Personalized word-learning based on technique feature analysis and learning analytics

Di ZOU, Haoran XIE*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)

9 Citations (Scopus)

Abstract

Many studies have highlighted the importance of personalized learning, and most current e-learning systems are able to personalize materials, activities, etc., based on individualized learner-factors. However, none of the extant word-learning systems provides a personalized learning experience that is guided by a comprehensive word learning theory. In this study, we develop such a system based on Nation and Webb's checklist for technique feature analysis - a thorough set of factors that promote effective word learning. This system recommends personalized word learning tasks based on the technique feature analysis scores of different tasks and user models. To examine the effectiveness of the proposed system, we conducted an experiment among 105 English learners, grouped them into three teams randomly, and asked them to learn forty target words through three approaches: a non-personalized approach, a personalized approach guided by a partial version of the technique feature analysis, and a personalized approach guided by the full list of the technique feature analysis. Significant differences were observed among the effectiveness of the three approaches in promoting word learning, with the personalized approach guided by the complete checklist leading to the best learning performance. It is therefore suggested that e-learning systems should be designed based on comprehensive learning theories.

Original languageEnglish
Pages (from-to)233-244
Number of pages12
JournalEducational Technology and Society
Volume21
Issue number2
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Learning analytics
  • Personalized learning
  • Technique feature analysis
  • User model
  • Vocabulary acquisition

Fingerprint Dive into the research topics of 'Personalized word-learning based on technique feature analysis and learning analytics'. Together they form a unique fingerprint.

  • Cite this