9 results about "Cognitive development" patented technology

The invention belongs to the technical field of education artificial intelligence, and particularly relates to an adaptive learning path generation method based on a knowledge graph, which comprises a basic construction stage of constructing a double-layer knowledge graph and acquiring and standardizing multi-source dynamic data, and is used for constructing a course and cross-domain double-layer knowledge system and a data base, a three-dimensional learning condition analysis stage including knowledge dimension, dynamic learning condition dimension and cognitive development dimension; the cross-domain knowledge migration mechanism breaks course barriers, associates current learning with core course knowledge points of the same major, helps students establish a complete professional knowledge network, avoids knowledge fragmentation, embeds cross-domain knowledge points in combination with professional planning and competition requirements, realizes connection of current learning and future application, improves the practical value and migration ability of knowledge, and improves the learning efficiency. The knowledge association weight is dynamically optimized, so that path planning follows domain knowledge logic and fits individual learning feedback, students are assisted in understanding internal association among knowledge, and knowledge construction logicality is enhanced.

This invention discloses a highly absorbable, non-allergenic DHA brain-boosting oral liquid specifically designed for students of all ages, belonging to the field of functional nutritional beverage technology for teenagers. This invention uses high-purity Schizochytrium algae oil DHA and perilla seed active molecules as the core brain-boosting matrix, innovatively configuring a grade-specific ingredient system. The formula is customized for the physical condition and brain function needs of students at different grade levels: kindergarten, primary school, junior high school, high school, and university. Through a small-molecule cell-wall breaking extraction process, the nutrients are directly absorbed into the bloodstream and quickly converted into "brain gold" in the brain. This formula strictly avoids eight major food allergens, ensuring safety for all ages. It also specifically addresses sub-health issues related to learning across all grade levels, including early childhood brain development and emotional well-being, exam preparation efficiency and fatigue reduction for students preparing for higher education, and cognitive development and mental sustainability for university students. The product comes in standardized packaging of 50ml / vial and 15 vials / box, with a customized sweet taste tailored to different grade levels, making it highly acceptable to students. It has no side effects and is suitable for long-term healthy consumption by all school students, effectively improving brain efficiency and learning status across all grade levels, with broad market application prospects.

This invention discloses a method for constructing a knowledge base in the field of early childhood education that integrates knowledge graphs and large language models, belonging to the field of early childhood education method optimization technology. It involves collecting and cleaning preprocessing early childhood education data from multiple channels; extracting entities and relationships using a customized model to construct a structured knowledge graph; fine-tuning the large language model for the early childhood education domain; integrating knowledge from the knowledge graph and model through a gating mechanism with double verification; establishing a monthly update mechanism to achieve incremental learning; furthermore, it achieves personalized knowledge recommendations based on user profiles, optimizes interactive question answering through a combination of retrieval and models, and ensures data security through anonymization and access control. This invention, through the integration of multi-source data and intelligent technologies, constructs a precise and dynamic early childhood education knowledge base, more accurately understands children's needs, generates content that aligns with their cognitive development, achieves personalized learning recommendations, improves the interactive experience and knowledge service quality, while ensuring data security and promoting the digital upgrade of the early childhood education field.

The present application relates to the technical field of educational big data analysis and personalized education, and discloses a deep learning effect correlation mining method based on children's animation watching behavior data, which specifically comprises the following method steps: step one, constructing a three-dimensional data model, the three dimensions including behavior dimension, content dimension and effect dimension; step two, through a group difference analysis model, an effective animation watching behavior path is identified for children groups of different ages. The present application can comprehensively and accurately evaluate the learning process and effectiveness of children by constructing a three-dimensional data model (behavior dimension, content dimension and effect dimension). Experimental data shows that after using the method, the vocabulary standard rate of the small child group (0-6 years old) is improved to 68%, and the long-time immersion mode conversion rate of the big child group (7-12 years old) is improved by 75%. This data-driven optimization mechanism ensures that the learning path is highly matched with the cognitive development law of children, thereby significantly improving the English enlightenment effect.

This invention discloses an AI-based method for identifying and personally recommending children's reading interests, specifically relating to the fields of data processing and intelligent recommendation technology. The method includes: collecting multimodal data from children's reading and performing spatiotemporal alignment preprocessing; using a deep learning model to perform multimodal fusion identification of emotional pleasure, focus index, and interest focus; constructing a knowledge graph of reading materials and a graph of children's developmental stages; performing similarity retrieval based on interest focus; calculating the "zone of proximal development deviation value" to filter a preliminary recommendation list; using interactive trial reading verification; and generating a final personalized recommendation list. This invention accurately captures children's latent interests through multimodal emotion computing, combines dual knowledge graphs to match cognitive development stages, and forms a closed loop through interactive verification. It solves the technical problem that traditional recommendation methods are unable to adapt to the dynamic reading needs of young children, significantly improving the accuracy and personalization of recommendations.

The present application belongs to the technical field of intelligent education, and discloses a method and system for comprehensive education evaluation based on an intelligent education AI model, comprising: gathering multi-modal cognitive data and learning behavior trajectories, coupling multiple sources to obtain an education interaction information spectrum; decoding cognitive structure to obtain a learning behavior cognitive map, constructing a knowledge network topology to obtain a cognitive development topographic map; performing cognitive fault detection to obtain a cognitive transition critical point set, performing cognitive evolution tracking to obtain a learning development trajectory map; purifying learning modes to obtain a learning style spectrum, and constructing a cognitive sensitivity analysis model; intelligently combining learning navigation schemes to obtain a set of personalized learning enhancement strategies, coordinating teaching schemes to obtain a comprehensive education evaluation report; the present application realizes intelligentization of the education evaluation process, and can accurately identify cognitive characteristics and provide personalized learning guidance.

This invention relates to the use of a nutritional composition. Specifically, it is used for non-therapeutic purposes to increase the expression of brain-derived neurotrophic factor (BDNF) and / or glial cell-derived neurotrophic factor (GNF). The nutritional composition comprises medium- to long-chain fatty acid triglycerides and docosahexaenoic acid (DHA) in a mass ratio of 2:1 to 600:1. The nutritional composition can synergistically increase the expression of BDNF and / or GNF. This nutritional composition can also improve brain development and promote neural and / or cognitive development.

The application discloses a user preference classification-oriented continuous learning method based on prototype contrast, maps discrete user samples into a fixed two-dimensional matrix, and extracts features by using a neural network. According to a sustainable learning theory and method driven by cognitive development, a fixed memory buffer area is maintained in the continuous learning stage to save historical user type samples, and new inflow user samples are mixed with the historical samples in the buffer area to form a training batch. A joint contrast loss calculation method combining instance level and prototype level is provided, that is, not only the feature expression of the same user preference is narrowed through the supervised contrast loss between samples, but also the contrast loss between the samples and the class prototype is calculated by activating the current active class prototype and the dormant historical class prototype. The method effectively alleviates the catastrophic forgetting problem caused by frequent updating of network parameters in class incremental learning, and realizes accurate and stable user preference classification in a dynamic data stream.

This application relates to the non-therapeutic use of a nutritional composition to increase the expression of neurotrophic factors. The nutritional composition comprises medium- and long-chain fatty acid triglycerides and lactoferrin in a mass ratio of 1:1 to 600:1. This nutritional composition can synergistically increase the expression of brain-derived neurotrophic factor and / or glial cell-derived neurotrophic factor, thereby improving brain development, and / or nourishing neurons, and / or protecting neurons, and / or promoting neural development, and / or promoting cognitive development, and / or promoting visual development, and / or enhancing learning and / or memory function.