8 results about "E-assessment" patented technology

The application discloses a kind of mobile equipment online examination methods, comprising: the behavior information of triggering at least two events in first event, second event, third event;According to two behavior information, output warning information;Examination program triggers pause event, and examination program is switched to background suspension operation;Heartbeat packet is sent to server;According to whether success of heartbeat packet reception, determine the data submission state of examination program;The data indicated by the input operation of user is sent to server and is stored in local answer database with encryption;When current examination program state is submission failure, supplement in local answer database is identified;Login current examination program, and the data in local answer database is uploaded according to version number.The application not only realizes full coverage monitoring to online examination cheating behavior, but also improves recovery success rate in abnormal submission scene.The application also discloses a system, electronic equipment and storage medium for implementing the above method.

This application relates to an automatic grading method for flowchart-based operation questions and an online examination system. The method includes: acquiring examinee answer data and question answer data; constructing a first matrix based on the examinee answer data and a second matrix based on the question answer data; comparing the first and second matrices, and determining the examinee's score based on the comparison result. This method enables automatic grading of flowchart-based operation questions, solving the problem in related technologies where automatic grading of flowchart-based operation questions is not possible, greatly improving grading efficiency and reducing labor costs.

Disclosed herein are systems and methods for proctoring online examinations by determining a gaze of a user. The method includes: performing a calibration process to calibrate a field of view of a user taking an online examination on a computer; after the calibration process is completed, obtaining a gaze origin and a gaze vector of the user using a prepared neural network to obtain facial key points of the user and determining, by a webcam or camera, the gaze of the user in three-dimensional (3D) coordinates to determine whether the gaze vector of the user is within a defined boundary relative to the gaze origin; and based on a determination that the gaze vector of the user is outside the defined boundary for a period of time, transmitting a message that the gaze of the user is outside of the defined boundary.

Disclosed herein are systems and methods for a machine-learning based method for proctoring online examinations. In one aspect, an exemplary method includes monitoring a user taking an online examination. The method also includes detecting at least one suspicious cheating event from the user from a captured video stream or time-series telemetry data. The method further includes, in response to detecting a plurality of suspicious cheating events from the user, analyzing the detected suspicious cheating events using a trained AI proctoring model. The trained AI proctoring model is configured to: recognize a cheating pattern based on the detected plurality of suspicious cheating events, classify the cheating events based on the detected cheating pattern, and calculate a cheating risk score based on the recognized cheating patterns. The method further includes notifying a proctor of suspicious cheating by the user based on the calculated cheating risk score exceeding a predetermined threshold.

Aspects of the present disclosure include a method for verifying live user presence in anonline session, comprising obtaining at least one video stream of a user captured during at least one previous presentation of at least one user interface (UI) on a display, obtaining input events representing previous user interactions with the UI, generating a behavioral digital fingerprint profile based on the video stream and the input events, initiating an online exam during the online session by providing, for presentation on the display, at least one additional UI, receiving at least one additional video stream of the user during the exam, receiving additional input events representing user interactions with the additional UI, determining exam-specific behavioral features based on the additional video stream and the additional input events, and verifying whether the user is taking the exam based on at least one of the features or the profile.

This invention discloses an online education system and method based on dynamic constraint verification and a multimodal anti-cheating mechanism. A dynamic learning hour constraint rule engine is introduced into the dynamic learning hour constraint and course recommendation module to complete user course training. In the examination control module, a homogeneous heterogeneous recombination algorithm is used to generate examination papers, while a multimodal behavior circuit breaker mechanism is combined to address cheating and violations in online examinations. Multi-level buffering is used to batch store examination paper data into the database, and a state machine compensation mechanism ensures the validity of examination payments. This invention improves the training efficiency of continuing education, solves the problems of fairness and lack of process monitoring in online examinations, effectively solves the database congestion problem and data loss problem in payment callbacks under the scenario of thousands of concurrent examinations, and significantly improves the robustness of the system.

This application discloses a comprehensive online learning management method, apparatus, electronic device, and storage medium. The method determines the course category for online learning in response to a first operation request from a user, and allocates the corresponding course content to the user's terminal for online learning. In response to a second operation request from the user to take an online exam of the course content, the method allocates the corresponding exam content to the user's terminal for online exams. Simultaneously, it acquires the user's online learning information and exam information, and evaluates the user's online learning based on these information to determine the learning effectiveness. This achieves unified management and quantification of the effectiveness of multiple course types, integrating various course contents, supporting online learning and exams, and effectively evaluating learning outcomes.