234 results about "Online course" patented technology

Online course and learning material management system and methods are provided where students, instructors, publishers, administrators, publishers, software vendors, and industry partners utilize the disclosed systems' databases and networking features to interact, to network, to improve learning outcome, and to exchange information elements. The system furthermore allows for networking among different user categories based on users' roles, professional profiles, and professional interests.

A system and method for developing a course requires a plurality of participants that collaborate over a network, for example, a social network. A database stores an initial framework that defines a sequence of learning units for creating a desired learning environment for students, those learning units made of specifically ordered content objects. The learning units are identified by corresponding learning unit identifiers. A plurality of workstations coupled to the network are used for entry of reviewer information by the participants using the learning environment created for the students. The reviewer information comprise one or more comments entered by one participant about a learning unit or content object and a rank entered by anther participant about the comment, with the rank being correlated with a defined ranking standard. A processor processes the rank according to a predefined criteria to produce a ranking result that is associated with a learning unit identifier. The ranking result is used for associating learning content to the learning unit identified by the learning unit identifier.

The present invention uses an algorithm which evaluates learners' short free-text answers when the answer has as few as 10 words. The answer key uses only one correct answer, allowing instructors to ask learners to produce short open-ended text responses to questions. The algorithm automates the scoring of free-text answers, enabling instructors to embed such questions in online courses, and providing nearly immediate scoring and feedback on learners' responses. The algorithm is based on the semantic relatedness of the words in the learners' answer to the single correct answer. The semantic relatedness algorithm requires a dedicated domain specific index or collection of topic-focused documents (a corpus), which is created by an automated crawl mechanism that collects documents based upon descriptive domain keywords.

The invention relates to a method of soft detecting overflowing particle size index of mine-milling loop spiral grader, composed of ball mill and spiral grader and used to mill ore in mine milling working stage of preparation plant, adopting the online course data provided by routine distributed computer control system and routine detecting instrument and combining with time variable to reflect the change of mine milling medium, by a little artificial sampling, building a grader overflowing particle size soft-measuring model composed of neural network, realizing the soft measurement of grader overflowing particle size of mine milling system. It reduces the cost and maintenance working load; reduces the indeterminacy of artificial operation and enhance the time effectiveness of measurement, and at the same time helpful to realize the optimized control and operation of mine milling course.

The invention discloses a student modeling and personalized course recommendation method in an online learning system, and belongs to the education data mining field. According to the method, cognitive level modeling and personalized course recommendation of students are mainly studied, firstly, knowledge mastering states of the students are judged based on a cognitive diagnosis model, learning behaviors of the students are analyzed through data on a system platform, and then the cognitive abilities of the students are modeled by integrating course mastering conditions; secondly, an online course is modeled ; and finally, the features of the online course are fused according to the cognitive level of the student to perform personalized recommendation. According to the invention, personalized recommendation is carried out based on the cognitive level of students and in combination with the feature indexes of the online courses, so that the user can be helped to carry out more accurate personalized course recommendation, and the online course recommendation is more interpretable and acceptable.

The present invention is generally directed to a variety of tools, programs and methods for presenting or enhancing an online learning environment for a student viewing or participating in an online learning session. One embodiment is directed to a method of matching instructors and students by utilizing commonalities (or lack thereof) with a social media site and by having the student take a personality test to determine how the student learns most efficiently. Another embodiment is directed to a method of certifying instructors that want to teach an online course. And yet another embodiment is directed to a method presenting an online learning session to students using and toggling between varieties of viewing formats.

The invention discloses an online learning monitoring feedback system, which comprises a database server, an application server, a computer terminal and a control console. The data server, the application server and the computer terminal are respectively connected with the control console. The data server is interconnected with the application server. The database server is provided with a data management module for managing a learner online learning behavior information base, a learner information base and a course knowledge point structure information base. The application server is provided with a learning behavior record module, an information statistical analysis module, and a learning decision support module. The computer terminal is provided with a subsidiary effective learning feedback information module, a course design feedback information module and a learning content design feedback information module. By adopting the system, not only the time utilization ratio of learners and the learning efficiency are improved, but also scientific proposals and scientific testing methods are provided for designing and improving online courses for online course designers.

