CN109559580B - Online learning system - Google Patents
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- CN109559580B CN109559580B CN201811632783.XA CN201811632783A CN109559580B CN 109559580 B CN109559580 B CN 109559580B CN 201811632783 A CN201811632783 A CN 201811632783A CN 109559580 B CN109559580 B CN 109559580B
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
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Abstract
The invention provides an online learning system, which is characterized in that a first timing unit and a second timing unit are arranged to respectively record the learning time and the testing time of a user on a question, and the learning time and the testing time are compared to obtain the grasping condition of the user on the question, so that the problem is prevented from being checked under the condition that the user is not clear or is not skilled in grasping the knowledge point of the question, the system judges the condition of error on the learning condition of the user, and the learning efficiency is improved; by arranging the storage module, the learning and testing tracks of the user can be stored, the user stays on the question during the learning time and the testing of the user, the learning time, the testing time and the correctness of the user to the question are integrated to judge the grasping condition of the user to the question, the task and the testing content of next learning are made according to the grasping condition of the user to the question, the learning plan conforming to the user is made through the mode, the learning loophole is avoided, and the learning efficiency is increased.
Description
Technical Field
The invention relates to the field of education systems, in particular to an online learning system.
Background
At present, online education platforms on the market are all set with tasks online, users complete the tasks on the platforms, online learning is achieved through the method, generally, the number of days for learning of the users is counted or a certain task amount is reached, the users enter a next learning level, the users can insist on learning every day in the early period, the number of counts of the education platforms is large, when the users forget to learn in a certain day or due to some things, the learning is not in time in the day, the previous counts of the education platforms and the learning tracks are removed, in addition, when the users start learning again, as the previous learned tracks are removed, all things need to be learned again, part of knowledge is mastered, but the users need to practice again, especially for a long-time video or a task with large workload, the method greatly hits the learning enthusiasm of the users, when the user stops learning and then learns again, the user often learns again from a new learning or according to a previous wrong question, sometimes, the answer submitted by the user is uncertain, but also in the opposite situation, and when the answer is wrong, the system defaults that the user already grasps the question, so that the knowledge point of the question is not learned any more, a learning leak is caused, and an accurate learning plan cannot be made according to different people. Therefore, there is a need for an intelligent online learning system that can make learning schemes different from person to person.
Disclosure of Invention
In view of the above, the present invention provides an intelligent online learning system capable of making a learning scheme different from person to person.
The technical scheme of the invention is realized as follows: the invention provides an online learning system which comprises a login module, a storage module, an autonomous learning module and an assessment module, wherein the autonomous learning module comprises a first timing unit and a learning unit;
the examination module comprises a second timing unit and a test unit;
the login module verifies the identity information of the user, and the user with successful identity verification can enter the online learning system;
the learning unit sets tasks of online learning of the user every day and stores the task amount completed by the user in the storage module;
the first timing unit records the staying time of the user in each topic during learning, and divides the learning degree of the user into three levels according to the staying time range: deep learning, normal learning and light display learning;
the testing unit sets testing questions according to the task contents learned by the user, judges answers submitted by the user, counts the submitted accuracy and displays the accuracy to the user, and packs and sends the contents of each question, information about whether the correct information is available and the staying time of each question to the storage module;
the second timing unit records the staying time of the user in each question during the test, and divides the mastery degree of the user on the knowledge points of the question into three levels according to the staying time: mastery, understanding and deluxe;
the storage module stores the content of the user in the learning unit and the total learning time recorded by the first timing unit, stores the questions tested by the user in the test unit, the mastery degree and the accuracy of each question, and pushes the learning task to the learning unit according to the test result of the user test unit and the historical test result;
the logging module is in signal connection with the learning unit, the first timing unit is in signal connection with the learning unit, the testing unit is in signal connection with the second timing unit and the learning unit respectively, and the storage module is in signal connection with the first timing unit, the second timing unit, the testing unit and the learning unit respectively.
On the basis of the above technical solution, preferably, the question tested by the test unit includes the content learned by the learning unit this time and the question classified as unsolved by the mastery degree of the last knowledge point.
Preferably, the questions with wrong answers submitted by the user in the test unit need to be continuously included in the student unit and the test unit within three days after the test until the user submits the correct questions continuously for three times, the system judges that the knowledge points of the questions are basically mastered by the user, and the questions do not need to appear in the next learning unit and the test unit within seven times after the test.
