阅读说明：本技术 一种基于大数据的教学系统 (Big data-based teaching system ) 是由 周慧 周亚 于 2021-07-27 设计创作，主要内容包括：本发明提供了一种基于大数据的教学系统,其系统包括：数据获取模块,用于获取学生的知识数据图谱；数据分析模块,用于基于大数据对所述知识数据图谱进行分析,确定各个学生对应的教学方法；数据调整模块,用于在预设时间段内检测各个学生在对应的教学方法下的学习质量,并根据所述学习质量对所述教学方法进行调整,完成对各个学生制定合适的目标教学方法。通过对知识数据图谱分析,实现对各个学生制定合适的教学方法,提高学生的学习效率以及学习质量,完善了教学方法中存在的弊端,同时也提高了教学效率。(The invention provides a big data-based teaching system, which comprises: the data acquisition module is used for acquiring a knowledge data map of the student; the data analysis module is used for analyzing the knowledge data map based on big data and determining a teaching method corresponding to each student; and the data adjusting module is used for detecting the learning quality of each student under the corresponding teaching method in a preset time period, adjusting the teaching method according to the learning quality and finishing formulating a proper target teaching method for each student. Through the analysis of the knowledge data atlas, the appropriate teaching method is formulated for each student, the learning efficiency and the learning quality of the student are improved, the defects existing in the teaching method are overcome, and meanwhile, the teaching efficiency is also improved.)

1. A big-data based tutorial system comprising:

the data acquisition module is used for acquiring a knowledge data map of the student;

the data analysis module is used for analyzing the knowledge data map based on big data and determining a teaching method corresponding to each student;

and the data adjusting module is used for detecting the learning quality of each student under the corresponding teaching method in a preset time period, adjusting the teaching method according to the learning quality and finishing formulating a proper target teaching method for each student.

2. The big-data based instruction system of claim 1, wherein the data acquisition module comprises:

the data acquisition unit is used for acquiring a historical learning data set of the student knowledge field, wherein the historical learning data set comprises training data and test data;

the model construction unit is used for training a deep learning model based on the training data, testing the deep learning model based on the test data and obtaining a test result;

the first judgment unit is used for comparing the test result with a preset test result;

the map construction unit is used for acquiring target data for constructing a knowledge map in the student knowledge field when the test result meets the target requirement of the preset test result, and performing data extraction on the target data for constructing the knowledge map based on the deep learning model to obtain a data extraction result;

the map construction unit is also used for carrying out knowledge fusion on the data extraction result from a preset knowledge data map construction layer according to the data extraction result so as to complete the construction of the knowledge data map of the student;

and the model construction unit is further used for retraining the deep learning model when the test result does not meet the target requirement of the preset test result until the target requirement of the preset test result is met.

3. The big-data-based instruction system according to claim 2, wherein the map building unit further comprises:

the map updating unit is used for acquiring the constructed knowledge data map and determining data corresponding to each node in the knowledge data map and attribute values of the data based on a preset rule;

the map updating unit is further configured to generate at least one piece of updating data in a preset time period according to the attribute value of the data, update data corresponding to each node in the knowledge data map according to the updating data, and obtain target updating data;

the map updating unit is further configured to obtain a preset inference rule according to the target updating data, where the preset inference rule is a rule that is required to be used for generating an inference knowledge map according to the target updating data;

and the map updating unit is also used for generating a reasoning knowledge map according to the preset reasoning rule and the target updating data, and combining the reasoning knowledge map with the knowledge data map to obtain a final updated knowledge data map.

4. The big-data based tutorial system of claim 3, wherein the atlas update unit further comprises:

the first storage unit is used for acquiring a final updated knowledge data map, wherein the final updated knowledge data map is provided with a data type identifier;

the first storage unit is used for judging whether a preset storage area has a graph instance corresponding to the data type identifier according to the data type identifier;

if not, creating a graph instance corresponding to the data type identifier in the preset storage area, and storing the final updated knowledge data graph to a target storage area corresponding to the created graph instance;

otherwise, storing the final updated knowledge data map to a target storage area corresponding to the map example.

