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Song, Ningyuan; Chen, Kejun; Jin, Xiufang; Zhao, Yuehua – Journal of Computer Assisted Learning, 2023
Background: In the digital environment, users' academic reading behaviour has changed, working with many articles simultaneously to search, filter, scan, link, annotate and analyse content fragments. The semantic enhancement environment has been widely set with semantic technologies to offer additional and handy support for users and thus…
Descriptors: Reading Processes, Reading Skills, Semantics, Cognitive Processes
Andersen, Nico; Zehner, Fabian; Goldhammer, Frank – Journal of Computer Assisted Learning, 2023
Background: In the context of large-scale educational assessments, the effort required to code open-ended text responses is considerably more expensive and time-consuming than the evaluation of multiple-choice responses because it requires trained personnel and long manual coding sessions. Aim: Our semi-supervised coding method eco (exploring…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Dominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
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
Gu, Xiaoqing; Cai, Huiying – Journal of Computer Assisted Learning, 2019
A semantic diagram tool is proposed in this study in order to structure collaborative problem solving (CPS) based on cognitive load theory (CLT). To investigate its effects on transaction cost and the deepening of user understandings, a comparative quasi-experiment was designed and conducted with 49 participants from a university in East China.…
Descriptors: Semantics, Cooperative Learning, Cognitive Ability, Foreign Countries
van de Ven, M.; de Leeuw, L.; van Weerdenburg, M.; Steenbeek-Planting, E. G. – Journal of Computer Assisted Learning, 2017
This study examined the effects of an intervention with a multicomponent reading game on the development of reading skills in 60 Dutch primary school children with special educational needs. The game contains evidence-based reading exercises and is based on principles of applied gaming. Using a multiple baseline approach, we tested children's…
Descriptors: Foreign Countries, Early Intervention, Early Reading, Reading Instruction