Developing an Automated Writing Placement System for ESL Learners

Autor/inn/en	Yannakoudakis, Helen; Andersen, Øistein E.; Geranpayeh, Ardeshir; Briscoe, Ted; Nicholls, Diane
Titel	Developing an Automated Writing Placement System for ESL Learners
Quelle	In: Applied Measurement in Education, 31 (2018) 3, S.251-267 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2018.1464447
Schlagwörter	Automation; Data Processing; Student Placement; English Language Learners; Writing (Composition); Language Proficiency; Achievement Rating; Test Construction; Scores; Writing Improvement; Usability; Second Language Learning; Benchmarking; Foreign Countries; Europe + Suchen Sie Ihr Suchwort? Datenverarbeitung; Schülerpraktikum; Schreibübung; Language skill; Language skills; Sprachkompetenz; Achievement; Rating; Leistung; Beurteilung; Leistungsbeurteilung; Testaufbau; Zweitsprachenerwerb; Ausland; Europa
Abstract	There are quite a few challenges in the development of an automated writing placement model for non-native English learners, among them the fact that exams that encompass the full range of language proficiency exhibited at different stages of learning are hard to design. However, acquisition of appropriate training data that are relevant to the task at hand is essential in the development of the model. Using the Cambridge Learner Corpus writing scores, which have been subsequently benchmarked to Common European Framework of Reference for Languages (CEFR) levels, we conceptualize the task as a supervised machine learning problem, and primarily focus on developing a generic writing model. Such an approach facilitates the modeling of truly consistent, internal marking criteria regardless of the prompt delivered, which has the additional advantage of requiring smaller dataset sizes and not necessarily requiring re-training or tuning for new tasks. The system is developed to predict someone's proficiency level on the CEFR scale, which allows learners to point to a specific standard of achievement. We furthermore integrate our model into Cambridge English Write & Improve™--a freely available, cloud-based tool that automatically provides diagnostic feedback to non-native English language learners at different levels of granularity--and examine its use. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01