The invention discloses an online course applicability evaluation method which belongs to the technical field of intelligent recommendation. Based on the analysis of the personal learning characteristics of a learner and online course teaching policies, the method establishes a correlation model for the personal characteristics and the learning effect of the learner by statistically calculating the personal learning characteristics and the online course learning effect of a large numbers of learners. The correlation model is used to predict the learning effect of the learner in a course to be selected. According to prediction results, the learner is helped to select the most suitable course for learning, namely a course with the best prediction result is selected for learning. According to the method, the learner can easily find the course suitable for the personal characteristics from a large number of course resources, so that the learning effect is improved; and in addition, the method can help the learner to better understand the details of the selected course in the aspect of teaching style and the like, so that the weaknesses of the learner can be effectively overcome in learning.

Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback. Using developed techniques, it is possible to administer courses and automatically grade submitted work that takes the form of media encodings of artistic expression, computer programming and even signal processing to be applied to media content.

The invention provides an online course management method and system, wherein the online course management method comprises the following steps: when receiving an instruction of learning a random course, obtaining the total learning time of each learning resource in a learning resource packet corresponding to said course; counting the learning time of each learning resource cost by a user in real time; calculating the learning progress of each learning resource according to the total learning time of each learning resource and the learning time of each learning resource; and showing the learning progress of each learning resource to the user. By means of the technical scheme provided by the invention, the user can directly know the learning progress of each learning resource in the learning resource packet; therefore, the user can reasonably manage self learning time; the online learning efficiency of the user is increased; and the learning experience of the user is improved.

Methods and systems are disclosed for providing learning as a service in a learning network. At least certain embodiments connect users with providers of learning content using an online learning marketplace in a web-based user interface that allows users to control content and pace of learning. The learning can be self-directed, through self-paced online courses, as well as live online courses. The platform is configured to fulfill user requests for learning content without regard to location of the content or time of access. The learning content can be decomposed into monetized components, which can be accessed by users on a per-use or per-session basis.

The invention belongs to the technical field of teaching quality evaluation and application, and discloses an online course video resource content identification and evaluation method and an intelligent system. The method comprises the steps of obtaining a target online course video, and obtaining video stream information and audio stream information from a video file; processing the video streaminformation to obtain shot extraction and image texts, and performing audio fragment segmentation and voice recognition on the audio stream information to obtain voice texts; carrying out knowledge point identification on the content of the video resource according to the image text and the voice text; and comparing the video knowledge points with the course outline, and evaluating and auditing the teaching content. According to the invention, quality evaluation and auditing can be carried out on the online course teaching video from multiple angles, the evaluation result is based on the image, voice and specific course content of the video, the teaching content of the online course teaching video can be comprehensively analyzed and evaluated, and the invention has great reference value and practical significance for an online course platform and a producer.

The present invention is a system and method for maintaining the integrity of data during the offline mode of an e-learning process. The invention maintains the integrity of online course material in a learning management system by determining when a student attempts to access online course material and to downloaded the material from a learning management server to a student server for offline utilization resulting in an offline access mode. The invention then provides a set of procedures for monitoring the course material when it has been accessed. The set of procedures may determine that the course material is in an offline access mode and prevent the student from accessing the course object from the learning management server while the course object is in the offline access mode. Further, the system and method of the present invention may also serve to synchronize the course material between the learning management server and the student server by copying the course material from one server to another. In one embodiment, the system and method of the present invention can prevent a student from uploading the course material to the learning management server unless the student has completed the one or more progress objectives associated with the course object. Finally, the set of procedures can provide a security mechanism to insure the security of the online course material when it is in the offline access mode.

