An explicit learner profiling model for personalized word learning recommendation

Di ZOU; Haoran XIE; Tak-lam WONG; Fu Lee WANG; Reggie KWAN; Wai Hong CHAN

doi:10.1007/978-3-319-71084-6_58

An explicit learner profiling model for personalized word learning recommendation

Di ZOU, Haoran XIE^*, Tak-lam WONG, Fu Lee WANG, Reggie KWAN, Wai Hong CHAN

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.

Original language	English
Title of host publication	Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers
Editors	Tien-Chi HUANG, Rynson LAU, Yueh-Min HUANG, Marc SPANIOL, Chun-Hung YUEN
Publisher	Springer Nature Switzerland AG
Pages	495-499
Number of pages	5
ISBN (Electronic)	9783319710846
ISBN (Print)	9783319710839
DOIs	https://doi.org/10.1007/978-3-319-71084-6_58
Publication status	Published - 2017
Externally published	Yes
Event	2nd International Symposium on Emerging Technologies for Education - Cape Town, South Africa Duration: 20 Sept 2017 → 22 Sept 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10676
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Emerging Technologies for Education
Abbreviated title	SETE 2017
Country/Territory	South Africa
City	Cape Town
Period	20/09/17 → 22/09/17

Funding

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), the Internal Research Grant (RG 66/2016-2017) and the Start-Up Research Grant (RG 37/2016-2017R) of The Education University of Hong Kong.

Keywords

Language acquisition
Word learning
User modeling
Task recommendation

Access to Document

10.1007/978-3-319-71084-6_58

Cite this

ZOU, D., XIE, H., WONG, T., WANG, F. L., KWAN, R., & CHAN, W. H. (2017). An explicit learner profiling model for personalized word learning recommendation. In T.-C. HUANG, R. LAU, Y.-M. HUANG, M. SPANIOL, & C.-H. YUEN (Eds.), Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers (pp. 495-499). (Lecture Notes in Computer Science; Vol. 10676). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-319-71084-6_58

ZOU, Di ; XIE, Haoran ; WONG, Tak-lam et al. / An explicit learner profiling model for personalized word learning recommendation. Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers. editor / Tien-Chi HUANG ; Rynson LAU ; Yueh-Min HUANG ; Marc SPANIOL ; Chun-Hung YUEN. Springer Nature Switzerland AG, 2017. pp. 495-499 (Lecture Notes in Computer Science).

@inproceedings{df76d4d8f6094656aa3bac8e19206c4a,

title = "An explicit learner profiling model for personalized word learning recommendation",

abstract = "Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.",

keywords = "Language acquisition, Word learning, User modeling, Task recommendation",

author = "Di ZOU and Haoran XIE and Tak-lam WONG and WANG, {Fu Lee} and Reggie KWAN and CHAN, {Wai Hong}",

year = "2017",

doi = "10.1007/978-3-319-71084-6_58",

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editor = "HUANG, {Tien-Chi } and Rynson LAU and Yueh-Min HUANG and Marc SPANIOL and Chun-Hung YUEN",

booktitle = "Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers",

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ZOU, D , XIE, H, WONG, T, WANG, FL, KWAN, R & CHAN, WH 2017, An explicit learner profiling model for personalized word learning recommendation. in T-C HUANG, R LAU, Y-M HUANG, M SPANIOL & C-H YUEN (eds), Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers. Lecture Notes in Computer Science, vol. 10676, Springer Nature Switzerland AG, pp. 495-499, 2nd International Symposium on Emerging Technologies for Education, Cape Town, South Africa, 20/09/17. https://doi.org/10.1007/978-3-319-71084-6_58

An explicit learner profiling model for personalized word learning recommendation. / ZOU, Di ; XIE, Haoran; WONG, Tak-lam et al.
Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers. ed. / Tien-Chi HUANG; Rynson LAU; Yueh-Min HUANG; Marc SPANIOL; Chun-Hung YUEN. Springer Nature Switzerland AG, 2017. p. 495-499 (Lecture Notes in Computer Science; Vol. 10676).

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

TY - GEN

T1 - An explicit learner profiling model for personalized word learning recommendation

AU - ZOU, Di

AU - XIE, Haoran

AU - WONG, Tak-lam

AU - WANG, Fu Lee

AU - KWAN, Reggie

AU - CHAN, Wai Hong

PY - 2017

Y1 - 2017

N2 - Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.

AB - Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.

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T3 - Lecture Notes in Computer Science

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BT - Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers

A2 - HUANG, Tien-Chi

A2 - LAU, Rynson

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PB - Springer Nature Switzerland AG

T2 - 2nd International Symposium on Emerging Technologies for Education

Y2 - 20 September 2017 through 22 September 2017

ER -

ZOU D , XIE H, WONG T, WANG FL, KWAN R, CHAN WH. An explicit learner profiling model for personalized word learning recommendation. In HUANG TC, LAU R, HUANG YM, SPANIOL M, YUEN CH, editors, Emerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers. Springer Nature Switzerland AG. 2017. p. 495-499. (Lecture Notes in Computer Science). Epub 2017 Nov 18. doi: 10.1007/978-3-319-71084-6_58

An explicit learner profiling model for personalized word learning recommendation

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