Prediction of Grade, Gender, and Academic Performance of Children and Teenagers from Handwriting Using the Sigma-Lognormal Model

2026-03-12

AI Summary

This study investigates whether handwriting dynamics in children and adolescents encode developmental and individual differences associated with grade level, gender, and academic performance. Leveraging a large-scale dataset of online handwriting samples from Japanese elementary and secondary school students, the research integrates kinematic statistical features, entropy-based variability measures, and parameters derived from the Sigma-Lognormal model. These features, aggregated at the student level, are employed in three predictive tasks. The work presents the first systematic validation of the Sigma-Lognormal model in developmental handwriting analysis, revealing a tendency for handwriting movements to become increasingly organized according to lognormal principles with age. Results demonstrate that handwriting dynamics strongly reflect developmental stage, particularly excelling in grade-level prediction, thereby highlighting their potential for educational assessment and developmental monitoring.

Technology Category

• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Digital handwriting acquisition enables the capture of detailed temporal and kinematic signals reflecting the motor processes underlying writing behavior. While handwriting analysis has been extensively explored in clinical or adult populations, its potential for studying developmental and educational characteristics in children remains less investigated. In this work, we examine whether handwriting dynamics encode information related to student characteristics using a large-scale online dataset collected from Japanese students from elementary school to junior high school. We systematically compare three families of handwriting-derived features: basic statistical descriptors of kinematic signals, entropy-based measures of variability, and parameters obtained from the sigma-lognormal model. Although the dataset contains dense stroke-level recordings, features are aggregated at the student level to enable a controlled comparison between representations. These features are evaluated across three prediction tasks: grade prediction, gender classification, and academic performance classification, using Linear or Logistic Regression and Random Forest models under consistent experimental settings. The results show that handwriting dynamics contain measurable signals related to developmental stage and individual differences, especially for the grade prediction task. These findings highlight the potential of kinematic handwriting analysis and confirm that through their development, children's handwriting evolves toward a lognormal motor organization.

Problem

• handwriting dynamics
• grade prediction
• gender classification
• academic performance
• developmental characteristics

Innovation

• sigma-lognormal model
• handwriting dynamics
• developmental handwriting analysis
• kinematic features
• student characteristic prediction