An application software-based learning integrated management system

Abstract

The application provides a kind of learning integrated management system based on application software, it is related to learning system technical field, including user integrated module, course mall module, online learning module, online supervision module and evaluation optimization module, the user integrated module is used for user registration and login, and record the interest direction of user to course and relevant learning progress, historical data;The application records the interest direction of user to course and relevant learning progress, historical data in use, as the basis for subsequent self-adjustment, provides diversified courses through course mall module, for student selection, provides online learning function through online learning module, and recommends relevant subsequent learning content according to learning progress, provides periodic ability test through evaluation optimization module, assesses user ability level, whereby dynamically adjusts the recommended course and practice of course mall module, online learning module, meets the individual needs of different users.

Description

Technical Field

[0001] This invention relates to the field of learning system technology, and in particular to a comprehensive learning management system based on application software. Background Technology

[0002] A learning system is a technology that combines hardware and software, encompassing various types, including but not limited to online learning systems and network learning systems. Online learning systems primarily record courses and exams taken by students online, enabling full tracking and management of students' learning progress. Network learning systems allow users to learn in virtual classrooms via the internet or mobile wireless networks, without being restricted by time or location.

[0003] With the development of intelligent products, many application systems have been integrated with mobile apps. Students access learning systems online through these apps. Existing learning systems generally include basic functions such as student management, course management, homework management, exam management, and grade management. However, these systems lack detailed tracking of students' learning progress and cannot provide personalized learning recommendations for courses and exercises. Furthermore, existing learning systems are generally targeted at specific subjects and lack a comprehensive and diversified curriculum, making it difficult to adapt to the diverse learning needs of students. Therefore, this invention proposes an application software-based comprehensive learning management system to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes an application-based learning management system. This system provides personalized learning recommendations through big data matching, catering to the needs of different users.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a learning comprehensive management system based on application software, including a user comprehensive module, a course mall module, an online learning module, an online supervision module, and an evaluation and optimization module. The user comprehensive module is used for users to register and log in, and to record users' interests in courses, as well as related learning progress and historical data. The course mall module is used to classify courses according to the fields of concentration, thinking ability, memory, observation, imagination, and creativity, to provide detailed introductions of each course, and to connect to a payment channel for users to purchase.

[0006] The online learning module provides video learning, interactive learning, and practice exercises, and recommends relevant follow-up learning content based on the learning progress. The online supervision module collects the user's expressions and actions during learning, and, in conjunction with a BP neural network algorithm, judges the user's current learning status and provides suggestions and opinions. The evaluation and optimization module provides regular ability tests to assess the user's ability level, generate learning reports, provide improvement suggestions, and dynamically adjusts the recommended courses and exercises in the course mall module and online learning module based on the user's current ability level.

[0007] Further improvements include: the user integration module, course mall module, online learning module, online supervision module, and evaluation optimization module are all built into the APP, and the APP is applied to mobile terminals, connected to cloud servers to provide data support, and the APP is based on Android and iOS architecture.

[0008] Further improvements are made in the following aspects: The user integration module includes a user center and a recording unit. The user center provides multiple registration methods, login to the system APP, and functions such as querying basic user information, changing passwords, managing payment information, and viewing course orders. The recording unit, based on the storage space provided by the cloud server, records and stores the user's interest in courses, learning progress, browsing history data, and learning achievements, and simultaneously provides keyword search and timestamp search functions.

[0009] Further improvements are made in the following ways: The course mall module includes a course classification module, a course details module, and a purchase and payment module. The course classification module categorizes courses according to the areas of focus, thinking ability, memory, observation, imagination, and creativity. Each category provides courses of different difficulty levels, including basic training, advanced improvement, high-level cultivation, and extreme challenges in that area.

[0010] Further improvements include: the course details module is built into each course, displaying a detailed introduction to the course, teacher introduction, course outline, preview chapters, user reviews, and a score based on historical sales data to measure the course's popularity; the purchase and payment module provides multiple payment methods for users to purchase courses.

