CN111444391B - Video learning achievement evaluation method based on artificial intelligence - Google Patents
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Abstract
The invention discloses a video learning achievement assessment method based on artificial intelligence, and particularly relates to the field of digital learning. According to the invention, the process records of all users are collected to comprise data such as online visual evaluation watching time and playback times, question answering accuracy rate and answering time of test questions after learning and the like, the capability results after the test of the users are taken as classification basis to be artificial intelligence, and after a new teaching video is online, the collected user data is used for predicting whether the teaching video is proper or not, so that whether the video has the effect of improving learning is evaluated.
Description
Technical Field
The invention relates to the technical field of digital learning, in particular to a video learning effect evaluation method based on artificial intelligence.
Background
The digital learning is to build an internet platform in the education field, and a brand new learning mode is realized when students learn through a network. Also known as web learning or E-learning. The integration of information technology and courses with digital learning as a core is different from the traditional learning mode. The integration of information technology and courses with digital learning as a core is different from the traditional learning mode, and has the characteristics that (1) learning is centered on students, and learning is personalized and can meet individual needs; (2) learning is question or topic-centric; (3) the learning process is communication, and learners are in negotiation and cooperation; (4) learning is creative and regenerative; (5) learning is lifelong at any time and any place. Digital learning changes the spatiotemporal concept of learning. The global sharing of digital learning resources, the appearance of virtual classrooms and virtual schools and the rising of modern remote education lead the learning to be not limited in schools and families, and people can enter the digital virtual schools for learning at any time and any place through the Internet. In time, knowledge skills enjoyed by an ancestor cannot be obtained by only concentrated learning for a period of time. Humans will transition from receiving one-time education to life-long learning. Digital chemistry therefore requires learners to have a lifelong learning attitude and ability. In the information age, the individual study will be life-long, which is the process in which a learner determines the goal of continuing to learn according to the social and work demands, and consciously self-plans, self-manages, and autonomously strives to achieve the learning goal through various approaches. Of course, this requires that education must undergo profound changes in the connotation and function of education, the goals of cultivation, content and pathways to provide conditions for life-long learning of people.
The digital learning system enables students to learn through online videos. At present, all digital systems do not have an effective assessment method aiming at whether online visual assessment is really helpful for improving learning results, so that whether online visual assessment content is proper cannot be known.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a video learning achievement evaluation method based on artificial intelligence, which aims to solve the technical problems that: how to determine whether a video for evaluating digital learning has learning success or not and whether to successfully coach with user learning.
In order to achieve the above purpose, the present invention provides the following technical solutions: a video learning achievement evaluation method based on artificial intelligence comprises the following specific test steps:
s1, comprehensive test before study: preparing a set of pre-learning test examination paper, and carrying out knowledge point investigation on a learner to deeply know the mastering condition of the learner to be knowledge points and the type of the error-prone questions;
s2, wrong question arrangement: carrying out wrong question statistics on the test results in the step S1, preparing a wrong question set, and carrying out centralized marking on the questions with the highest wrong question rate, thereby determining universal knowledge blind points;
s3, video adjustment: the wrong question set determined in the step S2 is adjusted to focus on knowledge points of the teaching video, focus on the knowledge point with the highest wrong question rate is explained, and error-prone questions are explained by way of example;
s4, recording playing information: after video delivery, recording the total playing time of the corresponding video and the playing times of the corresponding video, collecting information and comparing the information with the knowledge blind points recorded in the step S2;
s5, testing along with the hall;
s5.1: after video learning is completed, carrying out a current day knowledge point along with the hall test, wherein the test questions are divided into two questions which are difficult and easy according to the knowledge points;
s5.2: respectively recording the correctness and the answering time of the hall test according to the difficulty level;
s6, establishing a judgment model: the ability result after the user tests is taken as a classification basis, the ability result can be divided into a plurality of groups of distances, and the classification prediction and judgment information module is used for carrying out classification prediction and judgment according to the difficulty level of the test questions;
s7, score judgment: judging the learning score according to the difficulty degree classification;
(1) if the results reach the standard, finishing the video learning work on the same day, and finishing the wrong questions on the same day by the system to generate a single learning result model;
(2) if the simple questions reach the standard and the difficulty questions do not reach the standard, the system generates a current day learning result model, and repeats the steps from the step S3 to the step S5, wherein the in-hall test questions in the step S5 are only the difficulty questions;
(3) if the scores of the difficult and easy questions reach the standard, the system generates a study result model on the same day, and then repeats the steps S3-S7 to judge the scores of the tests on the same hall again;
s8, building a single learning result model: and counting the learning results of the students after the video learning is completed, counting the types of wrong questions, and establishing a difficult problem set which is not mastered by the students of the system, so that the consolidation of wrong questions again in the next-day video teaching process is facilitated, and the review of yesterday information and the connection of knowledge points are ensured.