For carrying out structural sorting on the online education courses and test resources, marking is carried out through the knowledge point-based tag system, and then based on the location and the relationship of the knowledge points in the knowledge map, structural description of the courses and test resources is formed. The behavior data of a student in browsing, retrieving and learning online courses as well as conducting various online tests is collected, filtering, cleaning, sorting, associating and recalculation are carried out according to a certain method, and an effective behavior description log of the individual student learning process is established. Based on the structural description of resources and the behavior log description of the individual student, behavioral marking, performance quantification and ability rating are carried out on the student from the perspective of knowledge points, and a preliminary ability quantification model is established.

For courses that deal with media content, such as sound, music, photographic images, hand sketches, video, conventional techniques for automatically evaluating and grading assignments are generally ill-suited to direct evaluation of coursework submitted in media-rich form. Likewise, for courses whose subject includes programming, signal processing or other functionally-expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. Instead, it has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, submissions using feature extraction and machine learning techniques. In this way, e.g., in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Likewise, large collections of coursework submissions (whether or not graded or scored) or media content more generally, may be efficiently browsed and grouped using techniques described herein.

A system and method for creating and offering online courses and classes provides a computer program accessible over a computer network. The program includes a template for creating a course by assigning predefined attributes and/or parameters, and by adding lessons, activities, and/or material in conformance with predetermined rules. A selected course is converted into a class for offering over the system which may be archived or deleted after the term. New classes may subsequently be made from the original course. Included are translation and/or conversion functions, wherein inputs and outputs may be translated between human languages and/or converted between different analog and digital forms, and a payment system wherein fees may be charged for access to the system, a course, and/or a class. Courses and course content may be saved and shared with other users having valid access parameters.

The invention discloses a network crawler data mining-based online course teaching quality assessment management system. The system comprises a crawler collection unit, a data resource pool, an indexextraction and text analysis unit, an assessment system and an application system; online platform interaction information is collected by utilizing a crawler technology; multisource data is subjectedto data integration and data auditing and cleaning to form the data resource pool; dimension indexes with business values and statistical meanings are assessed and screened; and upper dimensions areintegrated and extracted based on the processed data to form a complete assessment system from top to bottom, and an assessment result is subjected to display output and scene application in a systemmode. Course construction data, course browsing data, teaching research activity data, teacher-student interaction data, student learning behavior data and student learning outcome data are fully fused; the course teaching quality is subjected to scientific assessment and efficient output display in the system mode; quantitative quality assessment and advantage disadvantage analysis of various online courses are effectively finished; and improvement directions are provided directly for various courses.

The present invention discloses a user portrait construction system and a method thereof. The user portrait construction system includes forming a corresponding theme distribution matrix of documentsby acquiring keywords of description documents, clustering description documents based on the theme distribution matrix of documents, forming a binomial tree with courses as leaf nodes and clusteringkeywords as father nodes in each layer by using a tree map formation module which can form a tree map reflecting the theme relevance between the courses based on the theme discovery, and further obtaining the user portrait including a plurality of clustering keywords as labels according to the courses purchased by users. The user portrait can effectively reflect the needs of users for online courses, so that users can understand what courses in online education system meet their needs and operators can carry out promotion to target users.

The invention belongs to the field of smart cities and can be applied to the technical field of smart education. The invention provides an online course recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the identity features of a current user, wherein the identity features of the current user comprise but not to be limited tobasic information and feature information; searching a historical user which is the same as or similar to the basic information and/or the feature information of the current user; and searching a prediction course corresponding to the identity characteristics of the historical user through a preset course prediction model, determining the prediction course as a prediction course result, and recommending the prediction course result to the current user. Relational learning of user identity features and predicted courses is achieved through the learning ability of artificial intelligence, and the accuracy of course recommendation is achieved. The problem of cold start of a new user is solved by utilizing the pre-constructed user index tree, and the experience satisfaction of the user is improved according to the analysis of the real-time user behavior data.