by the teacher terminal in the emotion label marking point receiving and prompting module are points which are used for replacing and recognizing emotion label categories and different colors of emotion label meanings and are pushed to the teacher terminal in visual information prompting. Each point in the visual information prompt is interactive and is associated with corresponding online course content and bullet screen text comment content.
Has the beneficial effects that: according to the method, a bullet screen cloud data system is newly added in a traditional bullet screen comment system framework, the traditional bullet screen emoticons and sending attributes are technically improved, and learning emotion tags are specially arranged for identifying content information of online courses. By means of the abnormal identification of the bullet screen cloud data technology, the correction flow architecture of the bullet screen comment data sample and the operation mechanism thereof, the online course content is identified and improved in real time and in a full process, the course content information with smaller granularity can be focused, the optimization of the design and quality of the online course content is promoted, the normalized diagnosis of the online course content can be realized, and the authenticity and the accuracy of the online course content evaluation are improved.
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FIG. 1 is a flow chart of an embodiment of the method of the present invention;
FIG. 2 is a terminal review interface diagram of a student of the present invention;
FIG. 3 is a diagram illustrating teacher terminal information according to the present invention;
fig. 4 is a block diagram of an embodiment of the system of the present invention.
Detailed Description
The invention provides an online course content evaluation method based on bullet screen evaluation cloud data, and the invention is further described in detail below in order to make the purpose, technical scheme and effect of the invention clearer and more clear and definite. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a flowchart of a preferred embodiment of an online course content evaluation method based on bullet screen comment cloud data of the present invention, which includes the steps of:
s001, packaging and sending bullet screen comment data of the learner to a bullet screen cloud data system through a student terminal;
s002, the bullet screen comment data are received, stored and verified through a bullet screen cloud data system, and then the bullet screen comment data which are qualified in verification are directly packaged and sent to a screen terminal;
s003, the screen terminal receives and displays the bullet screen comment data and returns the big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
s004, receiving and processing historical big data characteristic values of the bullet screen comments by the bullet screen cloud data system, and pushing processing and analyzing results to a teacher terminal;
and S005, the teacher terminal receives the processing and analyzing result, judges whether a part or a segment of the course content is abnormal or not according to the distribution condition of the emotion label mark points, and gives information prompt to the teacher for the abnormal part of the course content in a visual form.
According to the method, a barrage cloud data system is newly added in a traditional barrage comment system architecture, the traditional barrage emoticons and the sending attributes are technically improved, and emotion labels are specially arranged for identifying content information of online courses. By means of the abnormal identification of the bullet screen cloud big data technology, the correction flow architecture of the bullet screen comment data sample and the operation mechanism thereof, the online course content is identified and improved in real time and in a full process, the course content information with smaller granularity can be focused, the optimization of the design and quality of the online course content is promoted, the normalized diagnosis of the online course content can be realized, and the authenticity and the accuracy of the online course content evaluation are improved.
In step S001, when performing online course learning, each student participating in evaluation may send barrage comment data to the barrage cloud data system through the student terminal of the student. Specifically, the bullet screen comment data of the students relate to two types of text comment data and emotion comment data, including text comment content, text comment quantity, time node of each text comment relative to course content, emotion tag category, emotion tag quantity and time node of each emotion tag relative to course content. The invention relates to an online course content evaluation methodological student terminal comment interface based on bullet screen evaluation cloud data, which is shown in figure 2. For example, when online course content is learned, each student can send text comment content, time nodes of each text comment relative to the course content, emotion label categories, emotion label quantity, and time nodes of each emotion label relative to the course content to a barrage cloud data system through the student terminal of each student. The emotion label category can be any one of praise, question or spit. For example, in an online course learning process, a student feels that a certain part of course content is not easy to understand, and the student can explain a place where the part of course content is not easy to understand in a text comment through a student terminal and identify the part of course content by using a question emotion label. Meanwhile, the learning terminal can automatically collect bullet screen comment data of students and send the bullet screen comment data to the bullet screen cloud data system.
In the step S002, after receiving and storing the bullet screen comment data sent by all students, the bullet screen cloud data system verifies the integrity and the legality of the bullet screen comment data, and then packs and sends the qualified bullet screen comment data to the screen terminal. The integrity and validity of the barrage comment data are mainly checked to see whether various comment data are complete, complete and clear in data chain. For example, in the course content learning process, due to the fact that the bullet screen comment data are abnormal due to abnormal environments such as shutdown, network disconnection and network delay of the learning terminal, the bullet screen cloud data system verifies indexes such as data index loss and data chain self-verification to ensure the integrity and the legality of the bullet screen comment data.
