Journal article

Quantitative learning strategies based on word networks

  • Zhao, Yue-Tian-Yi School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China
  • Jia, Zi-Yang Department of Computer Science, Rutgers University, Piscataway, USA
  • Tang, Yong School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
  • Xiong, Jason Jie Department of Computer Information Systems and Supply Chain Management, Walker College of Business, Appalachian State University, Boone, USA
  • Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland
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    01.02.2018
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2018, vol. 491, p. 898–911
English Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/306306
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