CN114742677A - Teaching assistance interaction platform based on melt media - Google Patents
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Abstract
The invention discloses a teaching auxiliary interaction platform based on a converged medium, which comprises a user information management module, a resource management module, a video image-text teaching module, a test answer module, an integral ranking module, a learning resource recommendation module, a academic early warning module and a data storage module. The learning resource recommendation module in the platform can facilitate teachers to know learning conditions of students in time and recommend learning resources which accord with learning abilities of the students; the resource management module, the video image-text teaching module and the test answer module greatly improve the interactivity and the interestingness of the platform; the integral ranking module and the academic early warning module are convenient for students to locate the learning state of the students, and the learning enthusiasm of the students is stimulated; the academic early warning module points out the defects of the students in the current learning through learning weakness prediction and learning burnout early warning, and whips students with poor learning states to stimulate the learning enthusiasm.
Description
Technical Field
The invention relates to the technical field of teaching assistance, in particular to a teaching assistance interactive platform based on a fusion medium.
Background
With the rapid development of the internet industry, intelligent teaching platforms based on a media-blending concept and a multimedia technology are known by more and more educators, and become a beneficial supplement to traditional teaching. The intelligent teaching platform solves the problem that teaching resources are dispersed and teaching quality is uneven under the limitation of time and place, and teachers can further strengthen understanding of students on knowledge by using the platform as an auxiliary teaching means. Therefore, the intelligent teaching platform integrates various information resources through a brand-new thought, and the learning process becomes more intelligent.
The existing intelligent teaching auxiliary platform is simple in function, mainly focuses on the learning function, and can only meet the basic learning requirements of students. On one hand, the method mainly realizes the input of learning resources and student information, is convenient for students to inquire and acquire the resources, but cannot capture and supervise the learning state of the students, and teachers cannot know the learning conditions of the students in time; on the other hand, interaction between teachers and students and interaction between students are low, communication is lacked, students feel boring in the learning process, and learning power is reduced.
Based on the above, the invention provides a teaching assistance interaction platform based on a fusion medium, which can track the learning condition of students and evaluate the learning ability of the students, so that teachers can adjust the teaching progress along with the condition and actual feedback of the students. Meanwhile, the teacher-student interaction and the student-student interaction are carried out in diversified forms, so that the learning participation and learning interest of students are improved, and the intelligent interaction of teaching and learning is realized.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems with existing media-based teaching assistance interaction platforms.
Therefore, the problem to be solved by the present invention is how to provide a teaching assistance interaction platform based on a converged media.
In order to solve the technical problems, the invention provides the following technical scheme: a teaching assistance interactive platform based on a converged media comprises an information management unit, a user information management module and a data storage module, wherein the data storage module stores the identity verification information of a user in the user information management module, and the user information management module is used for user identity authentication and authority management; the recommendation and early warning unit comprises a learning resource recommendation module, wherein the learning resource recommendation module acquires the learning behavior record of the user from the data storage module and recommends the learning resource which is consistent with the ability of the user for the user; the testing and ranking unit comprises a testing answer module and an integral ranking module, wherein the answer result of the testing answer module is stored in the data storage module, the integral ranking module reads the learning behavior record of the data storage module, the accumulated experience value of the user is quantized through an integral rule, and ranking is carried out.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the system also comprises a resource uploading unit which comprises a resource management module and a video image-text teaching module, wherein a user uploads learning resources through the resource management module and stores the learning resources in the data storage module, and the user learns the learning resources in the data storage module through the video image-text teaching module.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the learning behavior record comprises the completion degree of the user on the learning resources, the repetition times of the learning resources, the scores obtained in the test corresponding to the learning resources and the time for completing the test; and the resource recommending module calculates the matching degree of the learning ability and the resource difficulty of the user according to the learning behavior record and recommends corresponding learning resources to the user according to the matching degree.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the recommendation and early warning unit further comprises a academic early warning module, wherein the academic early warning module acquires the learning behavior record of the user from the data storage module and is used for counting and analyzing the learning effect of the user and making early warning prompts when the learning condition is not good.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the test answer module comprises a common answer module and a PK battle answer module, and the common answer module is used for completing exercises corresponding to the resources after the user completes the learning of the learning resources; the PK fight question answering module is used for receiving a fight request input by a user, triggering a fight mode, randomly matching a fight object for the user, and after the match is successful, starting the fight.