CN112712218B - Online and offline teaching system based on Ebinghaos forgetting curve - Google Patents
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- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
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- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
Abstract
The invention provides an online and offline teaching system based on an Ebinghaos forgetting curve, which comprises a knowledge decomposition module, a content summarization module, a question issuing module, a question collecting and analyzing module, a prompting system and an assessment module. By adopting the teaching system, students can be effectively promoted to master the curriculum knowledge points, problems of teacher omission and student stress are effectively solved, short-term memorandum and hard back of students are effectively avoided, and the knowledge points become a real skill or ability of the students.
Description
Technical Field
The invention relates to the field of education, in particular to an online and offline teaching system based on an Ebinghaas forgetting curve.
Background
Under the new situation, industrial enterprises put higher requirements on comprehensive qualities such as innovation capability, design capability and the like of mechanical graduates. At present, although the relevant courses of all universities and colleges have been used for teaching reform for decades, certain teaching achievements are obtained, and course teaching modes are also greatly improved. However, many defects and shortcomings still exist in the past, and improvement is needed. Particularly, most students can understand the contents in class whether online or offline, but the contents spoken by the teacher cannot be completely repeated after class, even simple questions cannot be written, and the students forget the questions in the next class. Meanwhile, most students mainly learn the habit of the study to be tried, and put the main energy on 'assault' before the examination, do not understand and consolidate the study at ordinary times, but remember hard for a short time, and forget soon after the examination.
The Ebinghaos forgetting curve can well solve the problem, but the traditional teaching mode needs to teach according to school timetables whether on-line or off-line, and the timetables are fixed (twice or once a week), which is contradictory to the Ebinghaos forgetting curve.
Disclosure of Invention
The invention aims to provide an online and offline teaching system based on an Ebinghaos forgetting curve, so as to solve the problem that the teaching contents cannot be scientifically reviewed and consolidated in the background. By adopting the teaching system, students can be effectively promoted to master the curriculum knowledge points, problems of teacher omission and student stress are effectively solved, short-term memorandum and hard back of students are effectively avoided, and the knowledge points become a real skill or ability of the students.
In order to achieve the purpose, the invention provides the following technical scheme:
the utility model provides an online off-line teaching system based on bionghaos forgetting curve, a serial communication port, including knowledge decomposition module, content summarization module, topic issue module, topic collection analysis module, reminder system and examination module, knowledge decomposition module and content summarization module conclude and summarize according to the study demand, the topic issue module is according to the difficult and important problem requirement to give the topic of a certain quantity to the student, the topic is collected analysis module and is issued the back and regularly to collect the topic according to the topic volume for the topic, analyzes student's study condition according to the condition, reminder system includes topic receiving and dispatching time management, pre-class early warning, in-class early warning, innovation prediction, topic receiving and dispatching time management mainly sets up according to bionghaos forgetting curve, and is 1 day, the third consolidation cycle is 2 days according to every student according to the second consolidation cycle, and the theme receiving and dispatching time management is alone, The fourth consolidation period is 4 days, the fifth consolidation period is 7 days, and the sixth consolidation period is 15 days, issues questions of the question issuing module, collects the issued questions and sends the issued questions to the question collecting and analyzing module; the pre-class early warning is realized mainly through a machine learning algorithm by prompting a growth environment and associated course scores which are acquired in advance in a system, so as to remind students to learn the course successfully; the pre-class early warning machine learning algorithm model is obtained by training and cross validation according to the scores of the students in the same major at the previous stage; in-class early warning is realized by acquiring sample characteristics of subject receiving and sending conditions through a plurality of time points set by an Ebingo forgetting curve in a prompting system through a machine learning algorithm, a teacher can make teaching decisions according to actual conditions, the in-class early warning is a supplement to the execution process of the Ebingo forgetting curve, and short boards in the aspect of student learning can be predicted in time; the innovation prediction mainly realizes the prediction of innovation capability through a machine learning algorithm by prompting a growth environment and associated course scores which are acquired in advance in a system, so as to pay attention to the innovation practice of students with innovation capability or the training of innovation competitions in the course, a machine learning algorithm model for innovation prediction needs to perform cluster analysis according to the scores of the students, and the associated courses comprise but are not limited to courses with innovation significance; the assessment module takes the completion rate and the accuracy rate of the questions collected by the prompting system as main ordinary scores, increases the ordinary score proportion in the total assessment and reduces the end-of-term examination score proportion.
Further, the system also comprises a first consolidation period of an Ebinghaos forgetting curve, and the first consolidation period is carried out after learning for 30 minutes in an offline teaching mode.
