TY - GEN
T1 - A Supervised Learning Model for the Automatic Assessment of Language Levels Based on Learner Errors
AU - Ballier, Nicolas
AU - Gaillat, Thomas
AU - Simpkin, Andrew
AU - Stearns, Bernardo
AU - Bouyé, Manon
AU - Zarrouk, Manel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - This paper focuses on the use of technology in language learning. Language training requires the need to group learners homogeneously and to provide them with instant feedback on their productions such as errors [8, 15, 17] or proficiency levels. A possible approach is to assess writings from students and assign them with a level. This paper analyses the possibility of automatically predicting Common European Framework of Reference (CEFR) language levels on the basis of manually annotated errors in a written learner corpus [9, 11]. The research question is to evaluate the predictive power of errors in terms of levels and to identify which error types appear to be criterial features in determining interlanguage stages. Results show that specific errors such as punctuation, spelling and verb tense are significant at specific CEFR levels.
AB - This paper focuses on the use of technology in language learning. Language training requires the need to group learners homogeneously and to provide them with instant feedback on their productions such as errors [8, 15, 17] or proficiency levels. A possible approach is to assess writings from students and assign them with a level. This paper analyses the possibility of automatically predicting Common European Framework of Reference (CEFR) language levels on the basis of manually annotated errors in a written learner corpus [9, 11]. The research question is to evaluate the predictive power of errors in terms of levels and to identify which error types appear to be criterial features in determining interlanguage stages. Results show that specific errors such as punctuation, spelling and verb tense are significant at specific CEFR levels.
KW - CEFR level prediction
KW - Error tagset
KW - Proficiency levels
KW - Regression
KW - Unsupervised clustering
UR - https://www.scopus.com/pages/publications/85149992221
U2 - 10.1007/978-3-030-29736-7_23
DO - 10.1007/978-3-030-29736-7_23
M3 - Conference Publication
AN - SCOPUS:85149992221
SN - 9783030297350
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 308
EP - 320
BT - Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
A2 - Scheffel, Maren
A2 - Broisin, Julien
A2 - Pammer-Schindler, Viktoria
A2 - Ioannou, Andri
A2 - Schneider, Jan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019
Y2 - 16 September 2019 through 19 September 2019
ER -