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the academic early warning module comprises a learning weakness prediction module and a learning listlessness early warning module, and when the learning ability of a user is lower than the resource difficulty, the learning weakness prediction module performs learning weakness early warning on the learning resource and related knowledge points so that the user can timely check and repair deficiencies; the learning burnout early warning module acquires the integral of the user in the ranking unit, compares the integral with the user in the ranking unit, and gives out learning burnout early warning to the user of which the integral is lower than a preset value, so that the learning enthusiasm of the user is improved.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the user learning ability is calculated by the following formula:
in the formula, abilityijThe capability value of the user k to the resource j is between 0 and 1; fracCompijRepresenting the completion degree of the user i to the resource j; repijRepresenting the number of repetitions of learning resource j by user i; n is a threshold value of repeated learning times; scoreij,timeijRespectively representing the fraction obtained in the corresponding test after the user k finishes learning the resource j and the time spent on finishing the test; s is the total score of the test; t is the completion time specified by the test; k is a radical of1,k2,k3,k4To control the weight, k, of the importance of the four influencing factors1+k2+k3+k4＝1。
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the resource difficulty is calculated by the following formula:
in the formula, difficultyjIs the difficulty value of the resource j, and is between 0 and 1; difjRepresenting the initial difficulty set by the teacher for the resource j, which is divided into simplicity, generality and difficulty; repijRepresenting the repetition times of the ith user for learning the resource j; scoreijRepresents the score obtained in the corresponding test after the ith user finishes learning the resource j; mjThe number of users who have learned resource j; l1,l2,l3To control the importance of the three influencing factors,/1+l2+l3＝1。
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: the matching degree of the user learning ability and the resource difficulty is calculated by the following formula:
matchij＝1-|abilityij-difficultyj|
in the formula, matchijRepresenting student abilityijAnd resource difficultyjThe matching degree between the students is between 0 and 1, and the closer the value is to 1, the higher the matching degree between the student ability and the resource difficulty is.
As an optimized scheme of the teaching assistance interaction platform based on the converged media, the invention comprises the following steps: when availabilityij<θ1Firstly, returning a resource set corresponding to the first-repair knowledge point, and then selecting a resource with high matching degree to recommend to the user for learning through the matching degree of the learning ability of the user and the resource difficulty;
when theta is1<abilityij<θ2The interactive platform acquires the learning times of the user on the current resource, and when the repeated learning times are already reachedWhen N is exceeded or equal, the capacity value is still at theta1And theta2If the first repair knowledge is still lack, returning to the resource set corresponding to the first repair knowledge point; when the repeated watching times are less than N, the user needs to learn the resources repeatedly to enhance understanding, so that the current resources are directly returned to the user;
when availabilityij＞θ2Returning to a resource set corresponding to the subsequent knowledge point, and selecting resources with high matching degree to recommend to the user for learning through the matching degree of the learning ability of the user and the resource difficulty;
wherein, theta1And theta2The value range is as follows: 0<θ1<θ2<1。
The invention has the beneficial effects that: the learning resource recommendation module can facilitate teachers to know learning conditions of students in time and recommend learning resources which accord with learning abilities of the students to the students; the resource management module, the video image-text teaching module and the test answer module greatly improve the interactivity and the interestingness of the platform; the integral ranking module and the academic early warning module are convenient for students to locate the learning state of the students, and the learning enthusiasm of the students is stimulated; the academic early warning module points out the defects of the students in the current learning through learning weakness prediction and learning burnout early warning, and whips students with poor learning states to stimulate the learning enthusiasm.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is an overall block diagram of a teaching assistance interaction platform based on a converged media.
FIG. 2 is a schematic diagram of a learning resource recommendation module of a teaching assistance interaction platform based on a converged media.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures of the present invention are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention and that the present invention is not limited by the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 and fig. 2, a first embodiment of the present invention provides a teaching assistance interaction platform based on a converged media, which includes an information management unit, a testing and ranking unit, and a recommending and early warning unit.
The information management unit comprises a user information management module and a data storage module, wherein the data storage module stores the identity verification information of the user in the user information management module, and the user information management module is used for a user identity authentication and authority management and recommendation and early warning unit.
Specifically, the information management unit includes a user information management module and a data storage module, wherein the data storage module stores the authentication information of the user in the user information management module, and the user information management module is used for user identity authentication and authority management.
