Dear colleagues,
We are pleased to announce an upcoming edited volume on "Data-Driven Language Teaching and Learning: Theory, Research, and Practice", to be published by Springer. With this email, we warmly invite chapter proposals from researchers, teacher educators, curriculum designers, and practitioners working across corpus linguistics, applied linguistics, language education, and educational technology.
TOPIC OVERVIEW
Data-Driven Learning (DDL), first introduced by Tim Johns (1991) as a method of classroom concordancing, has evolved considerably over the past three decades. Despite a substantial body of empirical evidence supporting corpus-based approaches for raising learner awareness, developing language autonomy, and supporting inductive learning, a persistent gap remains between research-based insights and everyday instructional decision-making.
This volume addresses this gap directly. Through empirically grounded contributions, it demonstrates how data can be used to support instructional design, materials development, classroom interaction, and assessment -- enabling more informed and transparent pedagogical choices without requiring advanced technical expertise. By adopting a cross-contextual and language-independent perspective, the volume identifies transferable principles applicable across a wide range of teaching settings.
We invite chapter proposals addressing topics including, but not limited to:
- Corpus-based and data-driven approaches to language teaching, learning, and assessment - Data-informed instructional design and materials development - Classroom applications of DDL across educational levels and language learning contexts - Learner engagement with corpus tools and concordancing tasks - AI-assisted feedback, adaptive learning platforms, and intelligent tutoring systems in DDL contexts - Learner corpus research and its implications for pedagogy - Teacher education and professional development through data-informed practice - Digital tools, corpus technologies, and blended or online learning environments - Challenges and affordances of implementing DDL in multilingual or under-resourced settings - Theoretical and methodological frameworks for data-driven language pedagogy - Cross-contextual and comparative studies of data-informed teaching
HOW TO CONTRIBUTE
The volume operates a two-stage submission process.
Stage 1 - Abstract Submission Prospective contributors are invited to submit an abstract of 400-600 words by 1 May 2026. Abstracts should include:
- Title and full names and institutional affiliations of all contributors - A clear statement of the chapter’s focus, argument, research questions, and main contribution - The theoretical and/or empirical approach to be employed - The educational context, data, or corpus under investigation (if applicable) - The chapter’s relevance to the volume’s overarching themes - A short bibliography of key references (not counted in the word limit)
Stage 2 - Full Chapter Submission Authors of accepted abstracts will be invited to submit full chapters of 7,000-9,000 words (including references) by October 2026. All full chapters will undergo double-blind peer review. Authors will receive detailed reviewer feedback and will be asked to revise and resubmit accordingly.
IMPORTANT DATES
Abstract submission deadline: 1 May 2026 Notification of outcome: Within three weeks of deadline Full chapter deadline: October 2026
Please submit your abstract by email to the corresponding editor, using the subject line: Chapter Proposal - Data-Driven Language Teaching and Learning.
For questions related to the volume’s scope, submission guidelines, or any other matter, please do not hesitate to contact the editors.
We look forward to receiving your proposals!
Mehrdad Vasheghani Farahani (Corresponding Editor) submittingpapers@yahoo.com
Cansu Akan Chemnitz University of Technology, Germany cansu.akan@phil.tu-chemnitz.de
Sepideh Javdani Esfahani Chemnitz University of Technology, Germany sepideh.javdani-esfahani@phil.tu-chemnitz.de