Further, the growing environment of the pre-class pre-warning includes father occupation, father schooling, mother occupation and mother schooling, and the machine learning algorithm of the pre-class pre-warning includes, but is not limited to, linear regression, polynomial regression, neural network model, deep learning model.
Further, the sample characteristics of the question receiving and sending conditions in the in-class early warning include a feedback rate, a correct rate, a completion rate, question making duration, note times and note word number, and the machine learning algorithm of the in-class early warning includes but is not limited to a decision tree, K neighbor, naive Bayes, a random forest and a support vector machine.
Further, the innovation forecasted growing environment father occupation, father schooling, mother occupation and mother schooling, the innovation forecasted machine learning algorithm includes but is not limited to K-means clustering, hierarchical clustering, density clustering, spectral clustering, gaussian mixture clustering.
Compared with the prior art, the invention has the beneficial effects that:
1. the aim of strengthening cognition and forming long-term memory is achieved by adopting the Ebinghaos forgetting curve to carry out online and offline teaching design and reproducing hard knowledge points of the learned chapters in a learning cycle.
2. The prompting system is mainly completed in the background of the Internet, not only has an auxiliary effect on student memory consolidation, but also can timely master the mastery degree of each student on each knowledge point and carry out early warning and innovation capability prediction on the learning of the students. The teacher can pertinently strengthen review for students.
3. By executing the assessment mode, the student can be prevented from assault learning at the end of the term, and the ability of the student paying attention to understanding and mastering knowledge points is developed.
Drawings
FIG. 1 is a schematic diagram of the Erbingos consolidation cycle used in the present invention;
FIG. 2 is a schematic diagram of an online and offline teaching method and a prompting system based on an Ebingois forgetting curve of the invention;
FIG. 3 is a schematic diagram of the class-ahead early warning and innovation prediction for analyzing student data samples in the class-ahead presentation system of the present invention;
fig. 4 is a schematic diagram of in-class early warning for analyzing student data samples in an in-class prompt system according to the present invention.
In the figure: the first consolidation period in fig. 2 is equivalent to 30 minutes below the first line in fig. 1, the second consolidation period in fig. 2 is equivalent to 1 day above the second line in fig. 1, the third consolidation period in fig. 2 is equivalent to 2 days above the second line in fig. 1, the fourth consolidation period in fig. 2 is equivalent to 4 days above the second line in fig. 1, the fifth consolidation period in fig. 2 is equivalent to 7 days above the second line in fig. 1, and the sixth consolidation period in fig. 2 is equivalent to 15 days above the second line in fig. 1. The "/" in fig. 2 indicates that, alternatively, the content of the dashed box in fig. 2 is equivalent to the content of the dashed box on the right side of fig. 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution:
the online and offline teaching system based on the Einghaos forgetting curve is characterized by comprising a knowledge decomposition module, a content summarization module, a question issuing module, a question collecting and analyzing module, a prompting system and an assessment module, wherein the knowledge decomposition module comprises the content decomposition of the weight, difficulty and commonsense of the knowledge points of the course chapters before class.
The content summarizing module is used for summarizing the content according to the key knowledge points required by the chapters in the teaching outline in a targeted manner in the offline teaching process so as to achieve the effect of quick warm practice.
The question issuing module comprises an interactive mode adopted by on-line teaching, is mainly carried out in a question making mode, issues a certain number of questions (such as 3-channel selection questions, 2-channel simple answer questions and the like, but not limited to the questions) according to the difficult and serious point requirements, and automatically completes the process according to consolidated cycle time nodes set by an Ebinghaos forgetting curve by a prompting system in advance.
The problem collection module includes the interactive mode that the online teaching adopted, mainly with regularly collect the problem according to the problem volume after the problem is issued, and reminder system can carry out the analysis to student's study condition according to the condition, and the teacher can be targeted again and issue and collect to strengthen consolidating knowledge.
The prompting system comprises question receiving and sending management, class-ahead early warning, class-in early warning and innovation prediction. The theme receiving and dispatching management comprises theme issuing and theme collection, and the theme issuing and collection are independently carried out on each student according to five time points, namely a second consolidation period is 1 day, a third consolidation period is 2 days, a fourth consolidation period is 4 days, a fifth consolidation period is 7 days, a sixth consolidation period is 15 days and the like according to an Ebinghaos forgetting curve.
