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Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
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ExpMRC: explainability evaluation for machine reading comprehension
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In: Heliyon (2022)
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Multilingual multi-aspect explainability analyses on machine reading comprehension models
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In: iScience (2022)
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Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
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Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ...
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Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
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Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
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Neural Stylistic Response Generation with Disentangled Latent Variables ...
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Language learners' enjoyment and emotion regulation in online collaborative learning
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Abstract:
This study explores how language learners’ enjoyment and emotion regulation manifest themselves in online collaborative learning. In the study, we collected multiple data during online collaboration tasks carried out by six Chinese undergraduate EFL learners in two 3-member groups, facilitated by a social media app. We used an idiodynamic approach to examine the moment-to-moment evolution of enjoyment at both the individual and group levels, and carried out semi-structured interviews and video recordings of the online group conversation to study the mechanisms of emotion regulation underlying the group-level enjoyment in both groups. The findings documented the dynamic evolution of enjoyment within and across individuals during the collaboration tasks. Participants used different but mutually supported types of regulation such as self-, co-, and socially shared regulation to achieve group-level enjoyment. Within the interplay of these regulation types, participants mostly engaged in shared regulation processes including joint planning, monitoring, and evaluating. The study also revealed that participants adopted emojis, together with words, to realize emotion regulation in online collaborative settings. The findings will be helpful to teachers and learners in optimizing their collaborative language activities, especially those that are online.
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Keyword:
280109 - Expanding knowledge in education; 390409 - Learning sciences; affect (psychology); education; emotions; higher; second language acquisition
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URL: http://hdl.handle.net/1959.7/uws:58746 https://doi.org/10.1016/j.system.2021.102478
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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ...
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TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
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N-LTP: An Open-source Neural Language Technology Platform for Chinese ...
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Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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