A comparative study on linguistic theories for modeling EFL learners: facilitating personalized vocabulary learning via task recommendations

Di ZOU, Minhong WANG, Haoran XIE*, Gary CHENG, Fu Lee WANG, Lap-kei LEE

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)

Abstract

Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task recommendations in e-learning systems is a very effective way to facilitate personalized vocabulary learning. One of the key research issues regarding these personalized vocabulary learning systems is how to model the learning logs of each learner. Specifically, how to measure the learning effectiveness of each learned tasks has become a core issue for establishing personalized learning systems. Three theories, Spaced Learning (SL), Technique Feature Analysis (TFA), and Involvement Load Hypothesis (ILH), are commonly applied for achieving this purpose. In this study, we compared the effectiveness of these three linguistic theories for modeling EFL learners’ personalized vocabulary learning via task recommendations. By conducting experimental studies among different groups of participants, our findings revealed that the ILH and TFA were more suitable than SL for facilitating personalized vocabulary learning. It is therefore suggested that future personalized vocabulary learning systems ought to be designed and developed based on comprehensive theoretical frameworks such as the ILH and TFA.
Original languageEnglish
Number of pages13
JournalInteractive Learning Environments
DOIs
Publication statusE-pub ahead of print - 11 Jul 2020

Bibliographical note

Funding:
This research received grants from the Standing Committee on Language Education and Research (EDB(LE)/P&R/EL/175/2), the Education Bureau of the Hong Kong Special Administrative Region; Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005); National Natural Science Foundation of China (No. 61977023); the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20); the Research Cluster Fund (RG 78/2019-2020R) of The Education University of Hong Kong.

Acknowledgement:
This research was fully supported by the Standing Committee on Language Education and Research (EDB(LE)/P&R/EL/175/2), the Education Bureau of the Hong Kong Special Administrative Region; Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005); National Natural Science Foundation of China (No. 61977023); the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20); the Research Cluster Fund (RG 78/2019- 2020R) of The Education University of Hong Kong. A preliminary study was published in the International Conference on Blended Learning 2019 (Xie, Wang, et al., 2019), and this article has been thoroughly re-written with completely different research objectives and methods, as well as newly developed personalized vocabulary learning systems.

Keywords

  • Personalized learning
  • learner modeling
  • linguistic theories
  • task recommendations
  • vocabulary acquisition

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