Actionable companion · Assessment & reasoning

Learning with agency: Student Characteristics and ICT Usage as Predictors of Computational Thinking: An Explainable AI Approach

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

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.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The data for this study were drawn from ICILS 2023, which surveyed a total of 132,998 Grade 8 students from 35 countries and regions. The ICILS is designed to assess students’ readiness for life in a technology- and information-driven society. However, not all countries or students participated in the CT assessment. This study focused specifically on the subsample for which complete CT assessment data were available, comprising 81,871 students from 23 countries and one regional education system (North Rhine-Westphalia, Germany) (ICILS 2023). The CT assessment in ICILS 2023 was developed to evaluate students’ ability to transform real-world problems into computationally solvable forms, emphasizing skills in algorithm design and problem solving. The assessment consisted of four modules, each lasting 25 min, targeting two core dimensions of CT: problem conceptualization and solution operationalization.

Limitations and uncertainty · source passage

Although this study leverages large-scale international data and employs a hybrid approach that combines machine learning and XAI to systematically identify the key predictors of students’ CT and uncover complex nonlinear mechanisms, several limitations should be acknowledged. First, the ICT usage data are primarily derived from self-reported student questionnaires, which may be subject to social desirability bias. Future studies could enhance data accuracy and interpretability by incorporating objective behavioral data, such as usage duration, the platform type, or interaction logs. Second, although the dataset includes a large and diverse student population, the current findings may not fully capture the contextual nuances. There remains a need for stratified analyses across age groups, cultural contexts, and educational systems to validate the model’s generalizability and cross-contextual transferability. Third, the study employs a cross-sectional design, which limits causal inference. To better understand the dynamic interactions between ICT usage and CT development, longitudinal studies or experimental designs are warranted. Additionally, future research could expand the application of XAI techniques in educational assessment and learning trajectory modeling (Tiukhova et al. 2024) and incorporate multimodal behavioral data for fine-grained modeling to improve both the explanatory power and predictive accuracy (Chango et al. 2021).

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Tongtong Guan; Liqiang Zhang; Xingshu Ji; Yuze He; Yonghe Zheng. Student Characteristics and ICT Usage as Predictors of Computational Thinking: An Explainable AI Approach. Journal of Intelligence (2025-11-11). https://doi.org/10.3390/jintelligence13110145

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.