Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniques
Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, YeongWook [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 86(2015), 1 vom: 11. Sept., Seite 271-285 |
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| Übergeordnetes Werk: |
volume:86 ; year:2015 ; number:1 ; day:11 ; month:09 ; pages:271-285 |
| Links: |
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| DOI / URN: |
10.1007/s11277-015-3062-2 |
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OLC2053795441 |
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| 520 | |a Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. | ||
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10.1007/s11277-015-3062-2 doi (DE-627)OLC2053795441 (DE-He213)s11277-015-3062-2-p DE-627 ger DE-627 rakwb eng 620 VZ Yang, YeongWook verfasserin aut Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniques 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. Language proficiency Cognitive ability Second language Yu, WonHee aut Lim, HeuiSeok aut Enthalten in Wireless personal communications Springer US, 1994 86(2015), 1 vom: 11. Sept., Seite 271-285 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:86 year:2015 number:1 day:11 month:09 pages:271-285 https://doi.org/10.1007/s11277-015-3062-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 86 2015 1 11 09 271-285 |
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10.1007/s11277-015-3062-2 doi (DE-627)OLC2053795441 (DE-He213)s11277-015-3062-2-p DE-627 ger DE-627 rakwb eng 620 VZ Yang, YeongWook verfasserin aut Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniques 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. Language proficiency Cognitive ability Second language Yu, WonHee aut Lim, HeuiSeok aut Enthalten in Wireless personal communications Springer US, 1994 86(2015), 1 vom: 11. Sept., Seite 271-285 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:86 year:2015 number:1 day:11 month:09 pages:271-285 https://doi.org/10.1007/s11277-015-3062-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 86 2015 1 11 09 271-285 |
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10.1007/s11277-015-3062-2 doi (DE-627)OLC2053795441 (DE-He213)s11277-015-3062-2-p DE-627 ger DE-627 rakwb eng 620 VZ Yang, YeongWook verfasserin aut Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniques 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. Language proficiency Cognitive ability Second language Yu, WonHee aut Lim, HeuiSeok aut Enthalten in Wireless personal communications Springer US, 1994 86(2015), 1 vom: 11. Sept., Seite 271-285 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:86 year:2015 number:1 day:11 month:09 pages:271-285 https://doi.org/10.1007/s11277-015-3062-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 86 2015 1 11 09 271-285 |
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Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. © Springer Science+Business Media New York 2015 |
| abstractGer |
Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. © Springer Science+Business Media New York 2015 |
| abstract_unstemmed |
Abstract This paper proposes a novel method for predicting second language proficiency based on linguistic cognitive ability measured in linguistic cognitive response test. Our method is based on an assumption that there is a correlation between language aptitude test scores and linguistic cognitive ability. Our proposed method for predicting L2 language proficiency uses as input learner’s linguistic cognition aptitude data. In our experiment, the method produced promising results with the predictive power as high as 70 %. Linguistic cognitive ability is measured through linguistic cognition tasks, which are: reading lexical decision tasks (LDT), listening LDT, translation recognition tasks, and semantic recognition tasks. Each type of the tasks is related to a different linguistic function in the brain. After measuring the learner’s linguistic cognitive aptitude, the result is fed as input for a machine learning model, which makes predictions for the corresponding language proficiency level. In training the linguistic proficiency classifier, we used multi-layer perceptron, Naive Bayes, logistic regression, and random forest model. For input data set in our experiment, we had 42 participants take our cognitive aptitude tests and used the result. Our classifier showed an accuracy >70 % in predicting proficiency level. Among the models, random forest model produced the best predictive power. © Springer Science+Business Media New York 2015 |
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Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniques |
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Yu, WonHee Lim, HeuiSeok |
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