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

doi:10.1007/978-3-319-32865-2_12

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

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

4 Citations (Scopus)

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 preknowledge 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 language	English
Title of host publication	Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers
Editors	Di ZOU, Zhiguo GONG, Dickson K.W. CHIU
Publisher	Springer-Verlag GmbH and Co. KG
Pages	111-117
Number of pages	7
Volume	9584
ISBN (Print)	9783319328645
DOIs	https://doi.org/10.1007/978-3-319-32865-2_12
Publication status	Published - 2016
Externally published	Yes
Event	The 14th International Conference on Web-based Learning - Guangzhou, China Duration: 5 Nov 2015 → 8 Nov 2015 http://www.cityu.edu.hk/merc/icwl/icwl2015home.htm

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9584 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 14th International Conference on Web-based Learning
Abbreviated title	ICWL 2015
Country/Territory	China
City	Guangzhou
Period	5/11/15 → 8/11/15
Internet address	http://www.cityu.edu.hk/merc/icwl/icwl2015home.htm

Bibliographical note

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).

Keywords

Learner profile
Vocabulary pre-knowledge
Word learning

Access to Document

10.1007/978-3-319-32865-2_12

Cite this

ZOU, D., XIE, H., WONG, T. L., RAO, Y., WANG, F. L., & WU, Q. (2016). Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. In D. ZOU, Z. GONG, & D. K. W. CHIU (Eds.), Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers (Vol. 9584, pp. 111-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9584 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-32865-2_12

ZOU, Di ; XIE, Haoran ; WONG, Tak Lam et al. / Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. editor / Di ZOU ; Zhiguo GONG ; Dickson K.W. CHIU. Vol. 9584 Springer-Verlag GmbH and Co. KG, 2016. pp. 111-117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Predicting pre-knowledge on vocabulary from e-learning assignments for language learners",

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{\textquoteright} 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 preknowledge 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.",

keywords = "Learner profile, Vocabulary pre-knowledge, Word learning",

author = "Di ZOU and Haoran XIE and WONG, {Tak Lam} and Yanghui RAO and WANG, {Fu Lee} and Qingyuan WU",

note = "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). ; The 14th International Conference on Web-based Learning , ICWL 2015 ; Conference date: 05-11-2015 Through 08-11-2015",

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isbn = "9783319328645",

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pages = "111--117",

editor = "Di ZOU and Zhiguo GONG and CHIU, {Dickson K.W.}",

booktitle = "Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers",

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url = "http://www.cityu.edu.hk/merc/icwl/icwl2015home.htm",

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ZOU, D , XIE, H, WONG, TL, RAO, Y, WANG, FL & WU, Q 2016, Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. in D ZOU, Z GONG & DKW CHIU (eds), Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. vol. 9584, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9584 LNCS, Springer-Verlag GmbH and Co. KG, pp. 111-117, The 14th International Conference on Web-based Learning , Guangzhou, China, 5/11/15. https://doi.org/10.1007/978-3-319-32865-2_12

Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. / ZOU, Di ; XIE, Haoran; WONG, Tak Lam et al.
Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. ed. / Di ZOU; Zhiguo GONG; Dickson K.W. CHIU. Vol. 9584 Springer-Verlag GmbH and Co. KG, 2016. p. 111-117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9584 LNCS).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - WANG, Fu Lee

AU - WU, Qingyuan

N1 - 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).

PY - 2016

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N2 - 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 preknowledge 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.

AB - 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 preknowledge 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.

KW - Learner profile

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AN - SCOPUS:84981185383

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ZOU D , XIE H, WONG TL, RAO Y, WANG FL, WU Q. Predicting pre-knowledge on vocabulary from e-learning assignments for language learners. In ZOU D, GONG Z, CHIU DKW, editors, Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. Vol. 9584. Springer-Verlag GmbH and Co. KG. 2016. p. 111-117. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2016 Aug 2. doi: 10.1007/978-3-319-32865-2_12

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

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Keywords

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