Next-generation Knowledge /
Conocimiento de próxima generación
Innovation / Innovación
Advancement / Avance
Learn more about our cutting-edge AI technology:
At Emerging Rule, we employ LevelShip, an advanced Skill Recognition and Allocation Algorithm, to personalize your learning experience. Here’s how it works:
1. Understanding Your Strengths: Our algorithm analyzes various aspects such as your evaluation results, background information (like age and location), and your interests. This helps us identify what you excel in and where you might need additional support.
2. Personalized Learning Plan: Based on this analysis, we create a customized plan just for you. This plan includes lessons and activities that are tailored to your unique needs and interests, ensuring you get the most effective learning experience possible.
3. Expert Guidance: Our platform also connects you with experts who provide personalized support and guidance. They use insights from the algorithm to assist you in achieving your learning goals effectively.
4. Continuous Improvement: As you progress, our algorithm continues to learn and adapt. It updates your learning plan based on your performance and newly acquired skills, ensuring that you’re always challenged at the right level.
Example:
Meet Anna, a student leveraging our platform. Our algorithm identified that Anna excels in math but could benefit from more practice in writing. So, we designed a learning plan specifically for her, focusing on enhancing her writing skills while maintaining her strong performance in math.
LevelShip is like having a personal tutor who understands your strengths and weaknesses, guiding you towards achieving your full potential. Take the first step towards personalized learning with LevelShip today!
LevelShip by Emerging Rule: Enabling the systematic recognition of patterns within data sets, the structuring of skills, and the allocation of those skills to optimal career paths through an iterative learning process, enhanced for accuracy and personalization.
LevelShip’s Algorithm is an advanced, iterative system designed to systematically analyze data, recognize patterns, and structure skills for optimal career development, particularly within K-12 education. By aggregating diverse data sets into milestones and employing sophisticated analytical methods, the algorithm creates a detailed representation of skill progression. It then leverages this structured skill set to offer personalized career path recommendations. These recommendations are dynamically adjusted based on factors such as individual preferences, industry trends, and geographical constraints. Emphasizing ethical considerations, LevelShip ensures fairness and transparency while continuously improving its accuracy through user feedback and regular audits. This holistic approach not only fosters precise skill development but also aligns career paths with real-world opportunities, making it a powerful tool for navigating professional growth from an early educational stage.
Give it a try or contact Emerging Rule; we are more than happy to collaborate with your school, district, state, or institution to implement and tailor LevelShip to your specific needs. Ensure that your data is key, always secure and handled with the utmost confidentiality and integrity.
1. Data Input and Milestones
a. Data Sets Definition: \( D_i \) where \( i \) indexes the input modules provided to the system. Diverse and comprehensive sources of data capture a broad spectrum of skills and performance metrics.
b. Milestone Aggregation: Data set groups are structured such that each group represents a milestone \( M_k \), where \( k \) is the milestone index. Each milestone aggregates data sets relevant to a specific stage of skill development, as expressed by:
\[ M_k = \bigcup_{i=1}^n D_i \]
where \( D_i \) represents the individual data sets associated with milestone \( M_k \) and \( n \) is the number of data sets aggregated at that milestone.
c. Analysis: Each milestone \( M_k \) is a collection of \( n \) data sets \( \{D_{i_1}, D_{i_2}, \ldots, D_{i_n}\} \). Patterns are analyzed within these sets using CNN/RNN, ensemble, and clustering algorithms to form a structured representation of skills.
2. Pattern Recognition
a. Iterative ML Model: Patterns are recognized and extracted from the data sets within each milestone. A validation process ensures the patterns' accuracy.
b. Recognize Patterns: \( P_k \) denotes the set of recognized patterns at milestone \( M_k \). Continuous refinement of pattern recognition processes is performed based on feedback and new data, as expressed by:
\[ P_k = f\left(\bigcup_{i=1}^n D_i, \text{Feedback}, \text{New Data}\right) \]
where \( f \) represents the pattern recognition function that incorporates the aggregation of data sets \( D_i \) from milestone \( M_k \), along with feedback and new data to refine and update the patterns.
c. Develop Higher-Order Structure: Patterns are organized into a higher-order structure \( S_k \), representing the skill developed at milestone \( M_k \). This structure, when matched with real-world contexts, accurately reflects real-world skill applications and aligns with industry standards.