5. The big-data based instruction system of claim 1, wherein the data analysis module comprises:

the learning data monitoring unit is used for acquiring the learning records of a plurality of groups of students according to the preset frequency, calling a target teaching scheme currently accepted by each student, and determining the deviation information of each student according to the learning records and the target teaching scheme of the plurality of groups of students;

the learning data monitoring unit is also used for acquiring physiological data generated by each student under a currently accepted target teaching scheme and determining target reactions of each student under the target teaching scheme according to the physiological data;

the learning data monitoring unit is also used for determining the classification identification of the teaching mode in the currently accepted target teaching scheme according to the target reaction of each student and determining the interest points and the non-interest points of each student according to the classification identification;

the teaching resource integration unit is used for clustering teaching resources contained in the knowledge data map, extracting subject knowledge points corresponding to students and forming a subject knowledge point set, wherein the knowledge data map shows the association relationship between the element knowledge points and the element knowledge points in the subject knowledge point set;

the teaching method determining unit is used for determining a target meta knowledge point and a meta knowledge point combination which has an association relation with the target meta knowledge point from the subject knowledge point set through the knowledge data map according to deviation information of each student;

the teaching method determining unit is further used for acquiring historical test question information, determining respective teaching proportions of target element knowledge points and element knowledge point combinations having association relations with the target element knowledge points based on the historical test question information, and determining final teaching resources corresponding to students according to the teaching proportions;

the teaching method determining unit is further used for matching corresponding target teaching modes from a preset teaching mode library according to the interest points and the non-interest points of the students, and matching and recording the target teaching modes corresponding to the students and the final teaching resources to obtain the teaching methods corresponding to the students.

6. The big-data based tutoring system of claim 1, wherein the data tone module comprises:

the model building unit is used for obtaining influence factors of student learning quality evaluation and building an evaluation index system of the student learning quality evaluation according to the influence factors of the student learning quality evaluation;

the model construction unit is also used for determining the index weight of the evaluation index system based on a preset method and constructing a student learning quality evaluation model according to the index weight;

the learning data acquisition unit is used for determining the target achievement degree of each student based on the capability level of the preset knowledge points, and monitoring the completion conditions of the test questions, the class questions and answers and the class discussion of each student to obtain the course participation degree corresponding to each student;

the learning quality evaluation unit is used for inputting the target achievement degree and the course participation degree corresponding to each student into the learning quality evaluation model to obtain a learning quality evaluation result corresponding to each student;

the learning quality evaluation unit is further used for comparing the learning quality evaluation result corresponding to each student with a preset learning quality evaluation result;

the judging unit is used for judging that the teaching method accepted by each student is qualified when the learning quality evaluation result corresponding to each student is higher than or equal to the preset evaluation result, or judging that the teaching method accepted by each student is unqualified;

the teaching method adjusting unit is used for calling the detection data of each knowledge point based on the knowledge data map and determining the learning capacity value of each student on each knowledge point according to the detection data of each knowledge point when judging that the teaching method accepted by each student is not qualified;

the teaching method adjusting unit is further used for screening knowledge points mastered by students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points, and determining the average learning ability value of the students according to the knowledge points mastered by the students;

the teaching method adjusting unit is further used for adjusting the teaching method based on the average learning capacity value to finish establishing a proper target teaching method for each student.

7. The big data-based teaching system of claim 6, wherein the teaching method adjusting unit further comprises:

the detection unit is used for acquiring a target teaching method formulated for each student and determining whether the target teaching method is reasonable or not based on a preset detection method;

if the target teaching method is unreasonable, analyzing the knowledge data map based on big data, and re-determining the teaching method corresponding to each student until the target teaching method is judged to be reasonable;

if the target teaching method is reasonable, the target teaching method is sent to intelligent terminals corresponding to teachers and students on the basis of a preset network server;

and the feedback unit is used for executing the target teaching method after the teacher and the student receive the target teaching method, acquiring feedback information of the teacher and the student in real time, and perfecting the target teaching method based on the feedback information.

