AI-driven Adaptive Learning Platform

A comprehensive learner model collects behavioral information, learning history, preferences, and characteristics to build a personalized student profile. This data-driven approach, utilizing collaborative filtering and clustering, enables personalized recommendations and path adjustments, with continuous tracking of learner feature evolution. The model updates using the formula M(t+1) = αM(t) + (1-α)R(t), balancing historical data with recent feedback.

The platform uses user-driven collaborative filtering to recommend learning resources. By calculating the cosine similarity between users (sim(u, v) = Σ(ru,i * rv,i) / (√(Σru,i²) * √(Σrv,i²))), the system identifies similar users and recommends materials based on their interests. This ensures customized content matching and enhances the learning experience.

Real-time monitoring of learning progress and behavior data enables dynamic adjustment of learning task difficulty and arrangement. This mechanism, based on the learner model, recommendation algorithm, and feedback system, provides real-time feedback. Learner mastery is evaluated using M(t) = (ΣP(i,t)) / n, allowing the system to suggest relevant exercises and transition to more difficult content as appropriate.

The intelligent feedback and evaluation mechanism tracks real-time learning results, providing targeted feedback based on progress, knowledge mastery, and behavior. It identifies weaknesses, optimizes content, and adjusts difficulty. Key indicators like answer accuracy, learning time, and review frequency assess knowledge mastery, calculated as K(t) = (Σ(r(i,t) * W(i))) / (ΣW(i)), integrating task accuracy and difficulty.