Research into second language (L2) reading is an exponentially growing field. Yet, it still has a relatively short supply of comparable, ecologically valid data from readers representing a variety of first languages (L1). This article addresses this need by presenting a new data resource called MECO L2 (Multilingual Eye Movements Corpus), a rich behavioral eye-tracking record of text reading in English as an L2 among 543 university student speakers of 12 different L1s. MECO L2 includes a test battery of component skills of reading and allows for a comparison of the participants' reading performance in their L1 and L2. This data resource enables innovative large-scale cross-sample analyses of predictors of L2 reading fluency and comprehension. We first introduce the design and structure of the MECO L2 resource, along with reliability estimates and basic descriptive analyses. Then, we illustrate the utility of MECO L2 by quantifying contributions of four sources to variability in L2 reading proficiency proposed in prior literature: reading fluency and comprehension in L1, proficiency in L2 component skills of reading, extralinguistic factors, and the L1 of the readers. Major findings included (a) a fundamental contrast between the determinants of L2 reading fluency versus comprehension accuracy, and (b) high within-participant consistency in the real-time strategy of reading in L1 and L2. We conclude by reviewing the implications of these findings to theories of L2 acquisition and outline further directions in which the new data resource may support L2 reading research.
Bibliographical noteFunding Information:
Research reported in this publication was supported by the following grants: the Social Sciences and Humanities Research Council of Canada Partnered Research Training Grant, 895-2016-1008 (PI: G. Libben); the Canada Research Chair (Tier 2; PI: V. Kuperman); the CFI Leaders Opportunity Fund (PI: V. Kuperman); Concerted research action BOF13/GOA/032 of Ghent University; FWO Project (PI: M. Brysbaert); ERC Advanced grant, project 692502-L2STAT (PI: R. Frost), Estonian Research Council Mobilitas Pluss postdoctoral researcher grant MOBJD408 (PI: K. Lõo), the Israel Science Foundation (ISF) grant 48/20 (PI: N. Siegelman), and Saint Petersburg State University grant ID 75288744, 121050600033-7 (PI: N. Slioussar). K. Nisbet’s work has been supported by the Ontario Graduate Scholarship.
© 2022 The Author(s). Published by Cambridge University Press.