3. Skill Structuring
a. Hierarchical Skill Structure: The collection of skills \( \{S_1, S_2, \ldots, S_k\} \) forms a hierarchical structure where each \( S_k \) builds upon previous skills. This hierarchy is regularly updated based on industry trends and feedback from users.
b. Relationships and Dependencies Identification: Relationships and dependencies among these skills are identified to form a comprehensive skill set \( S \), encompassing both foundational and advanced skills.
c. Skills Validation: The identified skills are continuously validated against current industry requirements and real-world applications by employing regular assessments, feedback from industry experts, and updates to industry standards.
4. Career Path Allocation
a. Recommendation System: Maps skill sets \( S \) to optimal career paths \( C \), incorporating dynamic updates based on the latest job market trends and emerging career fields.
b. Recommendation Function Definition: \( R(S) \) assigns the skill set \( S \) to a career path based on factors such as interests, passions, geographical preferences, work-life balance, and long-term career goals. Interactive feedback mechanisms for users to adjust preferences are implemented.
c. Career Path Alignment Optimization: \( R \) optimizes the alignment of the skill set with potential career paths, producing the most suitable career path \( C^* \).
5. Ethical Considerations and UX
a. Biases: Measures are implemented to detect and mitigate biases in data and algorithms. Regular audits are conducted to ensure fairness and transparency, integrating ethical considerations throughout the process.
b. Continuous Learning and Improvement: A feedback loop \( F \) allows users to provide input on recommendations and skill assessments. System performance \( P \) and user satisfaction \( U \) are monitored to iteratively refine the algorithm \( A \), enhancing its accuracy \( \alpha \) and relevance \( \rho \).
I am always learning...
(You can achieve the best of yourself if you know your powers...)
Your voice matters, and we believe it should shape the future. At Emerging Rule, we're pioneering LevelShip to navigate the challenges of the 'Age of Cognitive Technologies'. Our goal is to harness data-driven educational models that promote a more equitable future.
LevelShip leverages cutting-edge Machine Learning to identify and match skills with optimal career paths. We're dedicated to enhancing K-12 education by continuously improving learning outcomes through the power of artificial intelligence.
Explore our downloadable educational resources spanning primary, secondary, undergraduate, and graduate levels. Our software supports downloading, streaming, and managing diverse educational content—from images and movies to text and digital media—while also facilitating online courses and educational testing.
Join us as we redefine education through innovation and personalized learning experiences.
Siempre estoy aprendiendo...
(Puedes alcanzar lo mejor de ti si conoces tus poderes...)
Tu opinión tiene valor y creemos que debería influir en el diseño de nuestro futuro. En Emerging Rule, estamos desarrollando LevelShip para ayudarte a enfrentar los desafíos de la 'Era de las Tecnologías Cognitivas'. Utilizamos modelos educativos basados en datos para mejorar la educación hacia un futuro más equitativo.
LevelShip aplica Machine Learning de última generación para reconocer habilidades y asignarlas a carreras óptimas. Nos dedicamos a mejorar la educación desde el nivel inicial hasta el universitario, utilizando inteligencia artificial para garantizar resultados rápidos y precisos.
Explora nuestros recursos educativos descargables en los niveles primario, secundario, universitario y de posgrado. Nuestro software facilita la descarga, transmisión y gestión de diversos contenidos educativos, desde imágenes y películas hasta texto y medios digitales, además de facilitar cursos en línea y pruebas educativas.
Únete a nosotros mientras redefinimos la educación a través de la innovación y experiencias de aprendizaje personalizadas.