Further preferably, when the learning degree of the question by the first timing unit is determined as deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system determines that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question is judged to be deep learning by the first timing unit, the test result of the question is correct, and the mastery degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning;
when the learning degree of the question by the first timing unit is judged to be deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as unsolved, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit.
Further preferably, when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into understanding, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning;
when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as unsolved, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit.
Preferably, when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question is judged to be shallow learning by the first timing unit, the test result of the question is correct, and the grasping degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically grasped by the user, and the learning degree does not need to appear in the next learning unit and the test unit within seven days after the learning;
when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as unsolved, the system judges that the knowledge point of the question is not mastered by the user, and the knowledge point needs to be continuously included as the content of the next learning unit and the next test unit for three times.
Compared with the prior art, the online learning system has the following beneficial effects:
(1) by arranging the first timing unit and the second timing unit, the learning time and the testing time of the user on the question are respectively recorded, and the learning time and the testing time are compared to obtain the mastering condition of the user on the question, so that the problem is prevented from being mastered by the user under the condition that the user is not clear or is unskilled in mastering the knowledge point of the question, the system judges the condition of error on the learning condition of the user, and the learning efficiency is improved;
(2) by arranging the storage module, the learning and testing tracks of the user can be stored, the learning time of the user on the question and the staying time of the user on the question during testing are stored, the learning time, the testing time and the correctness of the user on the question are integrated to judge the grasping condition of the user on the question, the task and the testing content of next learning are made according to the grasping condition of the user on the question, the learning plan conforming to the user is made through the mode, the learning loophole is avoided, and the learning efficiency is increased;
(3) the whole system can comprehensively analyze the question according to the learning time of the user on the question and the testing time and the correctness of the question, and the conditions that knowledge points are not firmly mastered and the knowledge points are vulnerable are avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram of an online learning system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in FIG. 1, the online learning system of the present invention comprises a login module, a storage module, an autonomous learning module and an assessment module.
And the login module is used for logging in the system by the user through the user name and the password.
And the autonomous learning module is used for autonomously completing the tasks formulated by the autonomous learning module after the user logs in the system. The autonomous learning module includes a first timing unit and a learning unit. The learning unit sets tasks for the user to learn online every day and stores the task amount completed by the user in the storage module; the learning unit sets that the task content of the user online learning every day comes from two parts, one part is the task amount set by the expert in the system, and the principle of the task amount is the same as that of the task amount of the user online learning in the prior art, wherein the task amount is the task amount of the user online learning, and the task amount is not redundant; the other part is the questions with wrong test results from the test unit, and the comprehensive learning time and the test time are judged as the questions which are basically mastered and not mastered, wherein the frequency of the questions which are comprehensively mastered, basically mastered and not mastered is different; the first timing unit records the staying time of the user in each topic during learning, and divides the learning degree of the user into three levels according to the staying time range: deep learning, normal learning and light learning.
And the examination module is used for scoring the learning content of the user and storing the test result in the storage module. The assessment module comprises a second timing unit and a testing unit. The testing unit sets testing questions according to the task contents learned by the user, judges answers submitted by the user, counts the submitted accuracy and displays the accuracy to the user, and packs and sends the contents of each question, information about whether the correct information is available and the staying time of each question to the storage module; the second timing unit records the staying time of the user in each question during the test, and divides the mastery degree of the user on the knowledge points of the question into three levels according to the staying time: mastery, understanding and deluxe; the user submits the questions with wrong answers in the test unit, the questions of the student unit and the test unit need to be continuously included in three days after the test until the user submits the correct questions continuously for three times, the system judges that the knowledge points of the user on the questions are basically mastered, and the questions do not need to appear in the next learning unit and the test unit in seven times after the test. When the learning degree of the question by the first timing unit is judged to be deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning; when the learning degree of the question is judged to be deep learning by the first timing unit, the test result of the question is correct, and the mastery degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning; when the learning degree of the question by the first timing unit is judged to be deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into unsolved degrees, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit; when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning; when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into understanding, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning; when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into unsolved degrees, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit; when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning; when the learning degree of the question is judged to be shallow learning by the first timing unit, the test result of the question is correct, and the grasping degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically grasped by the user, and the learning degree does not need to appear in the next learning unit and the test unit within seven days after the learning; when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as unsolved, the system judges that the knowledge point of the question is not mastered by the user, and the knowledge point needs to be continuously included as the content of the next learning unit and the next test unit for three times.
