Machine learning-based brief version of the Caregiver-Teacher Report Form for preschoolers

Gong Hong Lin, Shih Chieh Lee, Yen Ting Yu, Chien Yu Huang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Background: The Caregiver-Teacher Report Form of the Child Behavior Checklist for Ages 1½–5 (C-TRF) is a widely used checklist to identify emotional and behavioral problems in preschoolers. However, the 100-item C-TRF restricts its utility. Aims: This study aimed to develop a machine learning-based short-form of the C-TRF (C-TRF-ML). Methods and procedures: Three steps were executed. First, we split the data into three datasets in a ratio of 3:1:1 for training, validation, and cross-validation, respectively. Second, we selected a shortened item set and trained a scoring algorithm using joint learning for classification and regression using the training dataset. Then, we evaluated the similarity of scores between the C-TRF-ML and the C-TRF by r-squared and weighted kappa values using the validation dataset. Third, we cross-validated the C-TRF-ML by calculating the r-squared and weighted kappa values using the cross-validation dataset. Outcomes and results: Data of 363 children were analyzed. Thirty-six items of the C-TRF were retained. The r-squared values of C-TRF-ML scores were 0.86–0.96 in the cross-validation dataset. Weighted kappa values of the syndrome/problem grading were 0.72–0.94 in the cross-validation dataset. Conclusions and implications: The C-TRF-ML had about 60 % fewer items than the C-TRF but yielded comparable scores with the C-TRF.

Original languageEnglish
Article number104437
JournalResearch in Developmental Disabilities
Publication statusPublished - Mar 2023


  • Artificial intelligence
  • Assessment
  • Emotional and behavioral problems
  • Machine learning

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Clinical Psychology


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