Based on Kellogg's writing model, Skehan's Limited Attentional Capacity Model (LACM), and Robinson's Cognition Hypothesis, our study investigated the effects of cognitive task complexity on syntactic complexity, lexical complexity, accuracy, fluency, and functional adequacy in Chinese L2 students' argumentative writing, when students were under an online planning condition. Sixty-eight participants from a Chinese university were recruited to complete two writing tasks with task complexity varied in terms of [+ argument elements]. The findings showed that increasing task complexity led to decreased subordination in terms of clauses per T-unit and dependent clauses per clause, increased phrasal elaboration in terms of coordinate phrases per clause, and no changes in mean length of T-unit, T-units per sentence, mean length of clause, and complex nominals per clause. Neither significant differences in accuracy nor fluency were found as a function of increasing task complexity. Detrimental effects on functional adequacy in content, organization, and overall scores were identified with the increases in task complexity. The trade-offs between syntactic and lexical complexity and between syntactic complexity and functional adequacy support the basic principle of Skehan's LACM that human's information processing capacity is limited and Kellogg's claim that learners have a limited central executive capacity in writing. Implications of the results of this research are discussed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469471 | PMC |
http://dx.doi.org/10.3389/fpsyg.2023.1121994 | DOI Listing |
Rheumatol Adv Pract
January 2025
Rheumatology Unit, ERN ReCONNET Center, IRCCS Meyer Children's Hospital, Firenze, Italy.
Objectives: Two different European Reference Networks cover CTDs with paediatric onset, the European Reference Network on Rare and Complex Connective Tissue Diseases (ERN ReCONNET) and the European Reference Network on Rare Immunological Disorders (ERN RITA). The transition of care is a significant focus, with ReCONNET centres actively addressing this through updated programs. Despite these efforts, challenges persist.
View Article and Find Full Text PDFProc Conf Assoc Comput Linguist Meet
March 2024
Hierarchical text classification (HTC) is a complex subtask under multi-label text classification, characterized by a hierarchical label taxonomy and data imbalance. The best-performing models aim to learn a static representation by combining document and hierarchical label information. However, the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation.
View Article and Find Full Text PDFSci Rep
January 2025
GEOPS, Univ. Paris-Sud, CNRS, Université Paris-Saclay, Orsay, 91405, France.
Mass controls two major processes in volcanic islands: large-scale collapse and vertical movements. Therefore, large islands like Hawaii are gradually subsiding and have undergone massive landsliding. What if the mass is much smaller, and there is good evidence that the vertical movement is more complex than simple loading-related subsidence? Here, we show that small volcanic islands, seemingly stable because of the small mass, can undergo sector collapses that can affect the vertical movement of the island.
View Article and Find Full Text PDFSci Rep
January 2025
School of Rail Transportation, Shandong Jiaotong University, Jinan, 250357, China.
In the task of pavement distress recognition and classification, the complexity of the pavement environment, the small proportion of distresses in images, significant variation in distress scales, and the influence of features such as vehicles and traffic signs in the data make distress feature extraction challenging. This paper proposes a spectrum focus transformer (SFT) layer, which processes the signal spectrum and focuses on important frequency components. Initially, by thoroughly analyzing the frequency domain characteristics of image data, frequency value distribution information is obtained to achieve fine-tuning of different frequency components.
View Article and Find Full Text PDFNat Commun
January 2025
School of Information Science and Technology, Fudan University, Shanghai, China.
Accelerating the discovery of novel crystal materials by machine learning is crucial for advancing various technologies from clean energy to information processing. The machine-learning models for prediction of materials properties require embedding atomic information, while traditional methods have limited effectiveness in enhancing prediction accuracy. Here, we proposed an atomic embedding strategy called universal atomic embeddings (UAEs) for their broad applicability as atomic fingerprints, and generated the UAE tensors based on the proposed CrystalTransformer model.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!