19 results about "Student assessment" patented technology

The invention discloses an ecological classroom teaching system based on smart learning. The ecological classroom teaching system comprises a student information acquisition module, a classroom information acquisition module, a teacher information acquisition module, a data processing module and an information sending module. The student information acquisition module is used for acquiring student related information; the teacher information acquisition module is used for acquiring related information of teachers; the classroom information acquisition module is used for acquiring classroom related information; the data processing module is used for processing the student related information to obtain student evaluation information; processing the teacher related information to obtain teacher management information; processing the classroom related information to obtain classroom management information; and the information sending module is used for sending the student evaluation information, the teacher management information and the classroom management information to a preset receiving terminal after the student evaluation information, the teacher management information and the classroom management information are generated. According to the invention, computer teaching courses suitable for students can be pushed to the students, and the personalized learning requirements of the students are met.

The invention discloses an education evaluation method and system based on visual identification data analysis, and relates to the technical field of education analysis, and the method comprises the steps: presetting the understanding difficulty for each mapping node, and taking the understanding difficulty as a teaching path reference; performing semantic analysis on the explanation scheme, extracting knowledge points and secondary and supplementary key phrases, and associating the knowledge points and the secondary and supplementary key phrases to corresponding mapping nodes to realize content structuring; acquiring real-time classroom voice information, extracting and analyzing characters to determine real-time keywords, positioning corresponding nodes in the mind map, sequentially connecting the nodes to form a real-time map node sequence line, and capturing an actual explanation path; calculating student initial state evaluation, combining node difficulty correction to obtain final evaluation, and accumulating all student evaluation as a teaching effect; taking a preset explanation order line teaching effect corresponding to the real-time order line as a standard, determining the relative positioning of actual evaluation, and achieving deviation quantification; according to the invention, through multi-modal fusion and a dynamic order line mechanism, the real-time performance and accuracy of evaluation are improved, and teaching improvement guidance is provided.

The application discloses a Bloom cognitive hierarchy constraint-based achievement-oriented education diagnosis method and system. The method takes course outline text and student evaluation data as input, and realizes the automatic diagnosis of course goal achievement through three structural modifications in the large language model Transformer architecture: the logarithmic value of the cognitive hierarchy transfer matrix is embedded as a learnable bias item in the attention score calculation path to realize feature coding of cognitive hierarchy perception; the continuous differentiable relaxation technique is used to establish an end-to-end differentiable inference path for the course goal achievement weight; and the Bloom partial order constraint is coded as a training loss, so that the diagnosis result meets the cognitive hierarchy progressive relationship. The three modifications are optimized by a joint loss function, which significantly improves the diagnosis accuracy and promotes the interpretability and scientificity of the large language model diagnosis in the achievement-oriented education scene.

The invention discloses a medical internship student assessment management method and related equipment. The method comprises the following steps: acquiring an operated object key point, a student hand key point and an instrument target in an operation video of a trainee, and identifying each operation action step of the trainee; identifying the medical care document content of the trainee, and obtaining a simulation object and peripheral sensor data for practical operation of the trainee, so as to respectively calculate a sensor quantitative index vector and a document quality vector of the trainee; and inputting a video score vector, a sensor quantitative index vector and the document quality vector obtained based on the operation action step analysis into a multi-modal fusion model based on an attention mechanism to obtain a comprehensive assessment score of the student. The problems that assessment of medical skills of students seriously depends on subjective observation and manual recording of teachers, objective, quantitative and traceable assessment means are lacked, and evaluation consistency and fairness are difficult to guarantee can be solved.

This invention discloses a course recommendation method and system for an intelligent construction education platform, relating to the field of intelligent education information technology. The method includes constructing a knowledge graph to establish multi-level associations between tasks and knowledge points; analyzing assessment data, setting score thresholds to identify students' weak knowledge points, integrating knowledge point weakness weights and job weights to generate comprehensive knowledge point weights; generating course recommendation results based on these comprehensive knowledge point weights, updating score data during student assessments after course completion, and using feedback to optimize the recommendation logic in real time. The method described in this invention implements a multi-dimensional weight-driven course recommendation path construction mechanism; employing dynamic feedback and real-time scoring algorithms, it establishes a closed-loop logic of recommendation, assessment, and re-recommendation, effectively improving the personalization and job matching of recommendations, and solving the problems of untraceable knowledge points, weak job adaptability, and singular recommendation strategies in existing platforms.

