207 results about "Personalized learning" patented technology

The invention discloses an interactive learning system and interactive method based on an education service robot. The interactive learning system mainly comprises a core processor module, a voice processing module, a voice acquisition module, a voice playing module, an image processing module, an image acquisition module, a projection processing module, a motion control module, an environment sensing module, a motor driving module and a network communication module. The interactive method mainly comprises steps that the system acquires user faces and desktop image information in real time; the system records learning conditions of users in real time; and a network server end adopts a big data analysis method to establish a set of personalized learning guiding scheme for users and displays the personalized learning guiding scheme through voice responses, projection responses, rotation of the head of a robot, front, back, left and right movement of the robot, turning on of a body indication lamp of the robot, and the like when a user asks a question or interacts with the system. The beneficial effects of the invention are that the personalized learning guiding scheme can be provided for the user, so the user can easily grasp and improve learnt knowledge by using a proper method and self-learning and independent thinking abilities of the user can be exercised.

The invention provides an internet based method for recommending media courseware, which includes: obtaining courseware applicable attribute information by using a system terminal to analyze courseware attributes or courseware learning behavior data of users; determining preference information of the users by using the system terminal to analyze personal basic learning attribute information of the users or the courseware learning behavior data of the users; searching courseware lists suitable for the uses to learn according to the courseware applicable attribute information and the preference information of the users; and using a client to arrange and display the courseware lists searched from the system terminal according to the match degrees of the courseware lists suitable for the users to learn. The invention further provides an internet based system for recommending media courseware. By the aid of the internet based method and system for recommending media courseware, requirements of users on individual study can be met, precision and accuracy in recommending of suitable courseware to the users can be effectively improved, and the users can conveniently find the courseware suitable for learning.

The invention discloses an online learning cognitive map generation system and method for representing the knowledge learning mastering state of a leaner in a specific field. The system comprises an intelligent terminal and a server. The intelligent terminal is used for online learning interaction. The online learning cognitive map generation system runs in the server comprising a knowledge graph construction module, a learning interaction data acquisition module, a knowledge state calculation and prediction module and a visual output module. The online learning cognitive map generation system and method are suitable for a common online learning platform and can simulate the cognitive structure of the learner and diagnose the learning defect of the leaner to provide personalized learning resources and social network services for the learner, so that learning confusion is reduced, cognitive loads are reduced, and learning pertinency and accuracy are improved.

The invention discloses a personalized network learning resource recommendation method which comprises the following steps: analyzing behavioral data of learners for visiting a network leaning system based on expanded theme pictures, acquiring leaning interest path changing patterns of the learners and groups of the learners on concepts and knowledge elements related to learning contents, and then realizing active personalized recommendation of proper learning resources to the learners according learning interest path changing patterns of the learner individuals and the groups of the learners individuals, sequence of learning objects of the expanded theme pictures and the like. The personalized network learning resource recommendation method can provide personalized learning service for network learners, thereby improving learning efficiency of the network learners.

A mechanism for facilitating personalized learning. A computer receives graded response data including grades that have been assigned to answers provided by learners in response to a set of questions. Output data is computed based on the graded response data using a latent factor model. The output data includes at least: an association matrix that defines a set of K concepts implicit in the set of questions, wherein K is smaller than the number of questions, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the questions; and a learner knowledge matrix including, for each learner and each of the K concepts, an extent of the learner's knowledge of the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the learner knowledge matrix.

A computer-implemented method for automatically generating learning exercises, including determining a target learning item in response to an event, obtaining a knowledge level of a learner in relation to the target learning item based on a model of the learner as produced by an automated learner model, associating a level of difficulty with the obtained knowledge level of the learner, retrieving a learning exercise pattern from an exercise pattern database, automatically generating a learning exercise relating to the retrieved learning exercise pattern based on the model of the learner and the associated level of difficulty; and presenting the learning exercise to the learner via an exercise interface.

Assessment devices, and teaching methods involving the use of the assessment devices, are disclosed. The assessment devices include iterative homework, quizzes, and/or tests, each of which allows for individual students to answer an initial question, and, based on the answer to that question, the next question will be harder or easier. The assessment devices can be administered to the students in print form, or electronically, such as on a computer or a personal digital assistant. Once the data is collated, students can be screened based on their ability to grasp all or a portion of the questions in a given test, and separated into groups based on their understanding of the subject matter. Teachers can then individually teach the different groups of students, ideally using lesson plans designed to work in tandem with the assessment devices, based on the students' grasp of the material.

