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

Adrian Iste, Kazuki Nishizawa, Chisa Tanaka, Andrew Vargo, Anna Scius-Bertrand, Andreas Fischer, Koichi Kise

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.

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

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.
Paper Structure (18 sections, 1 equation, 8 figures, 4 tables)

This paper contains 18 sections, 1 equation, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Wacom Dataset
  4. Overview
  5. Subset of the Dataset : Japanese Language
  6. Prediction and Classification Methods
  7. Features
  8. Prediction and Classification Method
  9. Grade Prediction
  10. Gender Classification
  11. Academic Performance Classification
  12. Experimental Results and Discussion
  13. Methodology
  14. Results of Grade Prediction
  15. Results of Gender Classification
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Distribution of the ages of the students in Wacom dataset.
  • Figure 2: Examples of drills from subject “Japanese language” for grade 1 to 9.
  • Figure 3: Distribution of number of questions in the drills of the subject Japanese language.
  • Figure 4: Score distributions analysis in Wacome dataset
  • Figure 5: Prediction of the grade for each set of feature using Linear Regression (left) and Random Forest (right) using 5-fold cross validation. One point corresponds to a prediction of a point from the test fold.
  • ...and 3 more figures