In addition, the first consolidation period of the Ebinghaos forgetting curve is carried out after learning for 30 minutes, and an offline teaching mode is mainly adopted. The theme receiving and dispatching management is mainly automatically carried out on a mobile phone end, a PC end or a flat panel line without manual interference. The pre-class early warning is realized mainly through a growth environment (father occupation, father school calendar, mother occupation and mother school calendar) and associated course achievements (associated course 1, associated course 2, … and associated course N) which are collected in advance in a prompting system through a machine learning algorithm (including but not limited to linear regression, polynomial regression, a neural network model, a deep learning model and the like) so as to remind students of learning the current course with good success. The pre-class early warning machine learning algorithm model is obtained by training and cross validation according to the scores of the students in the same major at the previous stage. In-class early warning is realized by collecting sample characteristics (feedback rate, accuracy rate, completion rate, question making duration, note times and note word number) of question receiving and sending conditions through a plurality of time points set by an Ebinghaos forgetting curve in a prompting system through a machine learning algorithm (including but not limited to a decision tree, K neighbor, naive Bayes, random forests, a support vector machine and the like), so that the influence of artificial subjective impression of teachers is reduced, learning problems are found more easily, and academic risk early warning is sent out. The teacher can make teaching decision according to actual conditions, and the method is a supplement to the implementation process of the Ebinghas forgetting curve and can predict a short board in the aspect of student learning in time. The innovation prediction mainly realizes the prediction of innovation capability through the development environment (father occupation, father school calendar, mother occupation and mother school calendar) and the associated course achievements (associated course 1, associated course 2, …, associated course N) which are collected in advance in the prompting system through a machine learning algorithm (including but not limited to K mean clustering, hierarchical clustering, density clustering, spectral clustering, Gaussian mixed clustering and the like), so as to pay attention to the innovation practice or the training of innovation competition of students with innovation capability in the course. The innovation prediction machine learning algorithm model needs to perform clustering analysis according to the performances of students, and the associated courses comprise but are not limited to courses with innovative significance.
The assessment module takes the completion rate and the accuracy rate of the questions collected by the prompting system as main ordinary results, increases the ordinary result proportion in the overall assessment, reduces the end-of-term examination result proportion, promotes the students to strengthen the cultivation of ordinary learning habits, and avoids end-of-term assault learning.
The working principle is as follows: the Ebinghaos forgetting curve is discovered by the psychological domestic Ebinghaos in 18 th century. Note that forgetting starts immediately after learning, the process of forgetting is also not uniform, forgetting follows a fast-then-slow process, and remembers less things over time. As shown in fig. 1, a human memorizes one thing, and if the memory is not subjected to a fixed review, the memory is forgotten about 42% after 20 minutes, 56% after 1 hour, 74% after 1 day, 77% after 1 week, and 79% after 1 month. Therefore, timely consolidation and review can be the most effective means for learning the anti-forgetting ability. If the Einghaos forgetting curve can be applied to the process of student learning, the knowledge will be mastered greatly. Referring to fig. 2, a teacher decomposes knowledge points before class, and classifies chapters according to emphasis, difficulty and commonsense, because people forget to reach about 50% after 30 minutes as shown in fig. 1, the estimated lecture time length according to emphasis and difficulty does not exceed 30 minutes, and then consolidation of contents of emphasis and difficulty of at least 50% is performed after 30 minutes during on-line teaching, so as to strengthen the contents just learned. According to the Ebinghaos forgetting curve, the next forgetting 50 percent after the first consolidation period (namely 25 percent of figure 1) is 1 day later, and the common school can not arrange the same course for class in the time after one day, so the anti-forgetting ability training (review consolidation) after 1 day is carried out on line, namely the second consolidation period of on-line interaction of figure 2, comprising issue and collection of questions, the process mainly strengthens the review consolidation of the knowledge points by doing exercises, and has the advantages that the understanding degree of the students to the questions or the knowledge points after doing the questions can be timely collected on line, namely the mastering degree, and the teacher carries out in-class early warning on the learning condition of the students through the machine learning algorithm of the prompt system, can make certain judgment according to the specific situation, if not ideal, can adopt the mode of teaching review or issue again for the questions to strengthen the consolidation, the weak points of knowledge mastery of individual students can be found out, and the students can be purposefully taught. In order to enable students to form long-term memory on knowledge points so that the knowledge points become an inherent skill, according to the experimental result shown in fig. 1, the second consolidation period is far insufficient, and further review and consolidation are needed, so that the third consolidation period of 2 days of online interaction, the fourth consolidation period of 4 days of online interaction, the fifth consolidation period of 7 days of online interaction and the sixth consolidation period of 15 days of online interaction are respectively set to enhance the learning efficiency. Wherein, if online interaction is to respectively execute review consolidation to each student according to Ebinghaos time point, if students have no problem in synchronization of mastery degree, and actually, the memory of each student is not completely the same, the foundation is different, so that the condition that the knowledge points are mastered by individual students in asynchronization can occur when the second consolidation period is reached, at the moment, teachers are not conscious of reviewing while the management time node is considered to