In the step S003, the screen terminal receives and displays the bullet screen comment data, and returns the big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system. Specifically, the returned big data characteristic values include: the number of the text comments, the time nodes of the course content corresponding to all the text comments, the category of the emotion label, the number of the emotion label, and the time nodes of the course content corresponding to all the emotion labels.
In the step S004, the barrage cloud data system receives and processes the historical big data characteristic value of the barrage comment, and pushes the processing and analyzing result to the teacher terminal. The specifically processed historical big data characteristic values mainly comprise the number of text comments, the curriculum content time nodes corresponding to all the text comments, the emotion label categories, the number of emotion labels, and the curriculum content time nodes corresponding to all the emotion labels.
The step S004 further includes a step of processing analysis: and on the basis of the received barrage comment big data, counting and analyzing the barrage comment historical big data by means of a mathematical statistics method to obtain course content quality analysis results based on the barrage comment cloud data. The method is characterized in that a student terminal collects evaluation information of each student in online course learning each time through the student terminal on course content, the evaluation information is barrage comment data in the method, and then the barrage comment information such as each student text comment content, text comment quantity, time nodes of all text comments relative to the course content, emotion label categories, emotion label quantity, time nodes of all emotion labels relative to the course content and the like is sent to a barrage comment cloud data system and stored. Through data accumulation in the content learning process of daily online courses, historical comment data of each online course can be formed on the barrage cloud data system. The bullet screen cloud data system realizes continuous quantitative collection of the student comment data in the process of learning the content of the normalized online course, can truly and effectively record the bullet screen comment information data of students for each class in detail, and forms historical bullet screen comment big data of the students for each class. Furthermore, the historical barrage comment big data can be counted and calculated by using a mathematical statistical method, so that big data characteristic values such as the number of text comments in each class, the class content time nodes corresponding to all the text comments, the category of emotion labels, the number of emotion labels, the class content time nodes corresponding to all the emotion labels and the like are obtained. According to the barrage cloud data system, statistical analysis is carried out on historical barrage comment data of each class, so that teachers can conveniently monitor the content state of the courses, online course content design is optimized and improved, and the learning efficiency of the course content is improved.
The present invention will be described below by way of example with respect to the number of emotion labels, the category of emotion labels, and the corresponding time node of the course content. For example, according to the formed historical bullet screen comment data of each class, the number of emotion labels, the category of emotion labels and the time nodes of the course content corresponding to all emotion labels of each class are calculated, and the emotion label distribution situation of each course content on each time node can be obtained. The content quality design condition of a certain part or a certain segment of the course content can be roughly judged according to the category and quantity distribution condition of the emotion labels. If the praise emotion labels distributed on some part of course content are more and centralized, the learning experience of the student on the part of course content is better, and the course content is well designed; if the questioning emotion labels distributed on the course content of a certain part are more and centralized, the student does not understand the course content of the part, and the course content design is more advanced and exceeds the cognitive development area of the student; if more and concentrated groove-spitting emotional labels are distributed on a certain part of course content, the fact that the learning experience of students on the part of course content is more groove-spitting indicates that the course content design has serious problems and needs to draw special attention of teachers. At the moment, the teacher terminal can send out early warning prompts to the course teachers, so that the teachers can improve and optimize the online course contents in time, and the learning efficiency of the course contents is improved.
In step S005, the teacher terminal receives a bullet screen comment big data processing analysis result pushed by the bullet screen cloud data system, and determines whether a certain part or a certain segment of the course content is abnormal or not according to the identification and distribution of the emotion tag on the course content time node. And when the course content is judged to be abnormal, giving information prompt to the teacher for the abnormal course content part. That is, when the query emotion labels distributed in the course content of a certain part are more and concentrated, the teacher end prompts the teacher: the student does not understand the content of the course, and the design of the content of the course can be advanced; when the spit groove emotion labels distributed in the course content of a certain part are more and concentrated, the teacher terminal prompts a teacher: the study experience of the student on the course content is compared with that of the groove cake, and the course content design has serious problems and needs to draw special attention. When the praise emotion labels distributed in the course content of a certain part are more and concentrated, the teacher terminal prompts the teacher: the student experiences the study of this part course content relatively good, and the course content design is good. The invention relates to an online course content evaluation method based on bullet screen evaluation cloud data, and a teacher terminal information prompt legend is shown in figure 3.