The user information management module is used for user identity authentication and authority management, and safety of the platform is guaranteed. In this embodiment, the user includes a teacher and a student, and the specific management steps include:
and (1-1) inputting user identity authentication information by a user through equipment, and sending a login request to the cloud platform. The equipment comprises a PC end or mobile equipment such as a mobile phone and a pad; the user identity authentication information comprises teacher identity authentication information and student identity authentication information, the teacher identity authentication information comprises a teacher name, a work number, a managed class and a login password, and the student identity authentication information comprises a name, a class, a school number and a login password;
and (1-2) checking the user identity authentication information after the cloud platform receives the login request of the user. And the cloud platform acquires the information stored in the data storage module and matches the received user identity authentication information with the preset value. When the matching is successful, the user identity authentication is judged to be successful, and the user can log in the platform to perform teaching activities or learning activities;
and (1-3) the cloud platform determines an access authorization strategy for the user according to the user identity authentication result, and controls the user to access the platform content. After the user logs in successfully, the cloud platform respectively opens different access authorities to the teacher and the students, allows the teacher to perform teaching activities on the managed class and check the learning activity records of all the students, and allows the students to enter the class to perform learning activities and check the learning activity records of the students;
and (1-4) after the user logs in, the account information can be further managed, including personal data setting (head portrait, nickname, gender and the like), password modification, browsing cache removal and suggestion feedback submission. Wherein, the opinion feedback function mainly faces students and is presented in the form of questionnaire. The students fill in a course data satisfaction questionnaire, an on-site questionnaire and an experience questionnaire used by an interactive platform to feed back use opinions.
And the recommendation and early warning unit comprises a learning resource recommendation module, and the learning resource recommendation module acquires the learning behavior record of the user from the data storage module and recommends the learning resource which is consistent with the ability of the user for the user.
The learning resource recommending module is used for recommending learning resources corresponding to the ability of the student for the student and improving the trust of the student on the platform, and as shown in fig. 2, the concrete recommending steps are as follows:
(2-1) the cloud platform acquires part of learning behavior records from the data storage module, wherein the part of learning behavior records comprises the completion degree of the student on the learning resources, the repetition times of learning the resources, the scores obtained in the test corresponding to the learning resources and the time for completing the test, and the learning ability of the student on the current learning resources is measured by four factors of the completion degree of the resources, the frequency ratio of the repetition learning resources, the accuracy of the test and the time ratio for completing the test. The expression of the learning ability of the user is as follows:
in the formula, abilityijThe capability value of the user i to the resource j is between 0 and 1; fracCompijRepresenting the completion degree of the user i to the resource j; repijRepresenting the number of repetitions of user i learning resource j; n is a threshold value of repeated learning times; scoreij,timeijRespectively representing the fraction obtained in the corresponding test after the user i finishes learning the resource j and the time spent on finishing the test; s is the total score of the test; t is the completion time specified by the test; k is a radical of1,k2,k3,k4To control the weight, k, of the importance of the four influencing factors1+k2+k3+k4＝1；
In the above formula, the first two terms respectively represent the ratio of the average completion degree of the student i to the resource j to the number of times of repeatedly learning the resource j, and the second two terms respectively represent the ratio of the accuracy of the student i in performing the corresponding test on the resource j to the time for completing the test.
And (2-2) the cloud platform acquires part of learning behavior records from the data storage module, wherein the part of learning behavior records comprises scores obtained by all students in tests corresponding to the learning resources and the repetition times of the learning resources. Calculating the difficulty of the resource by combining the initial difficulty set for the resource by the teacher:
in the formula, difficultyjIs the difficulty value of the resource j, and is between 0 and 1; difjRepresenting the initial difficulty set by the teacher for resource j, divided into simple (0.25), general (0.5) and difficult (0.75); repijRepresenting the repetition times of the ith user for learning the resource j; scoreijRepresents the score obtained in the corresponding test after the ith user finishes learning the resource j; mjThe number of users who have learned resource j; l1,l2,l3To control the importance of the three influencing factors,/1+l2+l3＝1；
The three terms in the above equation represent the initial difficulty of the resource, the average repeated learning number ratio and the average accuracy of the corresponding test, respectively.
(2-3) calculating the matching degree of the student capacity and the resource difficulty:
matchij＝1-|abilityij-difficultyj|
in the formula, matchijRepresenting student abilityijAnd resource difficultyjThe matching degree between the students is between 0 and 1, and the closer the value is to 1, the higher the matching degree between the student ability and the resource difficulty is.