To smoothly recognize evaluation results of a questionnaire assessment that measures the realization state of learning.SOLUTION: A processing server 10 includes: a visualization information creation unit 135 configured to receive a display request for evaluation results of each student, which are divided into three categories of school education experience, growth, and school education results and the evaluation of a specific item related to at least one of the categories is quantified with reference to results of students' answers to a questionnaire survey that measures the realization state of learning, which can evaluate school education experience, learning ability and humanity, and school education results, and create control information for creating a visualization image that visualizes evaluation results at the granularity requested to be displayed using evaluation values and statistics of the evaluation values of students requested to be displayed among the evaluation results; and a visualization image display control unit 136 configured to cause a request source terminal device to display the visualization image based on the control information.SELECTED DRAWING: Figure 6

The invention discloses a teaching evaluation method and system and a storage medium, and relates to the field of teaching quality monitoring, and the system comprises an evaluation center which is in communication connection with a data collection module, a data processing module, a data evaluation module and an evaluation feedback module. The data acquisition module is used for performing information acquisition on a medical teaching process to obtain corresponding teaching information and a face database; the data processing module is used for performing data processing on the obtained face database to obtain corresponding visual information; the data evaluation module is used for performing teaching evaluation on a corresponding medical teaching process based on the obtained visual data to obtain a corresponding teaching evaluation score; the teaching evaluation score comprises a student evaluation score and a teacher evaluation score; the evaluation feedback module is used for taking corresponding feedback based on the obtained teaching evaluation score; the system is beneficial for assisting medical teachers in improving the teaching quality.

A computer-implemented method for an online student assessment includes: using a server computer for: gaining access to an information processing apparatus; the information processing apparatus having an output device, a camera, and at least one input device; identifying processes running on the information processing apparatus; stopping the processes on the information processing apparatus that have been determined as being unallowed during the online student assessment; verifying an identity of a user of the student computer; if the identity of the user is confirmed then, while remotely monitoring the user via the camera, displaying questions on the display monitor, and receiving answers via the at least one input device; restarting at least one of the unallowed processes; and restoring access to the student computer.

The invention relates to the technical field of man-machine interaction, in particular to an education auxiliary system based on artificial intelligence. The system comprises a learning resource management unit which is used for collecting and managing teaching resources and student scores; a student evaluation unit which evaluates the learning level of the student through a K-Means clustering algorithm according to the student score of the learning resource management unit; the personalized recommendation unit is used for recommending proper learning materials and paths to the students through a mixed recommendation algorithm according to the evaluation result of the student evaluation unit; and the support service unit is used for collecting opinions and suggestions of the students according to a recommendation result of the personalized recommendation unit, and improving the service quality and content in time. And the advantages and weak links of the students are identified through deep analysis of test results. And through a hybrid recommendation algorithm, the recommendation accuracy and diversity are improved.

Technical field: The invention relates to the use of artificial intelligence (AI) in education for the objective evaluation of student performance and the generation of detailed feedback. Abstract: The present invention describes an AI-supported assessment system for the objective analysis and evaluation of student performance. The system comprises a data acquisition component for collecting performance data from various sources such as tests, homework, and oral contributions. A machine learning component analyzes this data to assess specific learning aspects such as comprehension, application, and creativity. A natural language processing (NLP) component evaluates students' written and oral contributions with regard to their language skills and content comprehension. Based on these analyses, a feedback generation component generates personalized feedback that includes strengths, weaknesses, and suggestions for improvement. A teacher adaptation component provides teachers with insights into the performance data and suggests targeted teaching strategies and materials to address the individual learning needs of the students. Inventive achievement: The proposed system improves the objectivity and detail of student assessments through the use of advanced AI technologies and offers personalized feedback as well as targeted lesson adjustments.

The present disclosure relates to a method of predicting a user's score on a question by an electronic device. The method includes: training a DP-multi tasking learning (DP-MTL) model; verifying the DP-MTL model; receiving choice selection information related to the question from the user through the terminal, and predicting 1) a probability that the user answers the question correctly and 2) the user's score related to the question using the verified DP-MTL model based on the choice selection information, and the DP-MTL model may be a model for predicting the user's score based on 1) information on whether the user answers the question correctly, 2) information on which incorrect answer is selected among choices of the question when the user selects an incorrect answer, and 3) a skill level of the user.

This disclosure relates to a method for generating lesson plans, as well as an electronic device and computer program product, belonging to the field of intelligent education technology. The method includes: acquiring student assessment data, which includes baseline data for all students and target group assessment data corresponding to the target student group; comparing and analyzing the baseline data for all students and the target group assessment data to construct a teaching deviation feature vector corresponding to the target student group; performing attribution diagnosis processing on the teaching deviation feature vector to obtain lesson plan optimization strategy instructions; and iteratively updating the original lesson plan corresponding to the target student group based on the lesson plan optimization strategy instructions to obtain an updated lesson plan. This disclosure can automatically convert multi-dimensional assessment data into lesson plan optimization strategy instructions and can verify the effect, significantly improving the personalization of teaching content and the iteration efficiency of lesson plans.

