This document discusses using Markov chain analysis and difficulty level classification to improve personalized learning paths for one digit multiplication. Key points: - Researchers analyzed over 500,000 multiplication calculations from primary school students to cluster questions by difficulty and identify patterns in student answers. - Markov chain models were used to analyze transition probabilities between answer types (right, wrong) both at the individual question level and overall. - Questions were classified into 6 optimal difficulty clusters, and patterns were found regarding which operands make questions easier or harder. - The analysis informs an adaptive algorithm to select the next question based on the student's previous response and difficulty level of the prior question. This provides personalized recommendations to improve learning paths.