In a preferred embodiment, the performance determination in step S7 may perform classification determination on the questions of two kinds of difficulty and ease, so as to determine the mastery condition of the knowledge points.
In a preferred embodiment, the achievement values of the difficult and easy subjects in step S7 are different, and are performed according to the easy-high and difficult-low decision criteria.
In a preferred embodiment, the comprehensive test in step S1 includes yesterday knowledge points and today knowledge points, which not only can play a role in review, but also can review knowledge series points in the next video teaching process in advance, so that the user can clearly learn the key points.
In a preferred embodiment, the invention further comprises a control end host and a using end host, wherein the control end host is connected with the using end host through an internet of things signal.
In a preferred embodiment, the control end host also comprises a database, wherein the database comprises a plurality of difficult and easy questions corresponding to the knowledge points, so that a single user can not cause the situation that one question is done for a plurality of times during secondary hall-following test.
The invention has the technical effects and advantages that:
1. according to the invention, through collecting all user process records including online visual evaluation watching time and playback times, test question answering accuracy after learning, answering time and other data as training data sources, the capability result after user test is taken as a classification basis and is artificial intelligence, when a new teaching video is online, the collected user data is used for predicting whether the teaching video is proper or not, so that whether the video has the effect of improving learning effect is evaluated;
2. the method can be accurately used for evaluating whether the online teaching video is suitable or not, is helpful for teaching promotion assistance, and can remove turnip through the proposed scheme, so that the truly useful teaching video is reserved.
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FIG. 1 is a schematic diagram of the overall use flow of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
the invention provides a video learning achievement evaluation method based on artificial intelligence, which comprises the following specific test steps:
s1, comprehensive test before study: preparing a set of pre-learning test examination paper, carrying out knowledge point investigation on a learner, deeply knowing the mastering condition of the learner to be knowledge points and the type of error-prone questions, comprehensively testing yesterday knowledge points and today knowledge points, playing a review effect, and carrying out advanced review on the knowledge series points in the next video teaching process to ensure that a user clearly learns key points;
s2, wrong question arrangement: carrying out wrong question statistics on the test results in the step S1, preparing a wrong question set, and carrying out centralized marking on the questions with the highest wrong question rate, thereby determining universal knowledge blind points;
s3, video adjustment: the wrong question set determined in the step S2 is adjusted to focus on knowledge points of the teaching video, focus on the knowledge point with the highest wrong question rate is explained, and error-prone questions are explained by way of example;
s4, recording playing information: after video delivery, recording the total playing time of the corresponding video and the playing times of the corresponding video, collecting information and comparing the information with the knowledge blind points recorded in the step S2;
s5, testing along with the hall;
s5.1: after video learning is completed, carrying out a current day knowledge point along with the hall test, wherein the test questions are divided into two questions which are difficult and easy according to the knowledge points;
s5.2: respectively recording the correctness and the answering time of the hall test according to the difficulty level;
s6, establishing a judgment model: the ability result after the user tests is taken as a classification basis, the ability result can be divided into a plurality of groups of distances, and the classification prediction and judgment information module is used for carrying out classification prediction and judgment according to the difficulty level of the test questions;
s7, score judgment: according to the difficulty level classification judging learning achievement, if the achievement meets the standard, the video learning work on the same day is finished, the system sorts the wrong questions on the same day to generate a single learning achievement model, the achievement judgment can judge the difficulty two types of questions in a classified mode, so that the mastering condition of the knowledge points is determined, the achievement values of the difficulty questions meeting the standard are different, and the learning achievement is executed according to the easy-high and difficult-low judgment standards;
s8, building a single learning result model: and counting the learning results of the students after the video learning is completed, counting the types of wrong questions, and establishing a difficult problem set which is not mastered by the students of the system, so that the consolidation of wrong questions again in the next-day video teaching process is facilitated, and the review of yesterday information and the connection of knowledge points are ensured.
