Teaching management system for course teaching

Abstract

The invention discloses a teaching management system for course teaching, and relates to the technical field of course teaching, and the system comprises the steps: building a case discussion module, optimizing a case comment and guiding tool, enabling a teacher to upload a course case through a teacher end, enabling a student to discuss the case through a student end, and obtaining a final discussion result; deep communication between students and between students and teachers can be promoted, and case knowledge can be deeply expanded; an online debate management module is established, for a dispute case, a teacher initiates a debate topic to students, and a debate link and a debate form are set by a teacher end; the sense of participation of students can be enhanced; establishing user portraits, collecting learning behavior data of students in the system, and based on a data analysis algorithm, establishing detailed user portraits for each student to clarify knowledge mastering conditions, interests and preferences and learning requirements of the student; related data can be accurately provided for different students, the utilization rate of resources is improved, and the search time of the students is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of course teaching, and specifically relates to a teaching management system for course teaching. Background Art

[0002] The existing teaching management system for course teaching still has the following drawbacks in actual use:

[0003] During teaching communication, the course discussion area and the online Q&A function only achieve basic communication and lack in-depth interaction. Among them, although the teacher uploads a large number of resources, when students search for the required resources, the system lacks an accurate recommendation function, which may consume a lot of screening time for students, making the system have certain deficiencies in use. Summary of the Invention

[0004] The purpose of the present invention is to provide a teaching management system for course teaching to solve the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A teaching management system for course teaching includes establishing a case discussion module and optimizing case comment and guidance tools. The teacher uploads course cases through the teacher terminal, and students discuss the cases through the student terminal to obtain the final discussion result;

[0006] Establishing an online debate management module. For controversial cases, the teacher initiates a debate topic to the students, and sets the debate session and debate form through the teacher terminal;

[0007] Establishing user portraits, collecting students' learning behavior data in the system, and constructing detailed user portraits for each student based on data analysis algorithms to clarify their knowledge mastery, interest preferences, and learning needs.

[0008] Further, the establishment of the case discussion module and the optimization of the case comment and guidance tools, where the teacher uploads course cases through the teacher terminal, and students discuss the cases through the student terminal to obtain the final discussion result, specifically includes:

[0009] Setting up a group component function. By collecting the profile information of each student, the profile information includes different abilities such as being good at research, copywriting, presentation, etc., and the group component function groups according to the profile information, combining students with complementary advantages into a group;

[0010] Providing an independent collaboration space for the group, including functions such as group document sharing, task assignment, and real-time communication. The group leader can assign various tasks of case analysis within the collaboration space, and group members can upload and share research materials and the initial draft of the report, and communicate and discuss through real-time chat tools at the same time;

[0011] In addition to case studies and debates, interactive activities such as role-playing and mock press conferences are added. In the role-playing activity, the teacher sets the course theme scenario, such as simulating the characters in historical events. Students can deeply understand historical events and the thoughts of characters by playing different roles. In the mock press conference activity, students are grouped to play roles such as press spokespersons and journalists, and conduct Q&A sessions on hot topics of the course. The teacher provides real-time guidance and feedback on the final discussion results.

[0012] Furthermore, for the online debate management module, for controversial cases, the teacher initiates a debate topic for students, and sets the debate session and debate form from the teacher side, specifically including:

[0013] Establish a comprehensive evaluation index system for interactive effects, including multiple dimensions such as student participation (number of speeches, length of time participating in discussions, etc.), contribution (novelty of viewpoints put forward, promotion of discussions, etc.), and teamwork ability (degree of cooperation with group members, etc.);

[0014] Set reasonable weights for each evaluation index, and dynamically adjust according to different types of interactive activities. For example, in the role-playing activity, more attention may be paid to students' role understanding and expressiveness, and the weights of these aspects of indicators are appropriately increased;

[0015] During the interactive activity process, the system provides real-time feedback to students on their performance. For example, immediately after a student's speech, a short evaluation on aspects such as the quality of the viewpoint and the clarity of expression is given. For example, when a student puts forward a profound viewpoint during the discussion, the system prompts "Your viewpoint is very novel and helpful for the discussion";

[0016] Establish an interactive incentive mechanism to reward outstanding students or groups, such as point rewards, virtual badges, etc. These rewards can be used to exchange for learning resources, course score increases, etc.

[0017] Furthermore, for the establishment of user portraits, collect students' learning behavior data in the system, and based on data analysis algorithms, construct a detailed user portrait for each student to clarify their knowledge mastery, interest preferences, and learning needs, specifically including:

[0018] The data analysis algorithms include collaborative filtering algorithms and content-based recommendation algorithms;

[0019] Based on the collaborative filtering algorithm, by analyzing the resource usage of student groups with similar learning behaviors, recommend resources that they are interested in to target students;

[0020] The content-based recommendation algorithm matches and recommends according to the characteristics of resources such as theme, knowledge points, and difficulty, with the needs of students.