The invention discloses a classroom interaction teaching system based on cloud service. The classroom interaction teaching system based on the cloud service comprises a cloud service unit and a classroom teaching interaction unit, wherein the cloud service unit is connected with the classroom teaching interaction unit by Internet. The cloud service unit is composed of a cloud service login unit, a teaching resource management unit, a space unit and a unified resource port service unit, and the classroom teaching interaction unit comprises a cloud service resource introduction unit, an interaction teaching unit and a teaching feedback unit. By the mode, the classroom interaction teaching system based on the cloud service can facilitate good teaching resources shared by teachers, achieves unified management of the teaching resources, generates personalized teaching contents, and conducts high-efficient teaching on a classroom by the classroom interaction system in an on-line or off-line mode, collects classroom learning data by the cloud service unit, facilitates viewing and analysis of the teachers, students and parents on classroom interaction data, provides help for personalized learning of the students, and can effectively improve learning effect.

The invention relates to a programming-language-course-oriented personalized learning effect analysis system and method. The system comprises a student cognitive ability assessment module, a practical ability assessment module and a feedback module. The method, based on programming-language-course-oriented general homework and program homework answering behaviors of students, assesses cognitive ability and practical ability of the students by utilizing a mathematical model training learning method, processes the cognitive ability and practical ability data through a data statistical method and feeds learning effect information to the students to realize personalized display. The system and method can judge cognition and practice two-way learning effects of the students, not only can give fair scores to the students more accurately, but also can instruct teachers to find knowledge weak parts of the students and to feed back the knowledge weak parts to the students in a personalized manner in time, thereby realizing a programming-language-course-oriented knowledge point granularity microscopic learning effect and learning trajectory establishment.

The invention discloses an automatic generation method and system based on personalized learning process of mapping knowledge domains. The method includes: on the basis of personalized mapping knowledge domains described by a directed hypergraph, utilizing a relation between knowledge elements and learning activities, and automatically generating corresponding learning process frames according to personalized characteristics like learning performance targets, learning capability, learning habits and learning modes of learners; on the frames, automatically generating all possible supporting resources of activities according to characteristics of the learners, the knowledge elements learned by the activities and activity type; utilizing relation among learning resources, the learners and learning activity attributes, relation between the activity attributes and process attributes and the personalized objects of the learners for optimization of process structure and learning resources to generate optimized learning process. By the method, huge personalized learning process can be generated automatically; the method is used for optimizing the learning process structure and the learning resources according to specific conditions of the learners, and learning is analyzed and optimized globally. In addition, the automatic generation method and system is a foundation for formulating further personalized learning schemes.

The invention provides a learning resource push method based on learning preferences of users. The method includes the following steps: firstly, acquiring the learning preferences of different users,and establishing a user attribute library according to the different learning preferences; and then classifying various types of learning resources according to different attributes, and establishinga digital resource attribute library; and finally, establishing a recommendation model according to a corresponding relationship between the user attribute library and the digital resource attribute library, wherein the recommendation model automatically selects digital resources of the corresponding attribute to perform personalized learning resource pushing according to different user attributes. The invention also provides a learning resource push system based on the learning preferences of the users. According to the scheme of the invention, by adopting the learning resource push method based on the learning preferences of the users, the needs of learners for personalized learning can be met, the learning resources can be pushed rapidly and accurately in combination with learning activities of the learners, and the effect of resource recommendation services can be effectively improved. The scheme of the invention has relatively good application and promotion values, and also has relatively good market prospects and social benefits.

Employers or recruiters populate an online database with job postings. The concepts that job applicants need to have learned to be successful applicants for a job are stated or inferred from the job posting and optionally resumes of others who have held that job. A student's own learning activities are logged by an online education platform. From a comparison between the student's completed learning units and a job posting's required learning units, a personalized learning unit gap can be identified for a student. The online education platform can then recommend how the student can fill the gap by undertaking the study of learning units on the education platform.