[0011] Further improvements include: the online learning module includes a multimedia learning module, an interactive learning module, a practice module, and a real-time recommendation module. The multimedia learning module, based on the currently used mobile terminal, provides video courses and VR / AR courses included in the purchased courses and supports offline downloads. The interactive learning module is an online classroom, and it obtains microphone and camera permissions from the mobile terminal to provide online teaching by teachers who have purchased courses, real-time interactive Q&A, and online discussion and learning among students.

[0012] Further improvements include: the exercise module provides accompanying exercises for purchased courses, allowing for immediate assessment of learning outcomes, scoring of exercise results, and automatic collection of incorrect answers; the real-time recommendation module integrates with the multimedia learning module, interactive learning module, and exercise module, collects learning progress, uses deep learning algorithms to analyze scores and types of incorrect answers, and recommends relevant subsequent learning content based on these types.

[0013] Further improvements are made in the following aspects: The online supervision module includes a data acquisition module, a database, and an analysis and output module. The data acquisition module obtains microphone and camera permissions from the mobile terminal and collects the user's voice, learning demeanor, and body movements during the learning process. The database stores different body language parameters for normal, abnormal, and symptom-related learning states of users across multiple age groups, constructing various feature datasets. The analysis and output module divides the different feature datasets into training, validation, and test sets. The training set is used as input to the BP neural network, and the connection weights and thresholds are adjusted using the validation set and the actual output. After training is complete, the test set is provided to the trained neural network to further verify its correctness, resulting in a final prediction model. This final prediction model is used to predict the current user's voice, learning demeanor, and body movements, determining whether the user's learning state is normal or abnormal, and outputting a supervision report with suggestions and opinions.

[0014] Further improvements are made in the following aspects: The evaluation and optimization module includes a periodic testing module and a system self-adjustment module. The periodic testing module is used to provide periodic ability tests, including concentration tests, thinking ability tests, memory tests, observation tests, imagination tests, and creativity tests, to assess the user's current ability level, generate learning reports, analyze learning effectiveness, and propose improvement suggestions. The system self-adjustment module is used to formulate learning paths based on the user's current ability level, dynamically adjust the recommended courses and exercises in the course mall module and online learning module, and match the suitability of the courses.

[0015] A further improvement is that the user integration module also includes a social module, which is used to build learning communities. These communities are categorized according to course areas. Users can open social accounts based on their registered identities, join communities of interest, exchange learning experiences, and interact by answering questions about learning content.

[0016] The beneficial effects of this invention are as follows:

[0017] 1. This invention is based on an app. During use, it records the user's interest in courses, related learning progress, and historical data, serving as the basis for subsequent self-adjustment. The course store module provides diverse courses in areas such as focus, thinking, memory, observation, imagination, and creativity for students to choose from. The online learning module provides online learning functions and recommends relevant follow-up learning content based on learning progress. The assessment and optimization module provides regular ability tests to evaluate the user's ability level, thereby dynamically adjusting the recommended courses and exercises in the course store module and online learning module. In summary, big data matching provides personalized learning recommendations to suit the needs of different users.

[0018] 2. The course classification module of this invention classifies courses according to the fields of concentration, thinking ability, memory, observation, imagination and creativity. Each category provides courses of different difficulty levels, including basic training, advanced improvement, high-level cultivation and extreme challenges in that field. The diverse types of courses and multiple difficulty levels are adapted to the diverse learning needs of students.

[0019] 3. This invention provides multiple learning methods, such as video courses, VR / AR courses, online teaching by teachers, real-time interactive Q&A, and online discussions among students, to comprehensively improve students' abilities. It also uses deep learning algorithms to analyze grades and types of incorrect questions, and recommends relevant follow-up learning content based on these types to facilitate the filling of gaps in knowledge.

[0020] 4. This invention has an online monitoring module that collects the user's voice, learning expression and body movements, and compares them with different body language parameters of users of various ages in the database for normal learning status, abnormal learning status and disease learning status, to determine whether the user's learning status is normal or abnormal, which is convenient for screening for learning disabilities such as ADHD and autism in young users, and has diversified functions.

