Student Characteristics and ICT Usage as Predictors of Computational Thinking: An Explainable AI Approach
Year: 2025
Type: article
Abstract: Computational thinking (CT) is recognized as a core competency for the 21st century, and its development is shaped by multiple factors, including students' individual characteristics and their use of information and communication technology (ICT). Drawing on large-scale international data from the 2023 cycle of the International Computer and Information Literacy Study (ICILS), this study analyzes a sample of 81,871 Grade 8 students from 23 countries and one regional education system who completed the CT assessment. This study is the first to apply a predictive modeling framework that integrates two machine learning techniques to systematically identify and explain the key variables that predict CT and their nonlinear effects. The results reveal that various student-level predictors-such as educational expectations and the number of books at home-as well as ICT usage across different contexts, demonstrate significant nonlinear patterns in the model, including U-shaped, inverted U-shaped, and monotonic trends. Compared with traditional linear models, the SHapley Additive exPlanations (SHAP)-based approach facilitates the interpretation of the complex nonlinear effects that shape CT development. Methodologically, this study expands the integration of educational data mining and explainable artificial intelligence (XAI). Practically, it provides actionable insights for ICT-integrated instructional design and targeted educational interventions. Future research can incorporate longitudinal data to explore the developmental trajectories and causal mechanisms of students' CT over time.
Source: Journal of Intelligence
Authors: Tongtong Guan, Liqiang Zhang, Xingshu Ji, Yuze He, Yonghe Zheng
Institution: Beijing Normal University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416105183