be online consolidated, therefore, when the second consolidation period reaches the sixth consolidation period, the questions are issued and the questions are collected and analyzed by a prompting system, the prompting system has all student information of the class, the student information is not interfered and is respectively independent, each student can independently issue and collect and analyze the questions according to 1 day, 2 days, 4 days, 7 days and 15 days of the Ebinghaos forgetting curve, and the prompting system can determine the execution time of a certain consolidation period according to the actual situation by a teacher. At the present stage, after a course is taught, the mastery degree of students is checked, namely, the examination is performed, the study process of the students is emphasized by the implementation of the teaching method, the cultivation of usual study habits is enhanced, and end-of-term assault type study is avoided, so the examination mode implemented by the teaching method is that a prompt system collects the completion rate and the accuracy rate of questions as main usual scores, the proportion of the usual scores is increased in the total evaluation, and the proportion of the end-of-term exam scores is reduced. Fig. 3 and 4 are parts of the prompting system in fig. 2, and in fig. 3, pre-class early warning is mainly realized through a machine learning algorithm (including but not limited to linear regression, polynomial regression, neural network model, deep learning model, etc.) through a growth environment (father occupation, father calendar, mother occupation, and mother calendar) and associated course achievements (associated course 1, associated course 2, …, associated course N) acquired in advance in the prompting system in fig. 2, so as to remind students to learn the current course successfully. The machine learning algorithm is obtained by training and cross-verifying the previous grade and the scores of the professional students. Fig. 3 mainly realizes the prediction of innovation capability through the machine learning algorithm (including but not limited to K-means clustering, hierarchical clustering, density clustering, spectral clustering, gaussian mixture clustering, etc.) and the growth environment (father occupation, father schooling, mother occupation, and mother schooling) and the associated lesson achievements (associated lesson 1, associated lesson 2, …, associated lesson N) and collected in advance in the prompting system of fig. 2, so as to focus on the training of innovation practice or innovation competition of the students with innovation capability in the lesson. The machine learning algorithm model needs to perform cluster analysis according to the students' own achievements, and the associated courses include but are not limited to courses with innovative significance. Fig. 4 mainly collects sample characteristics (feedback rate, accuracy rate, completion rate, question making duration, note times and note word number) of question receiving and sending conditions through a plurality of time points set by an Ebinghaos forgetting curve in the prompting system of fig. 2, and realizes in-class early warning through a machine learning algorithm (including but not limited to a decision tree, K neighbor, naive Bayes, random forest, support vector machine and the like), thereby reducing the influence of artificial subjective impression of teachers, finding learning problems more easily and sending academic risk early warning. The teacher can make teaching decision according to actual conditions, and the method is a supplement to the implementation process of the Ebinghas forgetting curve and can predict a short board in the aspect of student learning in time.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The utility model provides an online off-line teaching system based on bionghaos forgetting curve, a serial communication port, including knowledge decomposition module, content summarization module, topic issue module, topic collection analysis module, reminder system and examination module, knowledge decomposition module and content summarization module conclude the collection according to the study demand, the topic issue module is according to the difficult and important problem requirement to give the topic of a certain quantity to the student, the topic is collected analysis module and is issued the back and regularly collects the topic for the topic, analyzes student's study condition according to the condition, reminder system includes topic receiving and dispatching time management, pre-class early warning, in-class early warning, innovation prediction, topic receiving and dispatching time management sets up according to bionghaos forgetting curve, and is 1 day, the third consolidation cycle is 2 days according to every student according to second consolidation cycle, The fourth consolidation period is 4 days, the fifth consolidation period is 7 days, and the sixth consolidation period is 15 days, issues questions of the question issuing module, collects the issued questions and sends the issued questions to the question collecting and analyzing module; the pre-class early warning realizes the pre-class early warning through a machine learning algorithm by prompting the growth environment and the associated class score which are acquired in advance in the system so as to remind students to learn the current class successfully; the pre-class early warning machine learning algorithm model is obtained by training and cross validation according to the scores of the students in the same major at the previous stage; in-class early warning is realized by acquiring sample characteristics of subject receiving and sending conditions through a plurality of time points set by an Ebingo forgetting curve in a prompting system through a machine learning algorithm, and teachers make teaching decisions according to actual conditions, so that the implementation process of the Ebingo forgetting curve is supplemented, and short boards in the aspect of student learning can be predicted in time; the innovation prediction realizes the prediction of innovation capability through a machine learning algorithm by prompting the pre-collected growth environment and associated course scores in a system, so as to pay attention to the innovation practice of students with innovation capability or the training of innovation competition in the course, a machine learning algorithm model for innovation prediction needs to perform cluster analysis according to the scores of the students, and the associated courses comprise but are not limited to courses with innovation significance; the assessment module takes the completion rate and the accuracy rate of the questions collected by the prompting system as main ordinary scores, increases the ordinary score proportion in the total assessment and reduces the end-of-term examination score proportion.
