Predictive Analytics and Insights for Personalizing Special Education of Students with Dyscalculia
Year: 2026
Type: article
Abstract: Learning disability encompasses the uniquechallenges faced by a student to acquire process anddemonstrate any knowledge or skill. Identification of learningdisability students helps the educators to create an inclusiveenvironment enabling the students to reach their fullpotential. The paper delves deep into the machine learningbased personalized learning pathways, prediction of studentperformances, intervention with assistive technologies, mathrelated educational tools and games along with behavioranalysis using machine learning. Classification algorithms ofmachine learning analyses and learns patterns from thedataset. This study analyses the statistics of all the features ofthe dataset and creates models with different classificationalgorithms like Logistic Regression, Decision Tree, RandomForest, K-nearest neighbor, Support Vector Machine and XGBoost for predicting quarterly goal for these students. Finallythe results are compared by creating the confusion matrix andcomputing the accuracy, precision, recall and F1 score topredict the optimum models for the purpose.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7214019852