8. The big data-based teaching system of claim 6, wherein the teaching method adjusting unit further comprises:

the knowledge point integration unit is used for screening the knowledge points which are not mastered by the students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points;

the knowledge point integrating unit is further used for determining influence factors influencing knowledge points mastered by students based on preset rules, and searching corresponding target solutions from a preset solution library based on the influence factors, wherein the preset solution library stores a plurality of solutions corresponding to teaching problems;

and the execution unit is used for re-teaching the knowledge points which are not mastered by the students according to the target solution until the students completely master the knowledge points which are not mastered.

Technical Field

The invention relates to the technical field of big data, in particular to a teaching system based on big data.

Background

At present, the traditional teaching method is that teachers give lessons collectively, teachers give lessons on a platform, students listen to the lessons under the platform, but the learning conditions of all students cannot be effectively managed by the teachers due to the difference of learning abilities of part of the students, so that the learning quality of part of students is possibly reduced, and the learning efficiency is lowered;

therefore, the invention provides a big data-based teaching system which is used for analyzing the knowledge data map, realizing the formulation of a proper teaching method for each student, improving the learning efficiency and the learning quality of the student, improving the defects in the teaching method and simultaneously improving the teaching efficiency.

Disclosure of Invention

The invention provides a big data-based teaching system which is used for formulating a proper teaching method for each student through analyzing a knowledge data map, improving the learning efficiency and the learning quality of the student, overcoming the defects in the teaching method and simultaneously improving the teaching efficiency.

The invention provides a big data-based teaching system, which comprises:

the data acquisition module is used for acquiring a knowledge data map of the student;

the data analysis module is used for analyzing the knowledge data map based on big data and determining a teaching method corresponding to each student;

and the data adjusting module is used for detecting the learning quality of each student under the corresponding teaching method in a preset time period, adjusting the teaching method according to the learning quality and finishing formulating a proper target teaching method for each student.

Preferably, the teaching system based on big data, the data obtaining module includes:

the data acquisition unit is used for acquiring a historical learning data set of the student knowledge field, wherein the historical learning data set comprises training data and test data;

the model construction unit is used for training a deep learning model based on the training data, testing the deep learning model based on the test data and obtaining a test result;

the first judgment unit is used for comparing the test result with a preset test result;

the map construction unit is used for acquiring target data for constructing a knowledge map in the student knowledge field when the test result meets the target requirement of the preset test result, and performing data extraction on the target data for constructing the knowledge map based on the deep learning model to obtain a data extraction result;

the map construction unit is also used for carrying out knowledge fusion on the data extraction result from a preset knowledge data map construction layer according to the data extraction result so as to complete the construction of the knowledge data map of the student;

and the model construction unit is further used for retraining the deep learning model when the test result does not meet the target requirement of the preset test result until the target requirement of the preset test result is met.

Preferably, the teaching system based on big data, the map building unit, further include:

the map updating unit is used for acquiring the constructed knowledge data map and determining data corresponding to each node in the knowledge data map and attribute values of the data based on a preset rule;

the map updating unit is further configured to generate at least one piece of updating data in a preset time period according to the attribute value of the data, update data corresponding to each node in the knowledge data map according to the updating data, and obtain target updating data;

the map updating unit is further configured to obtain a preset inference rule according to the target updating data, where the preset inference rule is a rule that is required to be used for generating an inference knowledge map according to the target updating data;

and the map updating unit is also used for generating a reasoning knowledge map according to the preset reasoning rule and the target updating data, and combining the reasoning knowledge map with the knowledge data map to obtain a final updated knowledge data map.

Preferably, the teaching system based on big data, the atlas updating unit, further includes:

the first storage unit is used for acquiring a final updated knowledge data map, wherein the final updated knowledge data map is provided with a data type identifier;

the first storage unit is used for judging whether a preset storage area has a graph instance corresponding to the data type identifier according to the data type identifier;

if not, creating a graph instance corresponding to the data type identifier in the preset storage area, and storing the final updated knowledge data graph to a target storage area corresponding to the created graph instance;

otherwise, storing the final updated knowledge data map to a target storage area corresponding to the map example.