And (2-4) setting a recommendation rule according to the relation between the current learning condition of the student and the learning resources, and recommending the learning resources which accord with the current learning ability of the student for the student. The specific recommendation rule is as follows:
A.abilityij<θ1the situation that the student learns the current resource is poor, and the student needs to learn the prior correction knowledge of the knowledge point corresponding to the video resource first to tighten the foundation. Therefore, returning to the resource set corresponding to the prior knowledge point, and searching the resource with the highest matching degree through the step (6-3) and recommending the resource to the student;
B.θ1<abilityij<θ2it is stated that the learning condition of the student on the current resource is general, and the knowledge points in the current resource need to be continuously consolidated. The cloud platform acquires the learning times of the student on the current resource, and when the repeated learning times exceeds or equals to N, the capacity value is still theta1And theta2BetweenIf the first repair knowledge is still deficient, returning to the resource set corresponding to the first repair knowledge point; when the repeated watching times are less than N, the student needs to learn the resource repeatedly to enhance understanding, so that the current resource is directly returned to the student;
C.abilityij＞θ2the method shows that the students can learn the current resources well, have already been mastered basically and can learn the subsequent knowledge. Therefore, returning to the resource set corresponding to the subsequent knowledge point, and searching the resource with the highest matching degree through the step (6-3) and recommending the resource to the student;
wherein, theta1And theta2Learning ability value set for division learning condition in recommendation rule setting process, 0<θ1<θ2<1。
The testing and ranking unit comprises a testing answer module and an integral ranking module, wherein the answer result of the testing answer module is stored in the data storage module, the integral ranking module reads the learning behavior record of the data storage module, the accumulated experience value of the user is quantized through an integral rule, and ranking is carried out.
Wherein, test answer module is used for the teacher to inspect student's study effect, includes ordinary answer module and PK fight answer module again, specifically is:
(3-1) a common answer module: the method is used for finishing the exercises corresponding to the learning resources after the students finish learning the learning resources. The examination form is an objective question, and the system automatically completes the correction and then gives instant analysis and knowledge points of each question investigation. The cloud platform automatically records the test scores of students and the time spent on completing the tests, and uploads the test scores and the time spent on completing the tests to the data storage module;
(3-2), PK fight answer module: the system is used for receiving a fight request input by a student, triggering a fight mode, randomly matching a combat object for the student, and after the match is successful, starting the fight. The student and the object of the battle need to complete the same n-channel selection questions, where n is the number of questions both the fighters need to complete, and in this embodiment, n is 5, that is, both the fighters need to complete 5 channels of the same selection questions. In the process, the students have 3 online states, namely match middle, fight middle and fight end, and the specific fight operation is as follows:
A. the students trigger the fight mode, select the knowledge theme of the fight answer, start to match PK battle objects, and set the state of the students as matching. The cloud platform searches other students which are also in a matching state, and selects any student with the knowledge theme consistent with the knowledge theme of the answer of the fight to the student as a fight object. In the process, the students can cancel matching and reselect the knowledge theme of the answer to battle;
B. after matching is successful, the cloud platform stores matching information and sends the matching information to the student and the opponent of the battle, and both parties can obtain the opponent information. And starting PK fight answer, and setting the state of the student into fight. When two parties answer the battle, the cloud platform automatically records and pushes updated scores and answer schedules to opponents every time the scores and the answer schedules are updated;
C. and when the students finish answering, the opponents waiting for the battle also finish answering. After both parties finish answering, the student status is set as the end of the battle, and the match result is displayed for both parties;
D. the cloud platform automatically records the times of the student triggering the fighting mode and the fighting result, calculates the fighting victory ratio and uploads the result to the data storage module.
The point ranking module is used for visualizing the accumulated learning experience value of the students, stimulating the enthusiasm of the students for continuous learning and improving the participation degree and the completion degree of the courses. And the integral ranking module reads the learning behavior record of the data storage module and quantifies the accumulated experience value of the student through an integral rule. Students can log in the platform at any time to check learning points and current point ranking, and teachers have the authority to check all the points and the total ranking of the students. The final accumulated points of the students are used for redeeming reward points of ordinary achievements in the total points at the end of the period. The specific integration rule is as follows:
(4-1) students log in the interactive platform for the first time every day to obtain the daily check-in points, such as 5 points;
(4-2) when the student learns the learning data uploaded by the teacher each time, including the image-text data and the video data, the completion degree reaches more than a%, the basic learning integral can be obtained, for example, 20 points are obtained, a% is a set completion degree standard value, the set completion degree standard value is set by the teacher, and the set range is as follows: 0< a% <1, in this example, a% may be 80%;
(4-3), the scores obtained by the students in each test are used as test reward points, for example, 10 questions are total, 10 points are obtained for each question in response, basic points for battle can be obtained in the PK battle response module when each challenge is initiated, for example, 10 points are obtained, and the reward points for battle can be obtained in success of the challenge, for example, 30 points are obtained;
(4-4) students can obtain excellent learning points after actively uploading learning materials and sharing learning notes, for example, 10 points, and can obtain specially approved points, for example, 20 points through auditing and obtaining teachers or classmates for access and approval;
(4-5) the students actively participate in interaction of questioning, discussing and the like in the discussion message leaving area, and positive learning points, such as 10 points, can be obtained for the class materials or the class comment and the degree of satisfaction of the class materials, but the total positive learning points obtained in the learning process of the section do not exceed the basic learning points of the section.