The invention will be illustrated below with reference to fig. 3 for information presentation. For example, when the teacher terminal receives the barrage comment big data processing and analyzing result pushed by the barrage cloud data system, the processing and analyzing result is as follows: the number of spitting groove emotion labels of students in a certain part of course content is large and concentrated, and the number of red marking points corresponding to the part of course content in the information prompt of the teacher end is large and concentrated; when the teacher terminal receives the bullet screen comment big data pushed by the bullet screen cloud data system, the processing and analysis result is as follows: the questioning emotion labels of students in a certain course content are more and centralized, and orange marking points corresponding to the part of the content are more and centralized in the information prompt of the teacher end; when the teacher terminal receives the bullet screen comment big data pushed by the bullet screen cloud data system, the processing and analysis result is as follows: the endorsement emotion labels of students in a certain course content are more and centralized, and the green annotation points corresponding to the part of content are more and centralized in the information prompt of the teacher end. In the information prompt of the teacher terminal, the annotation points with different colors are mainly used for representing different emotional label contents and meanings thereof. Green dots represent praise, orange dots represent question, and red dots represent spit groove.
Based on the above method, the present invention further provides a block diagram of a preferred embodiment of an online course content evaluation system based on bullet screen cloud data, as shown in fig. 4, which includes:
the student terminal sending module 100 is used for sending the bullet screen comment data of the students to the bullet screen cloud data system;
the bullet screen cloud data system receiving, storing and verifying module 200 is used for receiving, storing and verifying the bullet screen comment data;
the barrage cloud data system sending module 300 is used for packaging and distributing the checked and qualified barrage comment data to the screen terminal;
the screen terminal receiving and displaying module 400 is used for receiving and displaying the bullet screen comment data sent by the bullet screen cloud data system;
the screen terminal returning module 500 is used for returning the big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
the bullet screen cloud data system processing and pushing module 600 is used for extracting a big data characteristic value corresponding to bullet screen comment data, performing statistical analysis, and pushing a processing and analyzing result to a teacher terminal;
and the teacher terminal receiving and prompting module 700 is used for receiving the historical big data analysis result of the barrage comment and giving information prompt to the teacher in a visual mode according to the distribution condition of the emotion label annotation points.
In the student terminal sending module 100, the barrage comment data of the student specifically includes text comment content, text comment quantity, time node of each text comment relative to course content, emotion tag category, emotion tag quantity, and time node of each emotion tag relative to course content. In addition, the student terminal sending module also relates to the technical transformation of the emoticons and the sending attributes of the traditional barrage.
In the bullet screen cloud data system receiving, storing and checking module 200, checking refers to performing logic processing on the bullet screen comment data and aims to check the integrity and the legality of the bullet screen comment data.
In the screen returning module 500, the big data feature values include: the number of the text comments, the time nodes of the course content corresponding to all the text comments, the category of the emotion labels, the number of the emotion labels, and the time nodes of the course content corresponding to all the emotion labels.
In the barrage cloud data system processing and pushing module 600, the barrage comment historical big data participating in statistics mainly include the number of text comments, course content time nodes corresponding to all the text comments, emotion tag categories, the number of emotion tags, and course content time nodes corresponding to all the emotion tags.
In the receiving and prompting module 700 of the teacher terminal, the emotion tag marking point is a point of different colors used for replacing and identifying the emotion tag type and the emotion tag meaning in the visual information prompt pushed to the teacher terminal. The green mark points represent praise, the orange mark points represent question, and the red mark points represent spitting grooves. In addition, each point is interactive and is associated with corresponding online course content and text review information.
In summary, in the invention, a bullet screen cloud data system is newly added in the traditional bullet screen comment system architecture, the traditional bullet screen emoticons and the sending attributes are technically improved, and a learning emotion label is specially set for identifying the content information of the online course. By means of the abnormal identification of the bullet screen cloud data technology, the correction flow architecture of the bullet screen comment data sample and the operation mechanism thereof, the online course content is identified and improved in real time and in a full process, the course content information with smaller granularity can be focused, the optimization of the design and quality of the online course content is promoted, the normalized diagnosis of the online course content can be realized, and the authenticity and the accuracy of the online course content evaluation are improved.
It should be understood that the invention is not limited to the above-described embodiments, but that modifications and variations can be made by persons skilled in the art in light of the above teachings and are to be included within the scope of the invention as defined by the appended claims.
