Language proficiency classification during computer-based test with EEG pattern recognition methods

Federico Cirett-Galán; Raquel Torres-Peralta; Carole R. Beal

doi:10.1007/978-3-319-59226-8_28

Language proficiency classification during computer-based test with EEG pattern recognition methods

Federico Cirett-Galán^*, Raquel Torres-Peralta, Carole R. Beal

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The answering of any test represents a challenge for students; however, foreign students whose first language is not English have to deal with the difficulty of the understanding of a series of questions written on a different language in addition of the effort required to solve the problem. In this study, we recorded the behavior of the brain signals of 16 students, 10 whom first language was English and 6 who were English learners, and used two supervised classification algorithms in order to identify the students’ language proficiency. The results shown that in both approaches, harder problems which required longer time to be responded had a higher accuracy rate; however, more tests are needed in order to understand the physical processing of written math text problem and the difference among both groups.

Original language	English
Title of host publication	Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings
Editors	Jesus Ariel Carrasco-Ochoa, Jose Francisco Martinez-Trinidad, Jose Arturo Olvera-Lopez
Publisher	Springer Verlag
Pages	288-296
Number of pages	9
ISBN (Print)	9783319592251
DOIs	https://doi.org/10.1007/978-3-319-59226-8_28
State	Published - 1 Jan 2017
Event	9th Mexican Conference on Pattern Recognition, MCPR 2017 - Huatulco, Mexico Duration: 21 Jun 2017 → 24 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10267 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Mexican Conference on Pattern Recognition, MCPR 2017
Country/Territory	Mexico
City	Huatulco
Period	21/06/17 → 24/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Behavior
Data mining
Electroencephalography
Intelligent tutoring systems
Machine learning
Pattern recognition
Physiology

Access to Document

10.1007/978-3-319-59226-8_28

Cite this

Cirett-Galán, F., Torres-Peralta, R., & Beal, C. R. (2017). Language proficiency classification during computer-based test with EEG pattern recognition methods. In J. A. Carrasco-Ochoa, J. F. Martinez-Trinidad, & J. A. Olvera-Lopez (Eds.), Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings (pp. 288-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10267 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59226-8_28

Cirett-Galán, Federico ; Torres-Peralta, Raquel ; Beal, Carole R. / Language proficiency classification during computer-based test with EEG pattern recognition methods. Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings. editor / Jesus Ariel Carrasco-Ochoa ; Jose Francisco Martinez-Trinidad ; Jose Arturo Olvera-Lopez. Springer Verlag, 2017. pp. 288-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Language proficiency classification during computer-based test with EEG pattern recognition methods",

abstract = "The answering of any test represents a challenge for students; however, foreign students whose first language is not English have to deal with the difficulty of the understanding of a series of questions written on a different language in addition of the effort required to solve the problem. In this study, we recorded the behavior of the brain signals of 16 students, 10 whom first language was English and 6 who were English learners, and used two supervised classification algorithms in order to identify the students{\textquoteright} language proficiency. The results shown that in both approaches, harder problems which required longer time to be responded had a higher accuracy rate; however, more tests are needed in order to understand the physical processing of written math text problem and the difference among both groups.",

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author = "Federico Cirett-Gal{\'a}n and Raquel Torres-Peralta and Beal, {Carole R.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th Mexican Conference on Pattern Recognition, MCPR 2017 ; Conference date: 21-06-2017 Through 24-06-2017",

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doi = "10.1007/978-3-319-59226-8_28",

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Cirett-Galán, F , Torres-Peralta, R & Beal, CR 2017, Language proficiency classification during computer-based test with EEG pattern recognition methods. in JA Carrasco-Ochoa, JF Martinez-Trinidad & JA Olvera-Lopez (eds), Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10267 LNCS, Springer Verlag, pp. 288-296, 9th Mexican Conference on Pattern Recognition, MCPR 2017, Huatulco, Mexico, 21/06/17. https://doi.org/10.1007/978-3-319-59226-8_28

Language proficiency classification during computer-based test with EEG pattern recognition methods. / Cirett-Galán, Federico ; Torres-Peralta, Raquel; Beal, Carole R.
Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings. ed. / Jesus Ariel Carrasco-Ochoa; Jose Francisco Martinez-Trinidad; Jose Arturo Olvera-Lopez. Springer Verlag, 2017. p. 288-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10267 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Language proficiency classification during computer-based test with EEG pattern recognition methods

AU - Cirett-Galán, Federico

AU - Torres-Peralta, Raquel

AU - Beal, Carole R.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The answering of any test represents a challenge for students; however, foreign students whose first language is not English have to deal with the difficulty of the understanding of a series of questions written on a different language in addition of the effort required to solve the problem. In this study, we recorded the behavior of the brain signals of 16 students, 10 whom first language was English and 6 who were English learners, and used two supervised classification algorithms in order to identify the students’ language proficiency. The results shown that in both approaches, harder problems which required longer time to be responded had a higher accuracy rate; however, more tests are needed in order to understand the physical processing of written math text problem and the difference among both groups.

AB - The answering of any test represents a challenge for students; however, foreign students whose first language is not English have to deal with the difficulty of the understanding of a series of questions written on a different language in addition of the effort required to solve the problem. In this study, we recorded the behavior of the brain signals of 16 students, 10 whom first language was English and 6 who were English learners, and used two supervised classification algorithms in order to identify the students’ language proficiency. The results shown that in both approaches, harder problems which required longer time to be responded had a higher accuracy rate; however, more tests are needed in order to understand the physical processing of written math text problem and the difference among both groups.

KW - Behavior

KW - Data mining

KW - Electroencephalography

KW - Intelligent tutoring systems

KW - Machine learning

KW - Pattern recognition

KW - Physiology

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DO - 10.1007/978-3-319-59226-8_28

M3 - Contribución a la conferencia

AN - SCOPUS:85021218285

SN - 9783319592251

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 288

EP - 296

BT - Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings

A2 - Carrasco-Ochoa, Jesus Ariel

A2 - Martinez-Trinidad, Jose Francisco

A2 - Olvera-Lopez, Jose Arturo

PB - Springer Verlag

T2 - 9th Mexican Conference on Pattern Recognition, MCPR 2017

Y2 - 21 June 2017 through 24 June 2017

ER -

Cirett-Galán F , Torres-Peralta R, Beal CR. Language proficiency classification during computer-based test with EEG pattern recognition methods. In Carrasco-Ochoa JA, Martinez-Trinidad JF, Olvera-Lopez JA, editors, Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings. Springer Verlag. 2017. p. 288-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59226-8_28

Language proficiency classification during computer-based test with EEG pattern recognition methods

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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