A Comparative Study on Linguistic Theories for Modelling EFL Learners : Facilitating Personalized Vocabulary Learning via Task Recommendations

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

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

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
Title of host publicationPersonalized Learning : Approaches, Methods and Practices
EditorsSimon K.S. CHEUNG, Fu Lee WANG, Lam For KWOK, Petra POULOVÁ
PublisherRoutledge
Chapter7
Pages91-103
Number of pages13
ISBN (Electronic)9781003448952
ISBN (Print)9781032581866, 9781032581873
DOIs
Publication statusPublished - 2024

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