Preferably, the teaching system based on big data, the data analysis module includes:

the learning data monitoring unit is used for acquiring the learning records of a plurality of groups of students according to the preset frequency, calling a target teaching scheme currently accepted by each student, and determining the deviation information of each student according to the learning records and the target teaching scheme of the plurality of groups of students;

the learning data monitoring unit is also used for acquiring physiological data generated by each student under a currently accepted target teaching scheme and determining target reactions of each student under the target teaching scheme according to the physiological data;

the learning data monitoring unit is also used for determining the classification identification of the teaching mode in the currently accepted target teaching scheme according to the target reaction of each student and determining the interest points and the non-interest points of each student according to the classification identification;

the teaching resource integration unit is used for clustering teaching resources contained in the knowledge data map, extracting subject knowledge points corresponding to students and forming a subject knowledge point set, wherein the knowledge data map shows the association relationship between the element knowledge points in the subject knowledge point set;

the teaching method determining unit is used for determining a target meta knowledge point and a meta knowledge point combination which has an association relation with the target meta knowledge point from the subject knowledge point set through the knowledge data map according to deviation information of each student;

the teaching method determining unit is further used for acquiring historical test question information, determining respective teaching proportions of target element knowledge points and element knowledge point combinations having association relations with the target element knowledge points based on the historical test question information, and determining final teaching resources corresponding to students according to the teaching proportions;

the teaching method determining unit is further used for matching corresponding target teaching modes from a preset teaching mode library according to interest points and non-interest points of students, and matching and recording the target teaching modes corresponding to the students and final teaching resources to obtain teaching methods corresponding to the students.

Preferably, the teaching system based on big data, the data transferring module, includes:

the model building unit is used for obtaining influence factors of student learning quality evaluation and building an evaluation index system of the student learning quality evaluation according to the influence factors of the student learning quality evaluation;

the model construction unit is also used for determining the index weight of the evaluation index system based on a preset method and constructing a student learning quality evaluation model according to the index weight;

the learning data acquisition unit is used for determining the target achievement degree of each student based on the capability level of the preset knowledge points, and monitoring the completion conditions of the test questions, the class questions and answers and the class discussion of each student to obtain the course participation degree corresponding to each student;

the learning quality evaluation unit is used for inputting the target achievement degree and the course participation degree corresponding to each student into the learning quality evaluation model to obtain a learning quality evaluation result corresponding to each student;

the learning quality evaluation unit is further used for comparing the learning quality evaluation result corresponding to each student with a preset learning quality evaluation result;

the judging unit is used for judging that the teaching method accepted by each student is qualified when the learning quality evaluation result corresponding to each student is higher than or equal to the preset evaluation result, or judging that the teaching method accepted by each student is unqualified;

the teaching method adjusting unit is used for calling the detection data of each knowledge point based on the knowledge data map and determining the learning capacity value of each student on each knowledge point according to the detection data of each knowledge point when judging that the teaching method accepted by each student is not qualified;

the teaching method adjusting unit is further used for screening knowledge points mastered by students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points, and determining the average learning ability value of the students according to the knowledge points mastered by the students;

the teaching method adjusting unit is further used for adjusting the teaching method based on the average learning capacity value to finish establishing a proper target teaching method for each student.

Preferably, the teaching system based on big data, the teaching method adjusting unit, further include:

the detection unit is used for acquiring a target teaching method formulated for each student and determining whether the target teaching method is reasonable or not based on a preset detection method;

if the target teaching method is unreasonable, analyzing the knowledge data map based on big data, and re-determining the teaching method corresponding to each student until the target teaching method is judged to be reasonable;

if the target teaching method is reasonable, the target teaching method is sent to intelligent terminals corresponding to teachers and students on the basis of a preset network server;

and the feedback unit is used for executing the target teaching method after the teacher and the student receive the target teaching method, acquiring feedback information of the teacher and the student in real time, and perfecting the target teaching method based on the feedback information.