Further, in this embodiment, the rule of exchanging the student accumulated learning points for the ordinary achievement awards in the end total points is as follows: the ordinary score reward points are exchanged for 2 points every 100 points, and the ordinary score reward points are accumulated; the reward points of the highest ordinary score are 50 points at 2500 points or more.
Preferably, the interactive platform further comprises a resource uploading unit which comprises a resource management module and a video image-text teaching module, the user uploads learning resources through the resource management module and stores the learning resources in the data storage module, and the user learns the learning resources in the data storage module through the video image-text teaching module.
The resource management module is used for managing learning resources, and comprises a teacher uploading learning materials and a student sharing the learning resources, and the specific management functions are as follows:
and (5-1) uploading the learning resources to a resource management module by the teacher, wherein the learning resources comprise course data, course outlines and a question bank. Course data is presented in the form of graphics and text or video materials according to knowledge sections and is divided into 6 modules of basis, concept, property, example, application and small encyclopedia. The teacher needs to specify the dependency relationship of each course data, calibrate the learning difficulty of each course data, and then organize and present all the course data according to the teaching target. The course outline is presented in a text form and comprises a course teaching plan, a teaching outline, an experiment outline and an experiment instruction book, so that students can conveniently know the teaching process. The exercise library is attached to each learning resource and comprises selection exercises and judgment exercises, and teachers mark difficulty levels of each set of exercises;
and (5-2) based on the idea of 'knowledge crowd funding', the students can also share the learning resources to the resource management module. The study resources shared by the students are considered valuable resources or study notes which are helpful for learning after the students study, and the students can have uploading authority only after highly completing the study resources provided by the teachers. The teacher can access the learning resources uploaded by the students, and all students can access and learn after the verification.
The video image-text teaching module is used for students to carry out learning activities, comprises image-text data and video data in the learning resource management module, issues discussion messages and evaluates the satisfaction degree of the data, and has the specific learning functions of:
(6-1) the students can read the image-text data to obtain the knowledge of concepts, properties, examples, applications, bases and small encyclopedias. The cloud platform automatically records learning behavior data of students, including total reading duration, reading completion degree and skipping and reviewing times when each image and text is read, and uploads the data to the data storage module;
and (6-2) the students watch the video data to acquire knowledge on concepts, properties, examples and applications. The cloud platform automatically records learning behavior data of students, including total time for watching each course video, watching completion degree, pause times, pause time percentage, skip times and review times, and uploads the data to the data storage module;
and (6-3) reserving a discussion message area and a learning process satisfaction evaluation area at the bottom of each course material. When the students study course data, the students issue comments or ask questions in the discussion message leaving area to interact with teachers and classmates; the teacher can look over the messages in the discussion area to solve the questions for the students. After the students learn the course data, the satisfaction degree of the course data is scored, and the scoring standards comprise the presentation form and the difficulty degree of resources, the richness and the interestingness of contents and the like.
In this embodiment, the satisfaction score may be quantified as 5 levels: unsatisfactory (1 point), less satisfactory (2 points), general (3 points), more satisfactory (4 points), and very satisfactory (5 points).
Furthermore, the recommendation and early warning unit further comprises a academic early warning module, wherein the academic early warning module acquires the learning behavior record of the user from the data storage module and is used for counting and analyzing the learning effect of the user and giving an early warning prompt when the learning condition is not good.
The academic early warning module comprises a learning weakness prediction module and a learning burnout early warning module, and the learning burnout early warning module has the following functions:
(7-1), a learning vulnerability prediction module: and (3) respectively presenting a change curve according to the learning capacity of the user calculated in the step (2-1) and the resource difficulty calculated in the step (2-2) in the learning resource recommendation module, and reflecting the change of the student capacity and the change of the resource difficulty in the learning process. When the student ability is higher than the resource difficulty, the student already masters the corresponding knowledge point of the resource; when the ability of the student is lower than the difficulty of the resource, especially far lower than the difficulty, the module needs to make early warning of learning weakness of the part of learning resource and related knowledge points so as to facilitate the student to check and repair the deficiency in time;
(7-2), a learning burnout early warning module: the cloud platform obtains the learning points of the student, and compares the learning points with the points of other students in the same class and the same profession to obtain the ranking of the class and the profession. For students with scores lower than b% of students, learning burnout early warning is timely given, and the learning enthusiasm of the students is improved. b% is the set integral early warning division standard, 0< b% < 1. In this embodiment, 50% may be taken as b%.