[0021] Further, the collaborative filtering algorithm recommends resources that target students are interested in by analyzing the resource usage of student groups with similar learning behaviors, and further includes:

[0022] Similarity calculation: Use the cosine similarity formula to calculate the similarity between students, where A = (a 1 , a 2 , …, a n ) and B = (b 1 , b 2 , … b n ) are the user profile vectors of students, and n is the dimension of the user profile vector.

[0023] Prediction scoring formula N(A) is the student group with a relatively high similarity to student A, sim(A, k) is the similarity between student A and student k, and r ki is the score of student k for resource i;

[0024] Item-based collaborative filtering

[0025] Formula for calculating the similarity between resources where R(i) is the set of students who have given feedback (such as browsing, liking, commenting, etc.) on resource i, R(j) is the set of students who have given feedback on resource j, and |R(i) ∩ R(j)| represents the number of students who have given feedback on both resources i and j.

[0026] Formula for predicting the preference degree of students for similar resources where S(i) is the set of resources similar to resource i, sim(i, j) is the similarity between resources i and j, and r uj is the score of student u for resource j.

[0027] Further, the content-based recommendation algorithm matches and recommends according to the characteristics of resources such as themes, knowledge points, and difficulty levels, and further includes:

[0028] Text feature extraction

[0029] Term frequency (TF): Calculate the frequency of occurrence of word i in document j

[0030] where, n ij is the number of times word i appears in document j, is the total number of times all words appear in document j;

[0031] Inverse document frequency (IDF): Measure the importance of word i in the entire document set

[0032] N is the total number of documents in the document collection, |j:n ij >0| is the number of documents containing the word i. If a word appears in many documents, its IDF value will be low, indicating that its discrimination is not high;

[0033] TF-IDF value: Calculate the TF-IDF value of the word i in the document j. TF-IDF ij = TF ij × IDF i ;

[0034] Calculate the similarity between the target user requirement vector D and the resource feature vector R, Judge the matching degree between the user requirement and the resource, and then recommend appropriate resources for the user.

[0035] Compared with the prior art, a teaching management system for course teaching provided by the present invention has the following beneficial effects:

[0036] For the teaching management system of the course teaching, by establishing a case discussion module and optimizing the case review and guidance tools, the teacher uploads the course cases through the teacher terminal, and the students discuss the cases through the student terminal to obtain the final discussion results; it can promote in-depth communication between students and between students and teachers, and in-depth expansion of case knowledge;

[0037] Establish an online debate management module. For controversial cases, the teacher initiates a debate topic for the students, and sets the debate session and debate form through the teacher terminal; it can enhance the students' sense of participation and interactivity;

[0038] Establish user portraits, collect the learning behavior data of students in the system, and based on data analysis algorithms, build detailed user portraits for each student to clarify their knowledge mastery, interest preferences and learning needs; it can accurately provide relevant data for different students, improve the utilization rate of resources, reduce the search time of students, and make the device more convenient to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is a schematic structural diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail with reference to the drawings.

[0042] See also Figure 1 , a course teaching management system, including the establishment of a case discussion module, the optimization of case comments and guidance tools, the teacher uploads the course case through the teacher end, and the students discuss the case through the student end to obtain the final discussion results;

[0043] Establish an online debate management module. For controversial cases, teachers can initiate debate topics to students, and the teacher can set the debate links and format.

[0044] Establish user portraits, collect students' learning behavior data in the system, and build detailed user portraits for each student based on data analysis algorithms to clarify their knowledge mastery, interest preferences, and learning needs.

[0045] S101. Establish a case discussion module, optimize case review and guidance tools, teachers upload course cases through the teacher end, students discuss the cases through the student end, and obtain the final discussion results, including:

[0046] Set up a group component function. By collecting the information of each student, including their different abilities such as research, copywriting, presentation and reporting, the group component function groups students with complementary strengths into a group according to the information.

[0047] Provide independent collaboration space for groups, including group document sharing, task assignment, real-time communication and other functions. Group leaders can assign various tasks of case analysis in the collaboration space, and group members can upload and share research materials and draft reports, and communicate and discuss through real-time chat tools.