[0021] 5. This invention provides learning communities where users can join communities of interest, exchange learning experiences, and interact by answering questions about learning content, thereby increasing users' interest in learning. Attached Figure Description

[0022] Figure 1 This is a diagram illustrating the composition of the present invention. Detailed Implementation

[0023] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0024] Example 1

[0025] according to Figure 1As shown, this embodiment proposes a learning management system based on application software, including a user integration module, a course mall module, an online learning module, an online supervision module, and an evaluation and optimization module. The user integration module is used for users to register and log in, and to record users' interests in courses, as well as related learning progress and historical data. The course mall module is used to classify courses according to the fields of concentration, thinking ability, memory, observation, imagination, and creativity, to provide detailed introductions of each course, and to connect to a payment channel for users to purchase.

[0026] The online learning module provides video learning, interactive learning, and practice exercises, and recommends relevant follow-up learning content based on learning progress. The online monitoring module collects the user's expressions and actions during learning, and uses a BP neural network algorithm to determine the user's current learning status, providing suggestions and feedback. The evaluation and optimization module provides regular ability tests to assess the user's ability level, generate learning reports, provide improvement suggestions, and dynamically adjust the recommended courses and exercises in the course store module and online learning module based on the user's current ability level. During use, the system records the user's interest in courses, related learning progress, and historical data as a basis for subsequent self-adjustment. The course store module offers diverse courses in areas such as focus, thinking, memory, observation, imagination, and creativity for students to choose from. The online learning module provides online learning functions and recommends relevant follow-up learning content based on learning progress. The evaluation and optimization module provides regular ability tests to assess the user's ability level, thereby dynamically adjusting the recommended courses and exercises in the course store module and online learning module. In summary, big data matching provides personalized learning recommendations suitable for different users' needs.

[0027] The user integration module, course marketplace module, online learning module, online monitoring module, and assessment optimization module are all built into the app. The app is designed for mobile devices and connects to a cloud server for data support. The app is based on Android and iOS architectures and is compatible with Android and iOS tablets, smartphones, and other mobile devices.

[0028] The user integration module includes a user center and a recording unit. The user center provides multiple registration methods, login to the system APP, and functions such as querying basic user information, changing passwords, managing payment information, and viewing course orders. The recording unit, based on storage space provided by the cloud server, records and stores users' course interests, learning progress, browsing history data, and learning achievements. It also provides keyword search and timestamp search functions to facilitate users in querying learning progress, browsing history data, and learning achievements. Users' course interests are recorded implicitly by the system and are not available for user query.

[0029] The course marketplace module includes a course category module, a course details module, and a purchase and payment module. The course category module categorizes courses based on areas such as focus, thinking ability, memory, observation, imagination, and creativity. Each category offers courses of varying difficulty levels, from basic training and intermediate to advanced, and even extreme challenges. This diverse range of courses and difficulty levels caters to the diverse learning needs of students.

[0030] The course details module is built into each course, displaying a detailed introduction to the course, instructor information, course outline, preview chapters, and user reviews. It also measures the course's popularity based on historical sales data, providing a rating for each course. The purchase and payment module offers multiple payment methods for users to purchase the course. The system intuitively displays detailed course information, instructor information, course outline, preview chapters, and user reviews, and provides a rating system to help users make informed decisions, reducing the difficulty of selection for them.