Preferably, the teaching system based on big data, the teaching method adjusting unit, further include:

the knowledge point integration unit is used for screening the knowledge points which are not mastered by the students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points;

the knowledge point integrating unit is further used for determining influence factors influencing knowledge points mastered by students based on preset rules, and searching corresponding target solutions from a preset solution library based on the influence factors, wherein the preset solution library stores a plurality of solutions corresponding to teaching problems;

and the execution unit is used for re-teaching the knowledge points which are not mastered by the students according to the target solution until the students completely master the knowledge points which are not mastered.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is a block diagram of a big data based tutorial system in an embodiment of the present invention;

FIG. 2 is an internal structural diagram of a data acquisition module in a big data-based teaching system according to an embodiment of the present invention;

fig. 3 is an internal structure diagram of a data analysis module in a big data-based teaching system according to an embodiment of the present invention.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

Example 1:

the embodiment provides a big data-based teaching system, as shown in fig. 1, including:

the data acquisition module is used for acquiring a knowledge data map of the student;

the data analysis module is used for analyzing the knowledge data map based on big data and determining a teaching method corresponding to each student;

and the data adjusting module is used for detecting the learning quality of each student under the corresponding teaching method in a preset time period, adjusting the teaching method according to the learning quality and finishing formulating a proper target teaching method for each student.

In this embodiment, the knowledge-data graph is used to represent the relationship between the respective data and the respective knowledge points contained in the respective meta knowledge points.

In this embodiment, the preset time period is set in advance, and may be, for example, one week, two weeks, or the like.

In this embodiment, the learning quality refers to a degree to which a student receives knowledge points and grasps the knowledge points in a teaching method.

The beneficial effects of the above technical scheme are: through analyzing the knowledge data map, the teaching scheme corresponding to each student is determined, so that the teaching scheme is favorable for performing suitable teaching for each student, the quality of the students is monitored, the teaching method is convenient to adjust in real time, the effectiveness of teaching for the students is improved, and the learning quality and the learning efficiency of the students are also improved.

Example 2:

on the basis of the foregoing embodiment 1, this embodiment provides a big data-based teaching system, as shown in fig. 2, the data obtaining module includes:

the data acquisition unit is used for acquiring a historical learning data set of the student knowledge field, wherein the historical learning data set comprises training data and test data;

the model construction unit is used for training a deep learning model based on the training data, testing the deep learning model based on the test data and obtaining a test result;

the first judgment unit is used for comparing the test result with a preset test result;

the map construction unit is used for acquiring target data for constructing a knowledge map in the student knowledge field when the test result meets the target requirement of the preset test result, and performing data extraction on the target data for constructing the knowledge map based on the deep learning model to obtain a data extraction result;

the map construction unit is also used for carrying out knowledge fusion on the data extraction result from a preset knowledge data map construction layer according to the data extraction result so as to complete the construction of the knowledge data map of the student;

and the model construction unit is further used for retraining the deep learning model when the test result does not meet the target requirement of the preset test result until the target requirement of the preset test result is met.

In this embodiment, the historical learning data set refers to the degree of understanding of knowledge points by students in past learning.

In the embodiment, the deep learning model is used for processing historical learning data of students, and convenience is provided for constructing a knowledge data map.

In this embodiment, the preset test result is set in advance, and is used to measure whether the detection result of the deep learning model reaches the standard.

In this embodiment, the target data for constructing the knowledge graph refers to knowledge points that need to be mastered by students in the field of knowledge of students.

In this embodiment, the preset knowledge data map construction level is set in advance, for example, a primary knowledge point needs to be constructed, and extension of a secondary knowledge point is performed below the primary knowledge point.

The beneficial effects of the above technical scheme are: the historical data of the students are analyzed and processed by constructing the deep learning model, so that an accurate knowledge data map is convenient to construct, a proper teaching method is convenient to formulate according to the knowledge data map, and convenience is brought to the improvement of teaching quality and teaching efficiency.