[0048] In addition to case studies and debates, interactive activities such as role-playing and simulated press conferences are added. In role-playing activities, teachers set course theme scenarios, such as simulating characters in historical events. Students gain an in-depth understanding of historical events and characters' thoughts by playing different roles. In simulated press conferences, students are divided into groups to play the roles of spokespersons, reporters, etc., and ask and answer questions on hot topics of the course. The teacher provides real-time guidance and feedback on the final discussion results.

[0049] S102. Establish an online debate management module. For controversial cases, the teacher initiates a debate topic to the students, and the teacher sets the debate session and debate format, including:

[0050] Establish a comprehensive interactive effect evaluation index system, including student participation (number of speeches, length of time in discussion, etc.), contribution (novelty of ideas proposed, role in promoting discussion, etc.), teamwork ability (degree of cooperation with group members, etc.), and other dimensions;

[0051] Set reasonable weights for each evaluation index and adjust them dynamically according to different types of interactive activities. For example, in role-playing activities, more attention may be paid to students' role understanding and expressiveness, and the weights of relevant indicators may be appropriately increased.

[0052] During the interactive activities, the system provides students with real-time feedback on their performance. For example, immediately after a student speaks, a short evaluation on aspects such as the quality of the view and the clarity of expression is given. For instance, when a student puts forward a profound view during a discussion, the system prompts "Your view is very novel and helpful for the discussion."

[0053] Establish an interactive incentive mechanism to reward outstanding students or groups, such as point rewards, virtual badges, etc. These rewards can be used to exchange for learning resources, course score increases, etc.

[0054] S103. Build user profiles, collect students' learning behavior data in the system, and based on data analysis algorithms, construct detailed user profiles for each student to clarify their knowledge mastery, interest preferences, and learning needs, specifically including:

[0055] The data analysis algorithms include collaborative filtering algorithms and content-based recommendation algorithms;

[0056] Based on the collaborative filtering algorithm, by analyzing the resource usage of student groups with similar learning behaviors, recommend resources that the target students are interested in to them;

[0057] The content-based recommendation algorithm matches and recommends according to the characteristics of resources such as themes, knowledge points, and difficulty levels with the needs of students.

[0058] Based on the collaborative filtering algorithm, by analyzing the resource usage of student groups with similar learning behaviors, recommend resources that the target students are interested in to them, and also include:

[0059] Similarity calculation: Use the cosine similarity formula Calculate the similarity between students, where A=(a 1 ,a 2 ,…,a n ) and B=(b 1 ,b 2 ,…b n ) are the user profile vectors of students, and n is the dimension of the user profile vector.

[0060] Prediction scoring formula N(A) is the student group with a relatively high similarity to student A, sim(A,k) is the similarity between student A and student k, and r ki is the score of student k for resource i;

[0061] Item-based collaborative filtering

[0062] Formula for calculating the similarity between computing resources where R(i) is the set of students who have given feedback (such as browsing, liking, commenting, etc.) on resource i, R(j) is the set of students who have given feedback on resource j, and |R(i)∩R(j)| represents the number of students who have given feedback on both resource i and j

[0063] Formula for predicting the preference degree of students for similar resources where S(i) is the set of resources similar to resource i, sim(i,j) is the similarity between resource i and j, and r uj is the score given by student u to resource j

[0064] The content-based recommendation algorithm matches and recommends according to the characteristics of resources such as themes, knowledge points, and difficulty, and also includes

[0065] Text feature extraction

[0066] Term Frequency (TF): Calculate the frequency of occurrence of word i in document j

[0067] where, n ij is the number of times word i appears in document j is the total number of times all words appear in document j

[0068] Inverse Document Frequency (IDF): Measure the importance of word i in the entire document collection

[0069] N is the total number of documents in the document collection, |j:n ij >0| is the number of documents containing word i. If a word appears in many documents, its IDF value will be low, indicating that its discrimination is not high

[0070] TF-IDF value: Calculate the TF-IDF value of word i in document j, TF-IDF ij =TF ij ×IDF i ;

[0071] Calculate the similarity between the target user's demand vector D and the resource feature vector R Judge the matching degree between the user's demand and the resource, and then recommend appropriate resources for the user

[0072] Only certain exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A course teaching management system, characterized in that: This includes establishing a case discussion module, optimizing case comments and guidance tools, where the teacher uploads the course case through the teacher terminal, and the students discuss the case through the student terminal to obtain the final discussion results; Establish an online debate management module. For controversial cases, teachers can initiate debate topics to students, and the teacher can set the debate links and format. Establish user portraits, collect students' learning behavior data in the system, and build detailed user portraits for each student based on data analysis algorithms to clarify their knowledge mastery, interest preferences, and learning needs.