[0031] The online learning module includes a multimedia learning module, an interactive learning module, a practice module, and a real-time recommendation module. The multimedia learning module, based on the currently used mobile terminal, provides video courses and VR / AR courses included in the purchased courses and supports offline downloads. The interactive learning module is an online classroom that obtains microphone and camera permissions on the mobile terminal, providing online lectures from teachers, real-time interactive Q&A, and online discussions among students. The practice module provides accompanying exercises for the purchased courses, instantly assessing learning outcomes, scoring exercises, and automatically collecting incorrect answers. The real-time recommendation module integrates with the multimedia learning module, interactive learning module, and practice module, collecting learning progress data, using deep learning algorithms to analyze scores and error types, and recommending relevant follow-up learning content based on these types. It offers multiple learning methods, including video courses, VR / AR courses, online lectures from teachers, real-time interactive Q&A, and online discussions among students. Some courses provide an immersive learning experience to enhance learning interest and effectiveness, comprehensively improving students' abilities. Deep learning algorithms are used to analyze scores and error types, recommending relevant follow-up learning content based on these types to facilitate filling in gaps in knowledge.

[0032] The online supervision module includes a data acquisition module, a database, and an analysis and output module. The data acquisition module obtains microphone and camera access from the mobile terminal to collect the user's voice, learning demeanor, and body movements during the learning process. The database stores different body language parameters for normal, abnormal, and symptom-related learning states across multiple age groups, constructing various feature datasets. The analysis and output module divides these feature datasets into training, validation, and test sets. The training set is used as input to the BP neural network, and the connection weights and thresholds are adjusted using the validation set and actual output. After training, the test set is provided to the trained neural network to further validate its correctness, resulting in a final prediction model. This model is then used to predict the user's voice, learning demeanor, and body movements, determining whether the user's learning state is normal or abnormal, and outputting a supervision report with suggestions and feedback. The use of the BP neural network includes the following steps:

[0033] Establish the prediction model function: Z = dot(w,x) + b

[0034] Where: x represents the input feature vector.

[0035] w represents the weight coefficient, which measures the importance of each input feature.

[0036] b represents the threshold, which affects the prediction result.

[0037] Z represents the prediction result;

[0038] Perform network state initialization, assigning random weight values ​​w to different feature vectors and thresholds of different groups. ij and threshold b i ; Calculate the outputs of each unit in the hidden and output layers based on the given input-output pattern, and activate them using the LReLU activation function, the expression of which is:

[0039] Calculate the new connection weight w ij and threshold b ij Select the next input mode until the network outputs an error θ. ij Once the set requirements are met; after the training and validation sets are completed, input the test samples into the network to verify the correctness of the network classification; repeat the above steps for different groups i to obtain the connection weights w for each group. ij Threshold b ij and error θ ij .

[0040] The evaluation and optimization module includes a periodic testing module and a system self-adjustment module. The periodic testing module provides regular ability tests, including focus, thinking, memory, observation, imagination, and creativity tests, to assess the user's current ability level, generate learning reports, analyze learning effectiveness, and provide improvement suggestions. The system self-adjustment module, based on the user's current ability level, devises a learning path and dynamically adjusts the recommended courses and exercises in the course marketplace and online learning modules to match course suitability. It provides regular ability tests, such as focus and logical thinking tests, to accurately assess the user's ability level. This diversified testing assessment reflects a more comprehensive understanding of the user's abilities, comprehensively covering multiple abilities. Based on the test results, it intelligently generates personalized learning paths, recommends the most suitable courses and exercises, and regularly generates learning progress reports to analyze learning effectiveness and provide improvement suggestions.

[0041] Example 2

[0042] according to Figure 1 As shown, this embodiment proposes a learning management system based on application software, including a user integration module, a course mall module, an online learning module, an online supervision module, and an evaluation and optimization module. The user integration module is used for users to register and log in, and to record users' interests in courses, as well as related learning progress and historical data. The course mall module is used to classify courses according to the fields of concentration, thinking ability, memory, observation, imagination, and creativity, to provide detailed introductions of each course, and to connect to a payment channel for users to purchase.

