7 results about "Student assessment" patented technology

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.

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 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.