A construction method of a personalized learning feature model based on a knowledge graph comprises the steps: constructing a course-oriented knowledge graph, wherein entities correspond to knowledgepoints and learning resources; describing a relationship between knowledge point entities by adopting a directed acyclic graph; describing and learning a relationship between resource entities by utilizing a Bloom cognitive hierarchy; constructing a student personalized cognitive model, and adopting transfer learning to solve the problem of scarcity of personalized information of a single student;adopting active learning to solve the value evaluation problem of the selected data; and constructing a dynamically evolved student personalized cognitive model by utilizing a Markov chain theory. The invention discloses a construction method of a personalized learning feature model based on a knowledge graph. By comprehensively utilizing the knowledge graph, the graph theory, the Markov chain, the Bloom cognitive hierarchy theory and other methods, the personalized learning characteristics of the students are effectively positioned, the accuracy of positioning the personalized characteristics of the students is improved, and therefore quantitative decision support is provided for improving the learning efficiency and the teaching effect.

The invention discloses a personalized learning path optimization method based on an improved particle swarm optimization algorithm, comprising the following steps: (1) building a mathematical model of a learning path optimization problem; (2) performing learning path optimization based on an improved particle swarm optimization algorithm; and (3) analyzing the time complexity. According to the invention, by improving a standard particle swarm optimization algorithm, the defect that the standard particle swarm optimization algorithm is easily trapped into local optimum when solving an optimization problem is solved, and the method is advantaged in accuracy and success rate of search.

The present invention proposes a network learning space Renrentong platform and its method, including: an identity identification module, a Renren space module, a personalized learning resource module, an air classroom module, an interactive question answering module, and an interactive evaluation module. The teaching resources of the network learning space everyone-communication platform of the present invention can be seamlessly transmitted between different subjects, with both common content and common ways; resources can be actively transmitted and shared among different visitors ; More emphasis on individuality, with the characteristics of emphasizing the spatial content of different subjects to adapt to the subject.

The invention discloses a method for optimizing personalized learning resources based on integrated modeling. The method is modeled by multi-view integrated learning based on knowledge points and centered on the process, recommends a corresponding learning process according to a learning objective, and establishes a dynamic mapping among a learner, the learning process and the resource environment according to the actual background knowledge of the learner so as to obtain the hypergraph-described learning process and possible service resource states for the learner; on this basis, the method performs structural transformation on a process model, extracts a learning activity sequence (LAS), utilizes the relationship among the learner, the learning resources and the learning process attributes, and optimizes the learning process so as to obtain a learning plan with optimized process and resources. The method is capable of providing a relatively optimized learning sequence and a combined service resource according to the objective, the knowledge background and the resource environment of the learner, and performing dynamic adjustment according to the change of resources and objective.

The invention provides a personalized learning resource recommendation method based on learner preference modeling. The method is characterized in that related learning log files of learners are obtained from an online learning platform; data such as historical course registration records, corresponding course scores and course related attributes of learners are taken as input data; historical course preferences of learners can be better obtained by embedding an attention mechanism; the coding input is used as the coding input of an automatic coder neural network, then a course knowledge graphis established to obtain a course pre-determined knowledge relationship, decoding is performed according to the correlation between courses, the probability of learning a target course by a learner is finally calculated, and a target recommendation list of the learner is generated according to the probability from large to small, so the individuation and accuracy of a recommendation result is improved.

The invention provides a learning plan recommendation method and a mobile terminal. The method includes the steps of when a preset user operation is detected, acquiring target learning materials to belearned by a target user that logs in a learning type application program and a learning goal of learning the target learning materials input by the target user; determining a pre-learning knowledgereserve of the target user to the target learning materials through a test; generating personal learning characteristics of the target user according to pre-stored historical learning information of the target user; and determining a target learning plan suitable for the target user to learn the target learning materials according to the pre-learning knowledge reserve, the personal learning characteristics and the learning goal, and recommending the target learning plan to the target user. According to the invention, a targeted learning plan is recommended to the user by combining the learningcharacteristics, knowledge reserve and learning goal of the user, the learning efficiency of the user can be effectively improved, the personalized learning needs of the user are met, and the user experience is enhanced.