[0043] The online learning module provides video learning, interactive learning, and practice exercises, and recommends relevant follow-up learning content based on learning progress. The online monitoring module collects the user's expressions and actions during learning, and uses a BP neural network algorithm to determine the user's current learning status, providing suggestions and feedback. The evaluation and optimization module provides regular ability tests to assess the user's ability level, generate learning reports, provide improvement suggestions, and dynamically adjust the recommended courses and exercises in the course store module and online learning module based on the user's current ability level. During use, the system records the user's interest in courses, related learning progress, and historical data as a basis for subsequent self-adjustment. The course store module offers diverse courses in areas such as focus, thinking, memory, observation, imagination, and creativity for students to choose from. The online learning module provides online learning functions and recommends relevant follow-up learning content based on learning progress. The evaluation and optimization module provides regular ability tests to assess the user's ability level, thereby dynamically adjusting the recommended courses and exercises in the course store module and online learning module. In summary, big data matching provides personalized learning recommendations suitable for different users' needs.

[0044] The user integration module, course marketplace module, online learning module, online monitoring module, and assessment optimization module are all built into the app. The app is designed for mobile devices and connects to a cloud server for data support. The app is based on Android and iOS architectures and is compatible with Android and iOS tablets, smartphones, and other mobile devices.

[0045] The user module also includes a social module for building learning communities. These communities are categorized by course area. Users can open social accounts based on their registered identity, join communities of interest, exchange learning experiences, and interact by answering questions about learning content. This invention provides learning communities categorized by learning area. Students can join communities of interest to exchange learning experiences, share learning resources, and increase user learning interest. It supports adding friends, viewing friends' learning activities, mutual encouragement, and collaborative progress. A Q&A section is included where students can ask questions, and teachers or classmates can answer, fostering a supportive learning atmosphere.

[0046] This application-based learning management system, powered by an app, records users' course interests, learning progress, and historical data during use, serving as a basis for subsequent self-adjustment. The system offers diverse courses in areas such as focus, thinking, memory, observation, imagination, and creativity through a course store module. An online learning module provides online learning functionality and recommends relevant follow-up learning content based on learning progress. An assessment and optimization module provides regular ability tests to evaluate user skill levels, dynamically adjusting the recommended courses and exercises in the course store and online learning modules. In summary, big data matching provides personalized learning recommendations suitable for different users' needs. Furthermore, the course classification module categorizes courses according to the areas of focus, thinking, memory, observation, imagination, and creativity. Each category offers courses of varying difficulty levels, from basic training to advanced improvement, high-level cultivation, and extreme challenges, providing a diverse range of courses and difficulty levels to meet the diverse learning needs of students. Meanwhile, this invention offers multiple learning methods, including video courses, VR / AR courses, online teacher instruction, real-time interactive Q&A, and online discussions among students, comprehensively improving students' abilities. It also employs deep learning algorithms to analyze grades and error types, recommending relevant follow-up learning content based on these types to facilitate remediation. Furthermore, this invention includes an online monitoring module that collects the user's voice, facial expressions, and body language, comparing these with different body language parameters from a database of users of various ages exhibiting normal, abnormal, or pathological learning states. This helps determine whether the user's learning state is normal or abnormal, facilitating the screening of learning disabilities such as ADHD and autism in younger users, thus offering diverse functionalities. Finally, this invention provides learning communities where users can join groups of interest, exchange learning experiences, and interactively answer questions, increasing user engagement in learning.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A learning management system based on application software, comprising a user integration module, a course marketplace module, an online learning module, an online monitoring module, and an assessment and optimization module, characterized in that: The user integration module is used for user registration and login, and records users' interests in courses, as well as related learning progress and historical data. The course mall module is used to classify courses according to the fields of concentration, thinking ability, memory, observation, imagination and creativity, provide detailed information about each course, and connect to the payment channel for users to purchase. The online learning module provides video learning, interactive learning, and practice exercises, and recommends relevant follow-up learning content based on the learning progress. The online supervision module collects the user's expressions and actions during learning, and, in conjunction with a BP neural network algorithm, judges the user's current learning status and provides suggestions and opinions. The evaluation and optimization module provides regular ability tests to assess the user's ability level, generate learning reports, provide improvement suggestions, and dynamically adjusts the recommended courses and exercises in the course mall module and online learning module based on the user's current ability level.