A software platform creates and maintains a data structure in the form of a decision tree, with each location or node of the decision tree being associated with certain action steps that are automatically initiated, or are initiated in response to student assessment scores. The “trunk” of the decision tree includes a plurality of screening skills, while a plurality of branches extend outwards from each screening skill, with each branch node being a subsequent skill associated with the screening skill. Entire classes or individual students may traverse the tree based upon periodic time intervals (e.g., weekly assessment of screening skills) and results of prior assessments. Scores indicating mastery of assessed material will traverse the taker towards the trunk, while poor scores will traverse the taker away from the trunk into follow-up assessments. Interfaces are provided for teachers and administrators to monitor progress and generate materials for each node.

The invention relates to the technical field of teaching resource recommendation, and discloses a teaching resource personalized recommendation system and method based on deep learning. The system comprises a student data acquisition module, a student data evaluation module and a medical education resource recommendation module. The student data acquisition module comprises a student idle time acquisition unit, a student professional data acquisition unit, a student assessment score acquisition unit and a student tendency data acquisition unit. According to the system, through collecting student professional names and previous assessment results uploaded by students, and tendency data of the students for each kind of educational resources, a lifting coefficient and a learning strategy coefficient are calculated respectively; and then, based on the lifting coefficient and the learning strategy coefficient, carrying out preliminary screening of the educational resources for each student in each rated idle time, and after the preliminary screening is completed, outputting a final recommendation result in combination with tendency data of the student for each educational resource. Therefore, the medical teaching resources can be recommended in a personalized manner in combination with the current learning defect of the user.

A computer-implemented method of providing automatic evaluation of student assessment based on multiple independent large language model (LLMs), the method including transmitting one or more questions to a user device associated with a student; receiving, from the user device, a data object comprising one or more text answers, each to a corresponding one of the one or more questions; generating one or more prompts comprising the one or more questions and the corresponding one or more text answers and predefined scoring points; initiating a plurality of LLMs to independently evaluate the one or more text answers based on the predefined scoring points; receiving evaluation outputs for an individual question of the one or more questions respectively from the LLMs; selecting a first evaluation output from among the evaluation outputs from a first LLM of the LLMs; and outputting the first evaluation output for the individual question to the user device.

The invention discloses a digitization-based teaching system, and the system comprises a data processing module which is used for collecting and integrating teaching design related data and student assessment score data, and carrying out the preprocessing of the collected data; and the evaluation analysis module performs multi-aspect evaluation analysis based on the student assessment score data. According to the method, the score rates of the students on different questions are obtained, so that a teacher can know the mastering condition of the students on each knowledge point, personalized evaluation and guidance are carried out, the students are helped to improve academic records in a targeted manner, and comprehensive evaluation indexes corresponding to the achievement degrees of the courses are obtained by analyzing the score rates of the students in the courses. Therefore, the performance of the student on different questions and different courses can be comprehensively considered, the learning condition of the student is more comprehensively evaluated, and misjudgment possibly caused by one-sidedness evaluation is reduced.

The present invention relates to the field of enrollment assessment, and specifically provides a student assessment system based on student character profiling and answer verification. Block supervision module: used to build a blockchain supervision network and verify the answers of students during the re-examination process; difference balance module: used to build student character profiles based on student information, and determine the score difference through the relative deviation ratio, and determine the score data after eliminating the difference; student source assessment module: used to import the student score table after answer verification and the score data after eliminating the difference into a fuzzy cloud model to generate student admission assessment results; secretary module: used to generate a student score table based on the imported student information and student admission assessment results during the student re-examination process.

The present invention relates to the technical field of remote teaching. Disclosed in the present invention is a management system for college English teaching. An input end of a student information module is separately connected to an input end of a score management module and an input end of a student profile management module. According to the management system for college English teaching, timely archiving and feedback of student evaluation results are achieved by means of bidirectional connection between a student management unit and an assessment and evaluation unit, making evaluation results and student profile management synchronous, and ensuring the accuracy of the evaluation results and the integrity of student profiles; the cooperation of a courseware browsing module, an online assignment module, a discussion area module, and a leaning progress tracking module can fully support online learning and course exchange activities of students; the discussion area module provides a communication and discussion platform for the students, promoting interaction and cooperation among the students, and broadening the learning horizon; and the learning progress of the students is tracked in real time, helping teachers to know the learning conditions of the students.