It should be noted that the data storage module is used for storing and calling operation data in the interactive platform. The module stores the identity verification information of teachers and students in the user information management module, all learning materials in the resource management module, and the integral and ranking of students in the learning process, and collects various learning behavior records of students in the video image-text teaching module and all test records and results of students in the test answering module. When the teacher or the student sends an access request, the cloud platform verifies the identity information, judges whether the access request has the access authority or not, and calls corresponding data from the data management module to send the corresponding data to the teacher or the student.
It should be noted that the resource management module, the video image-text teaching module and the test question answering module greatly improve the interactivity and the interestingness of the platform. The resource management module improves the participation and autonomy of students by setting the functions of sharing and uploading learning resources of the students; the video image-text teaching module enables students to interact with classmates and teachers through setting a discussion message area and a satisfaction degree score, questions in the learning process are jointly solved, and teachers can timely know the quality of learning resources and make adjustments; the test answer module is provided with the battle PK answer module, so that the learning condition of students can be checked, the interestingness of answer can be increased, and the interaction of the students is promoted;
the learning resource recommendation module provided by the invention can facilitate teachers to know the learning conditions of students in time and recommend learning resources which accord with the learning ability of the students to the students. The module firstly tracks the learning condition of students, estimates the learning ability of the students, secondly sets the difficulty of learning resources according to the overall learning condition of all the students, and finally recommends the learning resources with the highest matching degree for the students according to the recommendation rule and the matching degree between the learning ability of the users and the difficulty of the learning resources, so as to provide correct guidance for the students;
the integral ranking module and the academic early warning module are convenient for students to locate the learning state of the students, and the learning enthusiasm of the students is stimulated. The score ranking module accumulates corresponding scores for various learning activities of students by setting score rules, and the students can check the obtained scores and the current ranking to check the learning state and progress of the students; the academic early warning module points out the defects of the students in the current learning through learning weakness prediction and learning burnout early warning, and whips students with poor learning states to stimulate the learning enthusiasm.
Example 2
Referring to FIG. 2, a second embodiment of the present invention is shown for verifying the feasibility of the learning resource recommendation module of the present invention.
Specifically, an existing data set is employed. This data set is derived from the online MOOC learning platform courerera, which provides a large amount of learning resources. The data set comprises 93 course videos, the average time length of each video is 17 minutes, each course video is provided with a corresponding practice problem, and each student completes one course video and needs to complete the practice problem to test and master the condition. In addition, the data set also contains 26934 pieces of learning behavior logs of students, namely interaction information between the students and the course videos. Fields related to the viewing of videos by students include: the proportion of time spent by the student on the video, the proportion of time the student watches the video, and the proportion of time the student pauses on the video. Other fields related to video interaction include the number of times the student pauses the video, the number of times the student fast forwards and replays while watching the video, etc. After preprocessing such as data cleaning, partial attributes are selected from the data set and recombined to verify the feasibility of the learning resource recommendation module.
Now calculate the student's ability value for the resource based on the above data set, userID denotes the number of student i and VidID denotes the number of resource j. fracCompijThe completion degree of the student i to the resource j is between 0 and 1; the total score S of the test was 100, scoreijBetween 0 and 100; the test specified a completion time T of 30 minutes, timeijBetween 0 and 30. The threshold value N of the number of repeated learning is 5, k1,k2,k3,k4The values of the four importance weights are 0.1,0.2,0.5 and 0.2 respectively. Taking student a as an example of learning resource 0, the specific process of calculating learning ability based on learning behavior record is as follows:
TABLE 1 student a learning resources 0 parameter Table
| userID | VidID | fracComp | score | rep | time |
| a | 0 | 0.9318965,0.995535585 | 84 | 2 | 13 |
Calculating to obtain: abilitya0＝0.7497
That is, the learning ability value of student a for resource 0 is 0.7497.
