Predicting potential difficulties in second language lexical tone learning with support vector machine models

Juqiang Chen; Catherine T. Best; Mark Antoniou

doi:10.1007/978-3-030-66906-5_36

Predicting potential difficulties in second language lexical tone learning with support vector machine models

Juqiang Chen, Catherine T. Best, Mark Antoniou

Western Sydney University

Research output: Chapter in Book / Conference Paper › Chapter

Abstract

Second language speech learning is affected by learners’ native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how naÃ¯ve listeners of diverse tone languages will assimilate non-native lexical tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to emphasize when instructing students from particular language backgrounds.

Original language	English
Title of host publication	Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings
Editors	Chaoyi Pang, Yunjun Gao, Guanliang Chen, Elvira Popescu, Lu Chen, Tianyong Hao, Bailing Zhang, Silvia Margarita Baldiris Navarro, Qing Li
Place of Publication	Switzerland
Publisher	Springer Nature
Pages	383-392
Number of pages	10
ISBN (Electronic)	9783030669065
ISBN (Print)	9783030669058
DOIs	https://doi.org/10.1007/978-3-030-66906-5_36
Publication status	Published - 2021

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10.1007/978-3-030-66906-5_36

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Chen, J., Best, C. T., & Antoniou, M. (2021). Predicting potential difficulties in second language lexical tone learning with support vector machine models. In C. Pang, Y. Gao, G. Chen, E. Popescu, L. Chen, T. Hao, B. Zhang, S. M. B. Navarro, & Q. Li (Eds.), Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings (pp. 383-392). Springer Nature. https://doi.org/10.1007/978-3-030-66906-5_36

Chen, Juqiang ; Best, Catherine T. ; Antoniou, Mark. / Predicting potential difficulties in second language lexical tone learning with support vector machine models. Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings. editor / Chaoyi Pang ; Yunjun Gao ; Guanliang Chen ; Elvira Popescu ; Lu Chen ; Tianyong Hao ; Bailing Zhang ; Silvia Margarita Baldiris Navarro ; Qing Li. Switzerland : Springer Nature, 2021. pp. 383-392

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title = "Predicting potential difficulties in second language lexical tone learning with support vector machine models",

abstract = "Second language speech learning is affected by learners{\textquoteright} native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how na{\~A}¯ve listeners of diverse tone languages will assimilate non-native lexical tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to emphasize when instructing students from particular language backgrounds.",

author = "Juqiang Chen and Best, {Catherine T.} and Mark Antoniou",

year = "2021",

doi = "10.1007/978-3-030-66906-5_36",

language = "English",

isbn = "9783030669058",

pages = "383--392",

editor = "Chaoyi Pang and Yunjun Gao and Guanliang Chen and Elvira Popescu and Lu Chen and Tianyong Hao and Bailing Zhang and Navarro, {Silvia Margarita Baldiris} and Qing Li",

booktitle = "Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings",

publisher = "Springer Nature",

address = "Switzerland",

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Chen, J, Best, CT & Antoniou, M 2021, Predicting potential difficulties in second language lexical tone learning with support vector machine models. in C Pang, Y Gao, G Chen, E Popescu, L Chen, T Hao, B Zhang, SMB Navarro & Q Li (eds), Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings. Springer Nature, Switzerland, pp. 383-392. https://doi.org/10.1007/978-3-030-66906-5_36

Predicting potential difficulties in second language lexical tone learning with support vector machine models. / Chen, Juqiang; Best, Catherine T.; Antoniou, Mark.
Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings. ed. / Chaoyi Pang; Yunjun Gao; Guanliang Chen; Elvira Popescu; Lu Chen; Tianyong Hao; Bailing Zhang; Silvia Margarita Baldiris Navarro; Qing Li. Switzerland: Springer Nature, 2021. p. 383-392.

Research output: Chapter in Book / Conference Paper › Chapter

TY - CHAP

T1 - Predicting potential difficulties in second language lexical tone learning with support vector machine models

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AU - Best, Catherine T.

AU - Antoniou, Mark

PY - 2021

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N2 - Second language speech learning is affected by learners’ native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how naÃ¯ve listeners of diverse tone languages will assimilate non-native lexical tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to emphasize when instructing students from particular language backgrounds.

AB - Second language speech learning is affected by learners’ native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how naÃ¯ve listeners of diverse tone languages will assimilate non-native lexical tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to emphasize when instructing students from particular language backgrounds.

UR - https://hdl.handle.net/1959.7/uws:67348

U2 - 10.1007/978-3-030-66906-5_36

DO - 10.1007/978-3-030-66906-5_36

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SN - 9783030669058

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EP - 392

BT - Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings

A2 - Pang, Chaoyi

A2 - Gao, Yunjun

A2 - Chen, Guanliang

A2 - Popescu, Elvira

A2 - Chen, Lu

A2 - Hao, Tianyong

A2 - Zhang, Bailing

A2 - Navarro, Silvia Margarita Baldiris

A2 - Li, Qing

PB - Springer Nature

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Chen J, Best CT, Antoniou M. Predicting potential difficulties in second language lexical tone learning with support vector machine models. In Pang C, Gao Y, Chen G, Popescu E, Chen L, Hao T, Zhang B, Navarro SMB, Li Q, editors, Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020 Ningbo, China, October 22–24, 2020, Proceedings. Switzerland: Springer Nature. 2021. p. 383-392 doi: 10.1007/978-3-030-66906-5_36

Predicting potential difficulties in second language lexical tone learning with support vector machine models

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