Example 3:

on the basis of the foregoing embodiment 2, this embodiment provides a teaching system based on big data, and the map building unit further includes:

the map updating unit is used for acquiring the constructed knowledge data map and determining data corresponding to each node in the knowledge data map and attribute values of the data based on a preset rule;

the map updating unit is further configured to generate at least one piece of updating data in a preset time period according to the attribute value of the data, update data corresponding to each node in the knowledge data map according to the updating data, and obtain target updating data;

the map updating unit is further configured to obtain a preset inference rule according to the target updating data, where the preset inference rule is a rule that is required to be used for generating an inference knowledge map according to the target updating data;

and the map updating unit is also used for generating a reasoning knowledge map according to the preset reasoning rule and the target updating data, and combining the reasoning knowledge map with the knowledge data map to obtain a final updated knowledge data map.

In this embodiment, the preset rule is set in advance and is used to determine the knowledge point data corresponding to the knowledge data map.

In this embodiment, the data corresponding to each node refers to all knowledge points included in the knowledge data map.

In this embodiment, the attribute value of the data refers to the quantitative value of the knowledge point and the difficulty level value of the knowledge point.

In this embodiment, the preset time period is set in advance, and may be two weeks, one month, or the like, for example.

In this embodiment, the update data refers to updating knowledge points that the student requires to master, that is, new knowledge points added on the original basis.

In this embodiment, the preset inference rule is a rule that is set in advance and is used to determine the association relationship between the updated data and the original data.

In this embodiment, the inference knowledge graph is used to represent the association relationship between the updated data and the original data.

The beneficial effects of the above technical scheme are: by determining the updating data, the knowledge data map is updated, the teaching of knowledge points of students is facilitated to be perfected, comprehensiveness of knowledge points mastered by the students is improved, and the learning quality of the students is improved.

Example 4:

on the basis of the foregoing embodiment 3, this embodiment provides a teaching system based on big data, and the map updating unit further includes:

the first storage unit is used for acquiring a final updated knowledge data map, wherein the final updated knowledge data map is provided with a data type identifier;

the first storage unit is used for judging whether a preset storage area has a graph instance corresponding to the data type identifier according to the data type identifier;

if not, creating a graph instance corresponding to the data type identifier in the preset storage area, and storing the final updated knowledge data graph to a target storage area corresponding to the created graph instance;

otherwise, storing the final updated knowledge data map to a target storage area corresponding to the map example.

In this embodiment, the data type identifier is used to distinguish the category of the knowledge point, and functions as a kind of label.

In this embodiment, a graph instance refers to a graph stored in a storage area that resembles a class of knowledge-graphs.

In this embodiment, the preset storage area is set in advance, and may be a solid state disk, for example.

In this embodiment, the target storage area refers to a storage area capable of storing the knowledge data spectrogram in the preset storage area.

The beneficial effects of the above technical scheme are: through the knowledge data map after will updating save, be convenient for know the knowledge point that the student will master in real time, be convenient for simultaneously explain the knowledge point comprehensively, improved student's learning quality, also improved mr's teaching quality simultaneously, provide convenience for mr and student.

Example 5:

on the basis of the foregoing embodiment 1, this embodiment provides a big data-based teaching system, as shown in fig. 3, the data analysis module includes:

the learning data monitoring unit is used for acquiring the learning records of a plurality of groups of students according to the preset frequency, calling a target teaching scheme currently accepted by each student, and determining the deviation information of each student according to the learning records and the target teaching scheme of the plurality of groups of students;

the learning data monitoring unit is also used for acquiring physiological data generated by each student under a currently accepted target teaching scheme and determining target reactions of each student under the target teaching scheme according to the physiological data;

the learning data monitoring unit is also used for determining the classification identification of the teaching mode in the currently accepted target teaching scheme according to the target reaction of each student and determining the interest points and the non-interest points of each student according to the classification identification;

the teaching resource integration unit is used for clustering teaching resources contained in the knowledge data map, extracting subject knowledge points corresponding to students and forming a subject knowledge point set, wherein the knowledge data map shows the association relationship between the element knowledge points in the subject knowledge point set;