Example 2:
the invention provides a video learning achievement evaluation method based on artificial intelligence, which comprises the following specific test steps:
s1, comprehensive test before study: preparing a set of pre-learning test examination paper, carrying out knowledge point investigation on a learner, deeply knowing the mastering condition of the learner to be knowledge points and the type of error-prone questions, comprehensively testing yesterday knowledge points and today knowledge points, playing a review effect, and carrying out advanced review on the knowledge series points in the next video teaching process to ensure that a user clearly learns key points;
s2, wrong question arrangement: carrying out wrong question statistics on the test results in the step S1, preparing a wrong question set, and carrying out centralized marking on the questions with the highest wrong question rate, thereby determining universal knowledge blind points;
s3, video adjustment: the wrong question set determined in the step S2 is adjusted to focus on knowledge points of the teaching video, focus on the knowledge point with the highest wrong question rate is explained, and error-prone questions are explained by way of example;
s4, recording playing information: after video delivery, recording the total playing time of the corresponding video and the playing times of the corresponding video, collecting information and comparing the information with the knowledge blind points recorded in the step S2;
s5, testing along with the hall;
s5.1: after video learning is completed, carrying out a current day knowledge point along with the hall test, wherein the test questions are divided into two questions which are difficult and easy according to the knowledge points;
s5.2: respectively recording the correctness and the answering time of the hall test according to the difficulty level;
s6, establishing a judgment model: the ability result after the user tests is taken as a classification basis, the ability result can be divided into a plurality of groups of distances, and the classification prediction and judgment information module is used for carrying out classification prediction and judgment according to the difficulty level of the test questions;
s7, score judgment: according to the difficulty classification judging the learning score, if the simple questions reach the standard and the difficulty questions do not reach the standard, the system generates a current day learning result model, and repeats the steps from the step S3 to the step S5, wherein the test questions along with the questions in the step S5 are only difficult questions, the classification judgment of the score can be carried out on the two types of difficult questions, so as to determine the mastering condition of the knowledge points, the standard reaching score values of the difficult questions are different, the invention is carried out according to the easy-high and difficult-low judgment standards, the invention also comprises a control end host and a using end host, the control end host is connected with the using end host through an Internet of things signal, the control end host also comprises a database, and the database comprises a plurality of sets of difficult questions corresponding to the knowledge points, so that a single user cannot make a plurality of times of cases during the secondary along with the questions;
s8, building a single learning result model: and counting the learning results of the students after the video learning is completed, counting the types of wrong questions, and establishing a difficult problem set which is not mastered by the students of the system, so that the consolidation of wrong questions again in the next-day video teaching process is facilitated, and the review of yesterday information and the connection of knowledge points are ensured.
Example 3:
the invention provides a video learning achievement evaluation method based on artificial intelligence, which comprises the following specific test steps:
s1, comprehensive test before study: preparing a set of pre-learning test examination paper, carrying out knowledge point investigation on a learner, deeply knowing the mastering condition of the learner to be knowledge points and the type of error-prone questions, comprehensively testing yesterday knowledge points and today knowledge points, playing a review effect, and carrying out advanced review on the knowledge series points in the next video teaching process to ensure that a user clearly learns key points;
s2, wrong question arrangement: carrying out wrong question statistics on the test results in the step S1, preparing a wrong question set, and carrying out centralized marking on the questions with the highest wrong question rate, thereby determining universal knowledge blind points;
s3, video adjustment: the wrong question set determined in the step S2 is adjusted to focus on knowledge points of the teaching video, focus on the knowledge point with the highest wrong question rate is explained, and error-prone questions are explained by way of example;
s4, recording playing information: after video delivery, recording the total playing time of the corresponding video and the playing times of the corresponding video, collecting information and comparing the information with the knowledge blind points recorded in the step S2;
s5, testing along with the hall;
s5.1: after video learning is completed, carrying out a current day knowledge point along with the hall test, wherein the test questions are divided into two questions which are difficult and easy according to the knowledge points;
s5.2: respectively recording the correctness and the answering time of the hall test according to the difficulty level;
s6, establishing a judgment model: the ability result after the user tests is taken as a classification basis, the ability result can be divided into a plurality of groups of distances, and the classification prediction and judgment information module is used for carrying out classification prediction and judgment according to the difficulty level of the test questions;
s7, score judgment: according to the difficulty classification judging learning score, if the difficulty score is up to standard, repeating the step S3-step S7 after the system generates a current day learning result model, judging the result of the follow-up test again, judging the result to classify and judge the difficulty and difficulty two types of questions so as to determine the mastering condition of the knowledge points, wherein the up-to-standard score values of the difficulty score are different and are executed according to the easy-high and difficult-low judging standards;
s8, building a single learning result model: and counting the learning results of the students after the video learning is completed, counting the types of wrong questions, and establishing a difficult problem set which is not mastered by the students of the system, so that the consolidation of wrong questions again in the next-day video teaching process is facilitated, and the review of yesterday information and the connection of knowledge points are ensured.
