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.
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Notes
- 1.
We re-scale the degree of the value to [0,1] for the two baselines.
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Acknowledgement
The work described in this paper was fully supported by a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), a grant from the National Natural Science Foundation of China (Grant No. 61502545), a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013), and “the Fundamental Research Funds for the Central Universities” (Grant No. 46000-31610009).
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Zou, D., Xie, H., Wong, TL., Rao, Y., Wang, F.L., Wu, Q. (2016). Predicting Pre-knowledge on Vocabulary from e-Learning Assignments for Language Learners. In: Gong, Z., Chiu, D., Zou, D. (eds) Current Developments in Web Based Learning. ICWL 2015. Lecture Notes in Computer Science(), vol 9584. Springer, Cham. https://doi.org/10.1007/978-3-319-32865-2_12
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