Evidence for Learning
We develop methods analyzing learner language to broaden the empirical evidence for development, both in terms of linguistic constructions and general linguistic complexity, including task effects and L1 transfer.Analyzing Learner Language
Intelligent Language Tutoring
We create interactive systems that support foreign language learners in practicing language skills with incremental, scaffolding feedback — like a human tutor would, who unfortunately we can't always have around.Interactivity
We design search engines and linguistic complexity measures needed to identify the input that best fosters learners in their language development. We also check whether educational materials are adapted to their audience.Adaptivity
the SFB 833-A4 project,
we are developing automatic meaning assessment methods for short-answer
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
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
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
Geertzen, Korhonen & Meurers, 2015) and in terms of linguistic
complexity, emphasizing the need to account for task effects
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).
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 Intelligent Tutoring System (ITS) TAGARELA
(Amaral & Meurers 2011)
designed to complement university instruction, in collaboration with a
German school book publisher we created
the FeedBook, an interactive
workbook for English 7th grade in a DFG-funded transfer project.
In the first randomized controlled field study with an ITS fully embedded in a regular German school context,
we established the effectiveness of the specific scaffolding feedback (Meurers et al. 2019).
The approach is extended to adaptively sequence activities in the DigBinDiff project. In Interact4School, we extend the ITS with motivational feedback and explore the interface between individual learning using an ITS and the teacher orchestrated learning in the classroom. In IL2 we work on improving that interface, both on the technical side (teacher dashboards) and in terms of developing teacher training components linking SLA concepts and research results with the use of digital tools based on this foundation.
In the new BMBF project AISLA, we develop an intelligent dialog system supporting the acquisition of English in authentic, spoken language contexts.
We also collaborated with the Tübinger Institut für Lerntherapie in developing Prosodiya, a mobile serious game for German dyslexic primary-school children currently being evaluated in a large field study with a waiting control group design.
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
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, we collaborate in the BMBF-funded KANSAS project with the German Institute for Adult Education (DIE) and the Mercator Institute for Literacy and Language Education to build a tool supporting 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.