After the method in the above embodiments 1-3 is used, the performance of each user is obviously improved, the playing time and the playing times of each video are stable, and the existence of unacceptable misuse teaching videos is avoided, so that the delivered teaching videos are ensured to have results for improving learning.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (3)
1. The utility model provides a video learning achievement evaluation method based on artificial intelligence, includes control end host computer and user end host computer, control end host computer passes through thing networking signal connection with the user end host computer, its characterized in that: the control end host also comprises a database, wherein the database comprises a plurality of sets of difficult and easy questions corresponding to the knowledge points, and the specific test steps are as follows:
s1, comprehensive test before study: preparing a set of pre-learning test examination paper, carrying out knowledge point investigation on a learner, deeply knowing the mastering condition of the learner to be knowledge points and the type of error-prone questions, and comprehensively testing the knowledge points including yesterday knowledge points and today knowledge points;
s2, wrong question arrangement: carrying out wrong question statistics on the test results in the step S1, preparing a wrong question set, and carrying out centralized marking on the questions with the highest wrong question rate, thereby determining universal knowledge blind points;
s3, video adjustment: the wrong question set determined in the step S2 is adjusted to focus on knowledge points of the teaching video, focus on the knowledge point with the highest wrong question rate is explained, and error-prone questions are explained by way of example;
s4, recording playing information: after video delivery, recording the total playing time of the corresponding video and the playing times of the corresponding video, collecting information and comparing the information with the knowledge blind points recorded in the step S2;
s5, testing along with the hall;
s5.1: after video learning is completed, carrying out a current day knowledge point along with the hall test, wherein the test questions are divided into two questions which are difficult and easy according to the knowledge points;
s5.2: respectively recording the correctness and the answering time of the hall test according to the difficulty level;
s6, establishing a judgment model: the ability result after the user tests is used as a classification basis to be divided into a plurality of groups of distances, and the classification prediction and judgment information module is used for carrying out classification prediction and judgment according to the difficulty level of the test questions;
s7, score judgment: judging the learning score according to the difficulty degree classification;
(1) if the results reach the standard, finishing the video learning work on the same day, and finishing the wrong questions on the same day by the system to generate a single learning result model;
(2) if the simple questions reach the standard and the difficulty questions do not reach the standard, the system generates a current day learning result model, and repeats the steps from the step S3 to the step S5, wherein the in-hall test questions in the step S5 are only the difficulty questions;
(3) if the scores of the difficult and easy questions reach the standard, the system generates a study result model on the same day, and then repeats the steps S3-S7 to judge the scores of the tests on the same hall again;
s8, building a single learning result model: and (3) counting the learning results of the students after the video learning is completed, counting the wrong question types, and establishing a difficult problem set which is not mastered by the students of the system.
2. The video learning success assessment method based on artificial intelligence of claim 1, wherein: the score determination in step S7 is to classify and determine the question types of the difficulty and the ease.
3. The video learning success assessment method based on artificial intelligence of claim 1, wherein: and step S7, the achievement values of the difficult and easy questions reaching the standard are different and are executed according to the easy-high and difficult-low judging standards.
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| CN112507792B (en) * | 2020-11-04 | 2024-01-23 | 华中师范大学 | Online video key frame positioning method, positioning system, equipment and storage medium |
| CN113065023B (en) * | 2021-03-24 | 2023-06-23 | 武汉大学 | Online asynchronous teaching feedback analysis method and system for video watching behaviors |
| CN113327066A (en) * | 2021-06-30 | 2021-08-31 | 重庆五洲世纪文化传媒有限公司 | Infant testing system and method |
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