Predicting pre-knowledge on vocabulary from e-learning assignments for language learners

Di ZOU, Haoran XIE, Tak Lam WONG, Yanghui RAO, Fu Lee WANG, Qingyuan WU

Research output: Contribution to conferencePaper

Abstract

In the current big data era, we have witnessed the prosperity of emerging massive open online courses, user-generated data and ubiquitous techniques. These evolving technologies and applications have significantly changed the ways for people to learn new knowledge and access information. To find users’ desired data in an effective and efficient way, it is critical to understand/model users in applications involving in such a large volume of learning resources. For instance, word learning systems can be promoted significantly in terms of learning effectiveness if the pre-knowledge on vocabulary of learners can be predicted accurately. In this research, we focus on the issue of how to model a specific group of users, i.e., language learners, in the context of e-learning systems. Specifically, we try to predict the pre-knowledge on vocabulary of learners from their previous learning documents such as writing assignments and reading essays. The experimental study on real participants shows that the proposed predicting model is very effective and can be exploited for various applications in the future.
Original languageEnglish
Publication statusPublished - Nov 2015

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Learning systems
Big data

Citation

Zou, D., Xie, H., Wong, T.-L., Rao, Y., Wang. F. L., & Wu, Q. (2015, November). Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. Paper presented at The 14th International Conference on Web-based Learning (ICWL 2015), South China University of Technology, Guangzhou, China.

Keywords

  • Learner profile
  • Word learning
  • Vocabulary pre-knowledge