the teaching method determining unit is used for determining a target meta knowledge point and a meta knowledge point combination which has an association relation with the target meta knowledge point from the subject knowledge point set through the knowledge data map according to deviation information of each student;

the teaching method determining unit is further used for acquiring historical test question information, determining respective teaching proportions of target element knowledge points and element knowledge point combinations having association relations with the target element knowledge points based on the historical test question information, and determining final teaching resources corresponding to students according to the teaching proportions;

the teaching method determining unit is further used for matching corresponding target teaching modes from a preset teaching mode library according to interest points and non-interest points of students, and matching and recording the target teaching modes corresponding to the students and final teaching resources to obtain teaching methods corresponding to the students.

In this embodiment, the preset frequency is set in advance, and may be, for example, one week or two weeks.

In this embodiment, the goal teaching plan refers to a teaching method or a teaching plan currently accepted by the student.

In this embodiment, the deviation information of the student refers to the direction of the partial family or the unmastered knowledge point that the student appears in the learning process, and the like.

In this embodiment, the physiological data refers to the reaction of the student under the teaching plan, such as excitement, calmness, silence, etc.

In this embodiment, the target response refers to one of excitement, calmness and silence of the student under the teaching plan.

In this embodiment, the classification mark is a label used for distinguishing different teaching modes, for example, the mark for ancient board teaching is 1, the mark for interesting teaching is 2, and the like.

In this embodiment, the subject matter knowledge points refer to knowledge points that may be involved in each subject by each student.

In this embodiment, the meta knowledge point combination refers to a data set composed of meta knowledge points having an association relationship.

The beneficial effects of the above technical scheme are: the knowledge data atlas analysis is adopted to determine subject knowledge point sets of students, incidence relations among the knowledge points are determined in the subject knowledge point sets, corresponding teaching modes are selected according to interest points and non-interest points of the students, results are carried out on the teaching modes and the knowledge points, the determination of the teaching method is completed, effective teaching schemes are formulated for the students, the learning quality of the students is improved, and the teaching efficiency of teachers is improved.

Example 6:

on the basis of the foregoing embodiment 1, this embodiment provides a teaching system based on big data, and the data tone module includes:

the model building unit is used for obtaining influence factors of student learning quality evaluation and building an evaluation index system of the student learning quality evaluation according to the influence factors of the student learning quality evaluation;

the model construction unit is also used for determining the index weight of the evaluation index system based on a preset method and constructing a student learning quality evaluation model according to the index weight;

the learning data acquisition unit is used for determining the target achievement degree of each student based on the capability level of the preset knowledge points, and monitoring the completion conditions of the test questions, the class questions and answers and the class discussion of each student to obtain the course participation degree corresponding to each student;

the learning quality evaluation unit is used for inputting the target achievement degree and the course participation degree corresponding to each student into the learning quality evaluation model to obtain a learning quality evaluation result corresponding to each student;

the learning quality evaluation unit is further used for comparing the learning quality evaluation result corresponding to each student with a preset learning quality evaluation result;

the judging unit is used for judging that the teaching method accepted by each student is qualified when the learning quality evaluation result corresponding to each student is higher than or equal to the preset evaluation result, or judging that the teaching method accepted by each student is unqualified;

the teaching method adjusting unit is used for calling the detection data of each knowledge point based on the knowledge data map and determining the learning capacity value of each student on each knowledge point according to the detection data of each knowledge point when judging that the teaching method accepted by each student is not qualified;

the teaching method adjusting unit is further used for screening knowledge points mastered by students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points, and determining the average learning ability value of the students according to the knowledge points mastered by the students;

the teaching method adjusting unit is further used for adjusting the teaching method based on the average learning capacity value to finish establishing a proper target teaching method for each student.

In this embodiment, the influencing factor for evaluating the learning quality of the student may be the learning attitude of the student, the course participation of the student, and the like.

In this embodiment, the evaluation index system for evaluating the learning quality of the student may evaluate the learning quality of the student according to the course participation degree of the student and how much the knowledge points are grasped.

