Generating incidental word learning tasks via topic-based and load-based profiles

Haoran XIE, Di ZOU, Yiu Keung Raymond LAU, Fu Lee WANG, Tak Lam WONG

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Incidental and intentional learning are the two main approaches to word learning. Compared to incidental learning, intentional learning is inferior in that it demotivates learners, concentrates solely on the learning of word knowledge and provides learners with very restricted contexts of target words. The effectiveness of incidental word learning tasks can also be further increased by providing learners with materials that they are more familiar with or interested in. Therefore, in this article, we present a framework to generate incidental word learning tasks via (i) load-based profiles measured through the involvement load hypothesis, a construct proposed to evaluate the effectiveness of incidental word learning tasks, and (ii) topic-based profiles obtained from social media. We also conduct an experiment on real participants and find that the proposed framework promotes more effective word learning and increases learning enjoyment compared to the intentional word learning. Copyright © 2016 IEEE Computer Society.
Original languageEnglish
Pages (from-to)60-70
JournalIEEE Multimedia
Volume23
Issue number1
Early online dateNov 2015
DOIs
Publication statusPublished - 2016

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Citation

Xie, H., Zou, D., Lau, R., Wang, F. L., & Wong, T.-L. (2016). Generating incidental word learning tasks via topic-based and load-based profiles. IEEE MultiMedia, 23(1), 60-70.