The data for the learning ability calculation of more students on resources are shown in table 2:
TABLE 2 learning competence calculation table for resources of students
| userID | VidID | fracComp | score | rep | time | ability |
| a | 0 | 0.9318965,0.995535585 | 84 | 2 | 13 | 0.7497 |
| b | 1 | 0.94189045 | 63 | 1 | 25 | 0.602522 |
| b | 2 | 0.545277071 | 90 | 1 | 18 | 0.744528 |
| b | 4 | 0.510896277,0.743598549,0.996348817 | 92 | 3 | 16 | 0.745876 |
| b | 6 | 0.353338771,0.593342222,0.996061575 | 57 | 3 | 18 | 0.542137 |
| d | 67 | 0.305581339,0.992366742 | 92 | 2 | 15 | 0.744897 |
| … | … | … | … | … | … | … |
The difficulty value of the resource is now calculated based on the data set. l1,l2,l3The three importance weights are 0.2,0.5 and 0.3 respectively. Using resource 30 as an example, a total of 7 students learned the resource, Mj＝7. The specific process of recording computational resource difficulty based on learning behavior is shown in table 3:
TABLE 3 learning behavior record sheet
Calculating to obtain: difficulty30＝0.304
I.e., the difficulty value of resource 30 is 0.304.
The difficulty value calculation data for more resources is shown in table 4:
TABLE 4 difficulty value calculation Table for resources
The matching degree between the student ability and the resource difficulty is calculated based on the calculation process. Using student d and resource 67 as an example, availability is calculatedd67＝0.744897,difficulty670.67. Thus, the degree of match between student d's ability and the difficulty of resource 67 is calculated as:
matchd67＝1-|abilityd67-difficulty67|＝1-|0.744897-0.67|＝0.925103
i.e., the degree of match between student d's ability and the difficulty of resource 67 is 0.925103.
The data for the calculation of the degree of matching between the ability of more students and the difficulty of resources is shown in table 5:
TABLE 5 matching degree calculation Table between student ability and resource difficulty
| userID | VidID | ability | difficulty | match |
| er | 16 | 0.489784 | 0.494609 | 0.995175 |
| er | 17 | 0.431554 | 0.487528 | 0.944027 |
| er | 18 | 0.54677 | 0.373639 | 0.826869 |
| er | 19 | 0.385127 | 0.335371 | 0.950243 |
| d | 23 | 0.502009 | 0.604268 | 0.897741 |
| d | 67 | 0.744897 | 0.67 | 0.925103 |
| d | 68 | 0.494609 | 0.545004 | 0.949605 |
| d | 69 | 0.487528 | 0.417193 | 0.929666 |
| d | 70 | 0.335371 | 0.377636 | 0.957735 |
| ad | 0 | 0.604268 | 0.469678 | 0.865409 |
| ad | 26 | 0.421189 | 0.505298 | 0.915891 |
| ad | 49 | 0.265233 | 0.408231 | 0.857002 |
| ad | 77 | 0.704398 | 0.293769 | 0.589371 |
| … | … | … | … | … |
Now, a part of data in the current data set is selected to specifically explain the recommendation rule. Take theta1＝0.3,θ2The learning ability of the students is classified into three categories, i.e., poor, general and good, 0.6. The resource recommendation process for three classes of students with different learning abilities is shown in table 6:
TABLE 6 resource recommendation Table for students
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. The utility model provides a teaching assistance interactive platform based on melt media which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the information management unit comprises a user information management module and a data storage module, wherein the data storage module stores the identity verification information of the user in the user information management module, and the user information management module is used for user identity authentication and authority management;
the recommendation and early warning unit comprises a learning resource recommendation module, wherein the learning resource recommendation module acquires the learning behavior record of the user from the data storage module and recommends the learning resource which is consistent with the ability of the user for the user;
the testing and ranking unit comprises a testing answer module and an integral ranking module, wherein the answer result of the testing answer module is stored in the data storage module, the integral ranking module reads the learning behavior record of the data storage module, the accumulated experience value of the user is quantized through an integral rule, and ranking is carried out.
2. The media-based tutorial-assisted interactive platform of claim 1, wherein: the system also comprises a resource uploading unit which comprises a resource management module and a video image-text teaching module, wherein a user uploads learning resources through the resource management module and stores the learning resources in the data storage module, and the user learns the learning resources in the data storage module through the video image-text teaching module.
3. A financial media-based instructional assisted interactive platform as claimed in claim 1 or 2 wherein: the learning behavior record comprises the completion degree of the user on the learning resources, the repetition times of the learning resources, the scores obtained in the test corresponding to the learning resources and the time for completing the test;
and the resource recommending module calculates the matching degree of the learning ability and the resource difficulty of the user according to the learning behavior record and recommends corresponding learning resources to the user according to the matching degree.
