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Jia-Hua Zhao; Shu-Tao Shangguan; Ying Wang – Journal of Computer Assisted Learning, 2025
Background: Computational thinking (CT) is a fundamental ability required of individuals in the 21st-century digital world. Past studies show that generative artificial intelligence (GenAI) can enhance students' CT skills. However, GenAI may produce inaccurate output, and students who rely too much on AI may learn little and be unable to think…
Descriptors: Artificial Intelligence, Technology Uses in Education, Skill Development, Computation
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
Ramirez-Arellano, Aldo; Acosta-Gonzaga, Elizabeth; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel – Journal of Computer Assisted Learning, 2018
In Mexico, approximately 504,000 students pursue a bachelor's degree by means of distance or blended programmes. However, only 42% of these students conclude their degree on time. In the context of blended learning, the focus of this research is to present a causal model, based on a theoretical framework, which describes the relationships…
Descriptors: Causal Models, Blended Learning, Outcomes of Education, Learning Strategies

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