2. A course teaching management system according to claim 1, characterized in that: The case discussion module is established to optimize the case review and guidance tools. The teacher uploads the course case through the teacher terminal, and the students discuss the case through the student terminal to obtain the final discussion results, which specifically include: Set up a group component function. By collecting the information of each student, including their different abilities such as research, copywriting, presentation and reporting, the group component function groups students with complementary strengths into a group according to the information. Provide independent collaboration space for groups, including group document sharing, task assignment, real-time communication and other functions. Group leaders can assign various tasks of case analysis in the collaboration space, and group members can upload and share research materials and draft reports, and communicate and discuss through real-time chat tools. In addition to case studies and debates, interactive activities such as role-playing and simulated press conferences are added. In role-playing activities, teachers set course theme scenarios, such as simulating characters in historical events. Students gain an in-depth understanding of historical events and characters' thoughts by playing different roles. In simulated press conferences, students are divided into groups to play the roles of spokespersons, reporters, etc., and ask and answer questions on hot topics of the course. The teacher provides real-time guidance and feedback on the final discussion results.

3. A course teaching management system according to claim 1, characterized in that: The online debate management module is established. For controversial cases, the teacher initiates a debate topic to the students, and the teacher sets the debate links and forms, including: Establish a comprehensive interactive effect evaluation index system, including student participation (number of speeches, length of time in discussion, etc.), contribution (novelty of ideas proposed, role in promoting discussion, etc.), teamwork ability (degree of cooperation with group members, etc.), and other dimensions; Set reasonable weights for each evaluation indicator and adjust them dynamically according to different types of interactive activities. For example, in role-playing activities, more attention may be paid to students' role understanding and expressiveness, and the weight of indicators in this area may be appropriately increased; During the interactive activities, the system provides real-time feedback to students on their performance. For example, after a student speaks, the system immediately gives a brief evaluation on the quality of their ideas and the clarity of their expression. For example, when a student raises a profound idea in a discussion, the system prompts "Your idea is very novel and helpful to the discussion"; Establish an interactive incentive mechanism to reward outstanding students or groups, such as point rewards, virtual badges, etc. These rewards can be used to exchange for learning resources, course extra points, etc.

4. A course teaching management system according to claim 1, characterized in that: The user portrait is established to collect students' learning behavior data in the system. Based on the data analysis algorithm, a detailed user portrait is constructed for each student to clarify their knowledge mastery, interest preferences and learning needs, including: The data analysis algorithm includes a collaborative filtering algorithm and a content-based recommendation algorithm; Based on the collaborative filtering algorithm, by analyzing the resource usage of student groups with similar learning behaviors, the resources of interest to the target students are recommended; The content-based recommendation algorithm matches recommendations with students' needs based on the resource's theme, knowledge points, difficulty and other characteristics.

5. A course teaching management system according to claim 4, characterized in that: The collaborative filtering algorithm analyzes the resource usage of a group of students with similar learning behaviors and recommends resources of interest to target students, and also includes: Similarity calculation: Use cosine similarity formula Calculate the similarity between students, where A = (a1, a2, ..., a n ) and B=(b1,b2,…b n ) is the user portrait vector of the student, n is the dimension of the user portrait vector, Prediction scoring formula N(A) is the group of students with high similarity to student A, sim(A,k) is the similarity between student A and student k, r ki is the rating of student k on resource i; Item-based collaborative filtering, Formula for calculating similarity between resources R(i) is the set of students who have feedback (browsing, liking, commenting, etc.) on resource i, R(j) is the set of students who have feedback on resource j, and |R(i)∩R(j)| represents the number of students who have feedback on both resources i and j. Formula for predicting students' preference for similar resources Where S(i) is the set of resources similar to resource i, sim(i,j) is the similarity between resources i and j, and r uj is the rating of resource j by student u.

6. A course teaching management system according to claim 4, characterized in that: The content-based recommendation algorithm matches and recommends resources to students’ needs based on the resource’s theme, knowledge points, difficulty, and other characteristics, and also includes: Text feature extraction, Term frequency (TF): calculates the frequency of occurrence of word i in document j, Among them, n ij is the number of times word i appears in document j, is the total number of occurrences of all words in document j; Inverse Document Frequency (IDF): measures the importance of word i in the entire document collection. N is the total number of documents in the document collection, |j:n ij >0| is the number of documents containing word i. If a word appears in many documents, its IDF value will be low, indicating that its discrimination is not high; TF-IDF value: Calculate the TF-IDF value of word i in document j, TF-IDF ij =TF ij ×IDF i ; Calculate the similarity between the target user demand vector D and the resource feature vector R, Determine the degree of match between user needs and resources, and then recommend appropriate resources to users.

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