Educational policy as predictor of computational thinking: A supervised machine learning approach

• Published in: Journal of computer assisted learning 2024-12, Vol.40 (6), p.2872-2885
• Main Authors: Ezeamuzie, Ndudi O., Leung, Jessica S. C., Fung, Dennis C. L., Ezeamuzie, Mercy N.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Autonomy; Computation; computational thinking; Curricula; data mining; Education; Educational Environment; Educational Methods; Educational Policy; Educational Strategies; Environmental Influences; Influence of Technology; Machine learning; Policies; policymaking; Predictions; Problem solving; School environment; Students; Supervised learning; Teachers; Teaching Methods
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.13041

Background Computational thinking is derived from arguments that the underlying practices in computer science augment problem‐solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational policies on computational thinking remains unclear. Objectives This study examines the impact of basic and technology‐related educational policies on the development of computational thinking. Methods Using supervised machine learning, the computational thinking achievements of 31,823 eighth graders across nine countries were analysed. Seven rule‐based and tree‐based classification models were generated and triangulated to determine how educational policies predicted students' computational thinking. Results and conclusions Predictions show that students have a higher propensity to develop computational thinking skills when schools exercise full autonomy in governance and explicitly embed computational thinking in their curriculum. Plans to support students, teachers and schools with technology or introduce 1:1 computing have no discernible predicted influence on students' computational thinking achievement. Implications Although predictions deduced from these attributes are not generalizable, traces of how educational policies affect computational thinking exist to articulate more fronts for future research on the influence of educational policies on computational thinking. Lay description What is already known about this topic Computational thinking (CT) is a problem‐solving skill. Inquiries on CT focus on learners' factors such as age, gender and attitudes. Also, the choice of instructional strategies and learning environment influence CT development. What this paper adds Articulated how the wider community structures influence the development of CT. Educational policies affect the development of computational thinking. Implications for practices Students have a higher predicted propensity to develop CT when schools exercise full autonomy in governance and embed CT in the curriculum explicitly. Plans to support students, teachers and schools with technology or plans to introduce 1:1 computing have no discernible influence on students' CT.