4. The media-based tutorial-assisted interactive platform of claim 3, wherein: the recommendation and early warning unit further comprises a academic early warning module, wherein the academic early warning module acquires the learning behavior record of the user from the data storage module and is used for counting and analyzing the learning effect of the user and giving an early warning prompt when the learning condition is not good.
5. The multimedia-based instructional assisted interaction platform of claim 4, wherein: the test answer module comprises a common answer module and a PK battle answer module, and the common answer module is used for completing exercises corresponding to the resources after the user completes the learning of the learning resources; the PK fight question answering module is used for receiving a fight request input by a user, triggering a fight mode, randomly matching a fight object for the user, and after the match is successful, starting the fight.
6. The multimedia-based instructional aide interaction platform of claim 5, wherein: the academic early warning module comprises a learning weakness prediction module and a learning listlessness early warning module, and when the learning ability of a user is lower than the resource difficulty, the learning weakness prediction module performs learning weakness early warning on the learning resource and related knowledge points so that the user can timely check and repair deficiencies;
the learning burnout early warning module acquires the integral of the user in the ranking unit, compares the integral with the user in the ranking unit, and gives out learning burnout early warning to the user of which the integral is lower than a preset value, so that the learning enthusiasm of the user is improved.
7. The media-based tutorial-assisted interactive platform of claim 6, wherein: the user learning ability is calculated by the following formula:
in the formula, abilityijThe capability value of the user i to the resource j is between 0 and 1; fracCompijRepresenting the completion degree of the user i to the resource j; repijRepresenting the number of repetitions of learning resource j by user i; n is a threshold value of repeated learning times; scoreij,timeijRespectively representing the fraction obtained in the corresponding test after the user i finishes learning the resource j and the time spent on finishing the test; s is the total score of the test; t is the completion time specified by the test; k is a radical of formula1,k2,k3,k4To control the weight, k, of the importance of the four influencing factors1+k2+k3+k4＝1。
8. A financial media-based instructional assisted interactive platform as claimed in claim 6 or 7 wherein: the resource difficulty is calculated by the following formula:
in the formula, difficultyjIs the difficulty value of the resource j, and is between 0 and 1; difjRepresenting the initial difficulty set by the teacher for the resource j, which is divided into simplicity, generality and difficulty; repijRepresenting the repetition times of the ith user learning resource j; scoreijRepresents the score obtained in the corresponding test after the ith user finishes learning the resource j; mjThe number of users who have learned resource j; l. the1,l2,l3To control the importance of the three influencing factors,/1+l2+l3＝1。
9. The media-based tutorial-assisted interactive platform of claim 8, wherein: the matching degree of the user learning ability and the resource difficulty is calculated by the following formula:
matchij＝1-|abilityij-difficultyj|
in the formula, matchijRepresenting user learning ability availabilityijAnd resource difficultyjThe matching degree between the learning ability and the resource difficulty is between 0 and 1, and the closer the value is to 1, the higher the matching degree between the learning ability and the resource difficulty of the user is.
10. The media-based tutorial-assisted interactive platform of claim 9, wherein: when availabilityij<θ1Firstly, returning a resource set corresponding to the first-repair knowledge point, and then selecting a resource with high matching degree to recommend to the user for learning through the matching degree of the learning ability of the user and the resource difficulty;
when theta is1<abilityij<θ2The interactive platform acquires the learning times of the user on the current resource, and when the repeated learning times exceed or are equal to N, the capacity value is still in theta1And theta2If the first repair knowledge is still deficient, returning to the resource set corresponding to the first repair knowledge point; when the repeated watching times are less than N, the user needs to learn the resources repeatedly to enhance understanding, so that the current resources are directly returned to the user;
when availabilityij＞θ2Returning to a resource set corresponding to the subsequent knowledge point, and selecting resources with high matching degree to recommend to the user for learning through the matching degree of the learning ability of the user and the resource difficulty;
wherein, theta1And theta2The value range of (A) is as follows: 0<θ1<θ2<1。
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| CN117874339A (en) * | 2024-01-03 | 2024-04-12 | 北京华乐思教育科技有限公司 | Intelligent recommendation system and method for testing and analyzing learning content |
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| CN117874339A (en) * | 2024-01-03 | 2024-04-12 | 北京华乐思教育科技有限公司 | Intelligent recommendation system and method for testing and analyzing learning content |
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