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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
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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