The invention discloses a personalized learning recommendation method based on deep reinforcement learning. The method comprises the steps of: defining knowledge points and difficulty attributes of questions, and constructing a knowledge point network graph according to relationships among the knowledge points; determining relationships among the questions under the knowledge points according to the relationships among the knowledge points, and constructing a question network graph; obtaining a sub-graph for a current state of a specified user in the question network graph according to user behavior data to use the same as a learning boundary; and then using a deep reinforcement learning algorithm and user history records for modeling, and obtaining, by training, a strategy of how to select a cut set in the sub-graph under the current state of the user. According to the method of the invention, a best question can be intelligently recommended for the user, user learning time can be saved, learning efficiency thereof can be enabled to be improved, and learning experience can be improved.

A system is disclosed that provides a goal based learning system utilizing a rule based expert training system to provide a cognitive educational experience. The system provides the user with a simulated environment that presents a business opportunity to understand and solve optimally. Mistakes are noted and remedial educational material presented dynamically to build the necessary skills that a user requires for success in the business endeavor. The system utilizes an artificial intelligence engine driving individualized and dynamic feedback with synchronized video and graphics used to simulate real-world environment and interactions. Multiple “correct” answers are integrated into the learning system to allow individualized learning experiences in which navigation through the system is at a pace controlled by the learner. A robust business model provides support for realistic activities and allows a user to experience real world consequences for their actions and decisions and a tutorial system analyzes student inputs to determine appropriate feedback to teach new skills.

The invention provides a personalized learning resource recommendation system and method under an E-learning platform.The system mainly comprises a management module mainly used for managing user information and learning resources, a recommendation strategy module used for recommending the learning resources for users logging into a learning platform, and a database module used for storing various required basic data including information tables.The method includes the steps that the users log into a learning system to learn and grade the learning resources; the learning resources which the users may be interested in are recommended for the users according to the optimized collaborative filtering recommendation algorithm.The optimized method solves the problem that in a traditional collaborative filtering algorithm, because of grade matrix collaborative filtering, the similarity calculation is not accurate.In addition, user interest drifting is considered, time factors are introduced, and finally the personalized learning resource recommendation efficiency and accuracy are improved.

The invention provides an intelligent dynamic grouping learning system based on student personalized learning. The intelligent dynamic grouping learning system comprises a cloud server, a teacher terminal and a student terminal, wherein the cloud server comprises a cloud server database and cloud server software, the cloud server database stores various forms of teaching resources, and the cloud server software is used for processing information interaction logic and providing various application function interfaces; the teacher terminal and the student terminal organize classroom teaching through interaction with the cloud server; a teacher user can carry out grouping teaching on student users in a real-time classroom module through the intelligent dynamic grouping system of the teacher terminal, the grouping system carries out intelligent matching in combination with questions issued by teachers by collecting parameters such as learning habits of students, cooperation with students and reaction to different types of questions, the students are automatically grouped, and meanwhile the teachers and the students can be manually adjusted on the basis of automatic grouping. The grouping teaching has the advantages that grouping teaching is fast and personalized, and meanwhile initiative of teachers is also kept.

The invention discloses a classroom data analysis method and a system. The method comprises the following steps: obtaining classroom data in the classroom, wherein the classroom data comprises face data, student behavior data, teacher behavior data and teacher voice data; analyzing the classroom according to the classroom data. The invention adds a data acquisition mode, enriches the data types, supports classroom behavior analysis, face recognition, speech recognition, etc., and carries out trend analysis and score/behavior prediction on the data of the class and the students, and carries outpersonalized recommendation according to the trend analysis and the score/behavior prediction of the class and the students. Through a variety of sources of classroom data collection, the method andsystem can carry out accurate analysis and detailed evaluation of the classroom, can help managers to make effective decisions, teachers precision teaching, students personalized learning.

The invention discloses a keyword extraction method for an MOOC (Massive Open Online Course). The method comprises the steps of firstly converting a voice of an MOOC video file into a text; performingChinese word segmentation, part-of-speech tagging addition and semantic tagging on the text; according to word positions, part-of-speech and semantic features of words, and in combination with presetweight setting, calculating first comprehensive weights of the words in the text; in combination with a graph model built by a TextRank algorithm, calculating second comprehensive weights of the words; and finally according to the weights from high to low, iteratively outputting X keywords, thereby finishing keyword extraction. By adopting the method, the keyword extraction accuracy can be improved and the personalized learning demands of the MOOC can be met.