The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Data‐Driven Learning: Taking the Computer Out of the Equation
Abstract
Despite considerable research interest, data‐driven learning (DDL) has not become part of mainstream teaching practice. It may be that technical aspects are too daunting for teachers and students, but there seems to be no reason why DDL in its early stages should not eliminate the computer from the equation by using prepared materials on paper—considerably easier for the novice learner to handle. This article reports on an experiment to see how lower level learners cope with such paper‐based corpus materials and a DDL approach compared to more traditional teaching materials and practices. Pretests and posttests show that both are effective compared to control items, with the DDL items showing the greatest improvement, and questionnaire responses are more favorable to the DDL activities. The results are argued to show that printed materials can counter a number of potential barriers and may thus enable DDL to reach a wider audience.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Longman Grammar of Spoken and Written English
Broussard, K. M., Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (2000) · TESOL Quarterly · doi:10.2307/3587792 · Cited by 8,193
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_1 · Cited by 7,776 · cited 2×
“Kirschner et al. (2006) make a convincing case that novice learners and those with lower aptitudes do not learn effectively from a purely inductive approach, as they do not have sufficient prior knowledge and schemas to draw on, resulting in overload of working memory.”
BackgroundCommon European Framework of Reference for Languages: learning, teaching, assessment
Verhelst, N. D., Avermaet, P. V., Takala, S., Figueras, N., & North, B. (2009) · Ghent University Academic Bibliography (Ghent University) · Cited by 6,085
Autonomy and independence in language learning
(1997) · System · doi:10.1016/0346-251x(97)90167-6 · Cited by 869
Holschuh, L. W., & Swan, M. (1998) · The Modern Language Journal · doi:10.2307/329223 · Cited by 746
From printout to handout: Grammar and vocabulary teaching in the context of Data-driven Learning
Johns, T. (1994) · Perspectives on Pedagogical Grammar · doi:10.1017/cbo9781139524605.014 · Cited by 489
Wichmann, A., & Fligelstone, S. (2014) · Routledge eBooks · doi:10.4324/9781315842677 · Cited by 479
ESL student attitudes toward corpus use in L2 writing
Yoon, H., & Hirvela, A. (2004) · Journal of Second Language Writing · doi:10.1016/j.jslw.2004.06.002 · Cited by 386
Can learners use concordance feedback for writing errors?
Gaskell, D., & Cobb, T. (2004) · System · doi:10.1016/j.system.2004.04.001 · Cited by 347
Testing the limits of data-driven learning: language proficiency and training
Boulton, A. (2009) · ReCALL · doi:10.1017/s0958344009000068 · Cited by 257
Self-citation
Cited by
The Effects of Corpus Use on Second Language Vocabulary Learning: A Multilevel Meta-analysis
Lee, H., Warschauer, M., & Lee, J. H. (2019) · Applied Linguistics · doi:10.1093/applin/amy012 · Cited by 256
Enhancing writing pedagogy with learner corpus data
Cotos, E. (2014) · ReCALL · doi:10.1017/s0958344014000019 · Cited by 132
Concordancing in L2 writing class: an overview of research and issues
Yoon, C. (2011) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2011.03.003 · Cited by 94
ChatGPT: Friend or foe (to corpus linguists)?
Lin, P. (2023) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100065 · Cited by 59
Bennett, C., & Dhonnchadha, E. U. (2023) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100048 · Cited by 19
Metadata
- Authors
- Boulton, A.
- Venue
- Language Learning
- Publisher
- Wiley
- Published
- 2010-08-02
- Volume
- 60
- Issue
- 3
- Pages
- 534-572
- ISSN
- 0023-8333 (ISSN-L)
- 1467-9922
- OpenAlex
- W1598288397
- Cited by
- 278
- References
- 42
- FWCI
- 15.62
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Data‐Driven Learning: Taking the Computer Out of the Equation. https://app.cochat.ai/papers/10.1111/j.1467-9922.2010.00566.x
- The paper
- Boulton, A. (2010). Data‐Driven Learning: Taking the Computer Out of the Equation. Language Learning. https://doi.org/10.1111/j.1467-9922.2010.00566.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