2. The learning integrated management system based on application software according to claim 1, characterized in that: The user integration module, course mall module, online learning module, online supervision module, and evaluation optimization module are all built into the APP. The APP is applied to mobile terminals and connects to cloud servers to provide data support. The APP is based on Android and iOS architectures.

3. The learning integrated management system based on application software according to claim 2, characterized in that: The user integration module includes a user center and a recording unit. The user center provides multiple registration methods, login to the system APP, and functions such as querying basic user information, changing passwords, managing payment information, and viewing course orders. The recording unit, based on the storage space provided by the cloud server, records and stores the user's interest in courses, learning progress, browsing history data, and learning achievements, and simultaneously provides keyword search and timestamp search functions.

4. The learning integrated management system based on application software according to claim 1, characterized in that: The course mall module includes a course category module, a course details module, and a purchase and payment module. The course category module classifies courses according to the fields of focus, thinking ability, memory, observation, imagination, and creativity. Each category provides courses of different difficulty levels, including basic training, intermediate improvement, advanced cultivation, and extreme challenges in that field.

5. The learning integrated management system based on application software according to claim 4, characterized in that: The course details module is built into each course, displaying a detailed introduction to the course, teacher introduction, course outline, preview chapters, user reviews, and a score based on historical sales data to measure the course's popularity. The purchase and payment module provides multiple payment methods for users to purchase courses.

6. The learning integrated management system based on application software according to claim 1, characterized in that: The online learning module includes a multimedia learning module, an interactive learning module, a practice module, and a real-time recommendation module. The multimedia learning module, based on the currently used mobile terminal, provides video courses and VR / AR courses included in the purchased courses and supports offline downloads. The interactive learning module is an online classroom that obtains microphone and camera permissions from the mobile terminal, providing online teaching by teachers, real-time interactive Q&A, and online discussion and learning among students.

7. A learning integrated management system based on application software according to claim 6, characterized in that: The exercise module provides accompanying exercises for purchased courses, allowing for immediate assessment of learning outcomes, scoring of exercise results, and automatic collection of incorrect answers. The real-time recommendation module integrates with the multimedia learning module, interactive learning module, and exercise module, collects learning progress data, analyzes scores and error types using deep learning algorithms, and recommends relevant subsequent learning content based on these types.

8. The learning integrated management system based on application software according to claim 1, characterized in that: The online supervision module includes a data acquisition module, a database, and an analysis and output module. The data acquisition module obtains microphone and camera permissions from the mobile terminal and collects the user's voice, learning demeanor, and body movements during the learning process. The database stores different body language parameters for normal, abnormal, and symptom-related learning states of users across multiple age groups, constructing various feature datasets. The analysis and output module divides the different feature datasets into training, validation, and test sets. The training set is used as input to a backpropagation (BP) neural network, and the connection weights and thresholds are adjusted using the validation set and actual output. After training, the test set is provided to the trained neural network to further verify its correctness, resulting in a final prediction model. This final prediction model is used to predict the current user's voice, learning demeanor, and body movements, determining whether the user's learning state is normal or abnormal, and outputting a supervision report with suggestions and opinions.

9. A learning integrated management system based on application software according to claim 1, characterized in that: The evaluation and optimization module includes a periodic testing module and a system self-adjustment module. The periodic testing module provides regular ability tests, including concentration tests, thinking ability tests, memory tests, observation tests, imagination tests, and creativity tests, to assess the user's current ability level, generate learning reports, analyze learning effectiveness, and propose improvement suggestions. The system self-adjustment module is used to formulate learning paths based on the user's current ability level, dynamically adjust the recommended courses and exercises in the course mall module and online learning module, and match the suitability of the courses.

10. A learning integrated management system based on application software according to any one of claims 1-9, characterized in that: The user module also includes a social module, which is used to build learning communities. These communities are categorized according to course areas. Users can open social accounts based on their registered identity, join communities of interest, exchange learning experiences, and interact by answering questions about learning content.

Images