2. The system for online and offline teaching based on the biorthos forgetting curve as claimed in claim 1, further comprising a first consolidation period of the biorthos forgetting curve, wherein the offline teaching is performed after learning for 30 minutes.
3. The system of claim 1, wherein the pre-class pre-warning growing environment comprises father occupation, father schooling calendar, mother occupation and mother schooling calendar, and the pre-class pre-warning machine learning algorithm comprises but is not limited to linear regression, polynomial regression, neural network model, deep learning model.
4. The online and offline teaching system based on the ibbingos forgetting curve as claimed in claim 1, wherein the sample features of the subject transceiving condition in the in-class early warning include feedback rate, correct rate, completion rate, duration of doing the subject, number of notes, number of words of note, the machine learning algorithm of the in-class early warning includes but is not limited to decision tree, K neighbor, naive bayes, random forest, support vector machine.
5. The system of claim 1, wherein the innovation prediction growing environment father occupation, father scholarly, mother occupation and mother scholarly comprises but is not limited to K-means clustering, hierarchical clustering, density clustering, spectral clustering, gaussian mixture clustering.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101281686A (en) * | 2008-02-04 | 2008-10-08 | 博采林电子科技（深圳）有限公司 | Hand-hold learning equipment as well as demonstration system thereof |
| CN108009954A (en) * | 2017-12-12 | 2018-05-08 | 联想(北京)有限公司 | A kind of Formulating Teaching Program method, apparatus, system and electronic equipment |
| CN108062733A (en) * | 2017-11-23 | 2018-05-22 | 吉林大学 | Teaching method based on this great forgetting curve of Chinese mugwort guest and network examination paper bank |
| CN108597273A (en) * | 2018-06-22 | 2018-09-28 | 南阳理工学院 | A kind of intelligent tutoring system for English study |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570008B (en) * | 2015-10-09 | 2020-03-27 | 阿里巴巴集团控股有限公司 | Recommendation method and device |
-
2021
- 2021-02-18 CN CN202110187162.0A patent/CN112712218B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101281686A (en) * | 2008-02-04 | 2008-10-08 | 博采林电子科技（深圳）有限公司 | Hand-hold learning equipment as well as demonstration system thereof |
| CN108062733A (en) * | 2017-11-23 | 2018-05-22 | 吉林大学 | Teaching method based on this great forgetting curve of Chinese mugwort guest and network examination paper bank |
| CN108009954A (en) * | 2017-12-12 | 2018-05-08 | 联想(北京)有限公司 | A kind of Formulating Teaching Program method, apparatus, system and electronic equipment |
| CN108597273A (en) * | 2018-06-22 | 2018-09-28 | 南阳理工学院 | A kind of intelligent tutoring system for English study |
Non-Patent Citations (5)
| Title |
|---|
| "互联网+"课堂：一种基于艾宾浩斯遗忘曲线和网络题库的教学新模式探索;李世超 等;《高教学刊》;20200628(第18期);第7-9,13页 * |
| Programming of English Word Review Planning Time Based on Ebinhaus Forgetting Curve;Zhu Dandan;《2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)》;20200608;第901-904页 * |
| The effect of flipped learning on academic performance as an innovative method for overcoming ebbinghaus" forgetting curve;Bo Ae Chun 等;《Proceedings of the 6th International Conference on Information and Education Technology》;20180106;第56-60页 * |
| 基于Ebbinghaus遗忘曲线的初中化学习题编排与实践研究;申云萍;《中国优秀博硕士学位论文全文数据库(硕士) 社会科学Ⅱ辑》;20200615(第6期);第H130-340页 * |
| 基于艾宾浩斯遗忘曲线的《纺织品印花技术》教学模式创新研究;赵虹娟 等;《科学咨询(科技•管理)》;20200101(第1期);第77页 * |
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