In this embodiment, the preset method is set in advance and is used to determine importance values of different evaluation criteria in the evaluation system in all the evaluation criteria.

In this embodiment, the capability level of the preset knowledge point refers to a difficulty level value of the knowledge point.

In this embodiment, the target degree refers to the number value of the knowledge points successfully mastered by the student and the difficulty level value of the mastered knowledge points.

In this embodiment, the preset learning quality evaluation result is set in advance, and is a requirement for the learning quality of students.

In this embodiment, the detection data of each knowledge point refers to data used for detecting whether or not the student grasps the knowledge point in each knowledge point.

In this embodiment, the learning ability value refers to the ability of the student to grasp the knowledge point, for example, the time taken for the student to grasp the knowledge point is one hour, the identification learning ability value is strong, the time taken for the student to grasp the knowledge point is one day, and the identification learning ability value is weak.

The beneficial effects of the above technical scheme are: by constructing a learning quality evaluation model and acquiring learning data of students under a teaching method, whether the formulated teaching method is reasonable or not is judged by analyzing the learning quality of the students, and the teaching method is adjusted according to the learning ability value of the students under unreasonable conditions, so that the corresponding teaching method is formulated for each student, the learning quality of the students is improved, the teaching method is continuously improved, and convenience in different directions is provided for teachers and students.

Example 7:

on the basis of the foregoing embodiment 6, this embodiment provides a teaching system based on big data, and the teaching method adjusting unit further includes:

the detection unit is used for acquiring a target teaching method formulated for each student and determining whether the target teaching method is reasonable or not based on a preset detection method;

if the target teaching method is unreasonable, analyzing the knowledge data map based on big data, and re-determining the teaching method corresponding to each student until the target teaching method is judged to be reasonable;

if the target teaching method is reasonable, the target teaching method is sent to intelligent terminals corresponding to teachers and students on the basis of a preset network server;

and the feedback unit is used for executing the target teaching method after the teacher and the student receive the target teaching method, acquiring feedback information of the teacher and the student in real time, and perfecting the target teaching method based on the feedback information.

In this embodiment, the preset detection method is set in advance, and for example, the teaching method may be evaluated according to a teaching outline to determine whether knowledge points involved in the teaching method are comprehensive.

In this embodiment, the intelligent terminal may be a mobile phone or a computer of a teacher or a student.

In this embodiment, the feedback information refers to the problems of the teacher in the teaching process and the incomprehensible parts of the student in the learning process.

The beneficial effects of the above technical scheme are: the feedback opinions of the teacher and the students are collected in real time, so that the teaching method is continuously improved, the learning quality of the students is improved, and meanwhile, the teacher can conveniently adjust the teaching method in time.

Example 8:

on the basis of the foregoing embodiment 6, this embodiment provides a teaching system based on big data, and the teaching method adjusting unit further includes:

the knowledge point integration unit is used for screening the knowledge points which are not mastered by the students from the knowledge points contained in the knowledge data map based on the learning ability values of the students on the knowledge points;

the knowledge point integrating unit is further used for determining influence factors influencing knowledge points mastered by students based on preset rules, and searching corresponding target solutions from a preset solution library based on the influence factors, wherein the preset solution library stores a plurality of solutions corresponding to teaching problems;

and the execution unit is used for re-teaching the knowledge points which are not mastered by the students according to the target solution until the students completely master the knowledge points which are not mastered.

In this embodiment, the learning ability value refers to how easily it is for the student to grasp the knowledge point.

In this embodiment, the preset rule is set in advance.

In this embodiment, the influencing factor may be a difficult-to-learn factor or a student's own factor.

In this embodiment, the target solution is used to solve the problem that the student does not know the knowledge point, for example, the student can make multiple interpretations of the knowledge point that is not known in various ways.

The beneficial effects of the above technical scheme are: by determining the influence factors of the knowledge points which are not mastered by the students and searching the solution of the corresponding amount according to the influence factors, the comprehensiveness of the students on the mastered knowledge points is improved, and the learning quality of the students is improved.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.