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In:New Frontiers and Connections in Second Language Acquisition: Selected Proceedings of the 17th Generative Approaches to Second Language Acquisition (GASLA-17) Conference
Edited by Tania Ionin and Silvina Montrul
[Language Acquisition and Language Disorders 71] 2026
► pp. 360–383

Big educational language learning data (BELL)

Any good for Generative SLA research?

 | University of Cambridge
This content is being prepared for publication; it may be subject to changes.
Abstract
I consider the potential research value of Big Educational Language Learning (BELL) data, that is, data generated in teaching and assessment environments as part of the everyday operations of relevant organizations and institutions. Online language learning and assessment has created unprecedented opportunities for the collection of large and diverse samples of learner data across proficiency, for a variety of tasks and from learners with variable linguistic and educational backgrounds. I argue that, thanks to their size and diverse samples, BELL data can support multi-factor empirical investigations that are not easily feasible through lab-based or small-scale field studies. Importantly, they enable the investigation of several abstract features in a single design, contributing essential empirical evidence for feature-based hypotheses and accounts of L2 acquisition, complementing experimental investigations.
Article outline
  • 1.Introduction
  • 2.BELL: Big Educational Language Learning Data
  • 3.What can SLA researchers learn from BELL data?
    • 3.1Typological effects on L2/L3 outcomes and their interaction with age, exposure, and educational background
    • 3.2Considering established generalizations: L1 influence and natural order in the acquisition of L2 English morphemes
    • 3.3Extending research designs
    • 3.4Overcoming data sparsity
    • 3.5Reproducibility of results and computational modeling
  • 4.Can BELL data support hypothesis driven research?
    • 4.1Usage-based approaches
    • 4.2Evaluating the predictions of alternative hypotheses: Derivational complexity vs. L1-L2 structural overlap in L2 English genitive structures
    • 4.3Distinguishing parameter-based vs. item-based L1-L2 similarity effects: The case of the L2 English articles
  • 5.The value of multifactorial designs for GenSLA research:
    The acquisition of L2 functional morphology
    • 5.1The morphological bottleneck of L2 acquisition: Theoretical views and empirical challenges.
    • 5.2Multifactorial designs: The case of the L2 English articles
    • 5.3Identifying opportunity for learning
    • 5.4Comparing interactions and effects
    • 5.5Extending existing hypotheses and formulating new ones
  • 6.Investigating theoretical hypotheses
    through computational modelling
  • 7.An empirical bridge between SLA research
    and classroom learning and teaching
  • 8.Conclusion
  • Notes
  • References
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