The storage module is used for storing the content of the user in the learning unit and the total learning time recorded by the first timing unit, storing the questions tested by the user in the testing unit, the mastery degree and the accuracy of each question, and pushing the learning task to the learning unit according to the test result of the user testing unit and the historical test result.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (3)
1. The utility model provides an online learning system, its includes login module, storage module, independently learns module and examination module, its characterized in that: the autonomous learning module comprises a first timing unit and a learning unit;
the examination module comprises a second timing unit and a test unit;
the login module verifies the identity information of the user, and the user with successful identity verification can enter the online learning system;
the learning unit sets tasks for the user to learn online every day and stores the task amount completed by the user in the storage module;
the first timing unit records the stay time of the user in each topic during learning, and divides the learning degree of the user into three levels according to the stay time range: deep learning, normal learning and light display learning;
the testing unit sets testing questions according to the task contents learned by the user, judges answers submitted by the user, counts the submitted accuracy and displays the accuracy to the user, and packs and sends the contents of each question, information about whether the correct information is available and the staying time of each question to the storage module;
the second timing unit records the staying time of the user in each question during testing, and divides the mastery degree of the user on the knowledge points of the question into three levels according to the staying time: mastery, understanding and deluxe;
the storage module stores the content of the user in the learning unit and the total learning time recorded by the first timing unit, stores the questions tested by the user in the test unit, the mastery degree and the accuracy of each question, and pushes the learning task to the learning unit according to the test result of the user test unit and the historical test result;
the logging module is in signal connection with the learning unit, the first timing unit is in signal connection with the learning unit, the testing unit is in signal connection with the second timing unit and the learning unit respectively, and the storage module is in signal connection with the first timing unit, the second timing unit, the testing unit and the learning unit respectively;
when the learning degree of the question by the first timing unit is judged to be deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question is judged to be deep learning by the first timing unit, the test result of the question is correct, and the mastery degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning;
when the learning degree of the question by the first timing unit is judged to be deep learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into unsolved degrees, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit;
when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into understanding, the system judges that the knowledge point of the question is basically mastered by the user, and the knowledge point does not need to appear in the next learning unit and the test unit within seven days after the current learning;
when the learning degree of the question by the first timing unit is judged to be normal learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is divided into unsolved degrees, the system judges that the knowledge point of the question is not mastered by the user and needs to be included as the content of the next learning unit and the test unit;
when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as skilled mastering, the system judges that the knowledge points of the question are comprehensively mastered by the user, and the knowledge points do not need to appear in the next learning unit and the test unit within a half month after the current learning;
when the learning degree of the question is judged to be shallow learning by the first timing unit, the test result of the question is correct, and the grasping degree of the question is divided into understanding by the second timing unit, the system judges that the knowledge point of the question is basically grasped by the user, and the learning degree does not need to appear in the next learning unit and the test unit within seven days after the learning;
when the learning degree of the question by the first timing unit is judged to be shallow learning, the test result of the question is correct, and the mastery degree of the question by the second timing unit is classified as unsolved, the system judges that the knowledge point of the question is not mastered by the user, and the knowledge point needs to be continuously included as the content of the next learning unit and the next test unit for three times.
2. An online learning system as claimed in claim 1, wherein: the questions tested by the test unit comprise the contents learned by the learning unit and the questions which are divided into unsolved questions according to the mastery degree of the knowledge points at the last time.
3. An online learning system as claimed in claim 2, wherein: the questions with wrong answers submitted by the user in the test unit need to be continuously brought into the student unit and the test unit within three days after the test until the user continuously submits the correct questions for three times, the system judges that the knowledge points of the questions are basically mastered by the user, and the questions do not need to appear in the next learning unit and the test unit within seven times after the test.
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| CN110414628A (en) * | 2019-08-07 | 2019-11-05 | 清华大学深圳研究生院 | A kind of learning process planning and management method and system from wound course |
| CN110704499A (en) * | 2019-09-10 | 2020-01-17 | 深圳市壹箭教育科技有限公司 | Job content feedback method and device, storage medium and electronic equipment |
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| CN106611522A (en) * | 2015-10-27 | 2017-05-03 | 金陵科技学院 | Application of learning software system based on objective questions |
| CN105225563A (en) * | 2015-11-02 | 2016-01-06 | 广东小天才科技有限公司 | A kind of learning data recommend method, device and Wearable facility for study |
| CN105869091A (en) * | 2016-05-12 | 2016-08-17 | 深圳市时尚德源文化传播有限公司 | Internet teaching method and system |
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