TY - JOUR
T1 - A comparative study on linguistic theories for modeling EFL learners
T2 - facilitating personalized vocabulary learning via task recommendations
AU - Zou, Di
AU - Wang, Minhong
AU - Xie, Haoran
AU - Cheng, Gary
AU - Wang, Fu Lee
AU - Lee, Lap Kei
N1 - Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Personalized learning
KW - learner modeling
KW - linguistic theories
KW - task recommendations
KW - vocabulary acquisition
UR - http://www.scopus.com/inward/record.url?scp=85087868988&partnerID=8YFLogxK
U2 - 10.1080/10494820.2020.1789178
DO - 10.1080/10494820.2020.1789178
M3 - Article
AN - SCOPUS:85087868988
SN - 1049-4820
SP - 1
EP - 13
JO - Interactive Learning Environments
JF - Interactive Learning Environments
ER -