Claims (4)
1. An online course content evaluation method based on bullet screen evaluation cloud data is characterized by comprising the following specific steps:
A. the bullet screen comment data of the learner are packaged and sent to a bullet screen cloud data system through a student terminal;
B. the bullet screen cloud data system receives, stores and verifies the bullet screen comment data, and then directly packs and sends the qualified bullet screen comment data to the screen terminal;
C. the screen terminal receives and displays the bullet screen comment data and returns a big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
D. the bullet screen cloud data system receives and processes the historical big data characteristic value of the bullet screen comment, and pushes the processing and analyzing result to a teacher terminal;
E. the teacher terminal receives the processing and analyzing result, judges whether a part or a segment of the course content is abnormal or not according to the distribution condition of the emotion label mark points, and gives information prompt to the teacher for the abnormal part of the course content in a visual form; wherein:
in the step A, the bullet screen comment data of the learner are of two types: one type is text comment data which comprises text comment contents, the number of text comments and time nodes relative to the course contents; the other type is emotion label data which comprises emotion label types, emotion label quantity and time nodes of the emotion label relative to course content; the limited comment content must include both text and emotion tags to be able to be sent normally and efficiently;
in the step B, the check refers to the logic processing of the barrage comment data, and aims to check the integrity and the legality of the data;
in step C, the big data characteristic value corresponding to the barrage comment data includes: the method comprises the steps of determining the number of text comments, time nodes of each text comment relative to course content, emotion label categories, the number of emotion labels and the time nodes of the course content corresponding to each emotion label;
in the step D, the processing and analyzing specifically means that historical big data of the bullet screen comments are counted and analyzed by means of a mathematical statistics method on the basis of the received big data of the bullet screen comments to obtain course content quality analysis results based on the cloud data of the bullet screen comments;
in the step E, the emotion label marking point refers to a point which is used for replacing and identifying the emotion label type and meaning and has different colors in the visual information prompt pushed to the teacher end; green marking points represent praise, orange marking points represent questions, and red marking points represent spitting grooves; each marking point in the visual chart is interactive and is associated with the corresponding online course content and the corresponding barrage character comment content; when a certain point is clicked, the corresponding online course content and the barrage text comment thereof can be automatically presented and played.
2. The on-line course content evaluation method based on bullet screen comment cloud data as claimed in claim 1, wherein the emotion labels refer to emoticons expressing learning experiences of students, including three types of learning expression parameters of praise, question and spit slot.
3. A system for implementing the method of claim 1, the system comprising:
the student terminal sending module is used for sending the barrage comment data of the students to the barrage cloud data system;
the bullet screen cloud data system receiving, storing and verifying module is used for receiving, storing and verifying the bullet screen comment data;
the bullet screen cloud data system sending module is used for packaging and distributing the bullet screen comment data which are qualified in verification to the screen terminal;
the screen terminal receiving and displaying module is used for receiving and displaying the bullet screen comment data sent by the bullet screen cloud data system;
the screen terminal returning module is used for returning the big data characteristic value corresponding to the bullet screen comment data to the bullet screen cloud data system;
the bullet screen cloud data system processing and pushing module is used for extracting a big data characteristic value corresponding to bullet screen comment data, performing statistical analysis and pushing a processing and analyzing result to a teacher terminal;
the teacher terminal receiving and prompting module is used for receiving historical big data analysis results of barrage comments and visually prompting information for a teacher according to the distribution condition of emotion label marking points; wherein:
in the student terminal sending module, the bullet screen comment data of the learner are of two types: one type is text comment data which comprises text comment contents, the number of text comments and time nodes relative to the course contents; the other type is emotion label data which comprises emotion label types, emotion label quantity and time nodes of the emotion label relative to course content; the limited comment content must include both text and emotion tags to be able to be sent normally and effectively;
the bullet screen cloud data system receives, stores and verifies the bullet screen comment data, wherein the verification refers to the logic processing of the bullet screen comment data and the verification of the integrity and the legality of the data;
in the screen returning module, the big data characteristic values comprise: the method comprises the following steps of (1) judging the number of text comments, course content time nodes corresponding to all the text comments, emotion label categories, the number of emotion labels and course content time nodes corresponding to all the emotion labels;
in the barrage cloud data system processing and pushing module, the barrage comment historical big data participating in statistics comprise the number of text comments, course content time nodes corresponding to all the text comments, emotion label categories, the number of emotion labels and course content time nodes corresponding to all the emotion labels;
in the receiving and prompting module of the teacher terminal, the emotion label marking point is a point which is used for replacing and identifying different colors of emotion label types and meanings in the visual information prompt pushed to the teacher terminal; each point in the visual information prompt is interactive and is associated with corresponding online course content and bullet screen text comment content.
4. The system according to claim 3, wherein the emotion label is an emoticon expressing the learning experience of the student, and comprises three types of learning expression parameters of praise, question and spit.
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