ICALL Research Group

In the SFB 833-A4 project, we are developing automatic meaning assessment methods for short-answer reading comprehension. To collect a rich task-based corpus in a real-life teaching context, we created the WELCOME app (Ott et al., 2012) and obtained the CREG corpus (36k answers to 1.5k questions). Our research showcases the importance of interpreting data in context (Ziai & Meurers, 2014; De Kuthy et al., 2015, 2016a, b; Ziai et al., 2016). The CoaLLA project explores the integration of top-down and bottom-up information. With Katrin Wisniewski we explored linguistic correlates of the CEFR as part of the MERLIN project.
As EFCamDat consultants, we collaborate with Dora Alexopoulou (Cambridge) and Marije Michel (Utrecht) to jointly analyze this very large English learner corpus (1.18 million writing tasks by 175k learners, CEFR A1–C2). We characterize language development both for specific constructions, e.g., relative clauses (Alexopoulou, Geertzen, Korhonen & Meurers, 2015) and in terms of linguistic complexity, emphasizing the need to account for task effects (Alexopoulou, Michel, Murakami & Meurers, 2017).
We also analyze L1 transfer effects using machine learning for Native Language Identification as an experimental testbed integrating shallow and deeper linguistic characteristics of learner data (Bykh & Meurers 2012, 2014, 2016; Meurers, Krivanek & Bykh 2014; Bykh, Vajjala, Krivanek & Meurers 2013).

Feedback is known to be very effective in fostering learning — yet human tutors are not always around, and the different amount of support students get at home is a major cause for inequality in education. While tutoring systems are increasingly taken hold in formal domains such as mathematics and the natural sciences, foreign language learning poses additional modeling challenges. We are combining NLP methods with SLA insights in designing foreign language tutoring systems that provide individual, scaffolding feedback to students while they work on homework. Students are stepwise led to successfully complete an exercise so that teachers in class can work with a more homogeneous student group in class. Following the Portuguese tutoring system TAGARELA (Amaral & Meurers 2011) designed to complement university instruction, in collaboration with a German school book publisher we are creating the FeedBook, an interactive workbook for English 7th grade and will be testing its effectiveness in a full-year randomized controlled field study as part of the DFG-funded transfer project.

We are developing linguistic complexity analyzers integrating a wide range of linguistic, psycholinguistic, and SLA complexity features for English (Vajjala & Meurers 12, 13, 14a, b, c, Chen & Meurers 2016a, b) and German (Hancke, Vajjala, Meurers 12; Hancke & Meurers 2013) — and tools such as CTAP making it easy to use these measures.
Applying these methods to education, we investigate the (in)appropriateness of textbooks for students of different grades and school types (Bryant et al. 2017, Berendes et al., in press).
To support teachers and learners in identifying texts that are both interesting and richly represent the language constructs to be acquired, we created the linguistically-aware search engine FLAIR (Chinkina & Meurers 16). On this basis, the KANSAS project builds a tool designed to support teachers of functional literacy courses.
Connecting foundational and applied issues, we are spelling out Krashen's i+1 input fostering learning in terms of linguistic complexity using SyB (Chen & Meurers 17), a syntactic benchmarking tool, and we investigate the impact of challenging learners with such input.