Analyzing Syntactic Complexity in ESL and EFL Learners’ Language Production Using an AI Classification Model
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This study aims to (i) identify the key syntactic complexity-related characteristics that distinguish between learners studying English as a second language (ESL) and learners studying English as a foreign language (EFL) across three communication modes, (ii) investigate whether Korean learners are closer to ESL or EFL in terms of the identified syntactic characteristics in each mode, and (iii) test whether a machine learning-based classification model can effectively perform in addressing these two objectives. For the first objective, this study utilized the feature importance metric within the XGBoost classifier to assess the importance of fourteen syntactic complexity measures in differentiating between ESL and EFL learners in essays, dialogues, and monologues. For the second objective, this study trained the XGBoost classifier to sort new input data into ESL and EFL based on the key measures obtained from the first objective. For the third objective, evaluation metrics to assess the XGBoost classifier’s performance were employed. The results demonstrated that the XGBoost classifier can successfully identify the main syntactic characteristics that differentiate between ESL and EFL learners, Korean learners are closer to EFL learners in every mode, and the XGBoost classifier has the potential to serve as a new approach to reveal these two findings.
Abstract
This study aims to (i) identify the key syntactic complexity-related characteristics that distinguish between learners studying English as a second language (ESL) and learners studying English as a foreign language (EFL) across three communication modes, (ii) investigate whether Korean learners are closer to ESL or EFL in terms of the identified syntactic characteristics in each mode, and (iii) test whether a machine learning-based classification model can effectively perform in addressing these two objectives. For the first objective, this study utilized the feature importance metric within the XGBoost classifier to assess the importance of fourteen syntactic complexity measures in differentiating between ESL and EFL learners in essays, dialogues, and monologues. For the second objective, this study trained the XGBoost classifier to sort new input data into ESL and EFL based on the key measures obtained from the first objective. For the third objective, evaluation metrics to assess the XGBoost classifier’s performance were employed. The results demonstrated that the XGBoost classifier can successfully identify the main syntactic characteristics that differentiate between ESL and EFL learners, Korean learners are closer to EFL learners in every mode, and the XGBoost classifier has the potential to serve as a new approach to reveal these two findings.
참고문헌 (16)
- Casal, J. E., "Syntactic Complexity and Writing Quality in Assessed First-Year L2 Writing", Journal of Second Language Writing, 44, pp.51~62 (2019)
- Ferris, D. R., "Lexical and Syntactic Features of ESL Writing by Students at Different Levels of L2 Proficiency", Tesol Quarterly, 28(2), pp.414~420 (1994)
- Halleck, G. B., "Assessing Oral Proficiency : A Comparison of Holistic and Objective Measures", The Modern Language Journal, 79(2), pp.223~234 (1995)
- Hu, X., "Predicting CEFR Levels in L2 Oral Speech, Based on Lexical and Syntactic Complexity", Asia Pacific Journal of Corpus Research, 2(1), pp.35~45 (2021)
- Hwang, H., "Effects of Written Versus Spoken Production Modalities on Syntactic Complexity Measures in Beginning-Level Child EFL Learners", The Modern Language Journal, 104(1), pp.267~283 (2020)
- Ishikawa, S., "Learner Corpus Studies in Asia and the World 1", pp.91~118 (2013), Kobe University
- Iwashita, N., "Syntactic Complexity Measures and Their Relation to Oral Proficiency in Japanese as a Foreign Language", Language Assessment Quarterly : An International Journal, 3(2), pp.151~169 (2006)
- Kim, J., "Predicting L2 Writing Proficiency Using Linguistic Complexity Measures: A Corpus-Based Study", English Teaching, 69(4), pp.27~51 (2014)
- Kim, W., "Finding an Optimal Classification Model for Analyzing Linguistic Data", Lingua Humanitatis, 26(2) (2024)
- Larsen-Freeman, D., "An ESL Index of Development", TESOL Quarterly, 12(4), pp.439~448 (1978)
- Lu, X., "Automatic Analysis of Syntactic Complexity in Second Language Writing", International Journal of Corpus Linguistics, 15(4), pp.474~496 (2010)
- Lu, X., "A Corpus-Based Evaluation of Syntactic Complexity Measures as Indices of College Level ESL Writers’ Language Development", TESOL Quarterly, 45(1), pp.36~62 (2011)
- Martínez, A. C. L., "Analysis of Syntactic Complexity in Secondary Education EFL Writers at Different Proficiency Levels", Assessing Writing, 35, pp.1~11 (2018)
- Ortega, L., "Syntactic Complexity Measures and Their Relationship to L2 Proficiency : A Research Synthesis of College-Level L2 Writing", Applied Linguistics, 24(4), pp.492~518 (2003)
- Park, S., "Syntactic Complexity in a Learner Written Corpus and L2 Speaking Quality : Suggestions for Distinguishing L2 Speaking Proficiency", Journal of Language and Linguistic Studies, 18(1), pp.361~371 (2022)
- Wolfe-Quintero, K., "Second Language Development in Writing: Measures of Fluency, Accuracy, and Complexity" (1998), University of Hawaii Press